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Top 10 real-world data science case studies.

Aditya Sharma

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Frequently Asked Questions

Real-world data science case studies differ significantly from academic examples. While academic exercises often feature clean, well-structured data and simplified scenarios, real-world projects tackle messy, diverse data sources with practical constraints and genuine business objectives. These case studies reflect the complexities data scientists face when translating data into actionable insights in the corporate world.

Real-world data science projects come with common challenges. Data quality issues, including missing or inaccurate data, can hinder analysis. Domain expertise gaps may result in misinterpretation of results. Resource constraints might limit project scope or access to necessary tools and talent. Ethical considerations, like privacy and bias, demand careful handling.

Lastly, as data and business needs evolve, data science projects must adapt and stay relevant, posing an ongoing challenge.

Real-world data science case studies play a crucial role in helping companies make informed decisions. By analyzing their own data, businesses gain valuable insights into customer behavior, market trends, and operational efficiencies.

These insights empower data-driven strategies, aiding in more effective resource allocation, product development, and marketing efforts. Ultimately, case studies bridge the gap between data science and business decision-making, enhancing a company's ability to thrive in a competitive landscape.

Key takeaways from these case studies for organizations include the importance of cultivating a data-driven culture that values evidence-based decision-making. Investing in robust data infrastructure is essential to support data initiatives. Collaborating closely between data scientists and domain experts ensures that insights align with business goals.

Finally, continuous monitoring and refinement of data solutions are critical for maintaining relevance and effectiveness in a dynamic business environment. Embracing these principles can lead to tangible benefits and sustainable success in real-world data science endeavors.

Data science is a powerful driver of innovation and problem-solving across diverse industries. By harnessing data, organizations can uncover hidden patterns, automate repetitive tasks, optimize operations, and make informed decisions.

In healthcare, for example, data-driven diagnostics and treatment plans improve patient outcomes. In finance, predictive analytics enhances risk management. In transportation, route optimization reduces costs and emissions. Data science empowers industries to innovate and solve complex challenges in ways that were previously unimaginable.

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10 Real World Data Science Case Studies Projects with Example

Top 10 Data Science Case Studies Projects with Examples and Solutions in Python to inspire your data science learning in 2023.

BelData science has been a trending buzzword in recent times. With wide applications in various sectors like healthcare , education, retail, transportation, media, and banking -data science applications are at the core of pretty much every industry out there. The possibilities are endless: analysis of frauds in the finance sector or the personalization of recommendations on eCommerce businesses.  We have developed ten exciting data science case studies to explain how data science is leveraged across various industries to make smarter decisions and develop innovative personalized products tailored to specific customers.

Walmart Sales Forecasting Data Science Project

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Table of Contents

Data science case studies in retail , data science case study examples in entertainment industry , data analytics case study examples in travel industry , case studies for data analytics in social media , real world data science projects in healthcare, data analytics case studies in oil and gas, what is a case study in data science, how do you prepare a data science case study, 10 most interesting data science case studies with examples.

So, without much ado, let's get started with data science business case studies !

With humble beginnings as a simple discount retailer, today, Walmart operates in 10,500 stores and clubs in 24 countries and eCommerce websites, employing around 2.2 million people around the globe. For the fiscal year ended January 31, 2021, Walmart's total revenue was $559 billion showing a growth of $35 billion with the expansion of the eCommerce sector. Walmart is a data-driven company that works on the principle of 'Everyday low cost' for its consumers. To achieve this goal, they heavily depend on the advances of their data science and analytics department for research and development, also known as Walmart Labs. Walmart is home to the world's largest private cloud, which can manage 2.5 petabytes of data every hour! To analyze this humongous amount of data, Walmart has created 'Data Café,' a state-of-the-art analytics hub located within its Bentonville, Arkansas headquarters. The Walmart Labs team heavily invests in building and managing technologies like cloud, data, DevOps , infrastructure, and security.

Walmart is experiencing massive digital growth as the world's largest retailer . Walmart has been leveraging Big data and advances in data science to build solutions to enhance, optimize and customize the shopping experience and serve their customers in a better way. At Walmart Labs, data scientists are focused on creating data-driven solutions that power the efficiency and effectiveness of complex supply chain management processes. Here are some of the applications of data science  at Walmart:

i) Personalized Customer Shopping Experience

Walmart analyses customer preferences and shopping patterns to optimize the stocking and displaying of merchandise in their stores. Analysis of Big data also helps them understand new item sales, make decisions on discontinuing products, and the performance of brands.

ii) Order Sourcing and On-Time Delivery Promise

Millions of customers view items on Walmart.com, and Walmart provides each customer a real-time estimated delivery date for the items purchased. Walmart runs a backend algorithm that estimates this based on the distance between the customer and the fulfillment center, inventory levels, and shipping methods available. The supply chain management system determines the optimum fulfillment center based on distance and inventory levels for every order. It also has to decide on the shipping method to minimize transportation costs while meeting the promised delivery date.

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iii) Packing Optimization 

Also known as Box recommendation is a daily occurrence in the shipping of items in retail and eCommerce business. When items of an order or multiple orders for the same customer are ready for packing, Walmart has developed a recommender system that picks the best-sized box which holds all the ordered items with the least in-box space wastage within a fixed amount of time. This Bin Packing problem is a classic NP-Hard problem familiar to data scientists .

Whenever items of an order or multiple orders placed by the same customer are picked from the shelf and are ready for packing, the box recommendation system determines the best-sized box to hold all the ordered items with a minimum of in-box space wasted. This problem is known as the Bin Packing Problem, another classic NP-Hard problem familiar to data scientists.

Here is a link to a sales prediction data science case study to help you understand the applications of Data Science in the real world. Walmart Sales Forecasting Project uses historical sales data for 45 Walmart stores located in different regions. Each store contains many departments, and you must build a model to project the sales for each department in each store. This data science case study aims to create a predictive model to predict the sales of each product. You can also try your hands-on Inventory Demand Forecasting Data Science Project to develop a machine learning model to forecast inventory demand accurately based on historical sales data.

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Amazon is an American multinational technology-based company based in Seattle, USA. It started as an online bookseller, but today it focuses on eCommerce, cloud computing , digital streaming, and artificial intelligence . It hosts an estimate of 1,000,000,000 gigabytes of data across more than 1,400,000 servers. Through its constant innovation in data science and big data Amazon is always ahead in understanding its customers. Here are a few data analytics case study examples at Amazon:

i) Recommendation Systems

Data science models help amazon understand the customers' needs and recommend them to them before the customer searches for a product; this model uses collaborative filtering. Amazon uses 152 million customer purchases data to help users to decide on products to be purchased. The company generates 35% of its annual sales using the Recommendation based systems (RBS) method.

Here is a Recommender System Project to help you build a recommendation system using collaborative filtering. 

ii) Retail Price Optimization

Amazon product prices are optimized based on a predictive model that determines the best price so that the users do not refuse to buy it based on price. The model carefully determines the optimal prices considering the customers' likelihood of purchasing the product and thinks the price will affect the customers' future buying patterns. Price for a product is determined according to your activity on the website, competitors' pricing, product availability, item preferences, order history, expected profit margin, and other factors.

Check Out this Retail Price Optimization Project to build a Dynamic Pricing Model.

iii) Fraud Detection

Being a significant eCommerce business, Amazon remains at high risk of retail fraud. As a preemptive measure, the company collects historical and real-time data for every order. It uses Machine learning algorithms to find transactions with a higher probability of being fraudulent. This proactive measure has helped the company restrict clients with an excessive number of returns of products.

You can look at this Credit Card Fraud Detection Project to implement a fraud detection model to classify fraudulent credit card transactions.

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Let us explore data analytics case study examples in the entertainment indusry.

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Netflix started as a DVD rental service in 1997 and then has expanded into the streaming business. Headquartered in Los Gatos, California, Netflix is the largest content streaming company in the world. Currently, Netflix has over 208 million paid subscribers worldwide, and with thousands of smart devices which are presently streaming supported, Netflix has around 3 billion hours watched every month. The secret to this massive growth and popularity of Netflix is its advanced use of data analytics and recommendation systems to provide personalized and relevant content recommendations to its users. The data is collected over 100 billion events every day. Here are a few examples of data analysis case studies applied at Netflix :

i) Personalized Recommendation System

Netflix uses over 1300 recommendation clusters based on consumer viewing preferences to provide a personalized experience. Some of the data that Netflix collects from its users include Viewing time, platform searches for keywords, Metadata related to content abandonment, such as content pause time, rewind, rewatched. Using this data, Netflix can predict what a viewer is likely to watch and give a personalized watchlist to a user. Some of the algorithms used by the Netflix recommendation system are Personalized video Ranking, Trending now ranker, and the Continue watching now ranker.

ii) Content Development using Data Analytics

Netflix uses data science to analyze the behavior and patterns of its user to recognize themes and categories that the masses prefer to watch. This data is used to produce shows like The umbrella academy, and Orange Is the New Black, and the Queen's Gambit. These shows seem like a huge risk but are significantly based on data analytics using parameters, which assured Netflix that they would succeed with its audience. Data analytics is helping Netflix come up with content that their viewers want to watch even before they know they want to watch it.

iii) Marketing Analytics for Campaigns

Netflix uses data analytics to find the right time to launch shows and ad campaigns to have maximum impact on the target audience. Marketing analytics helps come up with different trailers and thumbnails for other groups of viewers. For example, the House of Cards Season 5 trailer with a giant American flag was launched during the American presidential elections, as it would resonate well with the audience.

Here is a Customer Segmentation Project using association rule mining to understand the primary grouping of customers based on various parameters.

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In a world where Purchasing music is a thing of the past and streaming music is a current trend, Spotify has emerged as one of the most popular streaming platforms. With 320 million monthly users, around 4 billion playlists, and approximately 2 million podcasts, Spotify leads the pack among well-known streaming platforms like Apple Music, Wynk, Songza, amazon music, etc. The success of Spotify has mainly depended on data analytics. By analyzing massive volumes of listener data, Spotify provides real-time and personalized services to its listeners. Most of Spotify's revenue comes from paid premium subscriptions. Here are some of the examples of case study on data analytics used by Spotify to provide enhanced services to its listeners:

i) Personalization of Content using Recommendation Systems

Spotify uses Bart or Bayesian Additive Regression Trees to generate music recommendations to its listeners in real-time. Bart ignores any song a user listens to for less than 30 seconds. The model is retrained every day to provide updated recommendations. A new Patent granted to Spotify for an AI application is used to identify a user's musical tastes based on audio signals, gender, age, accent to make better music recommendations.

Spotify creates daily playlists for its listeners, based on the taste profiles called 'Daily Mixes,' which have songs the user has added to their playlists or created by the artists that the user has included in their playlists. It also includes new artists and songs that the user might be unfamiliar with but might improve the playlist. Similar to it is the weekly 'Release Radar' playlists that have newly released artists' songs that the listener follows or has liked before.

ii) Targetted marketing through Customer Segmentation

With user data for enhancing personalized song recommendations, Spotify uses this massive dataset for targeted ad campaigns and personalized service recommendations for its users. Spotify uses ML models to analyze the listener's behavior and group them based on music preferences, age, gender, ethnicity, etc. These insights help them create ad campaigns for a specific target audience. One of their well-known ad campaigns was the meme-inspired ads for potential target customers, which was a huge success globally.

iii) CNN's for Classification of Songs and Audio Tracks

Spotify builds audio models to evaluate the songs and tracks, which helps develop better playlists and recommendations for its users. These allow Spotify to filter new tracks based on their lyrics and rhythms and recommend them to users like similar tracks ( collaborative filtering). Spotify also uses NLP ( Natural language processing) to scan articles and blogs to analyze the words used to describe songs and artists. These analytical insights can help group and identify similar artists and songs and leverage them to build playlists.

Here is a Music Recommender System Project for you to start learning. We have listed another music recommendations dataset for you to use for your projects: Dataset1 . You can use this dataset of Spotify metadata to classify songs based on artists, mood, liveliness. Plot histograms, heatmaps to get a better understanding of the dataset. Use classification algorithms like logistic regression, SVM, and Principal component analysis to generate valuable insights from the dataset.

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Below you will find case studies for data analytics in the travel and tourism industry.

Airbnb was born in 2007 in San Francisco and has since grown to 4 million Hosts and 5.6 million listings worldwide who have welcomed more than 1 billion guest arrivals in almost every country across the globe. Airbnb is active in every country on the planet except for Iran, Sudan, Syria, and North Korea. That is around 97.95% of the world. Using data as a voice of their customers, Airbnb uses the large volume of customer reviews, host inputs to understand trends across communities, rate user experiences, and uses these analytics to make informed decisions to build a better business model. The data scientists at Airbnb are developing exciting new solutions to boost the business and find the best mapping for its customers and hosts. Airbnb data servers serve approximately 10 million requests a day and process around one million search queries. Data is the voice of customers at AirBnB and offers personalized services by creating a perfect match between the guests and hosts for a supreme customer experience. 

i) Recommendation Systems and Search Ranking Algorithms

Airbnb helps people find 'local experiences' in a place with the help of search algorithms that make searches and listings precise. Airbnb uses a 'listing quality score' to find homes based on the proximity to the searched location and uses previous guest reviews. Airbnb uses deep neural networks to build models that take the guest's earlier stays into account and area information to find a perfect match. The search algorithms are optimized based on guest and host preferences, rankings, pricing, and availability to understand users’ needs and provide the best match possible.

ii) Natural Language Processing for Review Analysis

Airbnb characterizes data as the voice of its customers. The customer and host reviews give a direct insight into the experience. The star ratings alone cannot be an excellent way to understand it quantitatively. Hence Airbnb uses natural language processing to understand reviews and the sentiments behind them. The NLP models are developed using Convolutional neural networks .

Practice this Sentiment Analysis Project for analyzing product reviews to understand the basic concepts of natural language processing.

iii) Smart Pricing using Predictive Analytics

The Airbnb hosts community uses the service as a supplementary income. The vacation homes and guest houses rented to customers provide for rising local community earnings as Airbnb guests stay 2.4 times longer and spend approximately 2.3 times the money compared to a hotel guest. The profits are a significant positive impact on the local neighborhood community. Airbnb uses predictive analytics to predict the prices of the listings and help the hosts set a competitive and optimal price. The overall profitability of the Airbnb host depends on factors like the time invested by the host and responsiveness to changing demands for different seasons. The factors that impact the real-time smart pricing are the location of the listing, proximity to transport options, season, and amenities available in the neighborhood of the listing.

Here is a Price Prediction Project to help you understand the concept of predictive analysis which is widely common in case studies for data analytics. 

Uber is the biggest global taxi service provider. As of December 2018, Uber has 91 million monthly active consumers and 3.8 million drivers. Uber completes 14 million trips each day. Uber uses data analytics and big data-driven technologies to optimize their business processes and provide enhanced customer service. The Data Science team at uber has been exploring futuristic technologies to provide better service constantly. Machine learning and data analytics help Uber make data-driven decisions that enable benefits like ride-sharing, dynamic price surges, better customer support, and demand forecasting. Here are some of the real world data science projects used by uber:

i) Dynamic Pricing for Price Surges and Demand Forecasting

Uber prices change at peak hours based on demand. Uber uses surge pricing to encourage more cab drivers to sign up with the company, to meet the demand from the passengers. When the prices increase, the driver and the passenger are both informed about the surge in price. Uber uses a predictive model for price surging called the 'Geosurge' ( patented). It is based on the demand for the ride and the location.

ii) One-Click Chat

Uber has developed a Machine learning and natural language processing solution called one-click chat or OCC for coordination between drivers and users. This feature anticipates responses for commonly asked questions, making it easy for the drivers to respond to customer messages. Drivers can reply with the clock of just one button. One-Click chat is developed on Uber's machine learning platform Michelangelo to perform NLP on rider chat messages and generate appropriate responses to them.

iii) Customer Retention

Failure to meet the customer demand for cabs could lead to users opting for other services. Uber uses machine learning models to bridge this demand-supply gap. By using prediction models to predict the demand in any location, uber retains its customers. Uber also uses a tier-based reward system, which segments customers into different levels based on usage. The higher level the user achieves, the better are the perks. Uber also provides personalized destination suggestions based on the history of the user and their frequently traveled destinations.

You can take a look at this Python Chatbot Project and build a simple chatbot application to understand better the techniques used for natural language processing. You can also practice the working of a demand forecasting model with this project using time series analysis. You can look at this project which uses time series forecasting and clustering on a dataset containing geospatial data for forecasting customer demand for ola rides.

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7) LinkedIn 

LinkedIn is the largest professional social networking site with nearly 800 million members in more than 200 countries worldwide. Almost 40% of the users access LinkedIn daily, clocking around 1 billion interactions per month. The data science team at LinkedIn works with this massive pool of data to generate insights to build strategies, apply algorithms and statistical inferences to optimize engineering solutions, and help the company achieve its goals. Here are some of the real world data science projects at LinkedIn:

i) LinkedIn Recruiter Implement Search Algorithms and Recommendation Systems

LinkedIn Recruiter helps recruiters build and manage a talent pool to optimize the chances of hiring candidates successfully. This sophisticated product works on search and recommendation engines. The LinkedIn recruiter handles complex queries and filters on a constantly growing large dataset. The results delivered have to be relevant and specific. The initial search model was based on linear regression but was eventually upgraded to Gradient Boosted decision trees to include non-linear correlations in the dataset. In addition to these models, the LinkedIn recruiter also uses the Generalized Linear Mix model to improve the results of prediction problems to give personalized results.

ii) Recommendation Systems Personalized for News Feed

The LinkedIn news feed is the heart and soul of the professional community. A member's newsfeed is a place to discover conversations among connections, career news, posts, suggestions, photos, and videos. Every time a member visits LinkedIn, machine learning algorithms identify the best exchanges to be displayed on the feed by sorting through posts and ranking the most relevant results on top. The algorithms help LinkedIn understand member preferences and help provide personalized news feeds. The algorithms used include logistic regression, gradient boosted decision trees and neural networks for recommendation systems.

iii) CNN's to Detect Inappropriate Content

To provide a professional space where people can trust and express themselves professionally in a safe community has been a critical goal at LinkedIn. LinkedIn has heavily invested in building solutions to detect fake accounts and abusive behavior on their platform. Any form of spam, harassment, inappropriate content is immediately flagged and taken down. These can range from profanity to advertisements for illegal services. LinkedIn uses a Convolutional neural networks based machine learning model. This classifier trains on a training dataset containing accounts labeled as either "inappropriate" or "appropriate." The inappropriate list consists of accounts having content from "blocklisted" phrases or words and a small portion of manually reviewed accounts reported by the user community.

Here is a Text Classification Project to help you understand NLP basics for text classification. You can find a news recommendation system dataset to help you build a personalized news recommender system. You can also use this dataset to build a classifier using logistic regression, Naive Bayes, or Neural networks to classify toxic comments.

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Pfizer is a multinational pharmaceutical company headquartered in New York, USA. One of the largest pharmaceutical companies globally known for developing a wide range of medicines and vaccines in disciplines like immunology, oncology, cardiology, and neurology. Pfizer became a household name in 2010 when it was the first to have a COVID-19 vaccine with FDA. In early November 2021, The CDC has approved the Pfizer vaccine for kids aged 5 to 11. Pfizer has been using machine learning and artificial intelligence to develop drugs and streamline trials, which played a massive role in developing and deploying the COVID-19 vaccine. Here are a few data analytics case studies by Pfizer :

i) Identifying Patients for Clinical Trials

Artificial intelligence and machine learning are used to streamline and optimize clinical trials to increase their efficiency. Natural language processing and exploratory data analysis of patient records can help identify suitable patients for clinical trials. These can help identify patients with distinct symptoms. These can help examine interactions of potential trial members' specific biomarkers, predict drug interactions and side effects which can help avoid complications. Pfizer's AI implementation helped rapidly identify signals within the noise of millions of data points across their 44,000-candidate COVID-19 clinical trial.

ii) Supply Chain and Manufacturing

Data science and machine learning techniques help pharmaceutical companies better forecast demand for vaccines and drugs and distribute them efficiently. Machine learning models can help identify efficient supply systems by automating and optimizing the production steps. These will help supply drugs customized to small pools of patients in specific gene pools. Pfizer uses Machine learning to predict the maintenance cost of equipment used. Predictive maintenance using AI is the next big step for Pharmaceutical companies to reduce costs.

iii) Drug Development

Computer simulations of proteins, and tests of their interactions, and yield analysis help researchers develop and test drugs more efficiently. In 2016 Watson Health and Pfizer announced a collaboration to utilize IBM Watson for Drug Discovery to help accelerate Pfizer's research in immuno-oncology, an approach to cancer treatment that uses the body's immune system to help fight cancer. Deep learning models have been used recently for bioactivity and synthesis prediction for drugs and vaccines in addition to molecular design. Deep learning has been a revolutionary technique for drug discovery as it factors everything from new applications of medications to possible toxic reactions which can save millions in drug trials.

You can create a Machine learning model to predict molecular activity to help design medicine using this dataset . You may build a CNN or a Deep neural network for this data analyst case study project.

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9) Shell Data Analyst Case Study Project

Shell is a global group of energy and petrochemical companies with over 80,000 employees in around 70 countries. Shell uses advanced technologies and innovations to help build a sustainable energy future. Shell is going through a significant transition as the world needs more and cleaner energy solutions to be a clean energy company by 2050. It requires substantial changes in the way in which energy is used. Digital technologies, including AI and Machine Learning, play an essential role in this transformation. These include efficient exploration and energy production, more reliable manufacturing, more nimble trading, and a personalized customer experience. Using AI in various phases of the organization will help achieve this goal and stay competitive in the market. Here are a few data analytics case studies in the petrochemical industry:

i) Precision Drilling

Shell is involved in the processing mining oil and gas supply, ranging from mining hydrocarbons to refining the fuel to retailing them to customers. Recently Shell has included reinforcement learning to control the drilling equipment used in mining. Reinforcement learning works on a reward-based system based on the outcome of the AI model. The algorithm is designed to guide the drills as they move through the surface, based on the historical data from drilling records. It includes information such as the size of drill bits, temperatures, pressures, and knowledge of the seismic activity. This model helps the human operator understand the environment better, leading to better and faster results will minor damage to machinery used. 

ii) Efficient Charging Terminals

Due to climate changes, governments have encouraged people to switch to electric vehicles to reduce carbon dioxide emissions. However, the lack of public charging terminals has deterred people from switching to electric cars. Shell uses AI to monitor and predict the demand for terminals to provide efficient supply. Multiple vehicles charging from a single terminal may create a considerable grid load, and predictions on demand can help make this process more efficient.

iii) Monitoring Service and Charging Stations

Another Shell initiative trialed in Thailand and Singapore is the use of computer vision cameras, which can think and understand to watch out for potentially hazardous activities like lighting cigarettes in the vicinity of the pumps while refueling. The model is built to process the content of the captured images and label and classify it. The algorithm can then alert the staff and hence reduce the risk of fires. You can further train the model to detect rash driving or thefts in the future.

Here is a project to help you understand multiclass image classification. You can use the Hourly Energy Consumption Dataset to build an energy consumption prediction model. You can use time series with XGBoost to develop your model.

10) Zomato Case Study on Data Analytics

Zomato was founded in 2010 and is currently one of the most well-known food tech companies. Zomato offers services like restaurant discovery, home delivery, online table reservation, online payments for dining, etc. Zomato partners with restaurants to provide tools to acquire more customers while also providing delivery services and easy procurement of ingredients and kitchen supplies. Currently, Zomato has over 2 lakh restaurant partners and around 1 lakh delivery partners. Zomato has closed over ten crore delivery orders as of date. Zomato uses ML and AI to boost their business growth, with the massive amount of data collected over the years from food orders and user consumption patterns. Here are a few examples of data analyst case study project developed by the data scientists at Zomato:

i) Personalized Recommendation System for Homepage

Zomato uses data analytics to create personalized homepages for its users. Zomato uses data science to provide order personalization, like giving recommendations to the customers for specific cuisines, locations, prices, brands, etc. Restaurant recommendations are made based on a customer's past purchases, browsing history, and what other similar customers in the vicinity are ordering. This personalized recommendation system has led to a 15% improvement in order conversions and click-through rates for Zomato. 

You can use the Restaurant Recommendation Dataset to build a restaurant recommendation system to predict what restaurants customers are most likely to order from, given the customer location, restaurant information, and customer order history.

ii) Analyzing Customer Sentiment

Zomato uses Natural language processing and Machine learning to understand customer sentiments using social media posts and customer reviews. These help the company gauge the inclination of its customer base towards the brand. Deep learning models analyze the sentiments of various brand mentions on social networking sites like Twitter, Instagram, Linked In, and Facebook. These analytics give insights to the company, which helps build the brand and understand the target audience.

iii) Predicting Food Preparation Time (FPT)

Food delivery time is an essential variable in the estimated delivery time of the order placed by the customer using Zomato. The food preparation time depends on numerous factors like the number of dishes ordered, time of the day, footfall in the restaurant, day of the week, etc. Accurate prediction of the food preparation time can help make a better prediction of the Estimated delivery time, which will help delivery partners less likely to breach it. Zomato uses a Bidirectional LSTM-based deep learning model that considers all these features and provides food preparation time for each order in real-time. 

Data scientists are companies' secret weapons when analyzing customer sentiments and behavior and leveraging it to drive conversion, loyalty, and profits. These 10 data science case studies projects with examples and solutions show you how various organizations use data science technologies to succeed and be at the top of their field! To summarize, Data Science has not only accelerated the performance of companies but has also made it possible to manage & sustain their performance with ease.

FAQs on Data Analysis Case Studies

A case study in data science is an in-depth analysis of a real-world problem using data-driven approaches. It involves collecting, cleaning, and analyzing data to extract insights and solve challenges, offering practical insights into how data science techniques can address complex issues across various industries.

To create a data science case study, identify a relevant problem, define objectives, and gather suitable data. Clean and preprocess data, perform exploratory data analysis, and apply appropriate algorithms for analysis. Summarize findings, visualize results, and provide actionable recommendations, showcasing the problem-solving potential of data science techniques.

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12 Data Science Case Studies: Across Various Industries

Home Blog Data Science 12 Data Science Case Studies: Across Various Industries

Data science has become popular in the last few years due to its successful application in making business decisions. Data scientists have been using data science techniques to solve challenging real-world issues in healthcare, agriculture, manufacturing, automotive, and many more. For this purpose, a data enthusiast needs to stay updated with the latest technological advancements in AI. An excellent way to achieve this is through reading industry data science case studies. I recommend checking out Data Science With Python course syllabus to start your data science journey.   In this discussion, I will present some case studies to you that contain detailed and systematic data analysis of people, objects, or entities focusing on multiple factors present in the dataset. Almost every industry uses data science in some way. You can learn more about data science fundamentals in this Data Science course content .

Let’s look at the top data science case studies in this article so you can understand how businesses from many sectors have benefitted from data science to boost productivity, revenues, and more.

List of Data Science Case Studies 2024

• Hospitality:  Airbnb focuses on growth by  analyzing  customer voice using data science.  Qantas uses predictive analytics to mitigate losses
• Healthcare:  Novo Nordisk  is  Driving innovation with NLP.  AstraZeneca harnesses data for innovation in medicine  
• Covid 19:  Johnson and Johnson use s  d ata science  to fight the Pandemic  
• E-commerce:  Amazon uses data science to personalize shop p ing experiences and improve customer satisfaction  
• Supply chain management:  UPS optimizes supp l y chain with big data analytics
• Meteorology:  IMD leveraged data science to achieve a rec o rd 1.2m evacuation before cyclone ''Fani''  
• Entertainment Industry:  Netflix  u ses data science to personalize the content and improve recommendations.  Spotify uses big   data to deliver a rich user experience for online music streaming  
• Banking and Finance:  HDFC utilizes Big  D ata Analytics to increase income and enhance  the  banking experience
• Urban Planning and Smart Cities:  Traffic management in smart cities such as Pune and Bhubaneswar
• Agricultural Yield Prediction:  Farmers Edge in Canada uses Data science to help farmers improve their produce
• Transportation Industry:  Uber optimizes their ride-sharing feature and track the delivery routes through data analysis
• Environmental Industry:  NASA utilizes Data science to predict potential natural disasters, World Wildlife analyzes deforestation to protect the environment

Top 12 Data Science Case Studies

1. data science in hospitality industry.

In the hospitality sector, data analytics assists hotels in better pricing strategies, customer analysis, brand marketing, tracking market trends, and many more.

Airbnb focuses on growth by analyzing customer voice using data science.  A famous example in this sector is the unicorn '' Airbnb '', a startup that focussed on data science early to grow and adapt to the market faster. This company witnessed a 43000 percent hypergrowth in as little as five years using data science. They included data science techniques to process the data, translate this data for better understanding the voice of the customer, and use the insights for decision making. They also scaled the approach to cover all aspects of the organization. Airbnb uses statistics to analyze and aggregate individual experiences to establish trends throughout the community. These analyzed trends using data science techniques impact their business choices while helping them grow further.  

Travel industry and data science

Predictive analytics benefits many parameters in the travel industry. These companies can use recommendation engines with data science to achieve higher personalization and improved user interactions. They can study and cross-sell products by recommending relevant products to drive sales and increase revenue. Data science is also employed in analyzing social media posts for sentiment analysis, bringing invaluable travel-related insights. Whether these views are positive, negative, or neutral can help these agencies understand the user demographics, the expected experiences by their target audiences, and so on. These insights are essential for developing aggressive pricing strategies to draw customers and provide better customization to customers in the travel packages and allied services. Travel agencies like Expedia and Booking.com use predictive analytics to create personalized recommendations, product development, and effective marketing of their products. Not just travel agencies but airlines also benefit from the same approach. Airlines frequently face losses due to flight cancellations, disruptions, and delays. Data science helps them identify patterns and predict possible bottlenecks, thereby effectively mitigating the losses and improving the overall customer traveling experience.  

How Qantas uses predictive analytics to mitigate losses  

Qantas , one of Australia's largest airlines, leverages data science to reduce losses caused due to flight delays, disruptions, and cancellations. They also use it to provide a better traveling experience for their customers by reducing the number and length of delays caused due to huge air traffic, weather conditions, or difficulties arising in operations. Back in 2016, when heavy storms badly struck Australia's east coast, only 15 out of 436 Qantas flights were cancelled due to their predictive analytics-based system against their competitor Virgin Australia, which witnessed 70 cancelled flights out of 320.  

2. Data Science in Healthcare

The  Healthcare sector  is immensely benefiting from the advancements in AI. Data science, especially in medical imaging, has been helping healthcare professionals come up with better diagnoses and effective treatments for patients. Similarly, several advanced healthcare analytics tools have been developed to generate clinical insights for improving patient care. These tools also assist in defining personalized medications for patients reducing operating costs for clinics and hospitals. Apart from medical imaging or computer vision,  Natural Language Processing (NLP)  is frequently used in the healthcare domain to study the published textual research data.     

A. Pharmaceutical

Driving innovation with NLP: Novo Nordisk.  Novo Nordisk  uses the Linguamatics NLP platform from internal and external data sources for text mining purposes that include scientific abstracts, patents, grants, news, tech transfer offices from universities worldwide, and more. These NLP queries run across sources for the key therapeutic areas of interest to the Novo Nordisk R&D community. Several NLP algorithms have been developed for the topics of safety, efficacy, randomized controlled trials, patient populations, dosing, and devices. Novo Nordisk employs a data pipeline to capitalize the tools' success on real-world data and uses interactive dashboards and cloud services to visualize this standardized structured information from the queries for exploring commercial effectiveness, market situations, potential, and gaps in the product documentation. Through data science, they are able to automate the process of generating insights, save time and provide better insights for evidence-based decision making.  

How AstraZeneca harnesses data for innovation in medicine.  AstraZeneca  is a globally known biotech company that leverages data using AI technology to discover and deliver newer effective medicines faster. Within their R&D teams, they are using AI to decode the big data to understand better diseases like cancer, respiratory disease, and heart, kidney, and metabolic diseases to be effectively treated. Using data science, they can identify new targets for innovative medications. In 2021, they selected the first two AI-generated drug targets collaborating with BenevolentAI in Chronic Kidney Disease and Idiopathic Pulmonary Fibrosis.   

Data science is also helping AstraZeneca redesign better clinical trials, achieve personalized medication strategies, and innovate the process of developing new medicines. Their Center for Genomics Research uses  data science and AI  to analyze around two million genomes by 2026. Apart from this, they are training their AI systems to check these images for disease and biomarkers for effective medicines for imaging purposes. This approach helps them analyze samples accurately and more effortlessly. Moreover, it can cut the analysis time by around 30%.   

AstraZeneca also utilizes AI and machine learning to optimize the process at different stages and minimize the overall time for the clinical trials by analyzing the clinical trial data. Summing up, they use data science to design smarter clinical trials, develop innovative medicines, improve drug development and patient care strategies, and many more.

C. Wearable Technology  

Wearable technology is a multi-billion-dollar industry. With an increasing awareness about fitness and nutrition, more individuals now prefer using fitness wearables to track their routines and lifestyle choices.  

Fitness wearables are convenient to use, assist users in tracking their health, and encourage them to lead a healthier lifestyle. The medical devices in this domain are beneficial since they help monitor the patient's condition and communicate in an emergency situation. The regularly used fitness trackers and smartwatches from renowned companies like Garmin, Apple, FitBit, etc., continuously collect physiological data of the individuals wearing them. These wearable providers offer user-friendly dashboards to their customers for analyzing and tracking progress in their fitness journey.

3. Covid 19 and Data Science

In the past two years of the Pandemic, the power of data science has been more evident than ever. Different  pharmaceutical companies  across the globe could synthesize Covid 19 vaccines by analyzing the data to understand the trends and patterns of the outbreak. Data science made it possible to track the virus in real-time, predict patterns, devise effective strategies to fight the Pandemic, and many more.  

How Johnson and Johnson uses data science to fight the Pandemic   

The  data science team  at  Johnson and Johnson  leverages real-time data to track the spread of the virus. They built a global surveillance dashboard (granulated to county level) that helps them track the Pandemic's progress, predict potential hotspots of the virus, and narrow down the likely place where they should test its investigational COVID-19 vaccine candidate. The team works with in-country experts to determine whether official numbers are accurate and find the most valid information about case numbers, hospitalizations, mortality and testing rates, social compliance, and local policies to populate this dashboard. The team also studies the data to build models that help the company identify groups of individuals at risk of getting affected by the virus and explore effective treatments to improve patient outcomes.

4. Data Science in E-commerce  

In the  e-commerce sector , big data analytics can assist in customer analysis, reduce operational costs, forecast trends for better sales, provide personalized shopping experiences to customers, and many more.  

Amazon uses data science to personalize shopping experiences and improve customer satisfaction.  Amazon  is a globally leading eCommerce platform that offers a wide range of online shopping services. Due to this, Amazon generates a massive amount of data that can be leveraged to understand consumer behavior and generate insights on competitors' strategies. Data science case studies reveal how Amazon uses its data to provide recommendations to its users on different products and services. With this approach, Amazon is able to persuade its consumers into buying and making additional sales. This approach works well for Amazon as it earns 35% of the revenue yearly with this technique. Additionally, Amazon collects consumer data for faster order tracking and better deliveries.     

Similarly, Amazon's virtual assistant, Alexa, can converse in different languages; uses speakers and a   camera to interact with the users. Amazon utilizes the audio commands from users to improve Alexa and deliver a better user experience. 

5. Data Science in Supply Chain Management

Predictive analytics and big data are driving innovation in the Supply chain domain. They offer greater visibility into the company operations, reduce costs and overheads, forecasting demands, predictive maintenance, product pricing, minimize supply chain interruptions, route optimization, fleet management, drive better performance, and more.     

Optimizing supply chain with big data analytics: UPS

UPS  is a renowned package delivery and supply chain management company. With thousands of packages being delivered every day, on average, a UPS driver makes about 100 deliveries each business day. On-time and safe package delivery are crucial to UPS's success. Hence, UPS offers an optimized navigation tool ''ORION'' (On-Road Integrated Optimization and Navigation), which uses highly advanced big data processing algorithms. This tool for UPS drivers provides route optimization concerning fuel, distance, and time. UPS utilizes supply chain data analysis in all aspects of its shipping process. Data about packages and deliveries are captured through radars and sensors. The deliveries and routes are optimized using big data systems. Overall, this approach has helped UPS save 1.6 million gallons of gasoline in transportation every year, significantly reducing delivery costs.    

6. Data Science in Meteorology

Weather prediction is an interesting  application of data science . Businesses like aviation, agriculture and farming, construction, consumer goods, sporting events, and many more are dependent on climatic conditions. The success of these businesses is closely tied to the weather, as decisions are made after considering the weather predictions from the meteorological department.   

Besides, weather forecasts are extremely helpful for individuals to manage their allergic conditions. One crucial application of weather forecasting is natural disaster prediction and risk management.  

Weather forecasts begin with a large amount of data collection related to the current environmental conditions (wind speed, temperature, humidity, clouds captured at a specific location and time) using sensors on IoT (Internet of Things) devices and satellite imagery. This gathered data is then analyzed using the understanding of atmospheric processes, and machine learning models are built to make predictions on upcoming weather conditions like rainfall or snow prediction. Although data science cannot help avoid natural calamities like floods, hurricanes, or forest fires. Tracking these natural phenomena well ahead of their arrival is beneficial. Such predictions allow governments sufficient time to take necessary steps and measures to ensure the safety of the population.  

IMD leveraged data science to achieve a record 1.2m evacuation before cyclone ''Fani''   

Most  d ata scientist’s responsibilities  rely on satellite images to make short-term forecasts, decide whether a forecast is correct, and validate models. Machine Learning is also used for pattern matching in this case. It can forecast future weather conditions if it recognizes a past pattern. When employing dependable equipment, sensor data is helpful to produce local forecasts about actual weather models. IMD used satellite pictures to study the low-pressure zones forming off the Odisha coast (India). In April 2019, thirteen days before cyclone ''Fani'' reached the area,  IMD  (India Meteorological Department) warned that a massive storm was underway, and the authorities began preparing for safety measures.  

It was one of the most powerful cyclones to strike India in the recent 20 years, and a record 1.2 million people were evacuated in less than 48 hours, thanks to the power of data science.   

7. Data Science in the Entertainment Industry

Due to the Pandemic, demand for OTT (Over-the-top) media platforms has grown significantly. People prefer watching movies and web series or listening to the music of their choice at leisure in the convenience of their homes. This sudden growth in demand has given rise to stiff competition. Every platform now uses data analytics in different capacities to provide better-personalized recommendations to its subscribers and improve user experience.   

How Netflix uses data science to personalize the content and improve recommendations  

Netflix  is an extremely popular internet television platform with streamable content offered in several languages and caters to various audiences. In 2006, when Netflix entered this media streaming market, they were interested in increasing the efficiency of their existing ''Cinematch'' platform by 10% and hence, offered a prize of $1 million to the winning team. This approach was successful as they found a solution developed by the BellKor team at the end of the competition that increased prediction accuracy by 10.06%. Over 200 work hours and an ensemble of 107 algorithms provided this result. These winning algorithms are now a part of the Netflix recommendation system.  

Netflix also employs Ranking Algorithms to generate personalized recommendations of movies and TV Shows appealing to its users.   

Spotify uses big data to deliver a rich user experience for online music streaming  

Personalized online music streaming is another area where data science is being used.  Spotify  is a well-known on-demand music service provider launched in 2008, which effectively leveraged big data to create personalized experiences for each user. It is a huge platform with more than 24 million subscribers and hosts a database of nearly 20million songs; they use the big data to offer a rich experience to its users. Spotify uses this big data and various algorithms to train machine learning models to provide personalized content. Spotify offers a "Discover Weekly" feature that generates a personalized playlist of fresh unheard songs matching the user's taste every week. Using the Spotify "Wrapped" feature, users get an overview of their most favorite or frequently listened songs during the entire year in December. Spotify also leverages the data to run targeted ads to grow its business. Thus, Spotify utilizes the user data, which is big data and some external data, to deliver a high-quality user experience.  

8. Data Science in Banking and Finance

Data science is extremely valuable in the Banking and  Finance industry . Several high priority aspects of Banking and Finance like credit risk modeling (possibility of repayment of a loan), fraud detection (detection of malicious or irregularities in transactional patterns using machine learning), identifying customer lifetime value (prediction of bank performance based on existing and potential customers), customer segmentation (customer profiling based on behavior and characteristics for personalization of offers and services). Finally, data science is also used in real-time predictive analytics (computational techniques to predict future events).    

How HDFC utilizes Big Data Analytics to increase revenues and enhance the banking experience    

One of the major private banks in India,  HDFC Bank , was an early adopter of AI. It started with Big Data analytics in 2004, intending to grow its revenue and understand its customers and markets better than its competitors. Back then, they were trendsetters by setting up an enterprise data warehouse in the bank to be able to track the differentiation to be given to customers based on their relationship value with HDFC Bank. Data science and analytics have been crucial in helping HDFC bank segregate its customers and offer customized personal or commercial banking services. The analytics engine and SaaS use have been assisting the HDFC bank in cross-selling relevant offers to its customers. Apart from the regular fraud prevention, it assists in keeping track of customer credit histories and has also been the reason for the speedy loan approvals offered by the bank.  

9. Data Science in Urban Planning and Smart Cities  

Data Science can help the dream of smart cities come true! Everything, from traffic flow to energy usage, can get optimized using data science techniques. You can use the data fetched from multiple sources to understand trends and plan urban living in a sorted manner.  

The significant data science case study is traffic management in Pune city. The city controls and modifies its traffic signals dynamically, tracking the traffic flow. Real-time data gets fetched from the signals through cameras or sensors installed. Based on this information, they do the traffic management. With this proactive approach, the traffic and congestion situation in the city gets managed, and the traffic flow becomes sorted. A similar case study is from Bhubaneswar, where the municipality has platforms for the people to give suggestions and actively participate in decision-making. The government goes through all the inputs provided before making any decisions, making rules or arranging things that their residents actually need.  

10. Data Science in Agricultural Prediction   

Have you ever wondered how helpful it can be if you can predict your agricultural yield? That is exactly what data science is helping farmers with. They can get information about the number of crops they can produce in a given area based on different environmental factors and soil types. Using this information, the farmers can make informed decisions about their yield and benefit the buyers and themselves in multiple ways.  

Farmers across the globe and overseas use various data science techniques to understand multiple aspects of their farms and crops. A famous example of data science in the agricultural industry is the work done by Farmers Edge. It is a company in Canada that takes real-time images of farms across the globe and combines them with related data. The farmers use this data to make decisions relevant to their yield and improve their produce. Similarly, farmers in countries like Ireland use satellite-based information to ditch traditional methods and multiply their yield strategically.  

11. Data Science in the Transportation Industry   

Transportation keeps the world moving around. People and goods commute from one place to another for various purposes, and it is fair to say that the world will come to a standstill without efficient transportation. That is why it is crucial to keep the transportation industry in the most smoothly working pattern, and data science helps a lot in this. In the realm of technological progress, various devices such as traffic sensors, monitoring display systems, mobility management devices, and numerous others have emerged.  

Many cities have already adapted to the multi-modal transportation system. They use GPS trackers, geo-locations and CCTV cameras to monitor and manage their transportation system. Uber is the perfect case study to understand the use of data science in the transportation industry. They optimize their ride-sharing feature and track the delivery routes through data analysis. Their data science case studies approach enabled them to serve more than 100 million users, making transportation easy and convenient. Moreover, they also use the data they fetch from users daily to offer cost-effective and quickly available rides.  

12. Data Science in the Environmental Industry    

Increasing pollution, global warming, climate changes and other poor environmental impacts have forced the world to pay attention to environmental industry. Multiple initiatives are being taken across the globe to preserve the environment and make the world a better place. Though the industry recognition and the efforts are in the initial stages, the impact is significant, and the growth is fast.  

The popular use of data science in the environmental industry is by NASA and other research organizations worldwide. NASA gets data related to the current climate conditions, and this data gets used to create remedial policies that can make a difference. Another way in which data science is actually helping researchers is they can predict natural disasters well before time and save or at least reduce the potential damage considerably. A similar case study is with the World Wildlife Fund. They use data science to track data related to deforestation and help reduce the illegal cutting of trees. Hence, it helps preserve the environment.  

Where to Find Full Data Science Case Studies?  

Data science is a highly evolving domain with many practical applications and a huge open community. Hence, the best way to keep updated with the latest trends in this domain is by reading case studies and technical articles. Usually, companies share their success stories of how data science helped them achieve their goals to showcase their potential and benefit the greater good. Such case studies are available online on the respective company websites and dedicated technology forums like Towards Data Science or Medium.  

Additionally, we can get some practical examples in recently published research papers and textbooks in data science.  

What Are the Skills Required for Data Scientists?  

Data scientists play an important role in the data science process as they are the ones who work on the data end to end. To be able to work on a data science case study, there are several skills required for data scientists like a good grasp of the fundamentals of data science, deep knowledge of statistics, excellent programming skills in Python or R, exposure to data manipulation and data analysis, ability to generate creative and compelling data visualizations, good knowledge of big data, machine learning and deep learning concepts for model building & deployment. Apart from these technical skills, data scientists also need to be good storytellers and should have an analytical mind with strong communication skills.    

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Conclusion  

These were some interesting  data science case studies  across different industries. There are many more domains where data science has exciting applications, like in the Education domain, where data can be utilized to monitor student and instructor performance, develop an innovative curriculum that is in sync with the industry expectations, etc.   

Almost all the companies looking to leverage the power of big data begin with a SWOT analysis to narrow down the problems they intend to solve with data science. Further, they need to assess their competitors to develop relevant data science tools and strategies to address the challenging issue.  Thus, the utility of data science in several sectors is clearly visible, a lot is left to be explored, and more is yet to come. Nonetheless, data science will continue to boost the performance of organizations in this age of big data.  

Frequently Asked Questions (FAQs)

A case study in data science requires a systematic and organized approach for solving the problem. Generally, four main steps are needed to tackle every data science case study: 

• Defining the problem statement and strategy to solve it  
• Gather and pre-process the data by making relevant assumptions  
• Select tool and appropriate algorithms to build machine learning /deep learning models 
• Make predictions, accept the solutions based on evaluation metrics, and improve the model if necessary. 

Getting data for a case study starts with a reasonable understanding of the problem. This gives us clarity about what we expect the dataset to include. Finding relevant data for a case study requires some effort. Although it is possible to collect relevant data using traditional techniques like surveys and questionnaires, we can also find good quality data sets online on different platforms like Kaggle, UCI Machine Learning repository, Azure open data sets, Government open datasets, Google Public Datasets, Data World and so on.  

Data science projects involve multiple steps to process the data and bring valuable insights. A data science project includes different steps - defining the problem statement, gathering relevant data required to solve the problem, data pre-processing, data exploration & data analysis, algorithm selection, model building, model prediction, model optimization, and communicating the results through dashboards and reports.  

Devashree Madhugiri

Devashree holds an M.Eng degree in Information Technology from Germany and a background in Data Science. She likes working with statistics and discovering hidden insights in varied datasets to create stunning dashboards. She enjoys sharing her knowledge in AI by writing technical articles on various technological platforms. She loves traveling, reading fiction, solving Sudoku puzzles, and participating in coding competitions in her leisure time.

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Case studies

Notes for contributors

Case studies are a core feature of the Real World Data Science platform. Our case studies are designed to show how data science is used to solve real-world problems in business, public policy and beyond.

A good case study will be a source of information, insight and inspiration for each of our target audiences:

• Practitioners will learn from their peers – whether by seeing new techniques applied to common problems, or familiar techniques adapted to unique challenges.
• Leaders will see how different data science teams work, the mix of skills and experience in play, and how the components of the data science process fit together.
• Students will enrich their understanding of how data science is applied, how data scientists operate, and what skills they need to hone to succeed in the workplace.

Case studies should follow the structure below. It is not necessary to use the section headings we have provided – creativity and variety are encouraged. However, the areas outlined under each section heading should be covered in all submissions.

• The problem/challenge Summarise the project and its relevance to your organisation’s needs, aims and ambitions.
• Goals Specify what exactly you sought to achieve with this project.
• Background An opportunity to explain more about your organisation, your team’s work leading up to this project, and to introduce audiences more generally to the type of problem/challenge you faced, particularly if it is a problem/challenge that may be experienced by organisations working in different sectors and industries.
• Approach Describe how you turned the organisational problem/challenge into a task that could be addressed by data science. Explain how you proposed to tackle the problem, including an introduction, explanation and (possibly) a demonstration of the method, model or algorithm used. (NB: If you have a particular interest and expertise in the method, model or algorithm employed, including the history and development of the approach, please consider writing an Explainer article for us.) Discuss the pros and cons, strengths and limitations of the approach.
• Implementation Walk audiences through the implementation process. Discuss any challenges you faced, the ethical questions you needed to ask and answer, and how you tested the approach to ensure that outcomes would be robust, unbiased, good quality, and aligned with the goals you set out to achieve.
• Impact How successful was the project? Did you achieve your goals? How has the project benefited your organisation? How has the project benefited your team? Does it inform or pave the way for future projects?
• Learnings What are your key takeaways from the project? Are there lessons that you can apply to future projects, or are there learnings for other data scientists working on similar problems/challenges?

Advice and recommendations

You do not need to divulge the detailed inner workings of your organisation. Audiences are mostly interested in understanding the general use case and the problem-solving process you went through, to see how they might apply the same approach within their own organisations.

Goals can be defined quite broadly. There’s no expectation that you set out your organisation’s short- or long-term targets. Instead, audiences need to know enough about what you want to do so they can understand what motivates your choice of approach.

Use toy examples and synthetic data to good effect. We understand that – whether for commercial, legal or ethical reasons – it can be difficult or impossible to share real data in your case studies, or to describe the actual outputs of your work. However, there are many ways to share learnings and insights without divulging sensitive information. This blog post from Lyft uses hypotheticals, mathematical notation and synthetic data to explain the company’s approach to causal forecasting without revealing actual KPIs or data.

People like to experiment, so encourage them to do so. Our platform allows you to embed code and to link that code to interactive coding environments like Google Colab . So if, for example, you want to explain a technique like bootstrapping , why not provide a code block so that audiences can run a bootstrapping simulation themselves.

Leverage links. You can’t be expected to explain or cover every detail in one case study, so feel free to point audiences to other sources of information that can enrich their understanding: blogs, videos, journal articles, conference papers, etc.

Data Analysis Case Study: Learn From Humana’s Automated Data Analysis Project

Lillian Pierson, P.E.

Playback speed:

Got data? Great! Looking for that perfect data analysis case study to help you get started using it? You’re in the right place.

If you’ve ever struggled to decide what to do next with your data projects, to actually find meaning in the data, or even to decide what kind of data to collect, then KEEP READING…

Deep down, you know what needs to happen. You need to initiate and execute a data strategy that really moves the needle for your organization. One that produces seriously awesome business results.

But how you’re in the right place to find out..

As a data strategist who has worked with 10 percent of Fortune 100 companies, today I’m sharing with you a case study that demonstrates just how real businesses are making real wins with data analysis. 

In the post below, we’ll look at:

• A shining data success story;
• What went on ‘under-the-hood’ to support that successful data project; and
• The exact data technologies used by the vendor, to take this project from pure strategy to pure success

If you prefer to watch this information rather than read it, it’s captured in the video below:

Here’s the url too: https://youtu.be/xMwZObIqvLQ

3 Action Items You Need To Take

To actually use the data analysis case study you’re about to get – you need to take 3 main steps. Those are:

• Reflect upon your organization as it is today (I left you some prompts below – to help you get started)
• Review winning data case collections (starting with the one I’m sharing here) and identify 5 that seem the most promising for your organization given it’s current set-up
• Assess your organization AND those 5 winning case collections. Based on that assessment, select the “QUICK WIN” data use case that offers your organization the most bang for it’s buck

Step 1: Reflect Upon Your Organization

Whenever you evaluate data case collections to decide if they’re a good fit for your organization, the first thing you need to do is organize your thoughts with respect to your organization as it is today.

Before moving into the data analysis case study, STOP and ANSWER THE FOLLOWING QUESTIONS – just to remind yourself:

• What is the business vision for our organization?
• What industries do we primarily support?
• What data technologies do we already have up and running, that we could use to generate even more value?
• What team members do we have to support a new data project? And what are their data skillsets like?
• What type of data are we mostly looking to generate value from? Structured? Semi-Structured? Un-structured? Real-time data? Huge data sets? What are our data resources like?

Jot down some notes while you’re here. Then keep them in mind as you read on to find out how one company, Humana, used its data to achieve a 28 percent increase in customer satisfaction. Also include its 63 percent increase in employee engagement! (That’s such a seriously impressive outcome, right?!)

Step 2: Review Data Case Studies

Here we are, already at step 2. It’s time for you to start reviewing data analysis case studies  (starting with the one I’m sharing below). I dentify 5 that seem the most promising for your organization given its current set-up.

Humana’s Automated Data Analysis Case Study

The key thing to note here is that the approach to creating a successful data program varies from industry to industry .

Let’s start with one to demonstrate the kind of value you can glean from these kinds of success stories.

Humana has provided health insurance to Americans for over 50 years. It is a service company focused on fulfilling the needs of its customers. A great deal of Humana’s success as a company rides on customer satisfaction, and the frontline of that battle for customers’ hearts and minds is Humana’s customer service center.

Call centers are hard to get right. A lot of emotions can arise during a customer service call, especially one relating to health and health insurance. Sometimes people are frustrated. At times, they’re upset. Also, there are times the customer service representative becomes aggravated, and the overall tone and progression of the phone call goes downhill. This is of course very bad for customer satisfaction.

Humana wanted to use artificial intelligence to improve customer satisfaction (and thus, customer retention rates & profits per customer).

Humana wanted to find a way to use artificial intelligence to monitor their phone calls and help their agents do a better job connecting with their customers in order to improve customer satisfaction (and thus, customer retention rates & profits per customer ).

In light of their business need, Humana worked with a company called Cogito, which specializes in voice analytics technology.

Cogito offers a piece of AI technology called Cogito Dialogue. It’s been trained to identify certain conversational cues as a way of helping call center representatives and supervisors stay actively engaged in a call with a customer.

The AI listens to cues like the customer’s voice pitch.

If it’s rising, or if the call representative and the customer talk over each other, then the dialogue tool will send out electronic alerts to the agent during the call.

Humana fed the dialogue tool customer service data from 10,000 calls and allowed it to analyze cues such as keywords, interruptions, and pauses, and these cues were then linked with specific outcomes. For example, if the representative is receiving a particular type of cues, they are likely to get a specific customer satisfaction result.

The Outcome

Customers were happier, and customer service representatives were more engaged..

This automated solution for data analysis has now been deployed in 200 Humana call centers and the company plans to roll it out to 100 percent of its centers in the future.

The initiative was so successful, Humana has been able to focus on next steps in its data program. The company now plans to begin predicting the type of calls that are likely to go unresolved, so they can send those calls over to management before they become frustrating to the customer and customer service representative alike.

What does this mean for you and your business?

Well, if you’re looking for new ways to generate value by improving the quantity and quality of the decision support that you’re providing to your customer service personnel, then this may be a perfect example of how you can do so.

Humana’s Business Use Cases

Humana’s data analysis case study includes two key business use cases:

• Analyzing customer sentiment; and
• Suggesting actions to customer service representatives.

Analyzing Customer Sentiment

First things first, before you go ahead and collect data, you need to ask yourself who and what is involved in making things happen within the business.

In the case of Humana, the actors were:

• The health insurance system itself
• The customer, and
• The customer service representative

As you can see in the use case diagram above, the relational aspect is pretty simple. You have a customer service representative and a customer. They are both producing audio data, and that audio data is being fed into the system.

Humana focused on collecting the key data points, shown in the image below, from their customer service operations.

By collecting data about speech style, pitch, silence, stress in customers’ voices, length of call, speed of customers’ speech, intonation, articulation, silence, and representatives’  manner of speaking, Humana was able to analyze customer sentiment and introduce techniques for improved customer satisfaction.

Having strategically defined these data points, the Cogito technology was able to generate reports about customer sentiment during the calls.

Suggesting actions to customer service representatives.

The second use case for the Humana data program follows on from the data gathered in the first case.

In Humana’s case, Cogito generated a host of call analyses and reports about key call issues.

In the second business use case, Cogito was able to suggest actions to customer service representatives, in real-time , to make use of incoming data and help improve customer satisfaction on the spot.

The technology Humana used provided suggestions via text message to the customer service representative, offering the following types of feedback:

• The tone of voice is too tense
• The speed of speaking is high
• The customer representative and customer are speaking at the same time

These alerts allowed the Humana customer service representatives to alter their approach immediately , improving the quality of the interaction and, subsequently, the customer satisfaction.

The preconditions for success in this use case were:

• The call-related data must be collected and stored
• The AI models must be in place to generate analysis on the data points that are recorded during the calls

Evidence of success can subsequently be found in a system that offers real-time suggestions for courses of action that the customer service representative can take to improve customer satisfaction.

Thanks to this data-intensive business use case, Humana was able to increase customer satisfaction, improve customer retention rates, and drive profits per customer.

The Technology That Supports This Data Analysis Case Study

I promised to dip into the tech side of things. This is especially for those of you who are interested in the ins and outs of how projects like this one are actually rolled out.

Here’s a little rundown of the main technologies we discovered when we investigated how Cogito runs in support of its clients like Humana.

• For cloud data management Cogito uses AWS, specifically the Athena product
• For on-premise big data management, the company used Apache HDFS – the distributed file system for storing big data
• They utilize MapReduce, for processing their data
• And Cogito also has traditional systems and relational database management systems such as PostgreSQL
• In terms of analytics and data visualization tools, Cogito makes use of Tableau
• And for its machine learning technology, these use cases required people with knowledge in Python, R, and SQL, as well as deep learning (Cogito uses the PyTorch library and the TensorFlow library)

These data science skill sets support the effective computing, deep learning , and natural language processing applications employed by Humana for this use case.

If you’re looking to hire people to help with your own data initiative, then people with those skills listed above, and with experience in these specific technologies, would be a huge help.

Step 3: S elect The “Quick Win” Data Use Case

Still there? Great!

It’s time to close the loop.

Remember those notes you took before you reviewed the study? I want you to STOP here and assess. Does this Humana case study seem applicable and promising as a solution, given your organization’s current set-up…

YES ▶ Excellent!

Earmark it and continue exploring other winning data use cases until you’ve identified 5 that seem like great fits for your businesses needs. Evaluate those against your organization’s needs, and select the very best fit to be your “quick win” data use case. Develop your data strategy around that.

NO , Lillian – It’s not applicable. ▶  No problem.

Discard the information and continue exploring the winning data use cases we’ve categorized for you according to business function and industry. Save time by dialing down into the business function you know your business really needs help with now. Identify 5 winning data use cases that seem like great fits for your businesses needs. Evaluate those against your organization’s needs, and select the very best fit to be your “quick win” data use case. Develop your data strategy around that data use case.

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8 case studies and real world examples of how Big Data has helped keep on top of competition

Fast, data-informed decision-making can drive business success. Managing high customer expectations, navigating marketing challenges, and global competition – many organizations look to data analytics and business intelligence for a competitive advantage.

Using data to serve up personalized ads based on browsing history, providing contextual KPI data access for all employees and centralizing data from across the business into one digital ecosystem so processes can be more thoroughly reviewed are all examples of business intelligence.

Organizations invest in data science because it promises to bring competitive advantages.

Data is transforming into an actionable asset, and new tools are using that reality to move the needle with ML. As a result, organizations are on the brink of mobilizing data to not only predict the future but also to increase the likelihood of certain outcomes through prescriptive analytics.

Here are some case studies that show some ways BI is making a difference for companies around the world:

1) Starbucks:

With 90 million transactions a week in 25,000 stores worldwide the coffee giant is in many ways on the cutting edge of using big data and artificial intelligence to help direct marketing, sales and business decisions

Through its popular loyalty card program and mobile application, Starbucks owns individual purchase data from millions of customers. Using this information and BI tools, the company predicts purchases and sends individual offers of what customers will likely prefer via their app and email. This system draws existing customers into its stores more frequently and increases sales volumes.

The same intel that helps Starbucks suggest new products to try also helps the company send personalized offers and discounts that go far beyond a special birthday discount. Additionally, a customized email goes out to any customer who hasn’t visited a Starbucks recently with enticing offers—built from that individual’s purchase history—to re-engage them.

2) Netflix:

The online entertainment company’s 148 million subscribers give it a massive BI advantage.

Netflix has digitized its interactions with its 151 million subscribers. It collects data from each of its users and with the help of data analytics understands the behavior of subscribers and their watching patterns. It then leverages that information to recommend movies and TV shows customized as per the subscriber’s choice and preferences.

As per Netflix, around 80% of the viewer’s activity is triggered by personalized algorithmic recommendations. Where Netflix gains an edge over its peers is that by collecting different data points, it creates detailed profiles of its subscribers which helps them engage with them better.

The recommendation system of Netflix contributes to more than 80% of the content streamed by its subscribers which has helped Netflix earn a whopping one billion via customer retention. Due to this reason, Netflix doesn’t have to invest too much on advertising and marketing their shows. They precisely know an estimate of the people who would be interested in watching a show.

3) Coca-Cola:

Coca Cola is the world’s largest beverage company, with over 500 soft drink brands sold in more than 200 countries. Given the size of its operations, Coca Cola generates a substantial amount of data across its value chain – including sourcing, production, distribution, sales and customer feedback which they can leverage to drive successful business decisions.

Coca Cola has been investing extensively in research and development, especially in AI, to better leverage the mountain of data it collects from customers all around the world. This initiative has helped them better understand consumer trends in terms of price, flavors, packaging, and consumer’ preference for healthier options in certain regions.

With 35 million Twitter followers and a whopping 105 million Facebook fans, Coca-Cola benefits from its social media data. Using AI-powered image-recognition technology, they can track when photographs of its drinks are posted online. This data, paired with the power of BI, gives the company important insights into who is drinking their beverages, where they are and why they mention the brand online. The information helps serve consumers more targeted advertising, which is four times more likely than a regular ad to result in a click.

Coca Cola is increasingly betting on BI, data analytics and AI to drive its strategic business decisions. From its innovative free style fountain machine to finding new ways to engage with customers, Coca Cola is well-equipped to remain at the top of the competition in the future. In a new digital world that is increasingly dynamic, with changing customer behavior, Coca Cola is relying on Big Data to gain and maintain their competitive advantage.

4) American Express GBT

The American Express Global Business Travel company, popularly known as Amex GBT, is an American multinational travel and meetings programs management corporation which operates in over 120 countries and has over 14,000 employees.

Challenges:

Scalability – Creating a single portal for around 945 separate data files from internal and customer systems using the current BI tool would require over 6 months to complete. The earlier tool was used for internal purposes and scaling the solution to such a large population while keeping the costs optimum was a major challenge

Performance – Their existing system had limitations shifting to Cloud. The amount of time and manual effort required was immense

Data Governance – Maintaining user data security and privacy was of utmost importance for Amex GBT

The company was looking to protect and increase its market share by differentiating its core services and was seeking a resource to manage and drive their online travel program capabilities forward. Amex GBT decided to make a strategic investment in creating smart analytics around their booking software.

The solution equipped users to view their travel ROI by categorizing it into three categories cost, time and value. Each category has individual KPIs that are measured to evaluate the performance of a travel plan.

Reducing travel expenses by 30%

Time to Value – Initially it took a week for new users to be on-boarded onto the platform. With Premier Insights that time had now been reduced to a single day and the process had become much simpler and more effective.

Savings on Spends – The product notifies users of any available booking offers that can help them save on their expenditure. It recommends users of possible saving potential such as flight timings, date of the booking, date of travel, etc.

Adoption – Ease of use of the product, quick scale-up, real-time implementation of reports, and interactive dashboards of Premier Insights increased the global online adoption for Amex GBT

5) Airline Solutions Company: BI Accelerates Business Insights

Airline Solutions provides booking tools, revenue management, web, and mobile itinerary tools, as well as other technology, for airlines, hotels and other companies in the travel industry.

Challenge: The travel industry is remarkably dynamic and fast paced. And the airline solution provider’s clients needed advanced tools that could provide real-time data on customer behavior and actions.

They developed an enterprise travel data warehouse (ETDW) to hold its enormous amounts of data. The executive dashboards provide near real-time insights in user-friendly environments with a 360-degree overview of business health, reservations, operational performance and ticketing.

Results: The scalable infrastructure, graphic user interface, data aggregation and ability to work collaboratively have led to more revenue and increased client satisfaction.

6) A specialty US Retail Provider: Leveraging prescriptive analytics

Challenge/Objective: A specialty US Retail provider wanted to modernize its data platform which could help the business make real-time decisions while also leveraging prescriptive analytics. They wanted to discover true value of data being generated from its multiple systems and understand the patterns (both known and unknown) of sales, operations, and omni-channel retail performance.

We helped build a modern data solution that consolidated their data in a data lake and data warehouse, making it easier to extract the value in real-time. We integrated our solution with their OMS, CRM, Google Analytics, Salesforce, and inventory management system. The data was modeled in such a way that it could be fed into Machine Learning algorithms; so that we can leverage this easily in the future.

The customer had visibility into their data from day 1, which is something they had been wanting for some time. In addition to this, they were able to build more reports, dashboards, and charts to understand and interpret the data. In some cases, they were able to get real-time visibility and analysis on instore purchases based on geography!

7) Logistics startup with an objective to become the “Uber of the Trucking Sector” with the help of data analytics

Challenge: A startup specializing in analyzing vehicle and/or driver performance by collecting data from sensors within the vehicle (a.k.a. vehicle telemetry) and Order patterns with an objective to become the “Uber of the Trucking Sector”

Solution: We developed a customized backend of the client’s trucking platform so that they could monetize empty return trips of transporters by creating a marketplace for them. The approach used a combination of AWS Data Lake, AWS microservices, machine learning and analytics.

• Reduced fuel costs
• Optimized Reloads
• More accurate driver / truck schedule planning
• Smarter Routing
• Fewer empty return trips
• Deeper analysis of driver patterns, breaks, routes, etc.

8) Challenge/Objective: A niche segment customer competing against market behemoths looking to become a “Niche Segment Leader”

Solution: We developed a customized analytics platform that can ingest CRM, OMS, Ecommerce, and Inventory data and produce real time and batch driven analytics and AI platform. The approach used a combination of AWS microservices, machine learning and analytics.

• Reduce Customer Churn
• Optimized Order Fulfillment
• More accurate demand schedule planning
• Improve Product Recommendation
• Improved Last Mile Delivery

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Data Analytics Case Study: Complete Guide in 2024

What are data analytics case study interviews.

When you’re trying to land a data analyst job, the last thing to stand in your way is the data analytics case study interview.

One reason they’re so challenging is that case studies don’t typically have a right or wrong answer.

Instead, case study interviews require you to come up with a hypothesis for an analytics question and then produce data to support or validate your hypothesis. In other words, it’s not just about your technical skills; you’re also being tested on creative problem-solving and your ability to communicate with stakeholders.

This article provides an overview of how to answer data analytics case study interview questions. You can find an in-depth course in the data analytics learning path .

How to Solve Data Analytics Case Questions

Check out our video below on How to solve a Data Analytics case study problem:

With data analyst case questions, you will need to answer two key questions:

• What metrics should I propose?
• How do I write a SQL query to get the metrics I need?

In short, to ace a data analytics case interview, you not only need to brush up on case questions, but you also should be adept at writing all types of SQL queries and have strong data sense.

These questions are especially challenging to answer if you don’t have a framework or know how to answer them. To help you prepare , we created this step-by-step guide to answering data analytics case questions.

We show you how to use a framework to answer case questions, provide example analytics questions, and help you understand the difference between analytics case studies and product metrics case studies .

Data Analytics Cases vs Product Metrics Questions

Product case questions sometimes get lumped in with data analytics cases.

Ultimately, the type of case question you are asked will depend on the role. For example, product analysts will likely face more product-oriented questions.

Product metrics cases tend to focus on a hypothetical situation. You might be asked to:

Investigate Metrics - One of the most common types will ask you to investigate a metric, usually one that’s going up or down. For example, “Why are Facebook friend requests falling by 10 percent?”

Measure Product/Feature Success - A lot of analytics cases revolve around the measurement of product success and feature changes. For example, “We want to add X feature to product Y. What metrics would you track to make sure that’s a good idea?”

With product data cases, the key difference is that you may or may not be required to write the SQL query to find the metric.

Instead, these interviews are more theoretical and are designed to assess your product sense and ability to think about analytics problems from a product perspective. Product metrics questions may also show up in the data analyst interview , but likely only for product data analyst roles.

TRY CHECKING: Marketing Analytics Case Study Guide

Data Analytics Case Study Question: Sample Solution

Let’s start with an example data analytics case question :

You’re given a table that represents search results from searches on Facebook. The query column is the search term, the position column represents each position the search result came in, and the rating column represents the human rating from 1 to 5, where 5 is high relevance, and 1 is low relevance.

Each row in the search_events table represents a single search, with the has_clicked column representing if a user clicked on a result or not. We have a hypothesis that the CTR is dependent on the search result rating.

Write a query to return data to support or disprove this hypothesis.

search_results table:

search_events table

Step 1: With Data Analytics Case Studies, Start by Making Assumptions

Hint: Start by making assumptions and thinking out loud. With this question, focus on coming up with a metric to support the hypothesis. If the question is unclear or if you think you need more information, be sure to ask.

Answer. The hypothesis is that CTR is dependent on search result rating. Therefore, we want to focus on the CTR metric, and we can assume:

• If CTR is high when search result ratings are high, and CTR is low when the search result ratings are low, then the hypothesis is correct.
• If CTR is low when the search ratings are high, or there is no proven correlation between the two, then our hypothesis is not proven.

Step 2: Provide a Solution for the Case Question

Hint: Walk the interviewer through your reasoning. Talking about the decisions you make and why you’re making them shows off your problem-solving approach.

Answer. One way we can investigate the hypothesis is to look at the results split into different search rating buckets. For example, if we measure the CTR for results rated at 1, then those rated at 2, and so on, we can identify if an increase in rating is correlated with an increase in CTR.

First, I’d write a query to get the number of results for each query in each bucket. We want to look at the distribution of results that are less than a rating threshold, which will help us see the relationship between search rating and CTR.

This CTE aggregates the number of results that are less than a certain rating threshold. Later, we can use this to see the percentage that are in each bucket. If we re-join to the search_events table, we can calculate the CTR by then grouping by each bucket.

Step 3: Use Analysis to Backup Your Solution

Hint: Be prepared to justify your solution. Interviewers will follow up with questions about your reasoning, and ask why you make certain assumptions.

Answer. By using the CASE WHEN statement, I calculated each ratings bucket by checking to see if all the search results were less than 1, 2, or 3 by subtracting the total from the number within the bucket and seeing if it equates to 0.

I did that to get away from averages in our bucketing system. Outliers would make it more difficult to measure the effect of bad ratings. For example, if a query had a 1 rating and another had a 5 rating, that would equate to an average of 3. Whereas in my solution, a query with all of the results under 1, 2, or 3 lets us know that it actually has bad ratings.

Product Data Case Question: Sample Solution

In product metrics interviews, you’ll likely be asked about analytics, but the discussion will be more theoretical. You’ll propose a solution to a problem, and supply the metrics you’ll use to investigate or solve it. You may or may not be required to write a SQL query to get those metrics.

We’ll start with an example product metrics case study question :

Let’s say you work for a social media company that has just done a launch in a new city. Looking at weekly metrics, you see a slow decrease in the average number of comments per user from January to March in this city.

The company has been consistently growing new users in the city from January to March.

What are some reasons why the average number of comments per user would be decreasing and what metrics would you look into?

Step 1: Ask Clarifying Questions Specific to the Case

Hint: This question is very vague. It’s all hypothetical, so we don’t know very much about users, what the product is, and how people might be interacting. Be sure you ask questions upfront about the product.

Answer: Before I jump into an answer, I’d like to ask a few questions:

• Who uses this social network? How do they interact with each other?
• Has there been any performance issues that might be causing the problem?
• What are the goals of this particular launch?
• Has there been any changes to the comment features in recent weeks?

For the sake of this example, let’s say we learn that it’s a social network similar to Facebook with a young audience, and the goals of the launch are to grow the user base. Also, there have been no performance issues and the commenting feature hasn’t been changed since launch.

Step 2: Use the Case Question to Make Assumptions

Hint: Look for clues in the question. For example, this case gives you a metric, “average number of comments per user.” Consider if the clue might be helpful in your solution. But be careful, sometimes questions are designed to throw you off track.

Answer: From the question, we can hypothesize a little bit. For example, we know that user count is increasing linearly. That means two things:

• The decreasing comments issue isn’t a result of a declining user base.
• The cause isn’t loss of platform.

We can also model out the data to help us get a better picture of the average number of comments per user metric:

• January: 10000 users, 30000 comments, 3 comments/user
• February: 20000 users, 50000 comments, 2.5 comments/user
• March: 30000 users, 60000 comments, 2 comments/user

One thing to note: Although this is an interesting metric, I’m not sure if it will help us solve this question. For one, average comments per user doesn’t account for churn. We might assume that during the three-month period users are churning off the platform. Let’s say the churn rate is 25% in January, 20% in February and 15% in March.

Step 3: Make a Hypothesis About the Data

Hint: Don’t worry too much about making a correct hypothesis. Instead, interviewers want to get a sense of your product initiation and that you’re on the right track. Also, be prepared to measure your hypothesis.

Answer. I would say that average comments per user isn’t a great metric to use, because it doesn’t reveal insights into what’s really causing this issue.

That’s because it doesn’t account for active users, which are the users who are actually commenting. A better metric to investigate would be retained users and monthly active users.

What I suspect is causing the issue is that active users are commenting frequently and are responsible for the increase in comments month-to-month. New users, on the other hand, aren’t as engaged and aren’t commenting as often.

Step 4: Provide Metrics and Data Analysis

Hint: Within your solution, include key metrics that you’d like to investigate that will help you measure success.

Answer: I’d say there are a few ways we could investigate the cause of this problem, but the one I’d be most interested in would be the engagement of monthly active users.

If the growth in comments is coming from active users, that would help us understand how we’re doing at retaining users. Plus, it will also show if new users are less engaged and commenting less frequently.

One way that we could dig into this would be to segment users by their onboarding date, which would help us to visualize engagement and see how engaged some of our longest-retained users are.

If engagement of new users is the issue, that will give us some options in terms of strategies for addressing the problem. For example, we could test new onboarding or commenting features designed to generate engagement.

Step 5: Propose a Solution for the Case Question

Hint: In the majority of cases, your initial assumptions might be incorrect, or the interviewer might throw you a curveball. Be prepared to make new hypotheses or discuss the pitfalls of your analysis.

Answer. If the cause wasn’t due to a lack of engagement among new users, then I’d want to investigate active users. One potential cause would be active users commenting less. In that case, we’d know that our earliest users were churning out, and that engagement among new users was potentially growing.

Again, I think we’d want to focus on user engagement since the onboarding date. That would help us understand if we were seeing higher levels of churn among active users, and we could start to identify some solutions there.

Tip: Use a Framework to Solve Data Analytics Case Questions

Analytics case questions can be challenging, but they’re much more challenging if you don’t use a framework. Without a framework, it’s easier to get lost in your answer, to get stuck, and really lose the confidence of your interviewer. Find helpful frameworks for data analytics questions in our data analytics learning path and our product metrics learning path .

Once you have the framework down, what’s the best way to practice? Mock interviews with our coaches are very effective, as you’ll get feedback and helpful tips as you answer. You can also learn a lot by practicing P2P mock interviews with other Interview Query students. No data analytics background? Check out how to become a data analyst without a degree .

Finally, if you’re looking for sample data analytics case questions and other types of interview questions, see our guide on the top data analyst interview questions .

Top Data Science Case Studies For Inspiration

A data science case study refers to a process comprising a practical business problem on which data scientists work to develop deep learning or machine learning algorithms and programs. These programs and algorithms lead to an optimal solution to the business problem. Working on a data science case study involves analysing and solving a problem statement.

Data Science helps to boost businesses’ performance and helps them to sustain their performance. Various case studies related to data science help companies to progress significantly in their fields. These case studies help companies to effectively fulfil customers’ requirements by deeply assessing data for valuable insights. Let’s go through the topmost data science case studies for inspiration.

1) A leading biopharmaceutical company uses Machine Learning and AI to forecast the used medical equipment’s maintenance cost: Healthcare industry

Pfizer employs Machine learning to forecast the maintenance cost of the equipment used in patients’ treatment. The following effective approach the pharmaceutical companies should take to decrease expenses is implementing predictive maintenance using machine learning and AI.

Artificial Intelligence has significantly contributed to this sector’s growth. Multiple advanced tools in this sector are created to develop insights for providing the best treatment to patients. The tools used by the healthcare data science case studies help in specifying treatments as per the patients’ physical conditions. Consequently, these tools help hospitals to save on the expenses incurred in their services.

In medical imaging, data science assists healthcare personnel with productive medications for patients. These case studies help biotech companies to redesign better experiments and modernise the process of developing innovative medicines. They ensure that healthcare companies can spot the problems and avoid them from moving forward. 

Check out our website if you want to learn data science .

2) The use of Big Data Analytics to monitor student requirements: Education

Data Science has revolutionised how instructors and students interact and improve students’ performance assessment. It helps the instructors to evaluate the feedback obtained from the students and enhance their teaching methods accordingly.

Advanced big data analytics techniques help teachers to analyse their students’ requirements depending on their academic performance.

For example, online education platforms use data science-based python case study to track student performance. Hence, it systematises the assignment evaluation and improves the course curriculum depending on students’ opinions. This case study helps instructors prepare predictive modelling to forecast students’ performance and make the required amendments to teaching methods.

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3) Airbnb uses data science and realised 43,000% growth in five years: Hospitality Industry

Data analytics case study in hospitality helps hotels provide customers with the best possible costs. It helps hotel management to effectively endorse their business, understand the customers’ needs, determine the latest trends in this industry, and more.

This strategy proved very effective for Airbnb because the company realised 43,000% growth in only five years. This case study aims to share a few critical issues Airbnb faced during its development journey. It also expresses information about how the data scientists resolved those issues. Moreover, it adopted data science techniques to process the data, better interpret customers’ opinions, and make reasonable decisions based on customer needs.

Top Data Science Skills to Learn

4) Bin Packing Problem uses data science for package optimisation: E-commerce industry

When people search for any product over the internet, the search engine provides suggestions for similar products. The companies selling those products use data science for marketing their products based on the user’s interest via the recommendation system. The suggestions involved in this data analytics case study are typically dependent on the users’ search history.    

Bin Packing problem is a common NP-Hard problem on which data scientists work for optimising packages.

In this sector, big data analytics helps analyse customers’ needs, check prices, determine ways to boost sales and ensure customer satisfaction.

Another best example of this case study is Amazon . It uses data science to ensure customer satisfaction by tailoring product choices. Consequently, the generated data analyses customers’ needs and helps the brand to tailor them accordingly. Amazon utilises its data to serve users with recommendations on offered services and products. As a result, Amazon can persuade its consumers to purchase and make more sales.

Our learners also read: Free Python Course with Certification

5) Loan Eligibility prediction using Machine Learning: Finance and Banking industry

Data science proves quite beneficial in the finance and banking industry. The corresponding data analyst case study helps identify this industry’s many crucial facets. This Python case study uses Python to predict whether or not a loan must be provided to an applicant. It predicts using a parameter like a credit score. 

It also uses a machine learning algorithm to detect customer anomalies or malicious banking behaviour. When it comes to customer segmentation, data science uses customers’ behaviour to offer tailored services and products. This case study can suggest ways to boost financial performance depending on customers’ transactions and behaviours. 

6) Machine learning models identify, automate and optimise the manufacturing process: Supply Chain Management

Machine learning models can determine efficient supply systems after automating and optimising the manufacturing procedure. It facilitates the customisation of supply drugs to several patients.

The factors like big data and predictive analytics ensure innovation in this industry. This case study analyses the company operations, customers’ demands, products’ costs, reduces supply chain anomalies, and more.   

Another decent example of the use of this data science case study is the package delivery business in supply chain management. Timely and safe package delivery is inevitable for this company’s success. This company can develop advanced navigation tools using cutting-edge big data or Hadoop algorithms. This tool helps the company’s driver to determine the optimum route based on time, distance, and other aspects. Hence, the customers are assured of a flawless shipping experience.

7) Netflix uses over 1300+ recommendation clusters to offer a personalised experience: Entertainment Industry

Netflix uses more than 1300 recommendation clusters to provide a customised experience. These clusters are dependent on consumers’ viewing priorities. Netflix collects users’ data like platform research for keywords optimisation, content pause/rewind time, user viewing duration, etc. This data predicts the viewers’ viewing preference and offers a customised recommendation of shows and series.

The demand for OTT media platforms has significantly increased in the last few years.  Nowadays, people prefer watching web series and movies or enjoying music in their comfort. The widespread adoption of these platforms has changed the face of the entertainment industry. So, many media platforms now use data analytics to ensure user satisfaction and provide necessary recommendations to subscribers.

This data analyst case study is used in renowned media platforms like Netflix and Spotify. Spotify includes a database of a myriad of songs. It uses big data to support online music streaming with a satisfying user experience and create tailored experiences for every user. It uses various algorithms and big data to train machine learning models for offering personalised content.

Read our popular Data Science Articles

8) The use of data analytics to create an interactive game environment: Gaming

There are excellent job opportunities for data scientists willing to embark on their careers in the gaming field. This field uses data science to develop innovative gaming technologies. 

Data inferred from game analytics is employed to obtain detailed information about players’ expectations, forecasting game issues, etc.

The data science case study plays a vital role in the game development path. It assists in obtaining insights from the data to develop games that keep its players engrossed in the play. Another usefulness of this case study is the monetisation of games. It leads to the rapid development of games at a cost-effective price.

Graphics and visual interfaces play key roles in gaming. This case study is used to improve the games’ visual interface. It facilitates attractive graphics in the game to give the users a satisfying game-playing experience.

Get Started With Your Data Science Journey on UpGrad

Hoping to start your data science journey somewhere reliable? UpGrad’s Professional Certificate Program in Data Science course can be your right choice!

This 8-month course is curated to impart in-demand skills such as knowledge of Business Problem Solving, Machine Learning and Statistics, and Data Science Strategy. With upGrad, you would benefit from the IIIT Bangalore Alumni Status, exclusive job opportunities portal, career mentorship, interview preparation, and more. Generally, this course is suitable for IT professionals, Managers, and Project Leads in IT/Tech Companies.

These data science case studies are run on some of the most prominent industry names, reflecting the significance of data science in today’s evolving tech world. Data science and its prominence is bound to grow even further in the coming days, and every field is susceptible to its influence. The best you can do is start preparing yourself for the big change, which could be made possible by inheriting in-demand data science skills and experience. 

Rohit Sharma

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Frequently Asked Questions (FAQs)

The first step to follow when working on a data science case study is clarifying. It is used to collect more relevant information. Generally, these case studies are designed to be confusing and indefinite. The unorganised data will be intentionally complemented with unnecessary or lost information. So, it is vital to dive deeper, filter out bad info, and fill up gaps.

Usually, a hotel recommendation system works on collaborative filtering. It makes recommendations according to the ratings provided by other customers in the category in which the user searches for a product. This case study predicts the hotel a user is most likely to select from the list of available hotels.

Two aspects of data science make it easier for the pharmaceutical industry to gain a competitive edge in the market. These aspects are the parallel pipelined statistical models’ processing and the advancements in analytics. The different statistical models, including Markov Chains, facilitate predicting the doctors’ likelihood of prescribing medicines depending on their interaction with the brand.

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20 Useful Data Engineering Case Studies [2024]

In today’s digital age, data engineering has emerged as the backbone of innovation, driving transformation across diverse industries worldwide. Whether it is global retail giants optimizing their supply chains, iconic newspapers tailoring digital journalism, or pioneering health-tech firms personalizing medical diagnostics, the intricate dance between vast datasets and their insightful interpretations is reshaping how businesses operate and serve their customers. This article delves into twemty handpicked case studies spanning various sectors to underscore the profound impact of data engineering. Each case vividly illustrates how companies, irrespective of their size or domain, harness data to overcome challenges, innovate solutions, and set new industry benchmarks. As we journey through these narratives, we witness the undeniable potency of data engineering as a transformative force bridging challenges with solutions and molding the very future of business. In the modern era, where data is the new gold, these case studies shine a spotlight on those striking the perfect balance between technology and user needs, pioneering a brighter, data-driven future.

Related: Future of Data Engineering

Case Study 1: The Evolution of Scalable Data Infrastructure

Company: Airbnb

Task or Conflict:

As Airbnb transformed from a fledgling startup to a global hospitality powerhouse, it faced the mounting challenge of data management. The complexity of dealing with vast amounts of data from millions of listings, user reviews, transactions, and user behaviors meant their existing infrastructure became inadequate, slowing down data-driven insights and affecting operational efficiencies.

With a vision for the future, Airbnb initiated the development of “Airflow.” Beyond merely managing datasets, this state-of-the-art data infrastructure was crafted to optimize data workflows, provide an intuitive interface for users, and ensure the platform remained agile for future challenges. Airflow was designed to be robust and flexible, catering to the company’s evolving needs.

Overall Impact:

• The introduction of Airflow heralded a new era of seamless data processing, resulting in quicker, more actionable insights.
• With the success of Airflow within Airbnb, the company decided to contribute to the broader tech community by making it an open-source project.
• This move cemented Airbnb’s position as not just a hospitality leader but also as a tech innovator.

Key Learnings:

• Building infrastructure with an eye on scalability is crucial for businesses aiming for global dominance.
• Companies can significantly impact industry-wide practices by transforming internal solutions into open-source projects.
• Staying ahead in the tech curve requires a blend of foresight, innovation, and adaptability.

Case Study 2: Balancing Demand and Supply Through Predictive Analytics

Company: Uber

Operating in numerous cities worldwide, Uber’s challenge was multifaceted. They needed to anticipate demand surges, ensure driver availability, and maintain optimal pricing. Achieving this balance was crucial to upholding their promise of quick, reliable rides at transparent prices.

To refine its operations, Uber turned to data engineering. A sophisticated real-time analytics platform was conceived. This platform was designed to not just retroactively analyze patterns but to predict forthcoming demand spikes actively. By synthesizing data from past trips, events, weather forecasts, and more, it offered a dynamic model to forecast demand.

• The real-time analytics platform empowered Uber to proactively align driver availability with user demand, dramatically reducing wait times.
• Dynamic pricing, guided by real-time data, ensured fair pricing while optimizing revenue.
• By addressing demand-supply imbalances, Uber enhanced its brand reliability and user trust.
• Harnessing real-time data can transform business models from being reactive to proactive.
• In service industries, efficient data utilization directly translates to enhanced customer satisfaction and trust.
• Continuous adaptation and learning from data insights are key to maintaining leadership in dynamic markets.

Case Study 3: Personalized Music for the Masses: Crafting the Perfect Playlist

Company: Spotify

Spotify’s diverse user base, spanning continents and cultures, presented an intriguing challenge: How to ensure each user felt the platform was tailor-made for them? With millions of songs, diverse genres, and billions of playlists, Spotify had to navigate the vast musical ocean and deliver personally relevant tracks to each user.

The solution lay at the intersection of data engineering and musicology. Spotify integrated Apache Beam for large-scale data processing, analyzing petabytes of user preferences, listening durations, skipped tracks, and more. By dissecting this intricate data tapestry, algorithms were refined, ensuring every song recommendation echoed with the user’s musical soul.

• Users experienced a newfound resonance with the platform as playlists began to reflect individual tastes more accurately.
• The enhanced personal touch led to increased user engagement, with users exploring and discovering more tracks daily.
• Retention rates soared as users felt a deeper connection to the platform’s offerings.
• With word-of-mouth and shared playlists, Spotify’s reach expanded further.
• Crafting personalized experiences in a mass market is both an art and a science.
• When harnessed effectively, data can create deeply resonant user experiences, driving loyalty and growth.
• Continuous refinement, based on user feedback and behavior, is crucial to maintain relevance in content-driven platforms.
• Innovative data solutions can transform user interaction from passive consumption to active engagement.

Case Study 4: Quality Streaming for Every Bandwidth: A Viewer-Centric Approach

Company: Netflix

Catering to diverse audiences worldwide, Netflix grappled with the challenge of delivering high-quality content to users with varying internet speeds. The task was to maintain the integrity of content quality without frequent interruptions or prolonged buffering, which could mar the viewing experience.

Netflix’s ingenious data engineers stepped up. They designed a dynamic streaming system that continuously gauges a user’s internet bandwidth and adjusts the streaming quality in real time. This adaptive system ensured that viewers received the best possible quality their internet could handle without disruptions.

• From high-speed broadband to slower connections, viewers across the spectrum enjoyed a more seamless viewing experience.
• The frequency of stream interruptions plummeted, ensuring immersive viewing sessions.
• Viewer drop-offs due to buffering frustrations significantly decreased.
• Positive user reviews and feedback showcased enhanced satisfaction levels.
• Adapting to diverse user environments is crucial for global platforms aiming for universal appeal.
• Seamless user experience is pivotal in content streaming, directly impacting viewer retention.
• Proactive solutions anticipate and address potential issues and can significantly boost user satisfaction.
• Investing in backend infrastructure can have direct front-end user experience dividends.

Case Study 5: E-commerce Recommendations: From General Browsing to Personal Shopping

Company: Zalando

With a vast inventory spanning countless brands, styles, and categories, Zalando’s challenge was to transition users from aimless browsing to targeted shopping. The e-commerce platform wanted to make every user feel like the store was curated just for them.

Zalando harnessed Big Data tools and crafted algorithms to refine its recommendation engine. By deep-diving into user behaviors, purchase histories, wish lists, and even product return patterns, Zalando could predict the products each user would gravitate toward with increasing accuracy.

• Users began experiencing a more personalized shopping journey, with product recommendations aligning closely with their preferences.
• The conversion rates from browsing to purchasing saw a notable uptick.
• Return rates decreased as users found products that matched their needs and desires better.
• Overall, user trust in the platform’s recommendations grew, leading to increased loyalty and repeat purchases.
• Personalization can be the distinguishing factor in the vast digital marketplace that sets a platform apart.
• When translated effectively, data-driven insights can guide users seamlessly through their shopping journey.
• Reducing the gap between user expectations and platform offerings can drive sales and enhance user satisfaction.
• Continuously evolving algorithms, based on fresh data, ensures the platform remains attuned to changing user preferences.

Related: Inspirational Data Engineering Quotes

Case Study 6: Decoding Reader Behavior: Digital Journalism Tailored to Taste

Company: The New York Times

The New York Times faced a dual challenge when transitioning from print to a digital platform. On one hand, they had to maintain their legacy and reputation. On the other, they needed to tailor content to the varied reading habits of their global online audience, ensuring that the vast ocean of content didn’t overwhelm or alienate readers.

The paper turned its attention to data engineering. By creating a sophisticated data pipeline and analytics platform, they could gather granular insights into reader behavior – which articles were read, which were shared, time spent on each article, and even which articles were left mid-way. These insights were then used to curate and tailor content, ensuring that each reader received a bespoke experience.

• Individualized content delivery led to a remarkable increase in user engagement.
• Subscription rates surged, with a noticeable decline in subscription churn.
• Reader feedback became more positive, with many praising the “personal touch” in their content feed.
• Advertisers saw better engagement metrics, leading to an increase in ad revenues.
• In the digital age, even legacy institutions must continuously adapt and innovate.
• Understanding and catering to individual user behavior can transform passive readers into engaged subscribers.
• The balance between maintaining brand identity and evolving with technology is delicate but crucial.
• Personalizing digital experiences can have a direct positive impact on revenue streams.

Case Study 7: Banking’s Digital Shield: Combatting Fraud with Data Engineering

Company: HSBC

The financial world is constantly under the threat of fraud, and HSBC, a banking behemoth, is no exception. Ensuring security and instilling confidence in millions of customers required a system that could detect and counteract fraudulent activities in real-time.

HSBC elevated its defenses by developing a data engineering platform to scrutinize real-time transaction data meticulously. Every transaction was analyzed against patterns, historical data, and predictive algorithms to detect irregularities. Suspicious transactions were instantaneously flagged and either auto-blocked or sent for rapid human review.

• Fraud detection became more proactive rather than reactive.
• Customers felt more secure, leading to increased trust in the bank’s digital operations.
• Financial losses due to fraud decreased substantially, protecting both the bank and its customers.
• The system’s success led to its adoption across multiple branches globally, standardizing fraud detection measures.
• Real-time data analysis is an indispensable asset in the battle against financial fraud.
• Protecting customer assets directly bolsters trust and loyalty.
• Continuous adaptation and system refinement are crucial in the ever-evolving landscape of cyber threats.
• Standardizing successful systems can lead to cohesive and enhanced security across global operations.

Case Study 8: Logistics in the IoT Era: Reinventing Package Tracking

Company: FedEx

In the fast-paced world of logistics, tracking accuracy is paramount. FedEx, catering to millions globally, aimed to revolutionize package tracking, providing real-time updates that went beyond mere location data.

FedEx saw the potential of integrating Internet of Things (IoT) with their data engineering framework. Every package became a data point in the vast logistics network. Advanced sensors and tracking devices, combined with real-time data processing, provided insights into package location, handling conditions, estimated delivery times, and even environmental factors like temperature.

• Customers enjoyed unparalleled transparency with detailed real-time package tracking.
• Handling disputes decreased, as there was clear data on package handling and delivery conditions.
• Enhanced operational efficiency as real-time data helped optimize routes and delivery schedules.
• The brand’s image was bolstered as a tech-forward and customer-centric logistics provider.
• When integrated with traditional services, modern tech, like IoT, can provide unprecedented value additions.
• Transparency and real-time data can dramatically enhance customer trust and satisfaction.
• Leveraging technology can lead to operational efficiencies, optimizing both time and costs.
• Brand image in traditional industries can be revolutionized by embracing and integrating modern tech solutions.

Case Study 9: Deciphering Cosmic Puzzles: Data Engineering in Particle Physics

Company: CERN

Handling the colossal data streams from the Large Hadron Collider presented CERN with a unique challenge. The complexity and volume of the data required a system that could not only store but also efficiently analyze the data to drive new scientific discoveries.

CERN’s data engineers rose to the occasion, designing a specialized data processing platform. This platform, tailored for particle physics, could efficiently sift through petabytes of experimental data, enabling researchers to draw accurate insights faster and further the boundaries of human knowledge.

• Like the Higgs boson, groundbreaking discoveries became possible due to swift and efficient data processing.
• Research papers and findings were published at a faster rate, propelling CERN to the forefront of particle physics research.
• Collaborative research became easier, with data being accessible to researchers globally.
• The platform set a new standard for data processing in large-scale scientific experiments.
• Tailored data engineering solutions can drive breakthroughs in specialized research fields.
• When applied to retail, predictive analytics can significantly enhance customer satisfaction and store profitability.
• Collaboration in the scientific community is enhanced by accessible and well-structured data.
• Setting industry-specific benchmarks can inspire and guide similar initiatives globally.

Case Study 10: Optimizing Retail Supply Chains: The Dance of Inventory and Demand

Company: Walmart

As a global retail giant, Walmart’s supply chain complexity is unparalleled. Balancing inventory across thousands of stores, considering the varied demand for millions of products, presented an immense challenge. Overstock meant increased holding costs, while stockouts could lead to missed sales opportunities and unhappy customers.

Walmart deployed a comprehensive data engineering solution. An advanced analytics platform was developed to continually analyze sales data, customer buying patterns, seasonality, and external factors like local events or holidays. This system provided predictive insights into product demand at each store, enabling real-time inventory adjustments, ensuring products were in the right place at the right time.

• Stockouts became a rarity, ensuring customers consistently found their desired products.
• Inventory turnover rates improved, leading to increased sales and reduced warehousing costs.
• By reducing overstock, wastage, especially in perishable categories, was minimized.
• Store managers received better forecasting tools, empowering them to make informed decisions locally.
• Data engineering can drive massive efficiencies in global supply chains.
• Predictive analytics, when applied to retail, can significantly enhance customer satisfaction and store profitability.
• Empowering local store managers with data-driven tools can lead to better ground-level decisions.
• Continual refinement of prediction models is essential to account for ever-changing consumer behavior and external factors.

Related: Challenges Faced by Data Engineers

Case Study 11: Enhancing Data Warehouse Capabilities

Company: Amazon

Amazon dealt with challenges related to its massive scale of operations, which required an overhaul of its data warehousing solutions to manage and analyze the growing volumes of customer, inventory, and transaction data efficiently. The existing solutions were becoming costly and cumbersome to scale in line with Amazon’s global expansion.

Amazon invested in enhancing its data warehousing capabilities by adopting Amazon Redshift, a petabyte-scale data warehouse service designed specifically for the cloud. Redshift offers significant performance improvements over traditional data warehouses by using columnar storage technology to improve I/O efficiency and parallelizing queries across multiple nodes.

• Achieved faster data query performance, reducing the time to generate insights from hours to minutes.
• Lowered operational costs by utilizing more efficient cloud-based data warehousing solutions.
• Improved the scalability of data operations, allowing for flexible expansion as Amazon continues to grow.
• Cloud-native data warehousing solutions can offer significant advantages in scalability and cost efficiency.
• The right technological investments in data infrastructure can drive substantial business efficiencies and growth.

Case Study 12: Improving Data Reliability and Accuracy

Company: LinkedIn

As LinkedIn’s user base and the data generated expanded rapidly, the platform faced significant challenges in ensuring the reliability and accuracy of its data. This was especially critical for features like job recommendations and networking suggestions, which rely heavily on precise and timely data. Glitches in data could lead to poor user experiences and mistrust in the platform.

LinkedIn developed DataHub, an advanced metadata search and discovery tool. DataHub enhances data visibility across the company and ensures better data governance by tracking data lineage, managing metadata, and providing tools for data discovery. This allows teams across LinkedIn to access reliable data quickly and efficiently, ensuring that data-driven decisions are based on the most accurate and current information.

• Significantly improved the accuracy and reliability of data across LinkedIn, enhancing core services such as job matching and network recommendations.
• Reduced the time needed for data discovery and management, allowing data teams to focus more on analysis rather than data maintenance.
• Enhanced user trust and satisfaction by consistently providing high-quality and relevant content and recommendations.
• Robust metadata management tools are crucial for maintaining high data quality in large-scale data environments.
• Transparent data lineage and metadata accessibility are essential for compliance and trust in data-driven decision-making processes.

Case Study 13: Streamlining Data Pipelines for Scalability

Company: Twitter

Twitter generates enormous volumes of data every minute through user interactions, tweets, and media uploads. The company’s existing data processing system, Apache Storm, struggled to handle data’s scale and velocity efficiently, leading to significant processing delays and data management challenges.

Twitter developed Heron, a real-time, distributed, and fault-tolerant stream processing engine to address these challenges. Heron was designed to be fully API-compatible with Apache Storm but with enhancements in performance, efficiency, and ease of management. This new system allowed Twitter to streamline its data pipelines and improve data flow management across its services.

• Enhanced processing capabilities allowed Twitter to handle larger data volumes with better speed and reliability.
• Reduced the system latency dramatically, improving the timeliness of tweet feeds and real-time analytics.
• Improved the overall system efficiency and reduced operational costs by optimizing resource usage in data processing.
• Upgrading data processing systems to handle increased scale can significantly improve performance and user experience.
• Strategic modernization enhances operational capabilities and strengthens security and compliance postures.

Related: High-Paying Data Engineering Jobs & Career Paths

Case Study 14: Modernizing Legacy Data Systems

Company: Bank of America

Bank of America was grappling with outdated and inefficient legacy data systems that were not equipped to handle the modern demands of high-speed transactions and real-time financial services. These legacy systems were slow, prone to errors, and costly to maintain, which impeded the bank’s ability to provide innovative and competitive services to its customers.

The bank embarked on a major data modernization initiative, which involved the integration of state-of-the-art data processing technologies and the redesign of its data architecture to be more agile and responsive. The initiative focused on enhancing data security, improving transaction processing speeds, and providing a foundation for innovative financial services.

• Improved the speed and reliability of transaction processing, enhancing customer satisfaction and trust.
• Reduced operational costs by decommissioning outdated systems and adopting more efficient, modern technologies.
• Investments in advanced data processing technologies can lead to drastic improvements in service delivery and cost efficacy.
• Strategic modernization not only enhances operational capabilities but also strengthens security and compliance postures.

Case Study 15: Scaling Data Operations Globally

Company: Google

Google’s unparalleled scale of operations and its need to deliver high-performance services globally necessitated a robust, scalable solution for managing and processing the vast amounts of data collected from billions of user interactions across its multiple platforms, including search, ads, and YouTube. Traditional databases were inadequate in handling the latency and throughput requirements needed to maintain performance and reliability across continents.

Google developed and refined its proprietary technologies, Bigtable and Spanner. Bigtable is a high performance, scalable NoSQL database service designed for large analytical and operational workloads, while Spanner is a global relational database service that offers transactional consistency at a global scale, real-time access, and automated multi-region replication and failover.

• Enabled consistent and reliable data access globally, enhancing the user experience across all Google services.
• Facilitated the handling of millions of transactions per second, supporting real-time data applications and analytics.
• Strengthened global data operations, ensuring high availability and durability even in the face of regional disruptions.
• Scalable and reliable database solutions are critical for global operations, ensuring consistent service delivery across different regions.
• The ability to manage real-time transactions at scale is a cornerstone of performance for data-intensive companies.
• Advanced database technologies that offer global consistency and high availability can significantly enhance operational resilience and flexibility.

Case Study 16: Ensuring Data Security and Compliance

Company: JPMorgan Chase

As a leading global financial institution, JPMorgan Chase handles sensitive financial data and is subject to stringent regulatory requirements. The bank faced challenges in ensuring the security, integrity, and compliance of its data amidst an evolving threat landscape and increasingly complex regulatory environment. Traditional data management practices were proving insufficient to meet these demands.

JPMorgan Chase implemented a comprehensive data governance framework that integrates robust security measures, such as advanced encryption and strict access controls, with real-time compliance monitoring. This framework is designed to be scalable and adaptable to new regulations and security challenges, ensuring that the bank remains compliant and can quickly respond to potential threats.

• Improved compliance with global financial regulations, avoiding potential fines and legal issues.
• Strengthened the trust of customers and stakeholders in the bank’s ability to protect their information and maintain privacy.
• Proactive adaptation to new security threats and regulatory changes can mitigate risks and enhance organizational agility.
• Maintaining customer trust requires continuous investment in data security and compliance measures.

Case Study 17: Data-Driven Customer Experience Enhancement

Company: Sephora

Sephora faced the challenge of personalizing the shopping experience for millions of customers across its online and physical stores. The beauty retailer needed to integrate and analyze customer data from multiple touchpoints to offer tailored recommendations and improve customer engagement.

Sephora developed a unified data platform that collects, integrates, and analyzes data from online surfing habits, purchase history, and in-store interactions. This platform uses advanced analytics and machine learning to create personalized customer profiles and recommend products dynamically.

• Enhanced personalization led to increased customer satisfaction and loyalty.
• Boosted sales through targeted marketing and personalized recommendations.
• Improved inventory management by aligning product offerings with consumer preferences and trends.
• Leveraging machine learning for customer profiling and recommendation systems can drive sales and customer engagement.
• Data-driven insights are crucial for optimizing inventory and marketing strategies in the retail sector.

Related: Future Evolution of Role of Data Engineers

Case Study 18: Enhancing Financial Risk Management

Company: Citibank

Citibank required an advanced solution to manage financial risk more effectively as it navigated the complex global financial landscape. The bank faced challenges in real-time risk assessment and credit analysis due to the vast amount of data that needed to be processed.

Citibank developed a sophisticated risk management platform that integrates data from various global markets and internal sources. This platform uses artificial intelligence to analyze patterns and predict potential risks, helping the bank make more informed lending and investment decisions.

• Improved the accuracy of risk assessment, reducing financial losses.
• Enhanced decision-making speed in credit and investment operations.
• Strengthened the bank’s compliance with global financial regulations.
• Integrating AI into risk management can significantly enhance the accuracy and timeliness of financial assessments.
• Continuous updating and integration of data sources are crucial for effective risk management.
• Advanced analytics platforms are essential for large financial institutions to manage global financial risks.

Case Study 19: Optimizing Energy Consumption

Company: Siemens

Siemens faced the challenge of optimizing energy consumption across its vast array of industrial equipment and facilities worldwide. The company needed to manage and analyze large datasets related to energy usage to enhance efficiency and reduce costs.

Siemens implemented an IoT-based energy management system that collects real-time data from equipment sensors and facility meters. The system uses advanced data analytics to identify patterns and inefficiencies in energy usage, allowing for the automated adjustment of operations to optimize energy consumption.

• Achieved significant reductions in energy costs across multiple facilities.
• Improved operational efficiencies through better energy management.
• IoT and real-time data analytics are crucial for active energy management in industrial settings.
• Systematic data collection and analysis can identify significant efficiencies and cost-saving opportunities.

Case Study 20: Advancing Precision Agriculture

Company: John Deere

As the demand for food increased globally, John Deere faced the challenge of improving agricultural productivity and sustainability. The company needed to help farmers optimize crop yield and reduce resource waste through precise and data-driven farming techniques.

John Deere developed an advanced precision agriculture system that integrates IoT sensors, GPS technology, and machine learning algorithms. This system collects data on soil conditions, crop health, etc., in real-time. The data is assessed to provide farmers with precise planting, watering, and fertilization suggestions.

• Increased crop yields through optimized farming practices tailored to real-time environmental and soil conditions.
• Enhanced profitability for farmers by reducing resource waste and improving crop production efficiency.
• Precision agriculture technologies are essential for meeting global food demands while minimizing environmental impact.

Data-driven insights can transform traditional farming practices, leading to more efficient and sustainable agriculture.

Related: Role of Data Engineering in Marketing

Closing Thoughts

In conclusion, these enhanced case studies exemplify the deep transformative power of data engineering across industries. Whether they are new-age tech giants or legacy establishments, enterprises stand to gain immensely from harnessing the power of data. As businesses grow and evolve, the role of data engineering in driving efficiency, enhancing customer experience, and optimizing operations cannot be overstated. From tailoring personal experiences in music and shopping to optimizing global operations in transport and media streaming, effective data engineering has proven its worth time and again. Through data, all businesses can pave the way for groundbreaking innovations, creating ripples of positive change across sectors.

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15 Real-Life Case Study Examples & Best Practices

Written by: Oghale Olori

Case studies are more than just success stories.

They are powerful tools that demonstrate the practical value of your product or service. Case studies help attract attention to your products, build trust with potential customers and ultimately drive sales.

It’s no wonder that 73% of successful content marketers utilize case studies as part of their content strategy. Plus, buyers spend 54% of their time reviewing case studies before they make a buying decision.

To ensure you’re making the most of your case studies, we’ve put together 15 real-life case study examples to inspire you. These examples span a variety of industries and formats. We’ve also included best practices, design tips and templates to inspire you.

Let’s dive in!

Table of Contents

What is a case study, 15 real-life case study examples, sales case study examples, saas case study examples, product case study examples, marketing case study examples, business case study examples, case study faqs.

• A case study is a compelling narrative that showcases how your product or service has positively impacted a real business or individual. 
• Case studies delve into your customer's challenges, how your solution addressed them and the quantifiable results they achieved.
• Your case study should have an attention-grabbing headline, great visuals and a relevant call to action. Other key elements include an introduction, problems and result section.
• Visme provides easy-to-use tools, professionally designed templates and features for creating attractive and engaging case studies.

A case study is a real-life scenario where your company helped a person or business solve their unique challenges. It provides a detailed analysis of the positive outcomes achieved as a result of implementing your solution.

Case studies are an effective way to showcase the value of your product or service to potential customers without overt selling. By sharing how your company transformed a business, you can attract customers seeking similar solutions and results.

Case studies are not only about your company's capabilities; they are primarily about the benefits customers and clients have experienced from using your product.

Every great case study is made up of key elements. They are;

• Attention-grabbing headline: Write a compelling headline that grabs attention and tells your reader what the case study is about. For example, "How a CRM System Helped a B2B Company Increase Revenue by 225%.
• Introduction/Executive Summary: Include a brief overview of your case study, including your customer’s problem, the solution they implemented and the results they achieved.
• Problem/Challenge: Case studies with solutions offer a powerful way to connect with potential customers. In this section, explain how your product or service specifically addressed your customer's challenges.
• Solution: Explain how your product or service specifically addressed your customer's challenges.
• Results/Achievements : Give a detailed account of the positive impact of your product. Quantify the benefits achieved using metrics such as increased sales, improved efficiency, reduced costs or enhanced customer satisfaction.
• Graphics/Visuals: Include professional designs, high-quality photos and videos to make your case study more engaging and visually appealing.
• Quotes/Testimonials: Incorporate written or video quotes from your clients to boost your credibility.
• Relevant CTA: Insert a call to action (CTA) that encourages the reader to take action. For example, visiting your website or contacting you for more information. Your CTA can be a link to a landing page, a contact form or your social media handle and should be related to the product or service you highlighted in your case study.

Now that you understand what a case study is, let’s look at real-life case study examples. Among these, you'll find some simple case study examples that break down complex ideas into easily understandable solutions.

In this section, we’ll explore SaaS, marketing, sales, product and business case study examples with solutions. Take note of how these companies structured their case studies and included the key elements.

We’ve also included professionally designed case study templates to inspire you.

1. Georgia Tech Athletics Increase Season Ticket Sales by 80%

Georgia Tech Athletics, with its 8,000 football season ticket holders, sought for a way to increase efficiency and customer engagement.

Their initial sales process involved making multiple outbound phone calls per day with no real targeting or guidelines. Georgia Tech believed that targeting communications will enable them to reach more people in real time.

Salesloft improved Georgia Tech’s sales process with an inbound structure. This enabled sales reps to connect with their customers on a more targeted level. The use of dynamic fields and filters when importing lists ensured prospects received the right information, while communication with existing fans became faster with automation.

As a result, Georgia Tech Athletics recorded an 80% increase in season ticket sales as relationships with season ticket holders significantly improved. Employee engagement increased as employees became more energized to connect and communicate with fans.

Why Does This Case Study Work?

In this case study example , Salesloft utilized the key elements of a good case study. Their introduction gave an overview of their customers' challenges and the results they enjoyed after using them. After which they categorized the case study into three main sections: challenge, solution and result.

Salesloft utilized a case study video to increase engagement and invoke human connection.

Incorporating videos in your case study has a lot of benefits. Wyzol’s 2023 state of video marketing report showed a direct correlation between videos and an 87% increase in sales.

The beautiful thing is that creating videos for your case study doesn’t have to be daunting.

With an easy-to-use platform like Visme, you can create top-notch testimonial videos that will connect with your audience. Within the Visme editor, you can access over 1 million stock photos , video templates, animated graphics and more. These tools and resources will significantly improve the design and engagement of your case study.

Simplify content creation and brand management for your team

• Collaborate on designs , mockups and wireframes with your non-design colleagues
• Lock down your branding to maintain brand consistency throughout your designs
• Why start from scratch? Save time with 1000s of professional branded templates

Sign up. It’s free.

2. WeightWatchers Completely Revamped their Enterprise Sales Process with HubSpot

WeightWatchers, a 60-year-old wellness company, sought a CRM solution that increased the efficiency of their sales process. With their previous system, Weightwatchers had limited automation. They would copy-paste message templates from word documents or recreate one email for a batch of customers.

This required a huge effort from sales reps, account managers and leadership, as they were unable to track leads or pull customized reports for planning and growth.

WeightWatchers transformed their B2B sales strategy by leveraging HubSpot's robust marketing and sales workflows. They utilized HubSpot’s deal pipeline and automation features to streamline lead qualification. And the customized dashboard gave leadership valuable insights.

As a result, WeightWatchers generated seven figures in annual contract value and boosted recurring revenue. Hubspot’s impact resulted in 100% adoption across all sales, marketing, client success and operations teams.

Hubspot structured its case study into separate sections, demonstrating the specific benefits of their products to various aspects of the customer's business. Additionally, they integrated direct customer quotes in each section to boost credibility, resulting in a more compelling case study.

Getting insight from your customer about their challenges is one thing. But writing about their process and achievements in a concise and relatable way is another. If you find yourself constantly experiencing writer’s block, Visme’s AI writer is perfect for you.

Visme created this AI text generator tool to take your ideas and transform them into a great draft. So whether you need help writing your first draft or editing your final case study, Visme is ready for you.

3. Immi’s Ram Fam Helps to Drive Over $200k in Sales

Immi embarked on a mission to recreate healthier ramen recipes that were nutritious and delicious. After 2 years of tireless trials, Immi finally found the perfect ramen recipe. However, they envisioned a community of passionate ramen enthusiasts to fuel their business growth.

This vision propelled them to partner with Shopify Collabs. Shopify Collabs successfully cultivated and managed Immi’s Ramen community of ambassadors and creators.

As a result of their partnership, Immi’s community grew to more than 400 dedicated members, generating over $200,000 in total affiliate sales.

The power of data-driven headlines cannot be overemphasized. Chili Piper strategically incorporates quantifiable results in their headlines. This instantly sparks curiosity and interest in readers.

While not every customer success story may boast headline-grabbing figures, quantifying achievements in percentages is still effective. For example, you can highlight a 50% revenue increase with the implementation of your product.

Take a look at the beautiful case study template below. Just like in the example above, the figures in the headline instantly grab attention and entice your reader to click through.

Having a case study document is a key factor in boosting engagement. This makes it easy to promote your case study in multiple ways. With Visme, you can easily publish, download and share your case study with your customers in a variety of formats, including PDF, PPTX, JPG and more!

4. How WOW! is Saving Nearly 79% in Time and Cost With Visme

This case study discusses how Visme helped WOW! save time and money by providing user-friendly tools to create interactive and quality training materials for their employees. Find out what your team can do with Visme. Request a Demo

WOW!'s learning and development team creates high-quality training materials for new and existing employees. Previous tools and platforms they used had plain templates, little to no interactivity features, and limited flexibility—that is, until they discovered Visme.

Now, the learning and development team at WOW! use Visme to create engaging infographics, training videos, slide decks and other training materials.

This has directly reduced the company's turnover rate, saving them money spent on recruiting and training new employees. It has also saved them a significant amount of time, which they can now allocate to other important tasks.

Visme's customer testimonials spark an emotional connection with the reader, leaving a profound impact. Upon reading this case study, prospective customers will be blown away by the remarkable efficiency achieved by Visme's clients after switching from PowerPoint.

Visme’s interactivity feature was a game changer for WOW! and one of the primary reasons they chose Visme.

“Previously we were using PowerPoint, which is fine, but the interactivity you can get with Visme is so much more robust that we’ve all steered away from PowerPoint.” - Kendra, L&D team, Wow!

Visme’s interactive feature allowed them to animate their infographics, include clickable links on their PowerPoint designs and even embed polls and quizzes their employees could interact with.

By embedding the slide decks, infographics and other training materials WOW! created with Visme, potential customers get a taste of what they can create with the tool. This is much more effective than describing the features of Visme because it allows potential customers to see the tool in action.

To top it all off, this case study utilized relevant data and figures. For example, one part of the case study said, “In Visme, where Kendra’s team has access to hundreds of templates, a brand kit, and millions of design assets at their disposal, their team can create presentations in 80% less time.”

Who wouldn't want that?

Including relevant figures and graphics in your case study is a sure way to convince your potential customers why you’re a great fit for their brand. The case study template below is a great example of integrating relevant figures and data.

This colorful template begins with a captivating headline. But that is not the best part; this template extensively showcases the results their customer had using relevant figures.

The arrangement of the results makes it fun and attractive. Instead of just putting figures in a plain table, you can find interesting shapes in your Visme editor to take your case study to the next level.

5. Lyte Reduces Customer Churn To Just 3% With Hubspot CRM

While Lyte was redefining the ticketing industry, it had no definite CRM system . Lyte utilized 12–15 different SaaS solutions across various departments, which led to a lack of alignment between teams, duplication of work and overlapping tasks.

Customer data was spread across these platforms, making it difficult to effectively track their customer journey. As a result, their churn rate increased along with customer dissatisfaction.

Through Fuelius , Lyte founded and implemented Hubspot CRM. Lyte's productivity skyrocketed after incorporating Hubspot's all-in-one CRM tool. With improved efficiency, better teamwork and stronger client relationships, sales figures soared.

The case study title page and executive summary act as compelling entry points for both existing and potential customers. This overview provides a clear understanding of the case study and also strategically incorporates key details like the client's industry, location and relevant background information.

Having a good summary of your case study can prompt your readers to engage further. You can achieve this with a simple but effective case study one-pager that highlights your customer’s problems, process and achievements, just like this case study did in the beginning.

Moreover, you can easily distribute your case study one-pager and use it as a lead magnet to draw prospective customers to your company.

Take a look at this case study one-pager template below.

This template includes key aspects of your case study, such as the introduction, key findings, conclusion and more, without overcrowding the page. The use of multiple shades of blue gives it a clean and dynamic layout.

Our favorite part of this template is where the age group is visualized.

With Visme’s data visualization tool , you can present your data in tables, graphs, progress bars, maps and so much more. All you need to do is choose your preferred data visualization widget, input or import your data and click enter!

6. How Workato Converts 75% of Their Qualified Leads

Workato wanted to improve their inbound leads and increase their conversion rate, which ranged from 40-55%.

At first, Workato searched for a simple scheduling tool. They soon discovered that they needed a tool that provided advanced routing capabilities based on zip code and other criteria. Luckily, they found and implemented Chili Piper.

As a result of implementing Chili Piper, Workato achieved a remarkable 75–80% conversion rate and improved show rates. This led to a substantial revenue boost, with a 10-15% increase in revenue attributed to Chili Piper's impact on lead conversion.

This case study example utilizes the power of video testimonials to drive the impact of their product.

Chili Piper incorporates screenshots and clips of their tool in use. This is a great strategy because it helps your viewers become familiar with how your product works, making onboarding new customers much easier.

In this case study example, we see the importance of efficient Workflow Management Systems (WMS). Without a WMS, you manually assign tasks to your team members and engage in multiple emails for regular updates on progress.

However, when crafting and designing your case study, you should prioritize having a good WMS.

Visme has an outstanding Workflow Management System feature that keeps you on top of all your projects and designs. This feature makes it much easier to assign roles, ensure accuracy across documents, and track progress and deadlines.

Visme’s WMS feature allows you to limit access to your entire document by assigning specific slides or pages to individual members of your team. At the end of the day, your team members are not overwhelmed or distracted by the whole document but can focus on their tasks.

7. Rush Order Helps Vogmask Scale-Up During a Pandemic

Vomask's reliance on third-party fulfillment companies became a challenge as demand for their masks grew. Seeking a reliable fulfillment partner, they found Rush Order and entrusted them with their entire inventory.

Vomask's partnership with Rush Order proved to be a lifesaver during the COVID-19 pandemic. Rush Order's agility, efficiency and commitment to customer satisfaction helped Vogmask navigate the unprecedented demand and maintain its reputation for quality and service.

Rush Order’s comprehensive support enabled Vogmask to scale up its order processing by a staggering 900% while maintaining a remarkable customer satisfaction rate of 92%.

Rush Order chose one event where their impact mattered the most to their customer and shared that story.

While pandemics don't happen every day, you can look through your customer’s journey and highlight a specific time or scenario where your product or service saved their business.

The story of Vogmask and Rush Order is compelling, but it simply is not enough. The case study format and design attract readers' attention and make them want to know more. Rush Order uses consistent colors throughout the case study, starting with the logo, bold square blocks, pictures, and even headers.

Take a look at this product case study template below.

Just like our example, this case study template utilizes bold colors and large squares to attract and maintain the reader’s attention. It provides enough room for you to write about your customers' backgrounds/introductions, challenges, goals and results.

The right combination of shapes and colors adds a level of professionalism to this case study template.

8. AMR Hair & Beauty leverages B2B functionality to boost sales by 200%

With limits on website customization, slow page loading and multiple website crashes during peak events, it wasn't long before AMR Hair & Beauty began looking for a new e-commerce solution.

Their existing platform lacked effective search and filtering options, a seamless checkout process and the data analytics capabilities needed for informed decision-making. This led to a significant number of abandoned carts.

Upon switching to Shopify Plus, AMR immediately saw improvements in page loading speed and average session duration. They added better search and filtering options for their wholesale customers and customized their checkout process.

Due to this, AMR witnessed a 200% increase in sales and a 77% rise in B2B average order value. AMR Hair & Beauty is now poised for further expansion and growth.

This case study example showcases the power of a concise and impactful narrative.

To make their case analysis more effective, Shopify focused on the most relevant aspects of the customer's journey. While there may have been other challenges the customer faced, they only included those that directly related to their solutions.

Take a look at this case study template below. It is perfect if you want to create a concise but effective case study. Without including unnecessary details, you can outline the challenges, solutions and results your customers experienced from using your product.

Don’t forget to include a strong CTA within your case study. By incorporating a link, sidebar pop-up or an exit pop-up into your case study, you can prompt your readers and prospective clients to connect with you.

9. How a Marketing Agency Uses Visme to Create Engaging Content With Infographics

SmartBox Dental , a marketing agency specializing in dental practices, sought ways to make dental advice more interesting and easier to read. However, they lacked the design skills to do so effectively.

Visme's wide range of templates and features made it easy for the team to create high-quality content quickly and efficiently. SmartBox Dental enjoyed creating infographics in as little as 10-15 minutes, compared to one hour before Visme was implemented.

By leveraging Visme, SmartBox Dental successfully transformed dental content into a more enjoyable and informative experience for their clients' patients. Therefore enhancing its reputation as a marketing partner that goes the extra mile to deliver value to its clients.

Visme creatively incorporates testimonials In this case study example.

By showcasing infographics and designs created by their clients, they leverage the power of social proof in a visually compelling way. This way, potential customers gain immediate insight into the creative possibilities Visme offers as a design tool.

This example effectively showcases a product's versatility and impact, and we can learn a lot about writing a case study from it. Instead of focusing on one tool or feature per customer, Visme took a more comprehensive approach.

Within each section of their case study, Visme explained how a particular tool or feature played a key role in solving the customer's challenges.

For example, this case study highlighted Visme’s collaboration tool . With Visme’s tool, the SmartBox Dental content team fostered teamwork, accountability and effective supervision.

Visme also achieved a versatile case study by including relevant quotes to showcase each tool or feature. Take a look at some examples;

Visme’s collaboration tool: “We really like the collaboration tool. Being able to see what a co-worker is working on and borrow their ideas or collaborate on a project to make sure we get the best end result really helps us out.”

Visme’s library of stock photos and animated characters: “I really love the images and the look those give to an infographic. I also really like the animated little guys and the animated pictures. That’s added a lot of fun to our designs.”

Visme’s interactivity feature: “You can add URLs and phone number links directly into the infographic so they can just click and call or go to another page on the website and I really like adding those hyperlinks in.”

You can ask your customers to talk about the different products or features that helped them achieve their business success and draw quotes from each one.

10. Jasper Grows Blog Organic Sessions 810% and Blog-Attributed User Signups 400X

Jasper, an AI writing tool, lacked a scalable content strategy to drive organic traffic and user growth. They needed help creating content that converted visitors into users. Especially when a looming domain migration threatened organic traffic.

To address these challenges, Jasper partnered with Omniscient Digital. Their goal was to turn their content into a growth channel and drive organic growth. Omniscient Digital developed a full content strategy for Jasper AI, which included a content audit, competitive analysis, and keyword discovery.

Through their collaboration, Jasper’s organic blog sessions increased by 810%, despite the domain migration. They also witnessed a 400X increase in blog-attributed signups. And more importantly, the content program contributed to over $4 million in annual recurring revenue.

The combination of storytelling and video testimonials within the case study example makes this a real winner. But there’s a twist to it. Omniscient segmented the video testimonials and placed them in different sections of the case study.

Video marketing , especially in case studies, works wonders. Research shows us that 42% of people prefer video testimonials because they show real customers with real success stories. So if you haven't thought of it before, incorporate video testimonials into your case study.

Take a look at this stunning video testimonial template. With its simple design, you can input the picture, name and quote of your customer within your case study in a fun and engaging way.

Try it yourself! Customize this template with your customer’s testimonial and add it to your case study!

11. How Meliá Became One of the Most Influential Hotel Chains on Social Media

Meliá Hotels needed help managing their growing social media customer service needs. Despite having over 500 social accounts, they lacked a unified response protocol and detailed reporting. This largely hindered efficiency and brand consistency.

Meliá partnered with Hootsuite to build an in-house social customer care team. Implementing Hootsuite's tools enabled Meliá to decrease response times from 24 hours to 12.4 hours while also leveraging smart automation.

In addition to that, Meliá resolved over 133,000 conversations, booking 330 inquiries per week through Hootsuite Inbox. They significantly improved brand consistency, response time and customer satisfaction.

The need for a good case study design cannot be over-emphasized.

As soon as anyone lands on this case study example, they are mesmerized by a beautiful case study design. This alone raises the interest of readers and keeps them engaged till the end.

If you’re currently saying to yourself, “ I can write great case studies, but I don’t have the time or skill to turn it into a beautiful document.” Say no more.

Visme’s amazing AI document generator can take your text and transform it into a stunning and professional document in minutes! Not only do you save time, but you also get inspired by the design.

With Visme’s document generator, you can create PDFs, case study presentations , infographics and more!

Take a look at this case study template below. Just like our case study example, it captures readers' attention with its beautiful design. Its dynamic blend of colors and fonts helps to segment each element of the case study beautifully.

12. Tea’s Me Cafe: Tamika Catchings is Brewing Glory

Tamika's journey began when she purchased Tea's Me Cafe in 2017, saving it from closure. She recognized the potential of the cafe as a community hub and hosted regular events centered on social issues and youth empowerment.

One of Tamika’s business goals was to automate her business. She sought to streamline business processes across various aspects of her business. One of the ways she achieves this goal is through Constant Contact.

Constant Contact became an integral part of Tamika's marketing strategy. They provided an automated and centralized platform for managing email newsletters, event registrations, social media scheduling and more.

This allowed Tamika and her team to collaborate efficiently and focus on engaging with their audience. They effectively utilized features like WooCommerce integration, text-to-join and the survey builder to grow their email list, segment their audience and gather valuable feedback.

The case study example utilizes the power of storytelling to form a connection with readers. Constant Contact takes a humble approach in this case study. They spotlight their customers' efforts as the reason for their achievements and growth, establishing trust and credibility.

This case study is also visually appealing, filled with high-quality photos of their customer. While this is a great way to foster originality, it can prove challenging if your customer sends you blurry or low-quality photos.

If you find yourself in that dilemma, you can use Visme’s AI image edit tool to touch up your photos. With Visme’s AI tool, you can remove unwanted backgrounds, erase unwanted objects, unblur low-quality pictures and upscale any photo without losing the quality.

Constant Contact offers its readers various formats to engage with their case study. Including an audio podcast and PDF.

In its PDF version, Constant Contact utilized its brand colors to create a stunning case study design.  With this, they increase brand awareness and, in turn, brand recognition with anyone who comes across their case study.

With Visme’s brand wizard tool , you can seamlessly incorporate your brand assets into any design or document you create. By inputting your URL, Visme’s AI integration will take note of your brand colors, brand fonts and more and create branded templates for you automatically.

You don't need to worry about spending hours customizing templates to fit your brand anymore. You can focus on writing amazing case studies that promote your company.

13. How Breakwater Kitchens Achieved a 7% Growth in Sales With Thryv

Breakwater Kitchens struggled with managing their business operations efficiently. They spent a lot of time on manual tasks, such as scheduling appointments and managing client communication. This made it difficult for them to grow their business and provide the best possible service to their customers.

David, the owner, discovered Thryv. With Thryv, Breakwater Kitchens was able to automate many of their manual tasks. Additionally, Thryv integrated social media management. This enabled Breakwater Kitchens to deliver a consistent brand message, captivate its audience and foster online growth.

As a result, Breakwater Kitchens achieved increased efficiency, reduced missed appointments and a 7% growth in sales.

This case study example uses a concise format and strong verbs, which make it easy for readers to absorb the information.

At the top of the case study, Thryv immediately builds trust by presenting their customer's complete profile, including their name, company details and website. This allows potential customers to verify the case study's legitimacy, making them more likely to believe in Thryv's services.

However, manually copying and pasting customer information across multiple pages of your case study can be time-consuming.

To save time and effort, you can utilize Visme's dynamic field feature . Dynamic fields automatically insert reusable information into your designs.  So you don’t have to type it out multiple times.

14. Zoom’s Creative Team Saves Over 4,000 Hours With Brandfolder

Zoom experienced rapid growth with the advent of remote work and the rise of the COVID-19 pandemic. Such growth called for agility and resilience to scale through.

At the time, Zoom’s assets were disorganized which made retrieving brand information a burden. Zoom’s creative manager spent no less than 10 hours per week finding and retrieving brand assets for internal teams.

Zoom needed a more sustainable approach to organizing and retrieving brand information and came across Brandfolder. Brandfolder simplified and accelerated Zoom’s email localization and webpage development. It also enhanced the creation and storage of Zoom virtual backgrounds.

With Brandfolder, Zoom now saves 4,000+ hours every year. The company also centralized its assets in Brandfolder, which allowed 6,800+ employees and 20-30 vendors to quickly access them.

Brandfolder infused its case study with compelling data and backed it up with verifiable sources. This data-driven approach boosts credibility and increases the impact of their story.

Bradfolder's case study goes the extra mile by providing a downloadable PDF version, making it convenient for readers to access the information on their own time. Their dedication to crafting stunning visuals is evident in every aspect of the project.

From the vibrant colors to the seamless navigation, everything has been meticulously designed to leave a lasting impression on the viewer. And with clickable links that make exploring the content a breeze, the user experience is guaranteed to be nothing short of exceptional.

The thing is, your case study presentation won’t always sit on your website. There are instances where you may need to do a case study presentation for clients, partners or potential investors.

Visme has a rich library of templates you can tap into. But if you’re racing against the clock, Visme’s AI presentation maker is your best ally.

15. How Cents of Style Made $1.7M+ in Affiliate Sales with LeadDyno

Cents of Style had a successful affiliate and influencer marketing strategy. However, their existing affiliate marketing platform was not intuitive, customizable or transparent enough to meet the needs of their influencers.

Cents of Styles needed an easy-to-use affiliate marketing platform that gave them more freedom to customize their program and implement a multi-tier commission program.

After exploring their options, Cents of Style decided on LeadDyno.

LeadDyno provided more flexibility, allowing them to customize commission rates and implement their multi-tier commission structure, switching from monthly to weekly payouts.

Also, integrations with PayPal made payments smoother And features like newsletters and leaderboards added to the platform's success by keeping things transparent and engaging.

As a result, Cents of Style witnessed an impressive $1.7 million in revenue from affiliate sales with a substantial increase in web sales by 80%.

LeadDyno strategically placed a compelling CTA in the middle of their case study layout, maximizing its impact. At this point, readers are already invested in the customer's story and may be considering implementing similar strategies.

A well-placed CTA offers them a direct path to learn more and take action.

LeadDyno also utilized the power of quotes to strengthen their case study. They didn't just embed these quotes seamlessly into the text; instead, they emphasized each one with distinct blocks.

Are you looking for an easier and quicker solution to create a case study and other business documents? Try Visme's AI designer ! This powerful tool allows you to generate complete documents, such as case studies, reports, whitepapers and more, just by providing text prompts. Simply explain your requirements to the tool, and it will produce the document for you, complete with text, images, design assets and more.

Still have more questions about case studies? Let's look at some frequently asked questions.

How to Write a Case Study?

• Choose a compelling story: Not all case studies are created equal. Pick one that is relevant to your target audience and demonstrates the specific benefits of your product or service.
• Outline your case study: Create a case study outline and highlight how you will structure your case study to include the introduction, problem, solution and achievements of your customer.
• Choose a case study template: After you outline your case study, choose a case study template . Visme has stunning templates that can inspire your case study design.
• Craft a compelling headline: Include figures or percentages that draw attention to your case study.
• Work on the first draft: Your case study should be easy to read and understand. Use clear and concise language and avoid jargon.
• Include high-quality visual aids: Visuals can help to make your case study more engaging and easier to read. Consider adding high-quality photos, screenshots or videos.
• Include a relevant CTA: Tell prospective customers how to reach you for questions or sign-ups.

What Are the Stages of a Case Study?

The stages of a case study are;

• Planning & Preparation: Highlight your goals for writing the case study. Plan the case study format, length and audience you wish to target.
• Interview the Client: Reach out to the company you want to showcase and ask relevant questions about their journey and achievements.
• Revision & Editing: Review your case study and ask for feedback. Include relevant quotes and CTAs to your case study.
• Publication & Distribution: Publish and share your case study on your website, social media channels and email list!
• Marketing & Repurposing: Turn your case study into a podcast, PDF, case study presentation and more. Share these materials with your sales and marketing team.

What Are the Advantages and Disadvantages of a Case Study?

Advantages of a case study:

• Case studies showcase a specific solution and outcome for specific customer challenges.
• It attracts potential customers with similar challenges.
• It builds trust and credibility with potential customers.
• It provides an in-depth analysis of your company’s problem-solving process.

Disadvantages of a case study:

• Limited applicability. Case studies are tailored to specific cases and may not apply to other businesses.
• It relies heavily on customer cooperation and willingness to share information.
• It stands a risk of becoming outdated as industries and customer needs evolve.

What Are the Types of Case Studies?

There are 7 main types of case studies. They include;

• Illustrative case study.
• Instrumental case study.
• Intrinsic case study.
• Descriptive case study.
• Explanatory case study.
• Exploratory case study.
• Collective case study.

How Long Should a Case Study Be?

The ideal length of your case study is between 500 - 1500 words or 1-3 pages. Certain factors like your target audience, goal or the amount of detail you want to share may influence the length of your case study. This infographic has powerful tips for designing winning case studies

What Is the Difference Between a Case Study and an Example?

Case studies provide a detailed narrative of how your product or service was used to solve a problem. Examples are general illustrations and are not necessarily real-life scenarios.

Case studies are often used for marketing purposes, attracting potential customers and building trust. Examples, on the other hand, are primarily used to simplify or clarify complex concepts.

Where Can I Find Case Study Examples?

You can easily find many case study examples online and in industry publications. Many companies, including Visme, share case studies on their websites to showcase how their products or services have helped clients achieve success. You can also search online libraries and professional organizations for case studies related to your specific industry or field.

If you need professionally-designed, customizable case study templates to create your own, Visme's template library is one of the best places to look. These templates include all the essential sections of a case study and high-quality content to help you create case studies that position your business as an industry leader.

Get More Out Of Your Case Studies With Visme

Case studies are an essential tool for converting potential customers into paying customers. By following the tips in this article, you can create compelling case studies that will help you build trust, establish credibility and drive sales.

Visme can help you create stunning case studies and other relevant marketing materials. With our easy-to-use platform, interactive features and analytics tools , you can increase your content creation game in no time.

There is no limit to what you can achieve with Visme. Connect with Sales to discover how Visme can boost your business goals.

Easily create beautiful case studies and more with Visme

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• Big Data Tutorials

Top 10 Big Data Case Studies that You Should Know

In less than a decade, Big Data is becoming a multi-billion-dollar industry. Big data has its uses and applications in almost every industry. Big data has a massive contribution to the advancement in technology, growth in business and organizations, profit in each sector, etc.

Looking at the non-stop growth and progress of Big data, companies started adopting it more frequently. Let us look at the contribution of Big data in different organizations.

Top 10 Big Data Case Studies

1. big data in netflix.

Netflix implements data analytics models to discover customer behavior and buying patterns. Then, using this information it recommends movies and TV shows to their customers. That is, it analyzes the customer’s choice and preferences and suggests shows and movies accordingly.

According to Netflix, around 75% of viewer activity is based on personalized recommendations. Netflix generally collects data, which is enough to create a detailed profile of its subscribers or customers. This profile helps them to know their customers better and in the growth of the business.

2. Big data at Google

Google uses Big data to optimize and refine its core search and ad-serving algorithms. And Google continually develops new products and services that have Big data algorithms.

Google generally uses Big data from its Web index to initially match the queries with potentially useful results. It uses machine-learning algorithms to assess the reliability of data and then ranks the sites accordingly.

Google optimized its search engine to collect the data from us as we browse the Web and show suggestions according to our preferences and interests.

3. Big data at LinkedIn

LinkedIn is mainly for professional networking. It generally uses Big data to develop product offerings such as people you may know, who have viewed your profile, jobs you may be interested in, and more.

LinkedIn uses complex algorithms, analyzes the profiles, and suggests opportunities according to qualification and interests. As the network grows moment by moment, LinkedIn’s rich trove of information also grows more detailed and comprehensive.

4. Big data at Wal-Mart

Walmart is using Big data for analyzing the robust information flowing throughout its operations. Big data helps to gain a real-time view of workflow across its pharmacy, distribution centers, and stores.

Here are five ways Walmart uses Big data to enhance, optimize, and customize the shopping experience.

• To make Walmart pharmacies more efficient.
• To manage the supply chain.
• For personalizinging the shopping experience.
• To improve store checkout.
• To optimize product assortment.

Big data is helping Walmart analyze the transportation route for a supply chain, optimizing the pricing, and thus acting as a key to enhancing customer experiences.

5. Big data at eBay

eBay is an American multinational e-commerce corporation based in San Jose, California. eBay is currently working with tools like Apache Spark, Kafka, and Hortonworks HDF. It is also using an interactive query engine on Hadoop called Presto.

eBay website uses Big data for several functions, such as gauging the site’s performance and detecting fraud. It also used Big data to analyze customer data in order to make them buy more goods on the site.

eBay has around 180 million active buyers and sellers on the website. And about 350 million items listed for sale, with over 250 million queries made per day through eBay’s auto search engine.

6. Big data at Sprint

Sprint Corporation is a United States telecommunications holding company that provides wireless services. The headquarters of the company is located in Overland Park, Kansas. It is also a primary global Internet carrier.

Wireless carrier Sprint uses smarter computing. Smarter computing primarily involves big data analytics to put real-time intelligence and control back into the network, driving a 90% increase in capacity. The company offers wireless voice, messaging, and also offers broadband services through its various subsidiaries.

Subsidiaries are under the Boost Mobile, Virgin Mobile, and Assurance Wireless brands.

7. Big data at Mint.com

Mint.com is a free web-based personal financial management service. It provides services in the US and Canada. It uses Big data to provide users with information about their spending by category. Big data also helps them to have a look at where they spent their money in a given week, month, or year.

Mint.com’s primary services allow users to track bank, investment, credit card, and loan balances. It also facilitates creating budgets and set financial goals.

8. Big data at IRS

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So far, the IRS has also saved billions of dollars in fraud, specifically with identity theft, and also recovered more than $2 billion over the last three years.

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Woolworth uses Big data to analyze customers’ shopping habits and behavior. The company spent nearly $20 million on buying stakes in the Data Analytics Company. Nearly 1 billion is being spent on analyzing consumer spending habits and boosting online sales.

Big data is emerging as a fantastic technology that provides solutions to almost every sector. It helps organizations generate profits, increase their customers, optimize their systems, and whatnot.

Big data brings a kind of revolution in the technological world. There is no denying the fact that Big data will continue to bring advancement and efficiency in its applications and solutions.

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Home » Case Study – Methods, Examples and Guide

Case Study – Methods, Examples and Guide

Table of Contents

A case study is a research method that involves an in-depth examination and analysis of a particular phenomenon or case, such as an individual, organization, community, event, or situation.

It is a qualitative research approach that aims to provide a detailed and comprehensive understanding of the case being studied. Case studies typically involve multiple sources of data, including interviews, observations, documents, and artifacts, which are analyzed using various techniques, such as content analysis, thematic analysis, and grounded theory. The findings of a case study are often used to develop theories, inform policy or practice, or generate new research questions.

Types of Case Study

Types and Methods of Case Study are as follows:

Single-Case Study

A single-case study is an in-depth analysis of a single case. This type of case study is useful when the researcher wants to understand a specific phenomenon in detail.

For Example , A researcher might conduct a single-case study on a particular individual to understand their experiences with a particular health condition or a specific organization to explore their management practices. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a single-case study are often used to generate new research questions, develop theories, or inform policy or practice.

Multiple-Case Study

A multiple-case study involves the analysis of several cases that are similar in nature. This type of case study is useful when the researcher wants to identify similarities and differences between the cases.

For Example, a researcher might conduct a multiple-case study on several companies to explore the factors that contribute to their success or failure. The researcher collects data from each case, compares and contrasts the findings, and uses various techniques to analyze the data, such as comparative analysis or pattern-matching. The findings of a multiple-case study can be used to develop theories, inform policy or practice, or generate new research questions.

Exploratory Case Study

An exploratory case study is used to explore a new or understudied phenomenon. This type of case study is useful when the researcher wants to generate hypotheses or theories about the phenomenon.

For Example, a researcher might conduct an exploratory case study on a new technology to understand its potential impact on society. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as grounded theory or content analysis. The findings of an exploratory case study can be used to generate new research questions, develop theories, or inform policy or practice.

Descriptive Case Study

A descriptive case study is used to describe a particular phenomenon in detail. This type of case study is useful when the researcher wants to provide a comprehensive account of the phenomenon.

For Example, a researcher might conduct a descriptive case study on a particular community to understand its social and economic characteristics. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a descriptive case study can be used to inform policy or practice or generate new research questions.

Instrumental Case Study

An instrumental case study is used to understand a particular phenomenon that is instrumental in achieving a particular goal. This type of case study is useful when the researcher wants to understand the role of the phenomenon in achieving the goal.

For Example, a researcher might conduct an instrumental case study on a particular policy to understand its impact on achieving a particular goal, such as reducing poverty. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of an instrumental case study can be used to inform policy or practice or generate new research questions.

Case Study Data Collection Methods

Here are some common data collection methods for case studies:

Interviews involve asking questions to individuals who have knowledge or experience relevant to the case study. Interviews can be structured (where the same questions are asked to all participants) or unstructured (where the interviewer follows up on the responses with further questions). Interviews can be conducted in person, over the phone, or through video conferencing.

Observations

Observations involve watching and recording the behavior and activities of individuals or groups relevant to the case study. Observations can be participant (where the researcher actively participates in the activities) or non-participant (where the researcher observes from a distance). Observations can be recorded using notes, audio or video recordings, or photographs.

Documents can be used as a source of information for case studies. Documents can include reports, memos, emails, letters, and other written materials related to the case study. Documents can be collected from the case study participants or from public sources.

Surveys involve asking a set of questions to a sample of individuals relevant to the case study. Surveys can be administered in person, over the phone, through mail or email, or online. Surveys can be used to gather information on attitudes, opinions, or behaviors related to the case study.

Artifacts are physical objects relevant to the case study. Artifacts can include tools, equipment, products, or other objects that provide insights into the case study phenomenon.

How to conduct Case Study Research

Conducting a case study research involves several steps that need to be followed to ensure the quality and rigor of the study. Here are the steps to conduct case study research:

• Define the research questions: The first step in conducting a case study research is to define the research questions. The research questions should be specific, measurable, and relevant to the case study phenomenon under investigation.
• Select the case: The next step is to select the case or cases to be studied. The case should be relevant to the research questions and should provide rich and diverse data that can be used to answer the research questions.
• Collect data: Data can be collected using various methods, such as interviews, observations, documents, surveys, and artifacts. The data collection method should be selected based on the research questions and the nature of the case study phenomenon.
• Analyze the data: The data collected from the case study should be analyzed using various techniques, such as content analysis, thematic analysis, or grounded theory. The analysis should be guided by the research questions and should aim to provide insights and conclusions relevant to the research questions.
• Draw conclusions: The conclusions drawn from the case study should be based on the data analysis and should be relevant to the research questions. The conclusions should be supported by evidence and should be clearly stated.
• Validate the findings: The findings of the case study should be validated by reviewing the data and the analysis with participants or other experts in the field. This helps to ensure the validity and reliability of the findings.
• Write the report: The final step is to write the report of the case study research. The report should provide a clear description of the case study phenomenon, the research questions, the data collection methods, the data analysis, the findings, and the conclusions. The report should be written in a clear and concise manner and should follow the guidelines for academic writing.

Examples of Case Study

Here are some examples of case study research:

• The Hawthorne Studies : Conducted between 1924 and 1932, the Hawthorne Studies were a series of case studies conducted by Elton Mayo and his colleagues to examine the impact of work environment on employee productivity. The studies were conducted at the Hawthorne Works plant of the Western Electric Company in Chicago and included interviews, observations, and experiments.
• The Stanford Prison Experiment: Conducted in 1971, the Stanford Prison Experiment was a case study conducted by Philip Zimbardo to examine the psychological effects of power and authority. The study involved simulating a prison environment and assigning participants to the role of guards or prisoners. The study was controversial due to the ethical issues it raised.
• The Challenger Disaster: The Challenger Disaster was a case study conducted to examine the causes of the Space Shuttle Challenger explosion in 1986. The study included interviews, observations, and analysis of data to identify the technical, organizational, and cultural factors that contributed to the disaster.
• The Enron Scandal: The Enron Scandal was a case study conducted to examine the causes of the Enron Corporation’s bankruptcy in 2001. The study included interviews, analysis of financial data, and review of documents to identify the accounting practices, corporate culture, and ethical issues that led to the company’s downfall.
• The Fukushima Nuclear Disaster : The Fukushima Nuclear Disaster was a case study conducted to examine the causes of the nuclear accident that occurred at the Fukushima Daiichi Nuclear Power Plant in Japan in 2011. The study included interviews, analysis of data, and review of documents to identify the technical, organizational, and cultural factors that contributed to the disaster.

Application of Case Study

Case studies have a wide range of applications across various fields and industries. Here are some examples:

Business and Management

Case studies are widely used in business and management to examine real-life situations and develop problem-solving skills. Case studies can help students and professionals to develop a deep understanding of business concepts, theories, and best practices.

Case studies are used in healthcare to examine patient care, treatment options, and outcomes. Case studies can help healthcare professionals to develop critical thinking skills, diagnose complex medical conditions, and develop effective treatment plans.

Case studies are used in education to examine teaching and learning practices. Case studies can help educators to develop effective teaching strategies, evaluate student progress, and identify areas for improvement.

Social Sciences

Case studies are widely used in social sciences to examine human behavior, social phenomena, and cultural practices. Case studies can help researchers to develop theories, test hypotheses, and gain insights into complex social issues.

Law and Ethics

Case studies are used in law and ethics to examine legal and ethical dilemmas. Case studies can help lawyers, policymakers, and ethical professionals to develop critical thinking skills, analyze complex cases, and make informed decisions.

Purpose of Case Study

The purpose of a case study is to provide a detailed analysis of a specific phenomenon, issue, or problem in its real-life context. A case study is a qualitative research method that involves the in-depth exploration and analysis of a particular case, which can be an individual, group, organization, event, or community.

The primary purpose of a case study is to generate a comprehensive and nuanced understanding of the case, including its history, context, and dynamics. Case studies can help researchers to identify and examine the underlying factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and detailed understanding of the case, which can inform future research, practice, or policy.

Case studies can also serve other purposes, including:

• Illustrating a theory or concept: Case studies can be used to illustrate and explain theoretical concepts and frameworks, providing concrete examples of how they can be applied in real-life situations.
• Developing hypotheses: Case studies can help to generate hypotheses about the causal relationships between different factors and outcomes, which can be tested through further research.
• Providing insight into complex issues: Case studies can provide insights into complex and multifaceted issues, which may be difficult to understand through other research methods.
• Informing practice or policy: Case studies can be used to inform practice or policy by identifying best practices, lessons learned, or areas for improvement.

Advantages of Case Study Research

There are several advantages of case study research, including:

• In-depth exploration: Case study research allows for a detailed exploration and analysis of a specific phenomenon, issue, or problem in its real-life context. This can provide a comprehensive understanding of the case and its dynamics, which may not be possible through other research methods.
• Rich data: Case study research can generate rich and detailed data, including qualitative data such as interviews, observations, and documents. This can provide a nuanced understanding of the case and its complexity.
• Holistic perspective: Case study research allows for a holistic perspective of the case, taking into account the various factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and comprehensive understanding of the case.
• Theory development: Case study research can help to develop and refine theories and concepts by providing empirical evidence and concrete examples of how they can be applied in real-life situations.
• Practical application: Case study research can inform practice or policy by identifying best practices, lessons learned, or areas for improvement.
• Contextualization: Case study research takes into account the specific context in which the case is situated, which can help to understand how the case is influenced by the social, cultural, and historical factors of its environment.

Limitations of Case Study Research

There are several limitations of case study research, including:

• Limited generalizability : Case studies are typically focused on a single case or a small number of cases, which limits the generalizability of the findings. The unique characteristics of the case may not be applicable to other contexts or populations, which may limit the external validity of the research.
• Biased sampling: Case studies may rely on purposive or convenience sampling, which can introduce bias into the sample selection process. This may limit the representativeness of the sample and the generalizability of the findings.
• Subjectivity: Case studies rely on the interpretation of the researcher, which can introduce subjectivity into the analysis. The researcher’s own biases, assumptions, and perspectives may influence the findings, which may limit the objectivity of the research.
• Limited control: Case studies are typically conducted in naturalistic settings, which limits the control that the researcher has over the environment and the variables being studied. This may limit the ability to establish causal relationships between variables.
• Time-consuming: Case studies can be time-consuming to conduct, as they typically involve a detailed exploration and analysis of a specific case. This may limit the feasibility of conducting multiple case studies or conducting case studies in a timely manner.
• Resource-intensive: Case studies may require significant resources, including time, funding, and expertise. This may limit the ability of researchers to conduct case studies in resource-constrained settings.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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• Published: 19 August 2024

Multi-objective economic operation of smart distribution network with renewable-flexible virtual power plants considering voltage security index

• Ehsan Akbari 1 ,
• Ahad Faraji Naghibi 2 ,
• Mehdi Veisi 3 ,
• Amirabbas Shahparnia 4 &
• Sasan Pirouzi 5  

Scientific Reports volume  14 , Article number:  19136 ( 2024 ) Cite this article

Metrics details

• Energy science and technology
• Engineering
• Mathematics and computing

This paper discusses the simultaneous management of active and reactive power of a flexible renewable energy-based virtual power plant placed in a smart distribution system, based on the economic, operational, and voltage security objectives of the distribution system operator. The formulated problem aims to specify the minimum weighted sum of energy cost, energy loss, and voltage security index, considering the optimal power flow model, voltage security formulation, and the operating model of the virtual power plant. The virtual unit includes renewable sources, like wind systems, photovoltaic, and bio-waste units. Flexibility resources include electric vehicle parking lot and price-based demand response. In the mentioned scheme, parameters of load, renewable sources, electric vehicles, and energy prices are uncertain. This paper utilizes the Unscented Transformation method for modeling uncertainties. Fuzzy decision-making is utilized to extract a compromised solution. The suggested approach innovatively considers the simultaneous management of active and reactive power of a virtual unit with electric vehicles and price-based demand response. This is performed to promote economic, operational, and network security objectives. According to numerical results, the approach with optimal power management of renewable virtual units is capable of boosting the economic, operation, and voltage security status of the network by approximately 43%, 47–62%, and 26.9%, respectively, to power flow studies. Only price-based demand response can improve the voltage security, operation, and economic states of the network by about 19.5%, 35–47%, and 44%, respectively, compared to the power flow model.

Introduction

A Virtual Power Plant (VPP) is a coordinating framework and an integrated unit of resources, storage systems, and various energy management programs 1 . Generally, utilization of renewable energy sources (RESs) such as wind turbines (WTs), photovoltaics (PVs), and bio-waste units (BUs) in VPPs is proposed by various organizations to reduce environmental pollutants. WT and PV generate electrical energy respectively from wind collision with the turbine and solar radiation to the panel 2 . BU also uses environmental waste for energy production 3 . The power generated by these RESs is uncertain, so the day-ahead and real-time operation results for a VPP with a RES may differ 4 . This situation is recognized under conditions of flexibility shortage, and following that, generation-consumption unbalance may occur in real-time mode 4 . To deal with the mentioned issue, the use of flexible resources (resources capable of controlling active power), including storage systems, demand response programs (DRPs), and RESs in VPPs is necessary for system flexibility management 4 . Mobile storage devices like electric vehicles (EVs) and DRP are more accessible than mobile storage and RESs because they are in the hands of energy customers. Because to use RESs and stationary storage, it is necessary to incur installation and construction costs. However, EVs and DRP can be utilized with various incentive solutions for the goals of the Distribution System Operator (DSO) such as flexibility. In addition to this issue, various resources and storage devices are capable of controlling their active and reactive power at the same time 5 . So, a VPP can have a role in the Power Management System (PMS) in the distribution system. In this situation, a VPP can play the role of a reactive power source in the distribution system, following which, by establishing optimal performance for the VPP, it can enhance various technical and economic metrics of the distribution system.

Background study

A vast amount of research has been presented to investigate the operation of VPPs within the distribution system. To attain optimal operation of technical VPPs in a rearrangeable network, the formulation of an optimization problem is utilized in Ref. 6 to handle the potential contingency issue in the system’s lines. The objective of this endeavor is to achieve optimal performance. Combined heat and power (CHP), renewable distributed generators (DGs), and dispatchable DGs are some of the carriers that are incorporated into the VPP, which is a sophisticated energy system that incorporates other carriers. The thermal and electrical storage systems, in addition to loads, are included in this category. To effectively plan and manage a VPP that is comprised of charging stations for EVs, stationary batteries, and renewable energy sources, it is recommended in Ref. 7 that an Energy Management System (EMS) be used. Through the utilization of a two-stage stochastic formulation, the model can optimize the bidding procedure in the Day-Ahead Market (DAM). In this formulation, the uncertainties that have an impact on the estimation of the amount of energy that will be required for the following day are taken into consideration. In Ref. 8 , carbon trading and green certificate trading methods are incorporated into the optimal dispatch model of a VPP that incorporates WTs, PVs, gas turbines, and energy storage devices. The objective of the VPP optimization process is to provide the highest possible net profit while considering both economic and emission variables. VPP’s involvement in carbon trading and green certificate trading is used to develop three different schemes, compare and evaluate them, and investigate their effectiveness. The Ref. 9 presents an effective strategy for maximizing the economic dispatch of a VPP. Taking into consideration the potential of energy storage systems (ESS) in EVs and data centers, the technique operates. In a test model of a data center facility, the optimization is carried out with the assistance of an advanced EMS. The problem is evaluated to maximize revenue for VPP, taking into consideration the pricing of the market as well as the hazards that are linked with distributed energy resources. The energy management of a VPP that includes a demand response program, energy storage technology, and a wind farm (WF) is the topic of discussion in the study 10 . The approach that has been deployed functions at the level of electricity transmission and takes into consideration the relationships between VPPs in day-ahead energy and reserve markets.

The proposed approach in Ref. 11 is a bi-level coordinated dispatch method that utilizes VPPs, consisting of battery storage devices and dispatchable EVs. This technique aims to boost the robustness of the electrical energy and gas systems. The Monte Carlo simulation technique was employed to replicate the unpredictable and sequential nature of cascade failures resulting from severe weather events affecting power and gas systems. Additionally, VPPs use the direct control mode to deploy battery storage resources. Because of the cost-conscious nature of EVs, the relationship between VPPs and EV owners is likened to a Stackelberg game. The objective of this game is to determine the optimal pricing and timetable for discharging. This enables the reduction of dispatch costs while simultaneously optimizing the resilience and revenues of EV owners. In Ref. 12 , a proposition is made regarding the utilization of a method to visually represent, measure, and effectively employ the collective operational adaptability of a set of units. The developed technique depends on five parameters that are linked to active and reactive power. These measures aid the VPP’s operator in decision-making when faced with uncertain circumstances. The authors propose a leasing method for coal-fired units, as outlined in Ref. 13 , which relies on the integration of carbon credits and prices. This technique would grant the privilege to employ coal-fired units for VPPs. Subsequently, a range of demand response solutions are implemented to manage the adjustable loads of different customers, finally generating specific controlled resources for the VPP. Furthermore, to provide optimal decision-making by the VPP operator, a cost model is built that accurately represents the state of capacity degradation of the energy storage system. Reference 14 investigates the challenges related to achieving complementary synergy between various power sources and micro-grids. Currently, there is a strong emphasis on enlarging the capacity of the power system for regulation, which is a crucial aspect. The study also considers various resources to create micro-grids with different features. These resources encompass both household and industrial loads, which serve as typical instances of demand-side resources. These resources possess unique power and energy attributes within the market for demand-side regulation. Literature 15 provides an analysis of the management and operational challenges that arise when implementing distributed PV and ESS for residential, commercial, and industrial users. When it comes to combining distributed energy resources (DERs), VPP aggregators in many locations face the dilemma of choosing between two separate pricing strategies. This is an overlooked component in several currently accessible studies. The presentation in Ref. 16 focuses on a bi-level power management approach for an active distribution network (ADN) that includes a VPP. The suggested technique coordinates the VPP operator (VPPO) and the distribution system operator. The VPP encompasses RES, ESS, and EV parking lots that are synchronized with the VPPO.

Reference 17 outlines the operation of a Distribution Network that couples a VPP and Electric Springs. In fact, this system participates simultaneously in energy and reactive service markets. The prime aim of the proposed scheme is to maximize the predicted profits of systems in the mentioned markets. The constraints in the problem formulation are the AC optimal power flow equations, flexibility limits in the network, and the operating model of VPPs. Reference 18 proposes a network state-based power scenario reduction strategy for renewable energy generation, where typical scenarios are selected by the state of the grid voltage. The proposed day-ahead scheduling model is a mixed-integer, nonlinear, large-scale, stochastic optimization problem with high dimensional random variables, which is difficult to solve directly by traditional centralized method. Reference 19 contributes with a VPP operating model considering a full AC Optimal Power Flow while integrating different paths for the use of green hydrogen, such as supplying hydrogen to a Combined Heat and Power (CHP), industry, and local hydrogen consumers. In Ref. 20 , an interaction-based VPP operation methodology using distribution system constraints is proposed for DSO voltage management, assuming that the VPP primarily participates in the wholesale energy market. The research background includes an overview of the studies, which is presented in Table 1 , the last portion of the document.

Research gaps

According to the previous review and Table 1 , some research gaps related to VPPs operation in the distribution network include the following.

Generally, in most research, the energy management or active power of VPPs in the power system has been considered. However, it should be noted that resources and storage devices that are in the VPP can also play a role in controlling reactive power. For example, in Refs. 21 , 22 , EVs control their active and reactive power with their charger. Also, RESs such as wind and solar systems are connected to the network by power electronic devices. These devices can also play a role in controlling reactive power. However, in only a small fraction of studies like 16 , the control or management of reactive power of VPPs in the power system has been considered. Note that reactive power management of the network can be effective in improving operational indices and voltage security 16 , 21 .

Most research attempts to boost the economic and operational metrics of the power system by VPPs have been considered. However, a network has various technical and economic metrics that are not correlated to each other. For instance, boosting the economic situation will require high power injection by local resources into the power system. But in this situation, a suitable situation for the operational index like voltage profile is not achieved. In addition to this, in the distribution network, the voltage is very sensitive to the power demand of the network. So much so that there is a high voltage drop at the end of the feeder buses of this network. Therefore, in this situation, estimating the voltage security index in the distribution network by local resources is of special importance. However, this issue has been discussed in fewer studies.

Power fluctuations in RESs arise from the inherent uncertainty in the power they generate. This results in VPPs with a RES having limited flexibility. This lack of flexibility can cause generation-consumption unbalance during real-time operation. To address this challenge, flexible sources are utilized alongside the RES. A flexible resource is an element used for controlling its active power. In most research, stationary storage like a battery has been used as a flexible resource. But note that EVs and demand response are also flexible resources that are more accessible. However, this issue has been discussed in fewer studies, so in Refs. 9 , 11 , 16 EVs were utilized as a flexible resource.

Energy management of VPP in the distribution system is part of operational problems. In these problems, the execution steps are small thus making the solving part a time-consuming task. To reduce computational time, one solution is to simplify the problem. However, scenario-based stochastic optimization (SBSO) has mostly been employed to model uncertain parameters. In this method, quite a few scenarios are needed to access a trustable solution, so the volume of the problem in this method is not low. To address this issue, methods are needed that have a low number of scenarios. One of these techniques is the unscented transformation (UT) method, which is a stochastic optimization with a minimum number of scenarios than their counterpart methods. However, the use of this technique has been focused in fewer studies.

Contributions

To deal with the mentioned gaps, simultaneous management of active and reactive power of renewable VPPs with EV parking lots (EVPL) and price-based load response (PBDR) is utilized in the present study, which is based on the economic, operational, and voltage security objectives of the DSO, as Fig.  1 depicts. The approach minimizes the weighted sum of expected energy cost functions, expected energy losses, and voltage security index. This problem is subject to AC power flow (AC-PF) constraints, operational and voltage security limitations of the Smart Distribution Network (SDN), operational model of RESs, EVPL, and PBDR in the form of VPP. RESs in the VPP include WT, PV, and BU. BU, by consuming environmental waste, produces gas and then uses this gas to generate electrical energy 3 . Therefore, it has a significant effect on reducing environmental pollutants. EVPL and PBDR are utilized in the VPP as a flexibility resource. By controlling the active power of these elements, the VPP is expected to experience desirable flexibility conditions. In the proposed scheme, load, energy price, renewable power, and EV parameters are uncertain. In the suggested approach, to deal with the last sturdy gap, the UT technique is adopted. Subsequently, to find an optimal or compromised solution, the fuzzy decision-making method is employed. In the end, by making a comparison between the background studies and the suggested approach, the following novelties are obtained for the proposed scheme:

Simultaneous active-reactive power management of flexible-renewable VPPs in the SDN to boost the economic, operational, and security indices of the distribution system simultaneously.

Investigation of the impact of the optimal performance of renewable VPPs with EV parking and price-based load response on the voltage security index of the distribution network.

Use of more accessible flexibility resources (elements that are in the hands of customers and do not require the installation of a new element) such as parking of electric vehicles and price-based load response alongside RESs in the form of VPP.

Use of the UT technique for simultaneous modeling of uncertainties of load, renewable power, EVPL, and energy price to simplify the problem.

Use of bio-waste in the VPP to reduce environmental pollutants by consuming environmental waste and examining its optimal performance in enhancing the economic and technical metrics of the distribution system.

Active and reactive power management framework of renewable VPPs in the distribution network according to the economic and technical objectives of DSO.

Paper organization

In the subsequent sections, we delve into the details of our study. “ Modelling of the suggested approach ” section presents the mathematical modeling of renewable VPP operation, incorporating EVPL and PBDR in the SDN. The uncertainty modeling is presented in “ Modelling of uncertainties based on the UT method ” section. “ Numerical results and discussion ” section reports the numerical findings derived from different cases. Lastly, “ Conclusions ” section provides a summary of the general conclusions drawn from this study.

Modelling of the suggested approach

The mathematical model of energy management of SDN in the presence of renewable VPPs with EVPL and PBDR is presented here following the economic, operational, and voltage security objectives. This scheme minimizes the weighted sum of operational costs, energy losses, and voltage security index, while it is subject to AC optimal power flow (AC-OPF), and the operational model of VPPs. The details of the proposed scheme formulation are explained below.

Objective function

Equation ( 1 ) gives the objective function and minimizes the weighted sum of the expected cost of energy purchased from the upstream network (first term), SDN energy losses (second term), and voltage security index (third term). The cost of SDN’s energy purchased from the upstream network per operating hour equals the product of the active power on the distribution substation located at the reference bus and the energy price 21 , 22 . The network energy losses, like the second term of Eq. ( 1 ), equal the sum of the active power of the distribution post and VPPs minus the active power of passive consumers in the network. Here, the worst security index (WSI) has been adopted 21 . In this method, the weak bus in SDN is first identified. The weak bus has the lowest voltage amplitude. Then, WSI is found for the weak bus. This quantity varies in the range of 0 to 1. Zero value shows voltage collapse conditions, and unity represents the no-load conditions of the network (the network with the minimum voltage drop). Therefore, the WSI should be maximized. Since the objective function is expressed as a minimum expression, the third term of Eq. ( 1 ) includes a negative coefficient 21 .

In Eq. ( 1 ), the parameters ω EC , ω EL , and ω VS represent the weight coefficients of the energy cost, energy loss, and voltage security functions, respectively. These coefficients have a value between zero and one, and their sum must always be equal to 1 23 . Therefore, by changing the values of these weight coefficients, it is expected that the mentioned functions’ outputs are different, the plot of which in a 3-dimensional reference frame denotes the Pareto front of the suggested approach 23 . Next, a fuzzy decision-making technique 24 is utilized to access an optimal or compromise point. The details of this technique for the suggested problem are as follows 24 :

Step 1 Find the minimum ( F min ) and maximum ( F max ) value of the energy cost, energy loss, and voltage security functions for three case studies ω EC  = 1, ω EL  = 1, and ω VS  = 1.

Step 2 Select a random value for the weight coefficients so that their sum equals 1 (ω EC  + ω EL  + ω VS  = 1)..

Step 3 Calculate the linear membership function for the energy cost, energy loss, and voltage security functions:

If the value of a function ( F ) is less than F min , the linear membership function value for this function is 1.

If the value of a function is between F min and F max , the linear membership function value for the mentioned function equals the difference of the function value to F max divided by the difference of F min and F max (( F − F max )/( F min  − F max )).

If the function value is greater than F max , then the linear membership function value is zero.

Step 4 Determine the minimum value between the linear membership function of energy cost, energy loss, and voltage security (this amount is denoted by φ ).

Step 5 Upon reaching the maximum number of specified members of the Pareto front, step 6 is executed. Otherwise, repeat steps 2 and 3.

Step 6 Select a solution or compromise point corresponding to a point from the Pareto front that has the maximum value of φ .

Constraints of SDN

The constraints of the SDN are presented in Eqs. ( 2 )–( 13 ). Constraints ( 2 )–( 7 ) describe the AC power flow model in the SDN 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 . These equations respectively express the active-reactive power balance on the buses, the active and reactive power on the distribution lines, and the voltage phase angle and magnitude at the reference bus. In this section, the desired voltage magnitude is equal to 1 p.u. The operational constraints of the SDN are stated in constraints ( 8 )–( 11 ) 16 , 21 , 22 . The maximum apparent power passing through the distribution lines and posts is modeled respectively in Eqs. ( 8 ) and ( 9 ). These constraints are also known as line and substation capacity constraints. Equation ( 10 ) presents the voltage magnitude constraint for the buses. The lower boundary leads to the prevention of SDN’s shutdown in severe voltage drop conditions. Its upper limit prevents insulation damage of SDN equipment due to high overvoltage 21 . Constraint ( 11 ) models the power factor limitation of the distribution posts. The power factor is equal to the ratio of active to apparent power. In the present study, the minimum power factor is set at 0.9 22 . The voltage security model corresponding to the WSI is stated in Eqs. ( 12 ) and ( 13 ) 21 . In constraint ( 12 ), the WSI for the weak bus ( p ) is calculated 21 . WSI constraint is presented in Eq. ( 13 ). In this paper, the minimum value of WSI is considered to be 0.8 21 .

Renewable VPP constraints

The operation model of renewable VPPs with EVPL and PBDR is presented in constraints ( 14 )–( 25 ). In constraints ( 14 ) and ( 15 ), the active and reactive power of VPPs from the SDN perspective is calculated. The active power of VPP is equal to the sum of the active power of RESs (wind, solar, and bio-waste), PBDR, and EVs in discharge mode minus the sum of the active power of EVs in charge mode and passive load. The reactive power of VPP is equal to the sum of the reactive power of RESs and EV chargers minus the reactive power of the passive load. In constraints ( 16 )–( 18 ), the apparent power capacity limitation of controllable PVs, WTs, and BUs is presented. These constraints also represent the capability curve of RESs. The operation model of PBDR is stated in constraints ( 19 ) and ( 20 ) 25 . The active power control limitation of consumers participating in the DRP is consistent with constraint ( 19 ). In constraint ( 20 ), it is also ensured that all the energy consumed by the consumer participating in PBDR is supplied from SDN during the operation horizon. Active power variations of PBDRs depend on the price signal so that the minimum energy cost based on Eq. ( 1 ) is achieved. In this DR model, in hours when the energy price is low (corresponding to off-peak hours), consumers increase their energy consumption in these hours. However, in the case of high energy prices (corresponding to peak hours), these consumers reduce their energy consumption 25 . In constraints ( 21 )–( 25 ), the operation model of EV parking is stated 21 , 22 , 26 . In constraint ( 21 ), the energy storage in the EV batteries is calculated. This quantity is the sum of energy stored in the previous hour, the primary energy of the EVs that are connected to the VPP recently, and the energy stored as a result of EVs operating in charge mode minus the energy discharged by EVs in discharge mode and the energy consumed by EVs 26 . The limitation of the charge and discharge rate of EV batteries is modeled in constraints ( 22 ) and ( 23 ). Equation ( 24 ) ensures that the charging and discharging operation of EVs does not occur simultaneously. Finally, the apparent power limitation or capability curve of EV chargers is stated in Eq. ( 25 ). In this equation, the limitation of active and reactive power controllable by EV chargers is stated 21 , 22 . CR and DR in each hour are the sum of the charge and discharge rate of the connection of EVs to the VPP. E A in each hour equals the primary energy of EVs newly linked to the VPP in that period. E D is also for the energy consumed by EVs leaving the VPP.

In this article, as shown in Fig.  1 , VPP is an aggregator and coordinator of resources, storage systems, and response loads managed by DSO. Based on this definition of VPP, its operation model will be in the form of relations ( 14 )–( 25 ). It is noteworthy that in this article, the optimal operation of VPP on the improvement of technical and economic indicators in the distribution network has been investigated. Therefore, VPP's goals, such as obtaining financial benefits, were not considered in this article. But there is no limit to the implementation of this issue, and VPP's objectives can be added to the objective function ( 1 ).

Modelling of uncertainties based on the UT method

Uncertain parameters of the problem ( 1 )–( 25 ) include the amount of load, P C and Q C ; renewable power generation, P WT , P PV , and P BU ; energy price, γ , charge and discharge rate of EVs, CR and DR , and initial and consumption energy of EVs, E A and E D . The proposed problem is an operation problem with a small execution step; so, the problem needs to be simplified and the computation burden is lessened 27 . To achieve this goal, the volume of the problem should be decreased. To this end, the UT-based stochastic optimization 27 has been adopted here so that uncertainties can be properly modeled. The method with the minimum number of scenarios can derive a trustable optimal solution. So, for b uncertainty parameters, it requires 2 n  + 1 scenarios. In the suggested approach, n  = 10, so the number of scenarios is equal to 21.

The formulation of the problem is denoted as y  =  f ( z ). Here, y   ∈   R r is an uncertain output vector with r elements and the z   ∈   R n represents the vector of uncertain inputs. Also, μ z and σ z are the mean and covariance of z . Symmetric and asymmetric elements of σ z are used to calculate the variance and covariance of uncertain parameters. Also, the UT method is applied to determine the mean and covariance of outputs, which are μ y and σ y 27 . The steps of the formulation of the problem are summarized here:

Step 1 Take 2 n  + 1 samples (z s ) from the input data:

here, W 0 shows the weight of μ z (mean).

Step 2 Evaluate the weighting factor of individual sample points:

Step 3 Take 2 n  + 1 samples from the nonlinear function to achieve output samples using Eq. ( 33 ).

Step 4 Evaluate σ y and μ y of the output variable θ .

In the proposed scheme, uncertainty parameters are P C , Q C , P WT , P PV , P BU ; γ , CR , DR , E A, and E D . The total number of uncertainty parameters ( n ) is 10. According to the UT method, the total number of scenario samples is 2 n  + 1, therefore, it is equal to 21 for the proposed problem. In each scenario, a specific value is selected for each uncertainty parameter including load, renewable sources, and EVs based on the UT technique, ( 26 )–( 33 ), and the mean and standard deviation value of these uncertainties. In other words, the UT method considers the simultaneous modeling of all uncertainties in this section.

The proposed scheme includes a mathematical model 28 , 29 , 30 , 31 , 32 . This model is based on the optimization formulation 33 , 34 , 35 , 36 , 37 . It includes the objective function that is in terms of min or max 38 , 39 , 40 , 41 , 42 , 43 . The optimization problem includes the different constraints 44 , 45 , 46 , 47 . Constraints are equality or inequality 48 , 49 , 50 , 51 , 52 . To apply the optimization model on the distribution network, the network needs smart devices 53 , 54 , 55 , 56 . Smart systems include Telecommunication devices and intelligent algorithms 57 , 58 , 59 , 60 .

Numerical results and discussion

The suggested scheme, consistent with the formulation ( 1 )–( 25 ) and uncertainty modeling based on UT, ( 26 )–( 33 ), is applied in this section to the 69-bus IEEE SDN as illustrated in Fig.  2 61 . The network has a base power of 1 MVA. The base voltage is 12.66 kV. The minimum and maximum permissible voltage magnitude are respectively 0.9 and 1.05 per unit 62 , 63 , 64 , 65 , 66 . The data of the distribution lines, such as resistance, reactance, conductance, susceptance, and capacity for the mentioned network are provided in Ref. 61 . This network has one distribution post that is connected to bus 1. The bus is considered as the reference bus. The capacity of the distribution post is also considered to be 5 MVA. The peak load data for different buses are stated in Ref. 61 . The load at different hours equals the multiplication of the peak load and load factor 67 , 68 , 69 , 70 , 71 , 72 . The expected daily curve of the load factor, consistent with the data of Isfahan, Iran, is plotted in Fig.  3 . The energy price is based on the time of use (TOU), which for low-load (peak-load) hours, 1:00–7:00 (17:00–22:00), is 16 $/MWh (30$/MWh). The energy price in the mid-load range, 8:00–16:00 and 23:00–00:00, is 24$/MWh 21 . The mentioned network, as shown in Fig.  2 , has 8 flexible-renewable VPPs. The location of these VPPs is shown in Fig.  2 . Their data are based on Table 2 . The power generated by a RES for different periods can be found by multiplying its capacity and the power generation rate of this resource. The expected daily curve of the power generation rate of WT, PV, and BU, based on the data of Isfahan, Iran, is plotted in Fig.  3 . The number of EVs in each VPP is stated in Table 2 . The specifications of each EV, such as battery capacity, vehicle type, charge and discharge rate, and charger capacity are stated in Refs. 21 , 22 . The efficiency of charging and discharging the EV battery is respectively considered to be 93% and 92% 26 . The initial and consumed energy of each EV is respectively equal to 20% and 80% of the EV battery capacity. The number of EVs present at each hour will be a multiplication of the number of EVs and the penetration rate of EVs. Figure  3 illustrates the expected daily curve of the EV penetration rate 22 . The peak load in each VPP equals 150% of the peak load at the VPP connection location.

IEEE 69 bus SDN 61 with flexi-renewable VPPs.

Daily curve of load factor, generation power rate of RESs, and EV penetration rate.

Numerical results

The findings of the suggested plan, consistent with the data from the previous section, are presented. The simulation is performed in the GAMS optimization software environment, and the IPOPT algorithm 72 is utilized in this software to solve the problem. This algorithm is suitable for solving non-linear problems, and it has a toolbox in the mentioned software. Therefore, this algorithm calculates the optimal values of the objective function, (1), considering constraints ( 2 )–( 25 ). The following part provides a complete report on the findings.

Evaluation of the compromise solution between economic, operational, and DSO security objectives

Table 3 tabulates the Pareto front for the suggested plan, where 0, 0.25, 0.33, 0.5, 0.75, and 1 are the values considered for different weight coefficients. Accordingly, the minimum values of energy cost, energy losses, and voltage security index (total WSI based on Eq. ( 1 )) are respectively equal to 1422.6 $, 1.354 MWh, and 19.8 p.u. The maximum values of these functions are respectively equal to 2943.5 $, 3.023 MWh, and 23.1 p.u. Therefore, the range of variations (the difference between maximum and minimum values) are respectively equal to 1520.9 $, 1.669 MWh, and 3.3 p.u. The minimum values of energy cost and energy losses are obtained respectively for ω EC  = 1 and ω EL  = 1. The maximum value of total WSI is obtained for ω VS  = 1. In Eq. ( 1 ), the coefficient of the voltage security index is negative, because WSI should be maximized. Therefore, at ω VS  = 1, the best (maximum) value of the voltage security index is obtained. The maximum values of energy cost and energy losses are obtained respectively under conditions ω EL  = 1 and ω EC  = 1. The minimum value of total WSI is also obtained at ω EC  = 1. Based on Table 3 , the ascending and descending trends of objective functions are not the same. With the decrease in energy cost, energy losses become higher. The reason is that VPPs should inject high active and reactive power into the network to minimize energy costs. However, in such conditions, the power in the direction of VPP may increase towards the reference bus, which corresponds to an increase in the current flowing on the distribution lines and ultimately an increase in energy losses. The findings for the compromised solution between energy cost, energy losses, and voltage security are reported in Table 4 . To extract a more accurate solution, the number of members of the Pareto front for Table 4 was increased. In such a way that the step of changes of each weight coefficient was considered equal to 0.01. According to Table 4 , for modeling uncertainties with UT, the optimal values of energy cost, energy losses, and voltage security at the compromise point are respectively equal to 1862.1 $, 1.902 MWh, and 22.4 p.u. At this point, the energy cost is about 28.9% (1520.9 ÷ (1422.6–1862.1)) away from its minimum value (1422.6 $). This value for energy losses and voltage security is respectively about 32.8% and 21.2%. In other words, fuzzy decision-making has been able to obtain an optimal value for different objective functions in such a way that they are a little away from their best value (minimum value equal to energy cost and energy losses, and maximum value for voltage security).

In Table 4 , the results for modeling uncertainties with the UT and SBSO methods are presented. The SBSO combines the roulette wheel mechanism and the Kantorovich method 73 . The former initially produces quite a few scenarios (in this section, 2000 scenarios). In each of the scenarios, the values of uncertainty parameters are specified using their mean and standard deviation. The probability of each uncertainty parameter in each scenario is found by utilizing the normal probability function. The probability of each generated scenario is the multiplication of the probabilities of uncertainties. Subsequently, the Kantorovich method was adopted to reduce the number of scenarios to select a specific number of generated scenarios with the minimum distance from each other. Then, these scenarios are applied to the problem. The complete details of this method are explained in Ref. 73 . In Table 4 , the results for 30, 60, 90, and 120 scenarios obtained from the Kantorovich method are stated. As per Table 4 , the mentioned objective functions for a high number of scenarios in SBSO (more than 90 scenarios) are close to the results obtained in the UT method. But UT has obtained the mentioned solution for 21 scenarios based on “ Modelling of uncertainties based on the UT method ” section. This has resulted in UT obtaining a reliable solution in a much lower computational time than SBSO. For a low number of scenarios for SBSO, the distance of results to UT is greater. In this situation, based on Table 4 , a more desirable situation for the objective functions compared to UT or a high number of scenarios in SBSO has been obtained. This desirable situation is because some of the important scenarios that have a significant impact on the problem have not been considered, and this is a limitation. Therefore, a reliable solution for SBSO is obtained for a high number of scenarios. The UT has a low number of scenarios, therefore, its computing time is much less than SBSO with several different scenarios. In SBSO, the computing time increases with the increasing of scenarios number. Because in this situation, the volume of the problem increases. Since for the number of scenarios more than 90, SBSO obtains a reliable solution, therefore, the suitable number of scenarios for SBSO is equal to 90, because compared to scenarios more than 90, it has less computing time.

In Table 4 , numerical results are presented for different solvers such as IPOPT, CONOPT, BARON, KNITRO, LGO, and MINOS 72 . These algorithms in GAMS software have toolboxes and they are useful for solving non-linear problems. According to Table 4 , among the mentioned algorithms, IPOPT has been able to obtain the most optimal solution in a lower computing time. So that it has the lowest amount of energy cost and energy loss and the highest amount of voltage security index. Its convergence time is equal to 472 s, but the calculation time in other algorithms is more than 600 s. Therefore, IPOPT is the most suitable solution algorithm for the proposed plan. Therefore, only the numerical results for IPOPT were expressed for SBSO.

Examination of the performance of renewable VPPs with PBDR and EVPL

In Figs. 4 and 5 , the expected daily active and reactive power curves for RESs, EVPL, PBDR, and VPPs for the compromise point are presented, respectively. As per Figs.  3 and 4 a, the trend of changes in the active power output of the RES is analogous to the daily power generation rate curve of this source, but they are numerically different. Based on this, it is seen that the highest level of power generated from all RESs is obtained in the hours of 8:00–18:00. But at other hours, the level of power generated by renewable wind, solar, and bio-waste resources is less. In Fig.  4 b, consumers involved in PBDR raise energy demand in low-load (1:00–7:00) and mid-load (8:00–16:00 and 23:00–24:00) hours. Energy consumption in off-peak periods is more than mid-load hours. Based on “ Case study ” section, the energy price in the mid-load range is higher than in the low-load range. In peak-load hours (17:00–22:00), these consumers reduce their energy consumption. These hours correspond to high energy prices. This mode of operation of consumers in PBDR corresponds to the price signal and its goal is to reduce energy cost based on Eq. ( 1 ). EVs, based on Fig.  4 b, receive high energy during off-peak periods (1:00–7:00) from VPP. This energy equals the energy consumption EVs need for future trips. They, because the energy price in these hours is low, obtain their required energy consumption in the low-load range to reduce energy costs. EVs also perform charging operations in the hours of 12:00–16:00. This operation is for storing energy in the EV batteries that can be injected into the VPP or network during peak-load hours. In other words, with the mode of operation of EVs, it is expected that their charging cost and ultimately the network energy cost will be reduced. Finally, the expected daily active power curve of VPPs from the network’s point of view is shown in Fig.  4 c. This power is calculated by Eq. ( 14 ). According to Fig.  4 c, VPPs inject a high level of active power into the network during 8:00–18:00. Because in these hours, RESs produce high energy. VPPs are also in the role of an electricity producer in peak-load hours. Because in these hours, EVPL and PBDR, along with RESs, wind, solar, and bio-waste, act as an electricity producer. But at other operating hours, the level of active power of VPPs is low, in such a way that VPPs are in the consumer mode in the low-load range. Because in these hours, EVPL and PBDR are in the consumer mode.

Expected daily active power curve of, ( a ) RESs, ( b ) flexibility sources, ( c ) VPP.

Expected daily reactive power curve of ( a ) RESs, ( b ) flexibility sources, ( c ) VPP.

Based on Fig.  5 , it is observed that RESs and EVPLs inject reactive power into VPPs in the hours of 1:00–7:00 and 19:00–24:00, and at other hours they do not produce reactive power according to Fig.  5 a,b. In the hours of 1:00–7:00, VPPs are in consumer mode according to Fig.  4 . Therefore, to compensate for voltage drop and improve operational conditions (reducing energy losses) and voltage security in these conditions, RESs and EVPL inject reactive power into VPPs. In the hours of 19:00–24:00, the level of load consumption in the network is high. Therefore, various resources in VPP produce reactive power to boost the economic, operation, and voltage security metrics during these hours. Figure  5 c shows the expected daily reactive power curve of VPPs. This power is calculated by Eq. ( 15 ). Accordingly, VPPs are in the mode of producing reactive power during 1:00–7:00 and 19:00–24:00. Because in these hours, RESs and EVPL produce reactive power according to Fig.  5 . But at other hours, VPPs are in the mode of consuming reactive power. Because in these hours, based on Fig.  4 , VPPs inject larger amounts of active power into the network. Therefore, in such conditions, an excess voltage may be created in the network. To address this issue, VPPs are in the mode of consuming reactive power to prevent severe voltage deviation.

Examination of the economic, security, and operational status of the SDN

In Table 5 , the value of economic (energy cost), security (weak bus and minimum WSI value), and operation indices (energy losses, maximum voltage drop, maximum over-voltage, and peak load carrying capability (PLCC)) for various case studies are reported:

Case I: Load flow study (network without VPPs).

Case II: VPP including only RESs.

Case III: Case II + PBDR.

Case IV: Case II + EVPL.

Case V: Case III + EVPL without considering reactive power management by VPPs.

Case VI: Case III + EVPL considering reactive power management by VPPs.

PLCC refers to the network’s ability to handle a certain peak load, considering the daily load factor curve according to Fig.  3 . As Table 5 expresses, the highest energy cost, energy losses, and voltage drop occur in Case I, where the minimum values of WSI, excess voltage, and PLCC exist. This condition originates because the entire network load is fed from different buses from the upstream network, and there are no local sources in the network. In Mode II, with the presence of RESs in VPPs, the situation of the mentioned indices improves, except for excess voltage. Compared to Case I, energy cost, energy losses, and voltage drop decrease by approximately 25% , 36.4%, and 44.6% respectively. WSI and PLCC increased by approximately 8.9% and 33.7% respectively. The maximum excess voltage increases to 0.034 p.u., but this value is smaller than its allowable boundary of 0.05 p.u. (1–1.05). In Case III, when PBDR are present along with RESs in the form of VPP, more desirable conditions for different indices compared to Cases I and II are obtained. In such a way that energy losses, energy cost, maximum voltage drop, WSI, and PLCC improve by approximately 40%, 44.2%, 46.7%, 19.5%, and 44.3% respectively than in Case I. In this condition, the maximum excess voltage decreases by about 35.3% compared to Case II. In Case IV, with the presence of EVPL along with RESs in VPP, a more desirable situation is obtained than in Case I, but in comparison with Cases II and III, the network situation is weaker. In this case study, compared to Case I, WSI, PLCC, energy losses and cost, and maximum voltage drop improve by approximately 17.7%, 29.7%, 32.5%, 23.6%, and 46.7% respectively. The maximum excess voltage also decreases by about 29.4% in Case IV compared to Case II. In Case V, EVPL and PBDR are placed in VPP along with RESs, but reactive power management by EVPL and RESs in VPP is eliminated. In this case, more desirable conditions for the network are obtained compared to Cases I to IV. However, the best economic, operational, and security situation of the network in Case VI is obtained compared to other case studies. Case VI is similar to Case V, with the difference that reactive power management of VPP is also considered. In Case VI, energy cost, energy loss, and maximum voltage drop decrease by approximately 43%, 47.9%, and 48.9% than in Case I. WSI and PLCC in this condition increase by approximately 26.9% and 51.4% respectively. In this condition, the maximum excess voltage decreases by about 61.8% compared to Case II.

Conclusions

This article presented an economic exploitation constrained by voltage security for a Smart Distribution Network, by focusing on the simultaneous active-reactive power management of a renewable VPP equipped with EV parking and price-responsive load. The suggested approach minimized the weighted sum of expected energy cost and energy losses minus the voltage security index. It was also subject to optimal AC power flow equations, voltage security constraints, and the operation model of resources, mobile storage, and energy consumption management program as a VPP. Subsequently, a fuzzy decision-making technique was adopted to extract a compromised solution between economic, operational, and voltage security objectives for the distribution system operator. The approach is subject to uncertainties of load, renewable power, energy price, and aggregation parameters of electric vehicles. In this article, the Unscented transformation method was incorporated so that uncertain parameters are perfectly modeled. Findings have shown that the fuzzy decision-making found values for economic, operational, and voltage security objective functions at a compromised point. The values had a small distance from their best value (minimum/maximum value of losses and energy cost/voltage security). This distance for energy loss, energy cost, and voltage security is approximately 32.8%, 28.9%, and 21.2%, respectively. The Unscented transformation method, compared to scenario-based stochastic optimization, was able to reach a trustable solution with the minimum possible number of scenarios and computational time. With the optimal simultaneous active-reactive power management of electric vehicles, load responsiveness, and RESs as a virtual power plant, the economic status, operation, and voltage security of the SDN have improved by approximately 43%, 47–62%, and 26.9% respectively, compared to power flow studies. Note that only PBDR can improve the economic, operation, and voltage security indices of the distribution network by about 44%, 35–47%, and 19.5%, respectively, compared to the power flow model.

Based on the numerical results, it was observed that the virtual power plants with their optimal performance in the distribution network can improve the economic and technical status of this network. Therefore, they can benefit from this in different markets. This topic was not included in the proposed plan; however, it was considered as future work.

Data availability

All data generated or analyzed during this study are included in this published article, “ Case study ” section. Also, the datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Abbreviations

AC optimal power flow

AC power flow

Active distribution network

Bio-waste unit

Combined heat and power

Day-ahead market

Distributed energy resource

Distributed generator

Distributionally robust optimization

Demand response program

Distribution System Operator

Energy Management System

Energy storage system

Electric vehicle

• Electric vehicles parking lot

Hybrid stochastic-robust optimization

• Price-based demand response

Peak load carrying capability

• Power management system

Photovoltaic

Renewable energy source

Scenario-based stochastic optimization

Smart distribution network

Time of use

Unscented transformation

Virtual power plant

Virtual power plant operator

Worst security index

Wind turbine

Stored energy in electric vehicles (EVs) batteries in MWh

Active power of EV battery (MW) for charge and discharge operating states

Active power (MW) of consumers participating in the price-based demand response (PBDR)

Active (MW) and reactive (MVAr) power on the distribution line

Active (MW) and reactive (MVAr) power on the distribution substation

Active (MW) and reactive (MVAr) power of the virtual power plant (VPP)

Reactive power (MVAr) of EV chargers

Reactive power (MVAr) of wind turbine (WT), photovoltaic (PV), and bio-waste unit (BU)

Voltage magnitude in per-unit (p.u.)

Worst security index (p.u.)

Voltage angle (radian)

Bus and distribution line incidence matrix

Bus and VPP incidence matrix

Susceptance and conductance of the distribution line (p.u.)

Charge and discharge rate (MW) of EV batteries

Initial and consumed energy (MWh) of EVs

Active (MW) and reactive (MVAr) power of passive consumers

Active power (MW) for WT, PV, and BU

Resistance and reactance of the distribution line (p.u.)

Maximum apparent power (MVA) passing through EV chargers

Maximum apparent power (MVA) flow on the distribution line

Maximum apparent power (MVA) flow on the distribution substation

Maximum apparent power (MVA) passing through WT, PV, and BU

Permissible minimum and maximum magnitude of the voltage (p.u.)

Energy price ($/MWh)

Scenario probability

The participation rate of consumers in PBDR

Efficiency of charge and discharge of EV batteries

Weighting coefficients

Poor bus (a bus with low voltage magnitude)

The prior bus connected to the poor bus

Auxiliary index corresponding to bus

Operation hour

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Ehsan Akbari

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Ehsan Akbari: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data Curation, Writing—Original Draft. Ahad Faraji Naghibi: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data Curation, Writing—Original Draft. Mehdi Veisi: Investigation, Resources, Data Curation, Writing—Original Draft. Amirabbas Shahparnia: Investigation, Resources, Data Curation, Writing—Original Draft Sasan Pirouzi: Supervisor, Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data Curation, Writing—Original Draft.

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Akbari, E., Faraji Naghibi, A., Veisi, M. et al. Multi-objective economic operation of smart distribution network with renewable-flexible virtual power plants considering voltage security index. Sci Rep 14 , 19136 (2024). https://doi.org/10.1038/s41598-024-70095-1

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Embracing Gen AI at Work

• H. James Wilson
• Paul R. Daugherty

The skills you need to succeed in the era of large language models

Today artificial intelligence can be harnessed by nearly anyone, using commands in everyday language instead of code. Soon it will transform more than 40% of all work activity, according to the authors’ research. In this new era of collaboration between humans and machines, the ability to leverage AI effectively will be critical to your professional success.

This article describes the three kinds of “fusion skills” you need to get the best results from gen AI. Intelligent interrogation involves instructing large language models to perform in ways that generate better outcomes—by, say, breaking processes down into steps or visualizing multiple potential paths to a solution. Judgment integration is about incorporating expert and ethical human discernment to make AI’s output more trustworthy, reliable, and accurate. It entails augmenting a model’s training sources with authoritative knowledge bases when necessary, keeping biases out of prompts, ensuring the privacy of any data used by the models, and scrutinizing suspect output. With reciprocal apprenticing, you tailor gen AI to your company’s specific business context by including rich organizational data and know-how into the commands you give it. As you become better at doing that, you yourself learn how to train the AI to tackle more-sophisticated challenges.

The AI revolution is already here. Learning these three skills will prepare you to thrive in it.

Generative artificial intelligence is expected to radically transform all kinds of jobs over the next few years. No longer the exclusive purview of technologists, AI can now be put to work by nearly anyone, using commands in everyday language instead of code. According to our research, most business functions and more than 40% of all U.S. work activity can be augmented, automated, or reinvented with gen AI. The changes are expected to have the largest impact on the legal, banking, insurance, and capital-market sectors—followed by retail, travel, health, and energy.

• H. James Wilson is the global managing director of technology research and thought leadership at Accenture Research. He is the coauthor, with Paul R. Daugherty, of Human + Machine: Reimagining Work in the Age of AI, New and Expanded Edition (HBR Press, 2024). hjameswilson
• Paul R. Daugherty is Accenture’s chief technology and innovation officer. He is the coauthor, with H. James Wilson, of Human + Machine: Reimagining Work in the Age of AI, New and Expanded Edition (HBR Press, 2024). pauldaugh

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2.9 billion records, including Social Security numbers, stolen in data hack: What to know

An enormous amount of sensitive information including Social Security numbers for millions of people could be in the hands of a hacking group after a data breach and may have been released on an online marketplace, The Los Angeles Times reported this week.

The hacking group USDoD claimed it had allegedly stolen personal records of 2.9 billion people from National Public Data, according to a class-action lawsuit filed in U.S. District Court in Fort Lauderdale, Florida, reported by Bloomberg Law. The breach was believed to have happened in or around April, according to the lawsuit.

Here's what to know about the alleged data breach.

Social security hack: National Public Data confirms massive data breach included Social Security numbers

What information is included in the data breach?

The class-action law firm Schubert, Jonckheer & Kolbe said in a news release that the stolen file includes 277.1 gigabytes of data , and includes names, address histories, relatives and Social Security numbers dating back at least three decades.

According to a post from a cybersecurity expert on X, formerly Twitter, USDoD claims to be selling the 2.9 billion records for citizens of the U.S., U.K. and Canada on the dark web for $3.5 million.

Since the information was posted for sale in April, others have released different copies of the data, according to the cybersecurity and technology news site Bleeping Computer.

A hacker known as " Fenice " leaked the most complete version of the data for free on a forum in August, Bleeping Computer reported.

The news you need to start your day smart. Sign up for USA TODAY's Daily Briefing newsletter.

2025 COLA: Estimate dips with inflation, but high daily expenses still burn seniors

What is National Public Data?

National Public Data is a Florida-based background check company operated by Jerico Pictures, Inc. USA TODAY has reached out to National Public Data for comment.

The company has not publicly confirmed a data breach, but The Los Angeles Times reported that it has been telling people who contacted via email that "we are aware of certain third-party claims about consumer data and are investigating these issues."

What to do if you suspect your information has been stolen

If you believe your information has been stolen or has appeared on the dark web, there are a few steps you can take to prevent fraud or identity theft.

Money.com recommends taking the following steps:

• Make sure your antivirus is up to date and perform security scans on all your devices. If you find malware, most antivirus programs should be able to remove it, but in some cases you may need professional help.
• Update your passwords for bank accounts, email accounts and other services you use, and make sure they are strong and different for every account. Include uppercase and lowercase letters, numbers and punctuation marks, and never use personal information that a hacker could guess.
• Use multifactor authentication for any accounts or services that offer it to ensure you are the person logging in.
• Check your credit report, and report any unauthorized use of of your credit cards. If you notice any suspicious activity, you can ask credit bureaus to freeze your credit.
• Be careful with your email and social media accounts, and beware of phishing, an attempt to get your personal information by misrepresenting who a message or email is from.

• Search for: Toggle Search

Problem Solved: STEM Studies Supercharged With RTX and AI Technologies

Editor’s note: This post is part of the AI Decoded series , which demystifies AI by making the technology more accessible, and showcases new hardware, software, tools and accelerations for RTX PC users.

AI powered by NVIDIA GPUs is accelerating nearly every industry, creating high demand for graduates, especially from STEM fields, who are proficient in using the technology. Millions of students worldwide are participating in university STEM programs to learn skills that will set them up for career success.

To prepare students for the future job market, NVIDIA has worked with top universities to develop a GPU-accelerated AI curriculum that’s now taught in more than 5,000 schools globally. Students can get a jumpstart outside of class with NVIDIA’s AI Learning Essentials , a set of resources that equips individuals with the necessary knowledge, skills and certifications for the rapidly evolving AI workforce.

NVIDIA GPUs — whether running in university data centers, GeForce RTX laptops or NVIDIA RTX workstations — are accelerating studies, helping enhance the learning experience and enabling students to gain hands-on experience with hardware used widely in real-world applications.

Supercharged AI Studies

NVIDIA provides several tools to help students accelerate their studies.

The RTX AI Toolkit is a powerful resource for students looking to develop and customize AI models for projects in computer science, data science, and other STEM fields. It allows students to train and fine-tune the latest generative AI models, including Gemma, Llama 3 and Phi 3, up to 30x faster — enabling them to iterate and innovate more efficiently, advancing their studies and research projects.

Students studying data science and economics can use NVIDIA RAPIDS AI and data science software libraries to run traditional machine learning models up to 25x faster than conventional methods, helping them handle large datasets more efficiently, perform complex analyses in record time and gain deeper insights from data.

AI-deal for Robotics, Architecture and Design

Students studying robotics can tap the NVIDIA Isaac platform for developing, testing and deploying AI-powered robotics applications. Powered by NVIDIA GPUs, the platform consists of NVIDIA-accelerated libraries, applications frameworks and AI models that supercharge the development of AI-powered robots like autonomous mobile robots, arms and manipulators, and humanoids .

While GPUs have long been used for 3D design, modeling and simulation, their role has significantly expanded with the advancement of AI. GPUs are today used to run AI models that dramatically accelerate rendering processes.

Some industry-standard design tools powered by NVIDIA GPUs and AI include:

• SOLIDWORKS Visualize : This 3D computer-aided design rendering software uses NVIDIA Optix AI-powered denoising to produce high-quality ray-traced visuals, streamlining the design process by providing faster, more accurate visual feedback.
• Blender : This popular 3D creation suite uses NVIDIA Optix AI-powered denoising to deliver stunning ray-traced visuals, significantly accelerating content creation workflows.
• D5 Render : Commonly used by architects, interior designers and engineers, D5 Render incorporates NVIDIA DLSS technology for real-time viewport rendering, enabling smoother, more detailed visualizations without sacrificing performance. Powered by fourth-generation Tensor Cores and the NVIDIA Optical Flow Accelerator on GeForce RTX 40 Series GPUs and NVIDIA RTX Ada Generation GPUs , DLSS uses AI to create additional frames and improve image quality.
• Enscape : Enscape makes it possible to ray trace more geometry at a higher resolution, at exactly the same frame rate. It uses DLSS to enhance real-time rendering capabilities, providing architects and designers with seamless, high-fidelity visual previews of their projects.

Beyond STEM

Students, hobbyists and aspiring artists use the NVIDIA Studio platform to supercharge their creative processes with RTX and AI. RTX GPUs power creative apps such as Adobe Creative Cloud, Autodesk, Unity and more , accelerating a variety of processes such as exporting videos and rendering art.

ChatRTX is a demo app that lets students create a personalized GPT large language model connected to their own content and study materials, including text, images or other data. Powered by advanced AI, ChatRTX functions like a personalized chatbot that can quickly provide students relevant answers to questions based on their connected content. The app runs locally on a Windows RTX PC or workstation, meaning students can get fast, secure results personalized to their needs.

Schools are increasingly adopting remote learning as a teaching modality. NVIDIA Broadcast — a free application that delivers professional-level audio and video with AI-powered features on RTX PCs and workstations — integrates seamlessly with remote learning applications including BlueJeans, Discord, Google Meet, Microsoft Teams, Webex and Zoom. It uses AI to enhance remote learning experiences by removing background noise, improving image quality in low-light scenarios, and enabling background blur and background replacement.

From Data Centers to School Laptops

@dannylum_ The GeForce RTX 40 Series Laptops are perfect for your back to school all-in-one computer! Get one today #STEM #Gaming #engineering #student #BeyondFast #ad @NVIDIA GeForce ♬ original sound – Danny Lum

NVIDIA RTX-powered mobile workstations and GeForce RTX and Studio RTX 40 Series laptops offer supercharged development, learning, gaming and creating experiences with AI-enabled tools and apps. They also include exclusive access to the NVIDIA Studio platform of creative tools and technologies, and Max-Q technologies that optimize battery life and acoustics — giving students an ideal platform for all aspects of campus life.

Say goodbye to late nights in the computer lab — GeForce RTX laptops and NVIDIA RTX workstations share the same architecture as the NVIDIA GPUs powering many university labs and data centers. That means students can study, create and play — all on the same PC.

Learn more about GeForce RTX laptops and NVIDIA RTX workstations .

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Older Adults Do Not Benefit From Moderate Drinking, Large Study Finds

Virtually any amount increased the risk for cancer, and there were no heart benefits, the researchers reported.

By Roni Caryn Rabin

Even light drinking was associated with an increase in cancer deaths among older adults in Britain, researchers reported on Monday in a large study. But the risk was accentuated primarily in those who had existing health problems or who lived in low-income areas.

The study, which tracked 135,103 adults aged 60 and older for 12 years, also punctures the long-held belief that light or moderate alcohol consumption is good for the heart.

The researchers found no reduction in heart disease deaths among light or moderate drinkers, regardless of this health or socioeconomic status, when compared with occasional drinkers.

The study defined light drinking as a mean alcohol intake of up to 20 grams a day for men and up to 10 grams daily for women. (In the United States, a standard drink is 14 grams of alcohol .)

“We did not find evidence of a beneficial association between low drinking and mortality,” said Dr. Rosario Ortolá, an assistant professor of preventive medicine and public health at Universidad Autónoma de Madrid and the lead author of the paper, which was published in JAMA Network Open.

On the other hand, she added, alcohol probably raises the risk of cancer “from the first drop.”

The findings add to a mounting body of evidence that is shifting the paradigm in alcohol research. Scientists are turning to new methodologies to analyze the risks and benefits of alcohol consumption in an attempt to correct what some believe were serious flaws in earlier research, which appeared to show that there were benefits to drinking.

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