walmart big data case study

How Big Data Analysis helped increase Walmarts Sales turnover?

Walmart Big Data Case Study-Understand how Walmart Big Data is used to leverage analytics to increase sales by improving Customer Emotional Intelligence Quotient.

With more than 245 million customers visiting 10,900 stores and with 10 active websites across the globe, Walmart is definitely a name to reckon with in the retail sector. Whether it is in-store purchases or social mentions or any other online activity, Walmart has always been one of the best retailers in the world. The Global Customer Insights analysis estimates that Walmart sees close to 300,000 social mentions every week. With 2 million associates and approximately half a million associates hired every year, Walmart’s employee numbers are more than some of the retailer’s customer numbers. It takes in approximately $36  million dollars from across 4300 US stores everyday.This article details into Walmart Big Data Analytical culture to understand how big data analytics is leveraged to improve Customer Emotional Intelligence Quotient and Employee Intelligence Quotient.

Walmart Sales Forecasting Data Science Project

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Table of Contents

How Walmart uses Big Data?

How walmart is tracking its customers, how walmart is making a real difference to increase sales.

• Social Media Big Data Solutions
• Mobile Big Data Analytics Solutions

Walmart’ Carts – Engaging Consumers in the Produce Department

World's biggest private cloud at walmart- data cafe, how walmart is fighting the battle against big data skills crisis, 2014 kaggle competition walmart recruiting – predicting store sales using historical data, description of walmart dataset for predicting store sales, use market basket analysis to classify shopping trips.

Walmart Data Analyst Interview Questions

Walmart Hadoop Interview Questions

• Walmart Data Scientist Interview Question

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American multinational retail giant Walmart collects 2.5 petabytes of unstructured data from 1 million customers every hour. One petabyte is equivalent to 20 million filing cabinets; worth of text or one quadrillion bytes. The data generated by Walmart every hour is equivalent to 167 times the books in America’s Library of Congress. With tons of unstructured data being generated every hour, Walmart is improving its operational efficiency by leveraging big data analytics . Walmart has created value with big data and it is no secret how Walmart became successful.

“The most important thing about Wal-Mart is the scale of Wal-Mart. Its scale in terms of customers, its scale in terms of products and its scale in terms of technology.”-said Anand Rajaram, head of WalmartLabs

“We want to know what every product in the world is. We want to know who every person in the world is. And we want to have the ability to connect them together in a transaction.” –said Walmart’s CEO of global e-commerce in 2013.

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Walmart was the world’s largest retailer in 2014 in terms of revenue. Walmart makes $36 million dollars from across 4300 retail stores in US, daily and employs close to 2 million people. Walmart started making use of big data analytics much before the term  Big Data became popular in the industry. In 2012, Walmart made a move from the experiential 10 node  Hadoop cluster to a 250 node Hadoop cluster. The main objective of migrating the Hadoop clusters was to combine 10 different websites into a single website so that all the unstructured data generated is collected into a new  Hadoop  cluster. Since then, Walmart has been speeding along big data analysis to provide best-in-class e-commerce technologies with a motive to deliver pre-eminent customer experience. The main objective of leveraging big data at Walmart is to optimize the shopping experience of customers when they are in a Walmart store, or browsing the Walmart website or browsing through mobile devices when they are in motion. Big data solutions at Walmart are developed with the intent of redesigning global websites and building innovative applications to customize shopping experience for customers whilst increasing logistics efficiency.Hadoop and NOSQL technologies are used to provide internal customers with access to real-time data collected from different sources and centralized for effective use.

Walmart acquired a small startup Inkiru based in Palo Alto, California to boost its big data capabilites. Inkiru Inc. helps in targeted marketing , merchandising and fraud prevention. Inkiru's predictive technology platform pulls data from diverse sources and helps Walmart improve personalization through data analytics. The predictive analytics platform of Inkiru incorporates machine learning technologies to automatically enhance the accuracy of algorithms and can integrate with diverse external and internal data sources.

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Walmart has a broad big data ecosystem. The big data ecosystem at Walmart processes multiple Terabytes of new data and petabytes of historical data every day. The analysis covers millions of products and 100’s of millions customers from different sources. The analytics systems at Walmart analyse close to 100 million keywords on daily basis to optimize the bidding of each keyword.The main objective of leveraging big data at Walmart is to optimize the shopping experience for customers when they are in a Walmart store, or browsing the Walmart website or browsing through mobile devices when they are in motion. Big data solutions at Walmart are developed with the intent of redesigning global websites.

Work with the world's largest retail dataset- Walmart Store Sales Forecasting Data Science Project

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Walmart has transformed decision making in the business world resulting in repeated sales. Walmart observed a significant 10% to 15% increase in online sales for $1 billion in incremental revenue. Big data analysts were able to identify the value of the changes Walmart made by analysing the sales before and after big data analytics were leveraged to change the retail giant’s e-commerce strategy.

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First Applications to Ride the Hadoop Data at Walmart

• Savings Catcher –An application that alerts the customers whenever its neighbouring competitor reduces the cost of an item the customer already bought. This application then sends a gift voucher to the customer to compensate the price difference.
• eReceipts application provides customers with the electronic copies of their purchases.
• A mapping application at Walmart uses Hadoop to maintain the most recent maps of 1000’s of Walmart stores across the globe. These maps specify the exact location where a small bar of soap resides in the widespread Walmart store.

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Mupd8- Map Update Application

To fulfil the need for a general purpose real time stream processing platform which can tackle issues like performance and scalability, Walmart developed Mupd8 for Fast Data. With Mupd8, stream processing applications could emphasize on the quality of generated data. Mupd8 does for fast data, what hadoop mapreduce computational model does for big data.

Mupd8 allows developers to write applications easily and process them using the Map Update framework (a workflow of Map and Update operators), an easy way to express streaming computation. Writing an application as a combination of customized map and update operators, big data developers can focus on the business logic of the application and let Mupd8 handle load and data distribution across various CPU cores.

For example, an application can be written to subscribe to the Twitter firehose of every tweet written; such an application can analyse the tweets to determine Twitter's most influential users, or identify suddenly prominent events as they occur. Alternatively, an application can be written to subscribe to a log of all user activity on a Web site; such an application can detect service problems users’ face as they occur, or compute suggestions for users' next steps based on up-to-the-moment activity.

“Our ability to pull data together is unmatched”- said Walmart CEO Bill Simon.

Walmart uses data mining to discover patterns in point of sales data. Data mining helps Walmart find patterns that can be used to provide product recommendations to users based on which products were bought together or which products were bought before the purchase of a particular product. Effective data mining at Walmart has increased its conversion rate of customers. A familiar example of effective data mining through association rule learning technique at Walmart is – finding that Strawberry pop-tarts sales increased by 7 times before a Hurricane. After Walmart identified this association between Hurricane and Strawberry pop-tarts through data mining, it places all the Strawberry pop-tarts at the checkouts before a hurricane. Another noted example is during Halloween, sales analysts at Walmart could look at the data in real-time and found that thought a specific cookie was popular across all walmart stores, there were 2 stores where it was not selling at all. The situation was immediately investigated and it was found that simple stocking oversight caused the cookies not being put on the shelves for sales. This issue was rectified immeadiately which prevented further loss of sales.

Walmart tracks and targets every consumer individually. Walmart has exhaustive customer data of close to 145 million Americans of which 60% of the data is of U.S adults. Walmart gathers information on what customer’s buy, where they live and what are the products they like through in-store Wi-Fi.The big data team at Walmart Labs analyses every clickable action on Walmart.com-what consumers buy in-store and online, what is trending on Twitter, local events such as San Francisco giants winning the World Series, how local weather deviations affect the buying patterns, etc. All the events are captured and analysed intelligently by big data algorithms to discern meaningful big data insights for the millions of customers to enjoy a personalized shopping experience.

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Launching New Products

 Walmart is leveraging social media data to find about the trending products so that they can be introduced to the Walmart stores across the world. For instance, Walmart analysed social media data to find out the users were frantic about “Cake Pops” .Walmart responded to this data analysis quickly and Cake Pops hit the Walmart stores.

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Better Predictive Analytics

​ Walmart has recently modified its shipping policy for products based on big data analysis. Walmart leveraged predictive analytics and increased the minimum amount for an online order to be eligible for free shipping. According to the new shipping policy at Walmart, the minimum amount for free shipping is increased from $45 to $50 with addition of several new products to enhance the customer shopping experience.

Customized Recommendations

​ Just the manner in which Google tracks tailor made advertisements, Walmart's big data algorithms analyse credit card purchases to provide specialized recommendation to its customers based on their purchase history.

Big Data Analytics Solutions at Walmart

1)  social media big data solutions.

Social Media Data is unstructured, informal and generally ungrammatical. Analysing and mining petabytes of social media data to find out what is important and then map it to meaning products at Walmart is an arduous task.

Social Media Data driven decisions and technologies are more of a norm than an exception at Walmart. A big part of Walmart’s data driven decision are based on social media data- Facebook comments, Pinterest pins, Twitter Tweets, LinkedIn shares and so on. WalmartLabs is leveraging social medial analytics to generate retail related  big data insights.

Walmart launched a social media crowdsourcing contest that helped entrepreneurs get their products on the shelf. The contest attracted more than 5000 entries and more than 1 million votes across US. Anybody could pitch in their products and get exposure to millions of audience. The best products were declared as winners and sold at Walmart stores to be made available to millions of customers.

“Social Media Analytics is all about mining retail-related insights from social channels, a perilous and personally exciting task to us. When our team spent the 22nd of November feverishly following the social retail pulse on Black Friday, we knew the world wasn’t preparing for an apocalypse.”- said Arun Prasath, a Principal Engineer at WalmartLabs

Social genome.

Social Genome is a big data analytics solution developed by WalmartLabs that analyses millions and billions of Facebook messages, tweets, YouTube videos, blog postings and more. Through the Social Genome analytics solution, Walmart is reaching customer or friends customers who tweet or mention something about the products of Walmart to inform them about the product and provide them special discount.

The Social Genome product combines public data from the web, social media data and proprietary data like contact information, email address and customer purchasing data. This data helps Walmart better analyse the context of their users.

For example, if the Social Genome identifies that a lady frequently tweets about movies, then when she tweets something like “I love Salt”, the social genome solution of Walmart is able to understand that the lady is referring to the popular Hollywood movie Salt and not the condiment salt.

“ It is only after conquering all of these multifold challenges that meaningful recommendation can be made….Our social media analytics project operates on top of a searchable index of 60 billion social documents and helps merchants at Walmart monitor sentiments and popular interests real-time, or inquire into trends in the past. One can also see geographical variations of social sentiments and buzz levels. There are also tools that marry search trends on walmart.com, sales trends in our brick-and-mortar stores and social buzz all in one place, to help make correlations. Together, these tools provide powerful social insights.”- said Arun Prasath, Principal Engineer at WalmartLabs.

Shopycat-Gift Recommendation Engine at Walmart

If you are confused on finding the perfect gift for your friends then Walmart’s Shopycat app will help you buy the ideal gift for your friend during the holiday buying rush. Walmart’s Shopycat recommends gifts for friends based on the social data extracted from their Facebook profiles. The app also provides links to the Walmart products so that users can easily purchase the product without any hassle and strive towards creating a broader marketplace . Shopycat is a part of Walmart’s Facebook page that has close to 10 million fans.

The app also suggests friends for whom users must by gifts depending on the level of interaction with them. When people click on a suggested gift, Shopycat also tells why a particular gift was suggested. For instance, the suggestions can show that a friend has liked the product on Facebook or has commented on a wall post or has a status update related to the product.

Shopycat allows the users to message their friends mutually through Facebook and ask them if they would like to buy a gift voucher or a product.

Inventory Management at Walmart using Predictive Analytics

Predictive analytics is at the heart of supply chain process that helps Walmart reduce overstock and stay properly stocked on the most in-demand products. Suppliers to Walmart are required to use the real-time vendor inventory management system that helps them minimize the inventory for a particular product if there are no significant sales for it. This helps retailers to save funds to buy products that have greater demand and have increased probability for greater profits.

• Improving the Store Checkout Process for Customers

Big data analytics is beign leveraged to determine the best form of checkout for a particular customer - facilitated checkout or self checkout. It is using predicitive analytics to predict the demand at specific hours and determine how many asociate would be needed at specific counters.

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2)  Mobile Big Data Analytics Solutions

According to Deloitte, the mobile influenced offline sales are anticipated to reach $700 billion by end of 2016. Walmart is harnessing the power of big data to drive tools and services in order to get its mobile strategy in order.

More than half of the Walmart’s customers use Smartphones and among these 35% of the shoppers are adults which is close to 3/4 th  of its overall customer base. Mobile phone customers are extremely important to Walmart as smartphone shoppers make 4 more trips and spend 77% more in-store. Thus, mobile users account for 1/3 rd of the Walmart traffic every year and approximately 40% during holidays.

“E-commerce is closely related to mobile purchase. The world’s largest retailer will use big data to enhance the consumers shopping experience in the store.” He also added: “Our mobile strategy is both simple and audacious. We want to make mobile tools become indispensable for our customers while they are shopping in our stores and online. The  retail will improve each customers personalized experience for competition in the future, and this all will happen on the small screen in their hands,” said Gib Thomas, Senior Vice President of Mobile and Digital at Walmart

Walmart is leveraging  big data analysis to develop predictive capabilities on their mobile app. The mobile app generates a shopping list by analysing the data of what the customers and other purchase every week. Walmart’s mobile application consists of a shopping list that can tell customers the position of their wants and helps them by providing discounts to similar products on Walmart.com.

Another way in which Walmart is harnessing the power of big data analysis is by leveraging analytics in real-time- when a customer actually enters the Walmart store. The geofencing feature of Walmart’s mobile app senses whenever a user enters the Walmart store in US. The app asks the user to enter into the “Store Mode”. The store mode of the mobile app helps users to scan QE codes for special discounts and offers on products they would like to buy.

With the intent of reduce waste and increasing consumer engagement, Walmart is introducing quality carts in produce departments across its stores. Walmart has employed quality carts in across 500 stores now and are expected to be present in all 5000 US stores by end of third quarter. Walmart knows that keeping its customers in the fresh produce department is the key to customer engagement and the implementation of quality carts has attractive offerings for them.Walmart is using big data and IoT sensors to find out how long people loiter in the fresh produce department. Big data analysis has helped them find that if the fresh produce looks fresh enough then people loiter for longer and this is the secret to make customers buy more things from the Walmart stores.

Walmart repurposed 200 of its existing outlets to provide grocery pickup in 30 cities. After knowing that consumers were increasingly concerned about the freshness of food, Walmart trained personnel to evaluate the quality of produce and showed food items to the customers before packing them.  If the wrap of frozen chicken is ripped or if the mango is not ripe, an exchange can be made immediately. All that the customers need to do is tap in their order through the app. Big data analytics helped Walmart win a bright spot in terms of grocery pickup.

Walmart is in the process of creating the world’s biggest private cloud for processing 2.5 PB of data every hour. Walmart has created its own analytics hub known as Data Café in Bentonville, Arkansas headquarters. At the data café, more than 200 streams of external and internal data along with 40 PB of transactional data can be manipulated, modelled and visualized.The data cafe pulls information from 200 varied sources that include Telecom data, social media data, economic data, meteorological data , Nielsen data , gas prices and local events databases  that accounts for 200 billion rows of transactional data for just few weeks. The solution to any particular problem can be found through these varied datasets and Walmart's analytic algorithms are designed to scan through the data in microsseconds to come up with a real-time solution for a particular problem.

Walmart Big Data is increasing exponentially at a rapid pace every day and the dearth of big data talent is a major roadblock for Walmart in performing analytics. With limited number of personnel possessing required big data skills –Walmart is taking every necessary step to overcome this challenge is that it does not have to fall behind its competitors. Whenever a new team member jobs the analytics team at Walmart Labs, he/she has to take part in the analytics rotation program. During this program the candidates are required to spend some time with the different departments in the company to understand how big data analytics is being leveraged across the company.

Walmart is having a tough time finding professionals with experience in cutting edge analytics applications and working knowledge of data science  programming languages like Python and R to build machine learning models. Walmart used the hashtag #lovedata for its recruitment campaign to raise its profile amongst the growing data science community in Bentonville and Arkansas.

Mandar Thakur, senior recruiter for Walmart’s Technology division said – “The staffing supply and demand gap is always there, especially when it comes to emerging technology”. With more than 40 petabytes of data available for analysis daily at Walmart, he says that there is going to be an unprecedented demand always for people who can do data science and analytics.

The secret to successful retailing of Walmart lies in delivering the right product at the right place and at the right time. Walmart continues to climb the retailing success ladder with remarkable results by leveraging big data analysis.

Walmart is fighting the big data skills gap by crowdsourcing analytics talent. Walmart hosted a Kaggle competition in 2014 where professionals where provided with historical sales dataset from sample of stores together with related sales events, price rollbacks and clearance sales. Candidates has to develop models that showed the impact of these events on the sales across various departments. The result of the competition helped Walmart find highly skilled and competent analytics talent.

In 2015, Walmart crowd sourced analytic talent with another Kaggle competition where candidates were required to predict the impact of weather on sales of different products in the store. Walmart has been able to hire skilled talent through these competition which they would not consider even interviewing based on the resume alone.

Mandar Thakur, senior recruiter for Walmart’s Technology division said- “One for example had a very strong background in physics but no formal analytics background. He has a different skillset – and if we hadn’t gone down the Kaggle route, we wouldn’t have acquired him.”

The biggest challenge for retailers like Walmart is to make predictions with limited historical data. If Thanksgiving or New Year comes once a year, retailers like Walmart have to make strategic decisions about how the sales will impact the bottom-line during the festive season. Walmart hosted a recruiting competition where job seekers were provided with historical sales data of 45 Walmart stores from different regions. Each store has multiple departments and the candidates participating in the crowdsourcing competition were required to predict the sales for each department in the store.Walmart also has promotional markdown events for prominent holidays like Christmas, Super Bowl, Labor Day, New Year, ThanksGiving, etc. Holiday markdown events were also included in the dataset provided by Walmart to add up to the challenge as the sales for holiday seasons were evaluated 5 times higher than the sales for non- holiday weeks.

The most challenging part of the competition was to predict which departments were largely affected by the holiday markdown events and what was the level of impact they had on the sales.

• stores.csv – This file contains data about all the 45 stores indicating the type and size of each Walmart store.
• train.csv- This file has historical training dataset from 2010 to 2012  containing the below information-

i) The Store Number

ii) The Department Number

iii) The Week

iv) Weekly Sales of a particular department in a particular store.

v) IsHoliday to indicate if it is a holiday week or not.

• Features.csv- This file contains additional information about each store, the department, and regional activity for the mentioned dates with details like the store number , the average temperature in the region , the cost of fuel in that region, the unemployment rate, the consumer pricing index, whether the give date/week is a special holiday week or not, data related to promotional markdowns that Walmart is running.
• Test.csv- It is just similar to train.csv except that the weekly sales are withheld in this file and the sales predictions have to be made for every triplet of the store, department and the date.

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To serve its customers better, Walmart enhances customer experiences by segmenting their store visits based on different trip types. Regardless of whether a customer is- on a last-minute run looking  for new puppy supplies or is just taking a leisurely troll down the store shopping for weekly grocery.

Classifying different trip types helps Walmart enhance customer shopping experience. Initially, Walmart’s trip types are created by combing art i.e. existing customer insights and science i.e. purchase history data. A new challenge that can be solved using the Walmart dataset is to classify customer trips to the Walmart store using only transactional dataset of the products purchased so that the segmentation process can be refined.

If you are preparing for a data analyst or data scientist interview at Walmart then here are few interview questions that will help you prepare for your data analyst or data scientist job interview at Walmart -

1) How will you deal with an experienced professional who consulted you but does not believe in  your analytical insights and sticks to his older analytical methods ?

2) Given the acess to Walmarts HR data, what would you be interested to search for ?

1) Explain about Hadoop architecture .

Walmart Data Scientist Interview Questions

1) How many sub-spaces can four hyperplanes divide in 3D?

2) How many sub-spaces can four lines divide in 2D ?

3) Write the code to reverse a linked list data structure.

If you want to work with one of the world's largest retail dataset, then drop us an email to [email protected]  to get the download link to Walmart Big dataset.

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Does Walmart use AWS or Azure?

Walmart has signed a five-year deal with Microsoft and turned to Azure cloud services.

Does Walmart use Teradata?

Walmart has the world's most giant data warehouse , capturing data on point-of-sale transactions every second from roughly 5,000 locations in six countries. It's a Teradata database with a capacity of 30 petabytes.

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Künstliche Intelligenz in Unternehmen: Innovative Anwendungen in 50 erfolgreichen Firmen

Der Bestsellerautor und Geschäfts renommierter KI-Experte Bernard zeigt, wie sterben Technologie des maschinellen Lernens das von Unternehmen verändert. Das Buch bietet einen Überblick über einzelne Unternehmen, beschreibt das spezifische Problem und erklärt, wie KI die Lösung erleichtert. Jede Fallstudie bietet einen umfassenden Einblick, der einige technische Details wichtige Lernzusammenfassungen enthält. Marrs Buch ist eine aufschlussreiche und informative Untersuchung der transformativen Kraft der Technologie in der Wirtschaft des 21. Jahrhunderts.

Bernard Marr

Bernard Marr is a world-renowned futurist, influencer and thought leader in the fields of business and technology, with a passion for using technology for the good of humanity. He is a best-selling author of over 20 books, writes a regular column for Forbes and advises and coaches many of the world’s best-known organisations. He has a combined following of 4 million people across his social media channels and newsletters and was ranked by LinkedIn as one of the top 5 business influencers in the world.

Bernard’s latest books are ‘Future Skills’, ‘The Future Internet’, ‘Business Trends in Practice’ and ‘ Generative AI in Practice ’.

Bernard Marr ist ein weltbekannter Futurist, Influencer und Vordenker in den Bereichen Wirtschaft und Technologie mit einer Leidenschaft für den Einsatz von Technologie zum Wohle der Menschheit. Er ist Bestsellerautor von 20 Büchern, schreibt eine regelmäßige Kolumne für Forbes und berät und coacht viele der weltweit bekanntesten Organisationen. Er hat über 2 Millionen Social-Media-Follower, 1 Million Newsletter-Abonnenten und wurde von LinkedIn als einer der Top-5-Business-Influencer der Welt und von Xing als Top Mind 2021 ausgezeichnet.

Bernards neueste Bücher sind ‘Künstliche Intelligenz im Unternehmen: Innovative Anwendungen in 50 Erfolgreichen Unternehmen’

Walmart: Big Data analytics at the world’s biggest retailer

23 July 2021

With over 20,000 stores in 28 countries, Walmart is the largest retailer in the world. So it’s fitting then that the company is in the process of building the world’s largest private cloud, big enough to cope with 2.5 petabytes of data every hour. To make sense of all this information, Walmart has created what it calls its Data Café – a state-of-the-art analytics hub located within its Bentonville, Arkansas headquarters.

Walmart uses Big Data in practice

The Data Café allows huge volumes of internal and external data, including 40 petabytes of recent transactional data, to be rapidly modelled, manipulated and visualised. Speaking to me about the project, senior statistical analyst Naveen Peddamail said, “If you can’t get insights until you’ve analysed your sales for a week or a month, then you’ve lost sales within that time.”

Quick access to insights is therefore vital. For example, Peddamail told me about a grocery team who could not understand why sales had suddenly declined in a particular product category. By drilling into the data, they were quickly able to see that pricing miscalculations had led to the products being listed at a higher price than they should have been.

The system also provides automated alerts, so, when particular metrics fall below a set threshold in any department, the relevant team is alerted so that they can find a fast solution. In one example of this, during Halloween, sales analysts were able to see in real time that, although a particular novelty cookie was very popular in most stores, it wasn’t selling at all in two stores. The alert prompted a quick investigation, which showed that, due to a simple stocking oversight, the cookies hadn’t been put on the shelves. The store was then able to rectify the situation immediately.

The technical details

As well as 200 billion rows of transactional data (representing only the past few weeks!), the Café pulls in information from 200 sources, including meteorological data, economic data, Nielsen data, telecom data, social media data, gas prices, and local events databases. Anything within these vast and varied datasets could hold the key to the solution to a particular problem, and Walmart’s algorithms are designed to blaze through them in microseconds to come up with real-time solutions.

Ideas and insights you can steal

Clearly, Walmart has huge amounts of data at its fingertips – and the resources to tackle all that data. But what any company can borrow from Walmart’s example is their ability to react to data quickly. After all, there’s little point investing in data capabilities if your internal setup doesn’t allow you to quickly make decisions and changes based on what the data is telling you.

You can read more about how Walmart is using Big Data to drive success in  Big Data in Practice: How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results .

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Bernard Marr is a world-renowned futurist, influencer and thought leader in the fields of business and technology, with a passion for using technology for the good of humanity.

He is a best-selling author of over 20 books, writes a regular column for Forbes and advises and coaches many of the world’s best-known organisations.

He has a combined following of 4 million people across his social media channels and newsletters and was ranked by LinkedIn as one of the top 5 business influencers in the world.

Bernard’s latest book is ‘ Generative AI in Practice ’.

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How Big Data and Data Science Are Reshaping Walmart’s Retail Philosophy?

Have you ever wondered how a modest discount retailer transformed into a global behemoth, operating in 10,500 stores and clubs scattered across 24 countries?

Or how a company with humble beginnings can amass a jaw-dropping $559 billion in revenue for the fiscal year ending in January 2021?

That’s a colossal $35 billion growth , primarily driven by the explosive expansion of its eCommerce sector.

But behind the scenes of Walmart’s meteoric rise is a fascinating tale of innovation and data-driven mastery.

Join us as we embark on a journey through the evolution of Walmart , uncovering the secrets of its unparalleled success.

So, are you ready to dive into the extraordinary world of Walmart Labs and explore how data science fuels the retail giant’s ‘ Everyday Low Cost ‘ commitment?

Walmart Labs: Powering the Data-Driven Retail Revolution

Walmart is not just a retail company; it’s a data-driven powerhouse. At the heart of this transformation is Walmart Labs, a veritable hub of innovation and technology , which has set out to harness the incredible potential of big data.

Walmart’s journey into the digital age has been nothing short of phenomenal, and it is leveraging big data and advances in data science to create solutions that enhance, optimize, and personalize the shopping experience.

The Mammoth Data Infrastructure

Walmart boasts the world’s largest private cloud, capable of managing a staggering 2.5 petabytes of data every hour . To handle this colossal volume of data, Walmart has created the ‘ Data Café ,’ a state-of-the-art analytics hub situated within its Bentonville, Arkansas headquarters .

Here, data scientists and analysts work tirelessly to extract valuable insights from the data deluge , driving Walmart’s operational and strategic decisions.

Applications of Data Science at Walmart

At the forefront of Walmart’s data-driven revolution is a team of dedicated data scientists focused on creating solutions that power the efficiency and effectiveness of complex supply chain management processes.

Here are some of the remarkable applications of data science at Walmart:

i) Personalized Customer Shopping Experience

Walmart believes in making every shopping trip unique. To achieve this, the retailer employs data science to analyze customer preferences and shopping patterns . This analysis informs the stocking and displaying of merchandise in Walmart’s vast network of stores.

It also helps Walmart make informed decisions regarding new item introductions, product discontinuations, and the performance of various brands. The result is a shopping experience tailored to individual preferences.

ii) Order Sourcing and On-Time Delivery Promise

Walmart’s eCommerce platform, Walmart.com , serves millions of customers daily. One of the most crucial aspects of online shopping is the estimated delivery date. Walmart’s data scientists have developed a sophisticated algorithm that calculates this date in real time, taking into account the distance between the customer and the nearest fulfillment center, current inventory levels, and available shipping methods .

The supply chain management system determines the optimal fulfillment center for each order, considering distance and inventory levels. It also decides on the most cost-effective shipping method, all while ensuring the promised delivery date is met.

iii) Packing Optimization: The Bin Packing Challenge

In the world of retail and eCommerce, efficient packing and shipping are paramount. Walmart has tackled the age-old problem of optimizing the packing process. When items from one or multiple orders are ready for packing, Walmart employs a cutting-edge recommender system .

This system selects the most suitable box size to contain all the ordered items with minimal in-box space wastage, all within a predefined time frame. The problem, known as the Bin Packing Problem , is a classic NP-Hard problem that challenges the skills of data scientists.

Unveiling the Power of Data Science with Walmart’s Case Studies

To truly understand the practical applications of data science in the real world, Walmart offers a fascinating case study on sales prediction. The Walmart Sales Forecasting Project delves into the use of historical sales data from 45 Walmart stores, each housing numerous departments.

The challenge is to build a predictive model that can accurately project sales for each department in each store . This case study provides insights into the complexity and precision required to meet the demands of the retail industry.

Another intriguing project awaits data science enthusiasts—the Inventory Demand Forecasting Data Science Project . The goal here is to develop a machine learning model that forecasts inventory demand with precision, drawing from historical sales data.

Walmart’s data-driven journey is a testament to the transformative power of big data and data science.

It’s a story of retail reinvention, personalization, and optimization, all powered by the remarkable ability to turn data into actionable insights.

As Walmart continues to explore the endless possibilities of data-driven retail, the future promises even more innovation and excitement in the world of shopping.

Are you ready to dive into the world of data-driven retail with Walmart?

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How Walmart Used Big Data in the Process of Improving Supermarket Operations – Case Study

Walmart is not only the largest retailer in the world, but it is also the largest company in the world, with over two million employees and 20,000 locations located in 28 different countries.

It should not come as a surprise that a corporation of this size would see the benefits of data analytics at an early stage in their development. In 2004, when Hurricane Sandy struck the United States, it was discovered that when data was reviewed as a whole, rather than as separate individual sets, surprising insights were revealed. This discovery was made in light of the fact that when Linda Dillman attempted to forecast the demand for emergency supplies in preparation for Hurricane Sandy, she came upon some data that surprised her. As a result of the forecast for severe weather, there was a surge in demand for strawberry Pop Tarts, in addition to flashlights and other emergency supplies. During the year 2012, while Hurricane Frances was bearing down on Florida, a considerable quantity was sent to retailers that were in the line of the hurricane.

Since then, Walmart has significantly increased the size of their division devoted to Big Data and analytics, which helps to ensure that the company stays at the forefront of their industry. In 2015, the company announced their aim to construct the world’s largest private data cloud, which would have the capability to process 2.5 petabytes of data in an hour.

What kind of problems may be solved by utilising Big Data?

Supermarkets are responsible for the daily sale of millions of products to millions of customers. It is a very competitive industry that satisfies the need of a massive population in the developed world for essential items. Markets compete with one another not just on the basis of pricing, but also on the basis of the service they provide to customers and, most crucially, on the basis of how handy their locations are. Finding a way to deliver the right things to the right people at the right time presents enormous logistical hurdles. In order to price things in a manner that is competitive, close attention to every single cent is required. Customers who learn that they cannot obtain all they require in a single, accessible location are more likely to purchase elsewhere.

Applications of Big Data

In 2011, Walmart established @WalmartLabs and their Fast Big Data Team in order to investigate and deploy new data-led initiatives across the business. This was done in response to a growing awareness of how data could be used to understand customer needs and provide them with products they wanted to buy.

As a direct result of implementing this strategy, the company’s headquarters in Bentonville, Arkansas now has a state-of-the-art analytics hub called The Data Cafe. In addition to monitoring the other 200 streams of internal and external data in real time at the Cafe’, the analytics division is able to maintain track of a staggering 40 petabytes of sales data from the weeks before.

The Senior Statistical Analyst of Walmart, Naveen Peddamail, was quoted as saying, “If you can’t obtain insights until you’ve analysed your sales for a week or a month, then you’ve lost sales inside that time.” This highlights the significance of real-time data analysis in increasing the efficiency of an organisation.

We want to get the word out to our business partners as quickly as we possibly can so that they can react and reduce the amount of time it takes for the entire process to be completed. Analytics has the ability to both anticipate and react to upcoming occurrences.

Teams from all throughout the organisation are encouraged to bring their data challenges to the Cafe’, where they can work together with the analysts to figure out a solution to the problem. In addition, the organisation has a system that monitors key performance indicators and sends out automated notifications when they reach a predetermined threshold. At this point, the teams responsible for those indicators are urged to talk with the data team about ways to make things better.

Peddamail offers a team from a grocery store as an example of a group that is seeking to determine why a generally popular item has suddenly seen a decline in sales. As soon as the data from the Cafe was in the hands of the analysts, it was obvious that the decline was due to an error in pricing that had been made. After a couple of days of realising the error, it was corrected, and business went back to its usual state.

In addition, it is possible to monitor sales in many locations in real time. Peddamail recalls that during one Halloween, analysts were monitoring the sales numbers of novelty cookies and found that in some places, the cookies were not selling at all. This caught the attention of Peddamail’s employer, who decided to investigate the situation. Because of this, the merchandising staff in the stores were notified, and they realised very quickly that they hadn’t even loaded the shelves with the products. Certainly not the most complicated algorithm, but without access to real-time data, it is difficult to implement.

The Walmart Social Genome Project is another programme that monitors conversations taking place on public social media platforms in an effort to anticipate the kinds of goods that shoppers would purchase. In addition to having their very own search engine, which is known as Polaris, and being able to analyse the search terms that customers enter on their websites, they also have a service known as Shopycat, which is designed to predict how people’s shopping habits are influenced by the shopping habits of their friends (again, using social media data).

Outcome from Investigation

According to Walmart, the amount of time it takes to get from identifying an issue in the data to suggesting a solution has been cut down to something in the neighbourhood of 20 minutes thanks to the Data Cafe’ technology. In the past, completing this task would often take between two and three weeks.

Employed Information

The Data Cafe’ is driven by a database that stores 200 billion rows of transaction data, and that’s only for the data pertaining to the most recent few weeks.

In addition to this, it compiles data from two hundred additional sources, such as reports on the weather and economy, data on telecommunications and social media platforms, statistics on gas costs, and a database of events that will take place in close proximity to Walmart stores.

Notable Particulars of the Study

The real-time transaction database that Walmart uses contains 40 petabytes’ worth of information currently. In spite of the massive magnitude of this amount of data, we are just including the transactions from the most recent weeks’ worth of data because that is where the value lies in terms of real-time analysis. For the retailer, Hadoop serves as a centralised data warehouse, containing records from its physical stores, its online branches, and its corporate offices (a distributed data storage and data management system).

The goal of the strategy is to ensure that a wide variety of users have access to the information contained inside the company’s databases. As a result, CTO Jeremy King has given the plan the name “data democracy” due to the fact that it may be utilised by any member of the organisation. After the broad adoption of the distributed Hadoop architecture in 2011, there came a time when analysts started to worry that their capacity to adequately analyse the data would be hindered by the fast expanding volume of data. As a consequence of this, a plan for “intelligently managing” the collection of data was decided upon. This plan required the establishment of a number of different systems in order to clean and organise the information prior to archiving it in a permanent location. Other technologies such as Spark and Cassandra, in addition to programming languages such as R and SAS, are utilised in the construction of analytical programmes.

Challenging Circumstances

The ambitious analytics operation that was planned presented a challenge for a quickly growing firm like Walmart because it was difficult to find the right employees with the proper talents. According to a recent poll conducted by experts at Gartner, more than half of firms believe that they do not possess the essential competence to carry out Big Data analytics. This problem is not specific to Walmart in any way.

One of the resources that Walmart utilised in its search for a solution was Kaggle, a website that hosts competitions in data science.

The users of Kaggle were given a challenge in which they were asked to predict the effect that Christmas sales and stock-clearing sales would have on the sales of a number of different products. Candidates whose models showed the greatest consistency with the retailer’s actual data were sought after to fill open positions on the data science team at Walmart. One of the people who took part in the competition and afterwards found job at Walmart is Naveen Peddamail, whose observations I have integrated into this section. Naveen is currently employed by Walmart.

All new analysts are required to participate in Walmart’s Analytics Rotation Program for training. They gain an understanding of the numerous business uses of analytics by taking part in a rotation among the various analytical teams the organisation has.

According to Mandar Thakur, a senior recruiter for Walmart’s Information Systems Operation, who shared this information with me, “The Kaggle competition created a lot of interest in Walmart and our analytics group.” The most beneficial aspect was that it demonstrated how we are making strategic use of the data that Walmart creates and stores.

Conclusion and Takeaways

Supermarkets are complex creatures that are made up of a large number of distinct subsystems that all work together to function at high speeds and are subject to ongoing change. The fact that this is the case makes them an excellent candidate for the incorporation of big data analytics into their firm.

The degree of rivalry that exists between different companies is one of the most important determinants of how successful each one is. When it comes to data-driven initiatives such as loyalty and reward programmes, Walmart has always been one step ahead of the competition. By making a full commitment to the most recent developments in real-time, responsive analytics, Walmart has demonstrated their intention to maintain their position at the forefront of the retail industry.

Walmart has demonstrated that traditional brick-and-mortar retailers can reap just as many benefits from cutting-edge Big Data research as their more high-profile online competitors, such as Amazon and Alibaba. This is the case because to Walmart’s use of this technology.

It would appear that consumers, either out of habit or out of personal preference, are still willing to get in their cars and drive to shops rather than use the more convenient options that are available to them today. These options include online shopping, in-store pickup, and grocery delivery services. Which implies there is still plenty of opportunity in the market for innovative enterprises to improve efficiency and enhance the consumer experience, and those that do so will likely find significant success in the market.

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“We Need People to Lean into the Future”

• Adi Ignatius

For years, Walmart’s unrivaled customer research capabilities helped it dominate retailing. Then along came the internet, and Walmart suddenly found itself playing catchup to e-commerce pioneers like Amazon. In 2014 the board appointed Doug McMillon as CEO and gave him an imperative: Bring Walmart into the future—without sacrificing its longtime strengths.

McMillon, who began his career unloading trucks at a neighborhood Walmart, respects tradition but is impatient for change. In this interview with HBR editor in chief Adi Ignatius, he describes the ups and downs of transforming America’s largest company. Going digital is a top priority—which is why Walmart recently paid $3 billion to acquire e-tailer Jet.com. But the company also wants to strengthen the in-store experience. “The reality,” notes McMillon, “is that customers want everything”—low prices, convenience, and seamless interactions online and in person. In this new world, all employees, including those on the sales floor, will need to be tech savvy. And the management team can no longer make strategic decisions on an annual or even quarterly basis; “strategy is happening on a much faster cycle time,” says the CEO.

A conversation with Walmart CEO Doug McMillon

For years, Walmart seemed to understand exactly what its customers wanted. It developed complicated consumer analytics and used that data, along with relentless pressure on suppliers, to become a retail powerhouse that sold practically everything at the lowest possible prices.

• Adi Ignatius is the editor in chief of Harvard Business Review.

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5 Ways Walmart Uses Big Data to Help Customers

In many industries, big data provides a way for companies to gain a better understanding of their customers and make better business decisions..

By Walmart Staff

Aug. 7, 2017

Walmart relies on big data to get a real-time view of the workflow in the pharmacy, distribution centers and throughout our stores and e-commerce.

Check out the infographic below to see how Walmart uses big data to make the company’s operations more efficient and improve the lives of customers.

Whether it’s analyzing the transportation route for a supply chain or using data to optimize pricing, big data analytics will continue to be a key way for Walmart to enhance the customer experience.

More From Forbes

How walmart is using machine learning ai, iot and big data to boost retail performance.

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Even though Walmart was founded in 1962, it’s on the cutting edge when it comes to transforming retail operations and customer experience by using machine learning , the Internet of Things (IoT) and Big Data . In recent years, its patent applications, position as the second largest  online retailer and investment in retail tech and innovation are just a few reasons they are among the retail leaders evolving to take advantage of tech to build their business and provide better service to their customers.

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Network of physical locations and online capabilities lead to innovation

Lauren Desegur, VP of customer experience engineering at WalmartLabs said, “We’re essentially creating a bridge where we are enhancing the shopping experience through machine learning. We want to make sure there is a seamless experience between what customers do online and what they do in our stores.”

While its arch nemesis in business may be Amazon.com, Walmart has the advantage of using the best of both worlds—with over 11,000 brick-and-mortar stores and its online experience—in its laboratory to develop retail tech that catapults sales and customer satisfaction. Walmart was an  early adopter of RFID to track inventory and has a tech incubator called Store No. 8 in Silicon Valley to“incubate, invest in, and work with other startups, venture capitalists and academics to develop its own proprietary robotics, virtual and augmented reality, machine learning and artificial intelligence technology.”

Recently, Walmart launched Pick-up Towers in some of its stores that are 16 x 8-foot self-service kiosks conveniently located at the entrance to the store that retrieves online orders for customers. Customers can just scan a barcode on their online receipt and within 45 seconds the products they purchased will appear on a conveyor belt. So far, customers give these Pick-up Towers positive reviews as an improvement over the store’s traditional pickup process.

Another way Walmart hopes to improve the customer experience with new retail tech is through Scan and Go Shopping. Customers in the pharmacy and money services areas will be able to use the Walmart app for some aspects of the checkout process instead of waiting until they reach the counter and then will be able to bypass the main queue to get in and out of the store more quickly. This is a step in the direction of being able to bypass the checkout process entirely with the use of computer vision, sensors and machine learning as used at the  Amazon Go concept store. Walmart already uses machine learning to optimize the delivery routes of their associate home deliveries.

Unhappy waiting in line?

One of the new ways Walmart might impact its operations is by using facial recognition technology to identify unhappy or frustrated shoppers. As the machines learn to identify different levels of frustration through the facial expressions of those in line, it could trigger additional associates to run the checkouts and eventually could analyze trends over time in a shoppers’ purchase behavior. In 2015, Walmart did also test out this technology to try to detect and prevent theft.

Tags to monitor product consumption

What’s next? According to a  patent application Walmart filed , it seems like its next step is integrating IoT tags to products in order to monitor product usage, auto replace products as necessary and monitor expiration dates or product recalls. These sensors would rely on a variety of technology such as Bluetooth, barcodes, radio frequencies and RFID tags and would provide Walmart with an incredible amount of data including the time of day products are used to where the products are kept in the house. This data could help create personalized advertising and expand cross-selling opportunities. If you had a tag reader installed on your fridge, it could scan everything you place inside and alert you when you need to restock or when items are expired. In another example, a RFID system could monitor how many times you pick up your laundry detergent and predict how much is left. This info could be added to your shopping list and fed to Walmart data vaults to illustrate consumer behavior.

Even though patents have been filed, it remains to be seen which technology Walmart will implement and make available to all its customers. One thing does seem certain: There’s no reason to believe that Walmart will slow down its investments in machine learning, IoT and Big Data to boost its performance and enhance the customer experience anytime soon.

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How Walmart makes data work for its customers

Each week, more than 240 million customers shop at Walmart (online and at its banner stores), making it the world’s largest retailer.

And these days, Walmart is relying on data to power the best shopping experience for its customers, whether they’re buying from the web, via mobile devices or at traditional brick and mortar locations, says Jaya Kolhatkar, vice president of global data for @walmartlabs.

Jaya Kolhatkar, Vice President of Global Data for @walmartlabs

At the National Retail Federation's (NRF) 2016 Big Show conference in NYC, Kolhatkar discussed how @walmartlabs created a structure around its large volume of shopping data and about the culture that enables data scientists and engineers to use data to quickly build and launch new shopping experiences to customers.

To make data work for its customers, Walmart first had to make data work for its eCommerce business. And its eCommerce business is huge. $12 billion in sales last year. 12 e-commerce websites around the globe with a constant influx of data. Walmart’s e-commerce branch alone employs more than 3,000 technologists from Silicon Valley to India, England and South America.

Kolhatkar says, “Our story is one of enormous data, and also one of huge data opportunity. Transactional data, online data, mobile data – it’s what allows us to serve our customers in new ways. But, the data is so large it would be hard to put it to work for us without having the proper underlying infrastructure.”

So how big is the data? Multiterabytes of new data are collected each day. And that is combined with petabytes of historical data. All covering millions of products and hundreds of millions of customers around the world. In addition, more than 100 million keywords are constantly analyzed to know what people near each store are saying on social media.

An infrastructure for making data work

• Data cleansing. “First things first,” says Kolhatkar. “When you have as much customer data as we do, it is absolutely imperative to cleanse it.” But on top of making sure the data is correct and of high quality, they also make sure it is kept appropriately and anonymized. Names, phone numbers and emails are segregated. Other details are encrypted. Those steps enable them to make data available for multiple users without worrying about violating privacy issues. That’s why data cleansing is the first step.
• People make data work.  Kolhatkar says that the second key to @walmartlabs success is people. They call it the Big Fast Data Team. The team helps @walmartlab data users – which include developers, data scientists and business analyst – use the data effectively. The Big Fast Data team acquire data, develop and operate data feeds, analysis tools and implement the infrastructure. It’s a very diverse group of people with a wide set of skills.
• Access. According to Kolhatkar, this is what really makes the magic happen. “Big data democracy is how we like to think about it,” she says. “We don’t have a data bureaucracy or a lot of approver steps. It doesn’t take months to get access to the data. Hundreds of teams can access the anonymized data simultaneously. And that’s how we make the data work for us, so it can then work for our customers. Because making the data available is key. If you have great data, it’s of no use if you keep it locked up.”
• Choice. Kolhatkar calls this “the not-so-secret-sauce.” @walmartlabs combines a plethora of tools from a variety of partners, vendors, open source and in-house developers. They started with an enterprise data warehouse and built an infrastructure for the data. What enables this approach to work is the use of multiple BI and analytic tools. This enables @walmartlabs to hire people with different skill sets. Whether it’s accessing a Teradata warehouse or using NoSQL, SAS and Hadoop technologies, they can bring in people with diverse talent. Using different technologies helps them go from data to prototypes to launching at scale.

Building apps for an improved customer experience

More than 240 million customers visit a brick-and-mortar Walmart store each week. So it’s not just about online shopping. “We wanted to develop mobile apps to give our in-store customers the best experience possible,” says Kolhatkar.

Twenty-two million customers actively use the Walmart app each month and it ranks among the top three retail apps in the Google and Apple app stores. The Walmart app enhances the shopping experience in Walmart stores with features that include checking in to pick up an online order at a Walmart store, refilling pharmacy prescriptions and finding an item’s store location.

An in-store mobile navigation system was developed so that customers can search for items and see exactly where they are located in the store. Another app, e-receipts helps customers eliminate the pain of keeping up with printed receipts. With real-time processing for thousands of transactions, it also helps reduce item-return fraud. Wishlist was introduced in the 2015 holiday season for customers who want to create lists of items they want or find someone else’s list.

Putting all of their customer data together is working. “People are now seeing the value of what is being generated,” says Kolhatkar. Her advice? “Figure out what data to collect and then look at areas that will deliver the most value. That’s how we’re building the next generation of e-commerce for our customers.”

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How Walmart Uses Big Data

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Introduction

The widespread adoption of technology has ushered in a new era of data-driven decision making in various industries, and retail is no exception. With the advent of e-commerce and the rise of online shopping, retailers have access to vast amounts of data that can be leveraged to gain valuable insights and drive business growth. Walmart, the retail giant, is at the forefront of this data revolution, utilizing big data to optimize its operations and enhance its customer experience.

Big data refers to the vast amount of structured and unstructured data generated from diverse sources, such as sales transactions, customer interactions, social media, and supply chain operations. This data is characterized by its volume, velocity, and variety, making it too complex for traditional data processing methods to handle. However, with advancements in technology and data analytics, businesses can now harness the power of big data to derive actionable insights and make data-driven decisions.

Walmart, with its massive customer base and extensive supply chain network, generates an enormous amount of data on a daily basis. By strategically collecting, storing, and analyzing this data, Walmart is able to gain a competitive edge, improve operational efficiency, and deliver personalized experiences to its customers.

In this article, we will explore how Walmart collects, processes, and utilizes big data across various aspects of its operations. We will examine the data sources used by Walmart, delve into its data storage and processing capabilities, and highlight some of the key applications of big data in inventory management, supply chain optimization, customer personalization, pricing, and fraud detection.

Furthermore, we will also discuss the challenges and ethical considerations associated with the use of big data in retail, as concerns around data privacy and security continue to grow.

Overall, Walmart’s use of big data demonstrates the immense power and potential of data analytics in transforming the retail industry. By leveraging the insights derived from big data, Walmart is able to make informed business decisions, enhance customer experiences, and maintain its position as a leader in the market.

What is Big Data?

Big data refers to the vast amount of data that is generated from various sources, both structured and unstructured. It encompasses the volume, velocity, and variety of data and includes information from customer transactions, social media interactions, sensor readings, and more. The term “big data” is derived from the immense size of datasets that cannot be easily managed or processed using traditional data processing methods.

There are three key characteristics of big data :

• Volume: Big data is characterized by the massive amount of information generated. With the proliferation of digital technologies and the interconnected world we live in, data is being created at an unprecedented rate. This includes everything from website clicks and social media posts to financial transactions and sensor readings. The sheer volume of data presents unique challenges in terms of storage, management, and analysis.
• Velocity: The velocity of data refers to the speed at which it is generated. Real-time data is becoming increasingly important for businesses to make timely and informed decisions. With the advent of the Internet of Things (IoT) and the proliferation of connected devices, data streams in at high speeds, requiring efficient processing and analysis to extract meaningful insights.
• Variety: Big data is not just limited to structured data, such as numbers and categories. It also includes unstructured data, such as text, images, and videos. The variety of data sources adds complexity to its analysis and requires advanced tools and techniques to derive meaningful insights.

The potential of big data lies in its ability to provide valuable insights, trends, and patterns that were previously hidden or difficult to uncover. By leveraging advanced analytics techniques like machine learning and artificial intelligence, businesses can derive actionable insights from big data, leading to better decision making and improved outcomes.

With respect to the retail industry, big data can provide valuable insights into customer behaviors and preferences, optimize supply chain operations, enhance personalization efforts, and improve overall business performance.

Now that we have a clear understanding of what big data is, let’s explore how Walmart collects and harnesses this data to optimize its operations and provide a superior customer experience.

How Walmart Collects Big Data

As one of the largest retailers in the world, Walmart has access to an immense volume of data generated by its various interactions with customers, suppliers, and its own operations. The company employs a variety of methods to collect and capture this data, allowing them to gain valuable insights and make data-driven decisions.

First and foremost, Walmart collects data through its vast network of physical stores. Every transaction made at the checkout counter generates valuable data about the products purchased, quantities, payment methods, and customer demographics. This information provides insights into customer preferences, buying patterns, and market trends.

Walmart also gathers data through its online platforms, including its website and mobile applications. When customers browse products, add items to their carts, or make purchases online, Walmart captures this data, enabling them to analyze customer behavior, track preferences, and optimize the online shopping experience.

In addition to transactional data, Walmart collects data through various other channels. For instance, the company gathers information from customer feedback forms, surveys, and product reviews. This helps Walmart gauge customer satisfaction, identify areas for improvement, and make informed decisions about product offerings and enhancements.

Walmart further enhances its data collection efforts through loyalty programs. With programs like Walmart+, customers can opt in to share additional information about their shopping habits and preferences in exchange for exclusive benefits and discounts. This data provides Walmart with valuable insights into individual customer preferences and allows for personalized marketing and offers.

Furthermore, Walmart taps into third-party data to supplement its internal data sources. By partnering with external data providers, such as market research firms and data analytics companies, Walmart can access a broader range of data, including demographics, consumer trends, and competitive insights.

With a strong focus on data privacy and security, Walmart ensures that the data collected is handled responsibly and in compliance with strict regulations. The company takes measures to anonymize and protect customer information, ensuring that personal data is used only for statistical analysis and for providing a better shopping experience.

By strategically collecting data from multiple sources, Walmart has access to a comprehensive and diverse dataset. This allows them to gain a deep understanding of their customers, identify trends, and take proactive measures to improve various aspects of their business.

In the next sections, we will explore how Walmart stores and processes this data, and the various ways in which it is utilized to optimize inventory management, supply chain operations, customer personalization, pricing, and fraud detection.

Walmart’s Data Sources

Walmart, with its extensive reach and customer base, has access to a wide range of data sources that provide invaluable insights into various aspects of its operations. By collecting and analyzing data from diverse sources, Walmart is able to gain a comprehensive understanding of its customers, optimize its supply chain, and improve its overall business performance.

Let’s take a closer look at some of the key data sources that Walmart leverages:

• In-Store Transactions: One of the primary sources of data for Walmart is the transactions that take place in its physical stores. Every time a customer makes a purchase, information such as the products bought, quantities, payment methods, and timestamps are recorded. This transactional data provides insights into customer preferences, buying patterns, and market trends.
• Online Interactions: Walmart’s online platforms, including its website and mobile applications, generate a wealth of data. Customer interactions such as browsing products, adding items to the cart, and completing online purchases are tracked and analyzed. This data helps Walmart understand customer behavior, optimize the online shopping experience, and personalize offers and recommendations.
• Customer Feedback and Surveys: Walmart actively seeks feedback from its customers through feedback forms, surveys, and product reviews. This information provides valuable insights into customer satisfaction, preferences, and areas for improvement. By analyzing customer feedback, Walmart can identify trends, address concerns, and enhance the overall customer experience.
• Loyalty Programs: Walmart’s loyalty programs, such as Walmart+, allow customers to share additional information about their shopping habits and preferences in exchange for benefits and discounts. This data includes purchase history, preferences, and demographic information. By leveraging this data, Walmart can provide personalized marketing, tailored recommendations, and targeted promotions to its loyal customers.
• External Data Partnerships: Walmart also leverages partnerships with external data providers to complement its internal data sources. By collaborating with market research firms, data analytics companies, and other third-party data providers, Walmart gains access to a broader range of data. This includes demographic information, consumer trends, competitive insights, and market intelligence, providing a more comprehensive view of the retail landscape.

These are just a few examples of the data sources that Walmart taps into. By collecting and analyzing data from diverse channels, Walmart can gain valuable insights and make data-driven decisions across various aspects of its business.

In the next sections, we will explore how Walmart stores and processes this data, and how it is utilized to optimize inventory management, supply chain operations, customer personalization, pricing, and fraud detection.

Walmart’s Data Storage and Processing

With a massive volume of data generated from various sources, Walmart requires robust storage and processing capabilities to effectively manage and analyze this data. The company has established a sophisticated infrastructure that enables efficient data storage, processing, and analysis, ultimately supporting their data-driven decision-making processes.

Walmart leverages both on-premises data centers and cloud computing services to store and manage its data. The company has its own data centers equipped with high-capacity servers and storage systems to handle the massive influx of data. Additionally, Walmart also utilizes cloud computing platforms, such as Microsoft Azure and Google Cloud, to leverage their scalability and flexibility advantages, allowing seamless expansion of storage and processing capabilities as needed.

The data collected from various sources is stored in large data warehouses, organized in a structured format that allows for efficient retrieval and analysis. These data warehouses provide a centralized repository for different types of data, allowing Walmart’s analysts and data scientists to access and analyze the data easily.

In addition to structured data, Walmart also deals with unstructured data, such as customer reviews, social media posts, and images. To handle this unstructured data, Walmart employs advanced data processing techniques, including natural language processing (NLP) and image recognition. These tools allow Walmart to extract insights and sentiments from text data and analyze visual content, aiding in understanding customer preferences and trends.

Data processing at Walmart involves various stages, including data cleansing, transformation, and analysis. The process begins with cleansing the data to remove any errors, inconsistencies, or duplicates, ensuring the accuracy and reliability of the data. Next, the data is transformed and prepared for analysis, which may involve aggregating, segmenting, or categorizing the data as per the specific objectives. Finally, the data is analyzed using a combination of statistical analysis, machine learning algorithms, and other data analytics techniques to derive meaningful insights.

Walmart relies heavily on data visualization tools and dashboards to present the insights derived from the data. Interactive visualizations allow Walmart’s managers and stakeholders to easily understand complex data patterns, trends, and correlations, aiding in decision making and strategy development.

In addition to internal data storage and processing capabilities, Walmart also collaborates with external technology partners and data analytics firms to enhance its data management and analysis capabilities. These partnerships enable Walmart to leverage advanced analytics technologies and expertise to extract deeper insights from its data.

With a comprehensive data storage and processing infrastructure in place, Walmart is able to efficiently handle the massive volume, velocity, and variety of data. This enables the company to derive valuable insights, make data-driven decisions, and continually optimize their operations to deliver a superior retail experience to their customers.

Walmart’s Use of Big Data for Inventory Management

Inventory management is a critical aspect of Walmart’s operations, and the company leverages big data to optimize its inventory levels, reduce costs, and improve overall efficiency. By analyzing vast amounts of data, including historical sales, customer demand patterns, and supply chain information, Walmart can make data-driven decisions to ensure the availability of products while minimizing excess stock.

One key way Walmart utilizes big data for inventory management is through demand forecasting and predictive analytics. By analyzing historical sales data, seasonal fluctuations, and external factors like weather patterns and economic indicators, Walmart can accurately predict future demand for different products. This allows Walmart to adjust inventory levels and plan for replenishment, ensuring that products are available when and where customers need them.

Another aspect of inventory management where big data plays a significant role is in optimizing product assortment. Walmart collects data on sales, customer preferences, and market trends to understand which products are popular and in-demand. This information guides their decision-making process, enabling them to stock the right products and adjust their assortment to align with customer preferences. By aligning their inventory with customer demand, Walmart can maximize sales and minimize the risk of overstocking or understocking certain items.

Big data also helps Walmart in improving inventory turnover and reducing stockouts. By analyzing point-of-sale data, Walmart can identify which products are selling quickly and which are slow-moving. This information allows them to make informed decisions about stock replenishment, promotions, and markdowns. By ensuring that popular products are adequately stocked and reducing the inventory of slow-moving items, Walmart can optimize inventory turnover, reduce holding costs, and minimize stockouts.

Supply chain optimization is another area where Walmart harnesses the power of big data. By integrating data from suppliers, logistics partners, and internal operations, Walmart can gain real-time visibility into the movement of products throughout the supply chain. This visibility allows them to identify bottlenecks, optimize transportation routes, and streamline order fulfillment processes. By optimizing the supply chain, Walmart can shorten lead times, reduce inventory carrying costs, and enhance overall operational efficiency.

Additionally, Walmart uses big data and analytics to identify and mitigate risks in its supply chain. By analyzing data on supplier performance, inventory levels, and market conditions, Walmart can proactively address supply chain disruptions and minimize the impact on product availability. This proactive approach helps Walmart ensure that its inventory levels are well-managed, reducing the risk of stockouts or excess inventory due to unforeseen events.

Overall, by leveraging big data for inventory management, Walmart can optimize stock levels, improve product availability, reduce costs, and enhance operational efficiency. The utilization of data-driven approaches in inventory management allows Walmart to stay agile and responsive in meeting customer demands while maintaining an effective and profitable supply chain.

Walmart’s Use of Big Data for Supply Chain Management

Supply chain management is a critical component of Walmart’s operations, and the company utilizes big data to optimize its supply chain processes and ensure efficiency and effectiveness from sourcing to distribution. By leveraging the power of big data analytics , Walmart gains valuable insights into its supply chain operations, enabling them to make data-driven decisions, reduce costs, improve customer satisfaction, and enhance overall supply chain performance.

One way Walmart employs big data for supply chain management is by using real-time data analytics to monitor and track inventory levels across its vast network of stores and distribution centers. By analyzing data on stock levels, demand patterns, and sales trends, Walmart can identify potential stockouts or excess inventory situations. This real-time visibility allows Walmart to take proactive measures such as reallocating inventory or replenishing stock to meet customer demand while minimizing costs.

Big data analytics also plays a crucial role in Walmart’s demand forecasting and planning. By analyzing historical sales data, market trends, and external factors like weather patterns and economic indicators, Walmart can accurately forecast customer demand for different products and locations. This accurate demand forecasting helps Walmart optimize its inventory levels, minimize holding costs, reduce stockouts, and improve overall supply chain efficiency.

Furthermore, Walmart utilizes big data analytics to optimize its supplier relationships and strengthen its supplier network. By analyzing data on supplier performance, lead times, and quality metrics, Walmart can identify areas where improvements can be made. This data-driven approach enables Walmart to collaborate more effectively with suppliers, negotiate favorable terms, and enhance overall supply chain resilience.

Walmart also employs big data analytics to optimize its transportation and logistics operations. By integrating data from various sources such as GPS, traffic patterns, and delivery schedules, Walmart can efficiently plan and route its shipments. Real-time data analytics enables Walmart to dynamically adjust transportation routes, identify potential bottlenecks, and optimize delivery schedules to ensure timely and cost-effective supply chain operations.

Additionally, big data analytics aids Walmart in managing risk and enhancing supply chain resilience. By analyzing data on external factors such as natural disasters, political events, and market conditions, Walmart can proactively identify potential disruptions in the supply chain. This allows them to implement contingency plans, diversify sourcing strategies, and ensure business continuity, minimizing the impact of unforeseen events on product availability and customer satisfaction.

By leveraging big data for supply chain management, Walmart gains valuable insights into its operations, enabling the company to optimize inventory and demand planning, strengthen supplier relationships, improve transportation and logistics efficiency, and mitigate risks. This data-driven approach helps Walmart maintain a robust and effective supply chain, ensuring that products reach customers in a timely and cost-efficient manner.

Walmart’s Use of Big Data for Customer Personalization

Personalization is key to delivering a superior customer experience, and Walmart utilizes big data to understand individual customer preferences and tailor its offerings accordingly. By analyzing vast amounts of customer data, including purchase history, browsing behavior, and demographic information, Walmart can create personalized experiences that resonate with their customers and drive loyalty.

One way Walmart leverages big data for customer personalization is through targeted marketing and promotions. By analyzing customer purchase patterns and preferences, Walmart can send personalized offers and recommendations to each customer based on their individual needs and interests. This targeted approach helps Walmart enhance customer engagement and increase the relevancy of their marketing campaigns, resulting in higher conversion rates and customer satisfaction.

Furthermore, Walmart tracks and analyzes customer browsing behavior on its website and mobile applications. By capturing data on the products customers view, add to their carts, or purchase, Walmart can create personalized recommendations and product suggestions. This allows customers to discover new products and find items that align with their preferences, enhancing their shopping experience and increasing the likelihood of repeat purchases.

Walmart’s use of big data also extends to personalized pricing strategies. By analyzing customer purchase history, location, and browsing behavior, Walmart can offer personalized pricing promotions and discounts. For example, customers may receive exclusive discounts on products they frequently purchase or receive personalized coupons for items they have shown interest in. This personalized pricing approach helps Walmart strengthen customer loyalty and encourages repeat purchases.

In addition, Walmart leverages big data to provide personalized in-store experiences through its mobile applications and loyalty programs. By analyzing customer location data, past purchase history, and preferences, Walmart can offer personalized recommendations and promotions when customers are inside their stores. This level of personalization enhances the in-store shopping experience and encourages customers to continue shopping with Walmart.

Walmart also uses big data analytics to improve its customer service and support. By analyzing customer interactions, feedback, and sentiment analysis from different channels, such as call centers, social media, and online reviews, Walmart can identify areas for improvement and address customer concerns more effectively. This data-driven approach allows Walmart to provide a personalized and seamless customer support experience, resulting in higher customer satisfaction and loyalty.

Overall, Walmart’s use of big data for customer personalization allows them to understand individual preferences, deliver relevant offers and recommendations, and enhance the overall shopping experience. By leveraging big data analytics, Walmart can create personalized interactions with customers, fostering loyalty and ensuring that each customer feels valued and understood.

Walmart’s Use of Big Data for Pricing Optimization

Pricing optimization is a critical aspect of Walmart’s business strategy, and the company leverages big data to make informed pricing decisions and maximize profitability. By analyzing vast amounts of data, including market trends, customer behavior, and competitor prices, Walmart can optimize its pricing strategies to drive sales, increase market share, and provide value to its customers.

One way Walmart utilizes big data for pricing optimization is through dynamic pricing. By analyzing real-time data on demand, competition, and market conditions, Walmart can adjust prices dynamically to optimize revenue and competitiveness. This data-driven approach allows Walmart to respond quickly to changing market conditions and customer preferences, ensuring its prices remain competitive and appealing to consumers.

Walmart also uses big data to conduct price elasticity analysis. By analyzing historical sales data and price changes, Walmart can determine how price fluctuations impact customer demand. This analysis allows Walmart to identify price points that maximize revenue while maintaining customer demand. By understanding price elasticity, Walmart can set prices that are not only profitable but also attractive to customers.

Competitive pricing is another area where Walmart leverages big data. By continuously monitoring and analyzing competitor prices, Walmart can adjust its pricing strategies accordingly. Walmart gathers data on competitors’ pricing strategies through various sources, including web scraping and market intelligence tools. By understanding competitor pricing, Walmart can respond proactively to maintain its competitive position in the market and offer customers the best value.

Customer segmentation is crucial for effective pricing optimization, and big data plays a significant role in this area. Through the analysis of customer data, Walmart can segment its customer base into different groups based on factors such as demographics, purchase behavior, and preferences. This segmentation allows Walmart to personalize pricing strategies for different customer segments, tailoring offers and promotions to meet their specific needs and maximize sales.

Furthermore, Walmart takes advantage of big data analytics to identify pricing trends and patterns. By analyzing historical sales data, pricing changes, and customer responses, Walmart can identify optimal pricing strategies for different products and seasons. This helps Walmart learn which products are more price-sensitive, identify ideal promotional periods, and determine the most effective timing for price adjustments.

Walmart’s big data analytics capabilities also play a crucial role in understanding the impact of pricing decisions on overall profitability. By analyzing data on costs, margins, and pricing, Walmart can assess the profitability of various pricing strategies and make data-driven decisions to optimize profitability while remaining competitive in the market.

Overall, Walmart’s use of big data for pricing optimization allows the company to make informed pricing decisions, respond to market dynamics, and maximize profitability. By leveraging big data analytics, Walmart can set competitive prices, personalize offers, and deliver value to its customers while driving its own business success.

Walmart’s Use of Big Data for Fraud Detection

Fraud detection is a critical aspect of retail operations, and Walmart utilizes big data to identify and prevent fraudulent activities. By leveraging advanced analytics and machine learning algorithms, Walmart can analyze vast amounts of data to identify patterns, anomalies, and suspicious transactions, allowing them to take proactive measures to combat fraud.

One way Walmart uses big data for fraud detection is through transaction monitoring. By analyzing real-time data from various sources, including point-of-sale systems, online transactions, and loyalty programs, Walmart can identify abnormal transaction patterns that may indicate fraudulent activities. Unusual spending patterns, unusual locations, or suspicious purchase combinations are indicators that Walmart uses to flag potential fraud attempts.

Walmart also utilizes big data analytics to monitor and analyze customer behaviors and activities. By analyzing data on browsing behavior, cart abandonment rates, and previous purchase history, Walmart can detect unusual patterns within individual customer accounts. This analysis helps identify potential account takeovers, unauthorized activities, or fraudulent behavior.

Furthermore, Walmart employs big data analytics to identify patterns and trends in returned merchandise and refund requests. By analyzing data on return rates, refund amounts, and customer information, Walmart can identify abnormal return patterns that may be indicative of fraudulent activities, such as return fraud or organized retail crime. This data-driven approach enables Walmart to take proactive steps to mitigate fraudulent returns and minimize losses.

Big data analytics also plays a crucial role in detecting and preventing employee fraud. By analyzing employee data, transaction records, and access logs, Walmart can identify potential insider threats, unauthorized activities, or collusion. This proactive approach enables Walmart to take appropriate actions to address potential fraud risks and maintain a secure retail environment.

Walmart’s use of big data analytics is not limited to transactional data alone. The company also leverages external data sources, such as market intelligence, public records, and social media data, to enhance its fraud detection capabilities. By integrating and analyzing these diverse data sources, Walmart gains insights into broader fraud trends and patterns, allowing them to stay ahead of emerging fraud techniques and vulnerabilities.

Moreover, Walmart employs machine learning algorithms to continuously improve its fraud detection capabilities. By training models on historical fraud data and continuously feeding new data, Walmart’s fraud detection systems can adapt and evolve to detect new fraud patterns or techniques that may arise.

By harnessing big data analytics for fraud detection, Walmart can proactively identify and prevent fraudulent activities. This data-driven approach helps Walmart protect customer assets, minimize losses, and maintain trust and integrity within its retail operations.

Challenges and Ethical Considerations

While Walmart benefits from the use of big data in various aspects of its operations, there are also challenges and ethical considerations that arise from the collection, analysis, and utilization of such vast amounts of data.

One major challenge is ensuring the privacy and security of customer data. With the massive amount of data collected, stored, and processed by Walmart, there is a risk of data breaches and unauthorized access. It is vital for Walmart to establish robust data security measures to safeguard customer information and comply with data protection regulations.

Another challenge is managing data quality and accuracy. Big data is often characterized by its volume and variety, and ensuring the accuracy and reliability of the data can be complex. Data cleansing and validation processes are crucial to maintain the integrity of the insights derived from big data analytics.

Furthermore, ethical considerations arise in the collection and utilization of customer data. Walmart must obtain informed consent from customers and ensure the responsible use of their personal information. It is important to use big data to enhance the customer experience without compromising privacy or engaging in unethical practices, such as invasive profiling or discriminatory pricing.

There is also a concern about the potential for bias in big data analysis. Algorithms and machine learning models used in big data analytics may inadvertently perpetuate biases present in the data. Walmart needs to have mechanisms in place to identify and address biases, ensuring fair treatment and avoiding discrimination against any customer segment.

By leveraging big data, Walmart has the potential to significantly impact the retail market. However, this reliance on data can lead to a loss of the human touch and personalized experiences. It is important for Walmart to strike a balance between data-driven decision making and maintaining a customer-centric approach that values individual preferences and needs.

Additionally, Walmart must navigate the ever-evolving field of data regulation and compliance. As data protection laws and regulations continue to evolve, it is crucial for Walmart to stay updated and ensure that its data practices align with regulatory requirements to protect both the company and its customers.

Finally, an ongoing challenge is maintaining the trust of customers. With the collection and utilization of large amounts of data, there is a risk of customer skepticism and concerns about privacy. Walmart must be transparent about its data practices, educate customers about the benefits of data usage, and provide clear mechanisms for customers to exercise control over their data.

Overall, while the use of big data presents immense opportunities for Walmart, it is important for the company to address the challenges and ethical considerations associated with its use. By prioritizing data privacy, addressing biases, and maintaining transparency, Walmart can navigate the complexities of big data analytics responsibly and continue to deliver value to its customers while maintaining their trust.

Walmart’s utilization of big data has had a transformative impact on its operations and customer experience. By strategically collecting, storing, and analyzing vast amounts of data, Walmart has been able to gain valuable insights that drive data-driven decision making across various aspects of the business.

From inventory management to supply chain optimization, customer personalization to pricing optimization, and fraud detection to ethical considerations, Walmart has harnessed the power of big data to enhance its efficiency, improve customer satisfaction, and drive business growth.

Through robust data storage and processing capabilities, Walmart is able to handle the immense volume, velocity, and variety of data generated from numerous sources. By leveraging advanced analytics techniques, machine learning algorithms, and data visualization tools, Walmart extracts meaningful insights from the data and translates them into actionable strategies.

However, it is essential to acknowledge and address the challenges and ethical considerations that arise from the use of big data. Walmart must prioritize data privacy, security, and responsible data usage to protect customer information and maintain trust. Additionally, ensuring accuracy, managing biases, and striking a balance between data-driven decision making and personalized experiences are crucial to meet customer expectations and deliver value.

In conclusion, Walmart’s use of big data exemplifies its commitment to innovation and leveraging technology to drive its retail operations forward. By harnessing the power of big data analytics, Walmart continues to optimize inventory management, supply chain operations, customer personalization, pricing strategies, and fraud detection. With a customer-centric approach and a focus on data-driven decision making, Walmart remains at the forefront of the retail industry, delivering value to its customers and maintaining its leadership position in the market.

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13 Consumers, Big Data and Online Tracking in The Retail Industry-A Case Study of Walmart

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Is Big Data a household word? What’s going to push it that last mile?

~Everyday use by everybody.

Certainly, Big Data has become a large game-changer in the majority of the modern industries over the last few decades. As Big Data continues to pass through our daily lives, the number of different companies that are implementing Big Data continues to increase. Let us see how Big Data assist them to perform exponentially in the market with these big data case studies.

Below are the interesting big data case studies –

1. Big Data Case Study – Walmart

It is the largest retailer in the world and the world’s largest company by revenue, with more than 2 million employees and 20000 stores in 28 countries across the world. Walmart started making use of big data analytics much before the word Big Data came into the picture.

Walmart uses Data Mining to find outlines that can be used to provide product recommendations to the customer, based on which products were bought together. Walmart by applying efficient Data Mining has improved its conversion rate of customers. Walmart has been racing along big data analysis to provide best-in-class e-commerce technologies to deliver superior customer experience. The main purpose of holding big data at Walmart is to enhance the shopping experience of customers when they are in a store. Big data solutions at Walmart are utilized to redesign global websites and build innovative applications to personalize the shopping experience for customers whereas increasing logistics efficiency. Hadoop and NoSQL technologies are used to offer internal customers with access to real-time data gathered from different sources and unified for effective use.

2. Big Data Case Study – Uber

It is the primary choice for people around the world when they think of moving people and making deliveries. Uber uses the personal data of the user to carefully observe which features of the service are frequently used, to evaluate usage patterns, and to determine where the services should be more focused. Uber focuses on the supply and demand of the services due to which the prices of the services provided vary. Hence one of Uber’s biggest uses of data is rush in pricing. For example, if you are running late for an appointment and you book a cab in a congested place then you must be ready to pay twice the amount.

For instance, On New Year’s Eve, the price for driving for one mile can go from 200 to 1000. In the short term, rush in pricing changes the rate of demand, while long term use could be crucial to holding or losing customers. Machine learning algorithms are believed to conclude where the demand is intense.

3.Big Data Case Study – Netflix

NetFlix is the most esteemed American entertainment company specifying in online on-demand streaming videos for its customers. It has been decided to be able to forecast what exactly its users will enjoy watching with Big Data. For itself, Big Data analytics is the fuel that ignites the ‘recommendation engine’ designed to serve this purpose. More recently, Netflix started locating itself as a content creator, not just a distribution method. Naturally, this approach has been tightly driven by data. It’s recommendation engines and new content decisions are supported by data points such as what titles customers watch, how often playback stopped, ratings are given, etc. The company’s data structure includes Hadoop, Hive, and Pig with much other conventional business intelligence.

Netflix shows us that recognizing exactly what customers want is easy to understand if the companies just don’t go with the expectations and make decisions based on Big Data.

4.Big Data Case Study – eBay

A huge technical challenge for eBay as a data-intensive business to develop a system that can quickly evaluate and act on data as it arrives. There are many quickly advancing methods to support streaming data analysis. eBay is working with various tools including Apache Spark , Storm, Kafka. It permits the company’s data analysts to search for data tags that have been connected with the data and make it usable to as many people as possible with the right level of security and permissions. The company has been at the lead of using big data solutions and actively contributes its knowledge back to the open-source community.

5.Big Data Case Study – Procter & Gamble

Procter & Gamble whose products we all use 2-3 times a day is a 179-year-old company. The genius company has identified the capability of Big Data and put it to use in business units around the world. P&G has put a powerful prominence on using big data to make better, smarter, real-time business decisions. The international Business Services organization has developed tools, systems, and processes to grant managers with direct access to the latest data and advanced analytics. Therefore P&G being the oldest company, still holding a great share in the market despite having many promising companies.

Big Data predicting the uncertainties

An innovative study in Bangladesh has found that using data from mobile phone networks to trace movements of people across the country help to forecast where outbursts of diseases such as malaria are likely to occur, allowing health authorities to take precautionary measures.

Each year, malaria kills more than 400,000 people worldwide and most of them are children.

The numerous type of data, including data offered by the Bangladesh ministry of health, is used to create risk maps indicating the likely locations of malaria outbursts so the local health authorities can then be cautioned to take preventive action, including spraying insecticides and storing bed nets and medicines to protect the population from the disease.

With the different technologies it holds, Big Data assists almost every company or sector that aims to grow. Evaluating large datasets that are associated with the proceedings of the company can give them the vision to increase their customer satisfaction.