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How to Structure Your Data Analytics Team

• 09 Mar 2021

Some of the most successful companies are those that have embraced data-driven decision-making . Basing business decisions on real, tangible data brings many benefits, including the ability to spot trends, challenges, and opportunities before your competition. Perhaps most importantly, it allows you to measure progress toward goals so you can understand whether your strategy is working and, if it isn’t, how you might pivot.

If your organization consists of just yourself or a small group of employees, it’s likely everyone is versed in gathering and interpreting data to some extent. As your organization grows, however, it becomes increasingly important to have employees whose job is specifically anchored around data. Depending on your organization, this team may be called the data team or the analytics team .

Below is an overview of the job titles typically included on an analytics team, along with several considerations you should keep in mind as you build yours.

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Key Players on a Data Analytics Team

While team structure depends on an organization’s size and how it leverages data, most data teams consist of three primary roles: data scientists, data engineers, and data analysts. Other advanced positions, such as management, may also be involved. Here’s a look at these important roles.

1. Data Scientist

Data scientists play an integral role on the analytics team. These professionals leverage advanced mathematics, programming, and tools (such as statistical modeling, machine learning, and artificial intelligence) to perform large-scale analysis.

While their role and responsibilities vary from organization to organization, data scientists typically perform work designed to inform and shape data projects. They may, for example, identify challenges that can be addressed with a data project or data sources to collect for future use. Much of their time is spent designing algorithms and models to mine and organize data.

2. Data Engineer

Data engineers are responsible for designing, building, and maintaining datasets that can be leveraged in data projects. As such, they closely work with both data scientists and data analysts.

Much of the work data engineers perform is related to preparing the infrastructure and ecosystem that the data team and organization rely on. For example, data engineers collect and integrate data from various sources, build data platforms for use by other data team members, and optimize and maintain the data warehouse.

3. Data Analyst

Data analysts use data to perform reporting and direct analysis. Whereas data scientists and engineers typically interact with data in its raw or unrefined states, analysts work with data that’s already been cleaned and transformed into more user-friendly formats.

Depending on the challenge they’re trying to solve or address, their analysis may be descriptive, diagnostic, predictive, or prescriptive. Data analysts are often responsible for maintaining dashboards, generating reports, preparing data visualizations , and using data to forecast or guide business activity.

4. Advanced Positions

In addition to the job titles above, data teams often include a management or leadership role, especially in larger organizations. These positions include data manager , data director , and chief data officer .

3 Factors to Consider When Building Your Data Team

1. how large does the team need to be.

The answer to this question depends on several factors, and there’s no single answer that applies to all organizations. Generally speaking, the larger your organization is and the more data-driven it becomes, the larger your data team needs to be.

In thinking about your data team’s size and which roles it needs to include, ask yourself:

• How much data is the team responsible for managing and working with?
• How many projects will the data team work on in a given period?
• Who will the data team serve? Will they answer to a single stakeholder or department or assist employees organization-wide?

2. How Centralized Does the Team Need to Be?

In some organizations, analytics initiatives are highly centralized, with a single data team serving the entire organization. Other organizations take a more decentralized approach, where each department or business unit has access to its own resources, processes, and employees. Some apply a hybrid model.

While there are pros and cons to each approach, none is inherently right or wrong. The one you employ depends on your organization and its relationship to data. That being said, it can significantly impact your data team’s structure and the data governance processes, so it’s important to consider.

3. What Is the Overarching Data Strategy for the Organization?

Finally, your organization’s data strategy impacts how you structure your data team.

If, for example, there’s an initiative to back every business action in data, then this presumes your organization not only has access to that data, but the processes, tools, and professionals required to conduct significant analysis. On the other hand, if your organization intends to back its larger business strategy in data but is comfortable allowing smaller, daily decisions to be made without data, it may be possible to get by with a smaller team or fewer resources.

The Value of the Data Team

For organizations that pursue data-driven decision-making, a highly skilled data team is essential. Key players include data scientists, data engineers, data analysts, and managerial and leadership roles. If you’re in the process of building your organization’s data team—or expect to significantly interact with one—it’s crucial to understand the different professional roles and responsibilities that make it up.

Are you interested in improving your data literacy? Download our Beginner’s Guide to Data & Analytics to learn how you can leverage the power of data for professional and organizational success.

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How to Write Data Analysis Reports in 9 Easy Steps

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Enjoy reading this blog post written by our experts or partners.

If you want to see what Databox can do for you, click here .

Imagine a bunch of bricks. They don’t have a purpose until you put them together into a house, do they?

In business intelligence, data is your building material, and a quality data analysis report is what you want to see as the result.

But if you’ve ever tried to use the collected data and assemble it into an insightful report, you know it’s not an easy job to do. Data is supposed to tell a story about your performance, but there’s a long way from unprocessed, raw data to a meaningful narrative that you can use to create an actionable plan for making steady progress towards your goals.

This article will help you improve the quality of your data analysis reports and build them effortlessly and fast. Let’s jump right in.

What Is a Data Analysis Report?

Why is data analysis reporting important, how to write a data analysis report 9 simple steps, data analysis report examples.

A data analysis report is a type of business report in which you present quantitative and qualitative data to evaluate your strategies and performance. Based on this data, you give recommendations for further steps and business decisions while using the data as evidence that backs up your evaluation.

Today, data analysis is one of the most important elements of business intelligence strategies as companies have realized the potential of having data-driven insights at hand to help them make data-driven decisions.

Just like you’ll look at your car’s dashboard if something’s wrong, you’ll pull your data to see what’s causing drops in website traffic, conversions, or sales – or any other business metric you may be following. This unprocessed data still doesn’t give you a diagnosis – it’s the first step towards a quality analysis. Once you’ve extracted and organized your data, it’s important to use graphs and charts to visualize it and make it easier to draw conclusions.

Once you add meaning to your data and create suggestions based on it, you have a data analysis report.

A vital detail everyone should know about data analysis reports is their accessibility for everyone in your team, and the ability to innovate. Your analysis report will contain your vital KPIs, so you can see where you’re reaching your targets and achieving goals, and where you need to speed up your activities or optimize your strategy. If you can uncover trends or patterns in your data, you can use it to innovate and stand out by offering even more valuable content, services, or products to your audience.

Data analysis is vital for companies for several reasons.

A reliable source of information

Trusting your intuition is fine, but relying on data is safer. When you can base your action plan on data that clearly shows that something is working or failing, you won’t only justify your decisions in front of the management, clients, or investors, but you’ll also be sure that you’ve taken appropriate steps to fix an issue or seize an important opportunity.

A better understanding of your business

According to Databox’s State of Business Reporting , most companies stated that regular monitoring and reporting improved progress monitoring, increased team effectiveness, allowed them to identify trends more easily, and improved financial performance. Data analysis makes it easier to understand your business as a whole, and each aspect individually. You can see how different departments analyze their workflow and how each step impacts their results in the end, by following their KPIs over time. Then, you can easily conclude what your business needs to grow – to boost your sales strategy, optimize your finances, or up your SEO game, for example.

An additional way to understand your business better is to compare your most important metrics and KPIs against companies that are just like yours. With Databox Benchmarks , you will need only one spot to see how all of your teams stack up against your peers and competitors.

Instantly and Anonymously Benchmark Your Company’s Performance Against Others Just Like You

If you ever asked yourself:

• How does our marketing stack up against our competitors?
• Are our salespeople as productive as reps from similar companies?
• Are our profit margins as high as our peers?

Databox Benchmark Groups can finally help you answer these questions and discover how your company measures up against similar companies based on your KPIs.

When you join Benchmark Groups, you will:

• Get instant, up-to-date data on how your company stacks up against similar companies based on the metrics most important to you. Explore benchmarks for dozens of metrics, built on anonymized data from thousands of companies and get a full 360° view of your company’s KPIs across sales, marketing, finance, and more.
• Understand where your business excels and where you may be falling behind so you can shift to what will make the biggest impact. Leverage industry insights to set more effective, competitive business strategies. Explore where exactly you have room for growth within your business based on objective market data.
• Keep your clients happy by using data to back up your expertise. Show your clients where you’re helping them overperform against similar companies. Use the data to show prospects where they really are… and the potential of where they could be.
• Get a valuable asset for improving yearly and quarterly planning . Get valuable insights into areas that need more work. Gain more context for strategic planning.

The best part?

• Benchmark Groups are free to access.
• The data is 100% anonymized. No other company will be able to see your performance, and you won’t be able to see the performance of individual companies either.

When it comes to showing you how your performance compares to others, here is what it might look like for the metric Average Session Duration:

And here is an example of an open group you could join:

And this is just a fraction of what you’ll get. With Databox Benchmarks, you will need only one spot to see how all of your teams stack up — marketing, sales, customer service, product development, finance, and more. 

• Choose criteria so that the Benchmark is calculated using only companies like yours
• Narrow the benchmark sample using criteria that describe your company
• Display benchmarks right on your Databox dashboards

Sounds like something you want to try out? Join a Databox Benchmark Group today!

It makes data accessible to everyone

Data doesn’t represent a magical creature reserved for data scientists only anymore. Now that you have streamlined and easy-to-follow data visualizations and tools that automatically show the latest figures, you can include everyone in the decision-making process as they’ll understand what means what in the charts and tables. The data may be complex, but it becomes easy to read when combined with proper illustrations. And when your teams gain such useful and accessible insight, they will feel motivated to act on it immediately.

Better collaboration

Data analysis reports help teams collaborate better, as well. You can apply the SMART technique to your KPIs and goals, because your KPIs become assignable. When they’re easy to interpret for your whole team, you can assign each person with one or multiple KPIs that they’ll be in charge of. That means taking a lot off a team leader’s plate so they can focus more on making other improvements in the business. At the same time, removing inaccurate data from your day-to-day operations will improve friction between different departments, like marketing and sales, for instance.

More productivity

You can also expect increased productivity, since you’ll be saving time you’d otherwise spend on waiting for specialists to translate data for other departments, etc. This means your internal procedures will also be on a top level.

Want to give value with your data analysis report? It’s critical to master the skill of writing a quality data analytics report. Want to know how to report on data efficiently? We’ll share our secret in the following section.

• Start with an Outline
• Make a Selection of Vital KPIs
• Pick the Right Charts for Appealing Design
• Use a Narrative
• Organize the Information
• Include a Summary
• Careful with Your Recommendations
• Double-Check Everything
• Use Interactive Dashboards

1. Start with an Outline

If you start writing without having a clear idea of what your data analysis report is going to include, it may get messy. Important insights may slip through your fingers, and you may stray away too far from the main topic. To avoid this, start the report by writing an outline first. Plan the structure and contents of each section first to make sure you’ve covered everything, and only then start crafting the report.

2. Make a Selection of Vital KPIs

Don’t overwhelm the audience by including every single metric there is. You can discuss your whole dashboard in a meeting with your team, but if you’re creating data analytics reports or marketing reports for other departments or the executives, it’s best to focus on the most relevant KPIs that demonstrate the data important for the overall business performance.

PRO TIP: How Well Are Your Marketing KPIs Performing?

Like most marketers and marketing managers, you want to know how well your efforts are translating into results each month. How much traffic and new contact conversions do you get? How many new contacts do you get from organic sessions? How are your email campaigns performing? How well are your landing pages converting? You might have to scramble to put all of this together in a single report, but now you can have it all at your fingertips in a single Databox dashboard.

Our Marketing Overview Dashboard includes data from Google Analytics 4 and HubSpot Marketing with key performance metrics like:

• Sessions . The number of sessions can tell you how many times people are returning to your website. Obviously, the higher the better.
• New Contacts from Sessions . How well is your campaign driving new contacts and customers?
• Marketing Performance KPIs . Tracking the number of MQLs, SQLs, New Contacts and similar will help you identify how your marketing efforts contribute to sales.
• Email Performance . Measure the success of your email campaigns from HubSpot. Keep an eye on your most important email marketing metrics such as number of sent emails, number of opened emails, open rate, email click-through rate, and more.
• Blog Posts and Landing Pages . How many people have viewed your blog recently? How well are your landing pages performing?

Now you can benefit from the experience of our Google Analytics and HubSpot Marketing experts, who have put together a plug-and-play Databox template that contains all the essential metrics for monitoring your leads. It’s simple to implement and start using as a standalone dashboard or in marketing reports, and best of all, it’s free!

You can easily set it up in just a few clicks – no coding required.

To set up the dashboard, follow these 3 simple steps:

Step 1: Get the template 

Step 2: Connect your HubSpot and Google Analytics 4 accounts with Databox. 

Step 3: Watch your dashboard populate in seconds.

3. Pick the Right Charts for Appealing Design

If you’re showing historical data – for instance, how you’ve performed now compared to last month – it’s best to use timelines or graphs. For other data, pie charts or tables may be more suitable. Make sure you use the right data visualization to display your data accurately and in an easy-to-understand manner.

4. Use a Narrative

Do you work on analytics and reporting ? Just exporting your data into a spreadsheet doesn’t qualify as either of them. The fact that you’re dealing with data may sound too technical, but actually, your report should tell a story about your performance. What happened on a specific day? Did your organic traffic increase or suddenly drop? Why? And more. There are a lot of questions to answer and you can put all the responses together in a coherent, understandable narrative.

5. Organize the Information

Before you start writing or building your dashboard, choose how you’re going to organize your data. Are you going to talk about the most relevant and general ones first? It may be the best way to start the report – the best practices typically involve starting with more general information and then diving into details if necessary.

6. Include a Summary

Some people in your audience won’t have the time to read the whole report, but they’ll want to know about your findings. Besides, a summary at the beginning of your data analytics report will help the reader get familiar with the topic and the goal of the report. And a quick note: although the summary should be placed at the beginning, you usually write it when you’re done with the report. When you have the whole picture, it’s easier to extract the key points that you’ll include in the summary.

7. Careful with Your Recommendations

Your communication skills may be critical in data analytics reports. Know that some of the results probably won’t be satisfactory, which means that someone’s strategy failed. Make sure you’re objective in your recommendations and that you’re not looking for someone to blame. Don’t criticize, but give suggestions on how things can be improved. Being solution-oriented is much more important and helpful for the business.

8. Double-Check Everything

The whole point of using data analytics tools and data, in general, is to achieve as much accuracy as possible. Avoid manual mistakes by proofreading your report when you finish, and if possible, give it to another person so they can confirm everything’s in place.

9. Use Interactive Dashboards

Using the right tools is just as important as the contents of your data analysis. The way you present it can make or break a good report, regardless of how valuable the data is. That said, choose a great reporting tool that can automatically update your data and display it in a visually appealing manner. Make sure it offers streamlined interactive dashboards that you can also customize depending on the purpose of the report.

To wrap up the guide, we decided to share nine excellent examples of what awesome data analysis reports can look like. You’ll learn what metrics you should include and how to organize them in logical sections to make your report beautiful and effective.

• Marketing Data Analysis Report Example

SEO Data Analysis Report Example

Sales data analysis report example.

• Customer Support Data Analysis Report Example

Help Desk Data Analysis Report Example

Ecommerce data analysis report example, project management data analysis report example, social media data analysis report example, financial kpi data analysis report example, marketing data report example.

If you need an intuitive dashboard that allows you to track your website performance effortlessly and monitor all the relevant metrics such as website sessions, pageviews, or CTA engagement, you’ll love this free HubSpot Marketing Website Overview dashboard template .

Tracking the performance of your SEO efforts is important. You can easily monitor relevant SEO KPIs like clicks by page, engaged sessions, or views by session medium by downloading this Google Organic SEO Dashboard .

How successful is your sales team? It’s easy to analyze their performance and predict future growth if you choose this HubSpot CRM Sales Analytics Overview dashboard template and track metrics such as average time to close the deal, new deals amount, or average revenue per new client.

Customer Support Analysis Data Report Example

Customer support is one of the essential factors that impact your business growth. You can use this streamlined, customizable Customer Success dashboard template . In a single dashboard, you can monitor metrics such as customer satisfaction score, new MRR, or time to first response time.

Other than being free and intuitive, this HelpScout for Customer Support dashboard template is also customizable and enables you to track the most vital metrics that indicate your customer support agents’ performance: handle time, happiness score, interactions per resolution, and more.

Is your online store improving or failing? You can easily collect relevant data about your store and monitor the most important metrics like total sales, orders placed, and new customers by downloading this WooCommerce Shop Overview dashboard template .

Does your IT department need feedback on their project management performance? Download this Jira dashboard template to track vital metrics such as issues created or resolved, issues by status, etc. Jira enables you to gain valuable insights into your teams’ productivity.

Need to know if your social media strategy is successful? You can find that out by using this easy-to-understand Social Media Awareness & Engagement dashboard template . Here you can monitor and analyze metrics like sessions by social source, track the number of likes and followers, and measure the traffic from each source.

Tracking your finances is critical for keeping your business profitable. If you want to monitor metrics such as the number of open invoices, open deals amount by stage by pipeline, or closed-won deals, use this free QuickBooks + HubSpot CRM Financial Performance dashboard template .

Rely on Accurate Data with Databox

“I don’t have time to build custom reports from scratch.”

“It takes too long and becomes daunting very soon.”

“I’m not sure how to organize the data to make it effective and prove the value of my work.”

Does this sound like you?

Well, it’s something we all said at some point – creating data analytics reports can be time-consuming and tiring. And you’re still not sure if the report is compelling and understandable enough when you’re done.

That’s why we decided to create Databox dashboards – a world-class solution for saving your money and time. We build streamlined and easy-to-follow dashboards that include all the metrics that you may need and allow you to create custom ones if necessary. That way, you can use templates and adjust them to any new project or client without having to build a report from scratch.

You can skip the setup and get your first dashboard for free in just 24 hours, with our fantastic customer support team on the line to assist you with the metrics you should track and the structure you should use.

Enjoy crafting brilliant data analysis reports that will improve your business – it’s never been faster and more effortless. Sign up today and get your free dashboard in no time.

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At Databox, we’re obsessed with helping companies more easily monitor, analyze, and report their results. Whether it’s the resources we put into building and maintaining integrations with 100+ popular marketing tools, enabling customizability of charts, dashboards, and reports, or building functionality to make analysis, benchmarking, and forecasting easier, we’re constantly trying to find ways to help our customers save time and deliver better results.

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Stefana Zarić is a freelance writer & content marketer. Other than writing for SaaS and fintech clients, she educates future writers who want to build a career in marketing. When not working, Stefana loves to read books, play with her kid, travel, and dance.

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Top 10 Data Analysis Templates with Samples and Examples

Mohammed Sameer

If people could eat data instead of food, we could end world hunger with enough spare data left over to tackle 3 famines.

This startling but obvious statement underscores the abundance of data available to the human race today and the humungous rate at which it has grown in our digital age. Just as sustenance nourishes our bodies, data fuels our intellect, satiating the hunger for insights and understanding. 

Data is the foundation upon which the structure of information stands tall. Imagine gazing at a puzzle's scattered pieces – each is important, might be beautiful and vital, but the true picture emerges only when the pieces interlock. Similarly, data is the root of knowledge for today’s businesses. Our new Data Analysis Templates are the masterful hands that bring all that scattered knowledge and wisdom together.

These PPT Presentations emerge as essential companions in a landscape where accurate decision-making means the difference between thriving and surviving. Understanding data is pivotal in the symphony of business strategies, marketing endeavors, and research pursuits. 

The 100% customizable nature of the templates provides you with the desired flexibility to edit your presentations. The content-ready slides give you the much-needed structure.

Let’s explore!

Template 1: Data Analysis Process PPT Set

Use this PPT Set to help stakeholders understand difficulties that mar the data analysis process and gain valuable insights. Explore the crucial stages of data analysis, from establishing data requirements and efficient data collection to thorough data processing and cleaning. This PPT Design highlights the often underestimated yet pivotal phase of data cleaning. With this template, you'll understand how data lays the foundation for seamless analysis, leading to more accurate results and impactful communication. Download now!

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Template 2: Data Analysis Business Evaluation Process for Visualization and Presentation

This holistic PPT Bundle guides you through the complex stages of visualization and presentation while offering a profound understanding of each crucial phase. Use this presentation template to understand the essence of successful data analysis, as it breaks down the process into digestible segments. From the initial steps of business issue comprehension and data understanding to data preparation, exploratory analysis, monitoring, validation, and finally, captivating visualization and presentation – every facet is covered. This PPT Preset goes beyond mere process explanation, offering a robust framework for the holistic development of data conceptualization, collection, analysis, and cleaning procedures. Get it today!

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Template 3: Data Requirement Analysis PPT Bundle

Navigating challenges of problem-solving, prioritization, and data insight, this PPT Presentation presents a strategic roadmap that transforms raw information into actionable intelligence. It starts with a deep dive into the heart of your business challenges. Focusing on defining the core problems, this presentation template guides you through the process of setting priorities, ensuring every move is a step closer to your objectives. Data collection, a crucial cornerstone, is explained through insightful visual aids and organized segments. Witness the transformation of disparate data points into a coherent narrative, empowering you to decipher trends, anomalies, and opportunities.

This PPT Template equips you with the tools to not only gather data but also comprehend its implications, turning information into true knowledge. Navigating the challenges of data requirement analysis is no longer a daunting task. From security gaps that demand attention to complex data systems that require expertise, our template ensures you're prepared to overcome these hurdles with confidence. The high costs that often come with data analysis are confronted head-on, unraveling budget-friendly strategies that don't compromise on quality. Get this template today!

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Template 4: Big Data Analysis PPT Set

This comprehensive PPT Deck presents a pre-made Big Data Analysis funnel that guides you through the rather complex process of turning data into gold. Gain a competitive edge by understanding effective data analysis techniques of association rule learning, classification tree analysis, genetic algorithm, regression analysis, and sentiment analysis. It's more than a run-of-the-mill PPT Presentation; it's a transformative tool. Invest in a big data analysis PPT like resource that's not just about graphs and numbers; get it now. Download now!

Template 5: Data Management Analysis PPT Framework

For achieving business excellence, the quest for efficient and time-saving solutions is a universal endeavor. Recognizing your aspirations, we present the Data Management Analysis PowerPoint Presentation — an invaluable asset for seamless change management and effective data analysis. It incorporates PPT Slides designed to provide an effortless avenue for embracing change management and conducting incisive data analysis. It offers a cohesive platform for centralizing your objectives, ready to be shared with your team. The judicious use of text boxes empowers you to articulate your perspectives with precision on each pertinent subject. Download today!

Template 6: Predictive Data Analysis PPT Layout

Get this PPT Preset to consolidate your stakeholder's grasp on predictive analytics, a discipline that uses statistical methodologies, cutting-edge machine learning algorithms, and a suite of tools to dissect historical data. This PPT Layout guides you through a well-structured journey, unfolding the essentials of predictive analytics, its foundational framework, and a suite of models that constitute its core. The significance of predictive analytics takes center stage, underscored by its multifaceted applications. Additionally, this resource has an Estimation Model PPT Slide, which explains the key tenets of diverse predictive analytics tools and their closely-knit workflows. The demarcation between the four pivotal categories of advanced analytics in this PPT deck receives careful attention. It sheds light on predictive analytics models – from classification to clustering models and beyond. Download now!

Template 7: Dashboard For IT Operations Data Analysis

This PPT Template Dashboard is a dynamic representation of your operational landscape. This PPT Set helps track the total number of cases from inception to resolution. Visualize trends with a graph showcasing the weekly ebb and flow of opened and closed cases. Prioritize effectively, allocating resources where they matter most, as the presentation template depicts it across departments. Efficiency meets clarity as you explore the time distribution of tickets on a day-by-day basis. Gain a better understanding of workflow patterns and resource utilization. Analyze open case statuses, fostering an environment of proactive response and swift action. Download now!

Template 8: Quarterly Sales Data Analysis Report

Visualize your progress with ease using this PPT Template's intuitive presentation of monthly sales data. Get a clear view of team-wise statistics that showcase individual contributions, fostering a culture of recognition and growth. Uncover finer details through the nuanced comparison of total versus actual sales values, empowering you to identify trends and opportunities. Engage stakeholders in strategy evaluation as you assess team goals versus actual achievements. Pinpoint areas of excellence and those warranting attention, refining your approach. Download now!

Template 9: Real-Time  Marketing Data Analysis

Here's a dynamic marketing analysis tool blending insights and aesthetics. It presents a pie chart comparing planned vs. actual budgets while diving deep into sections showcasing real-time marketing benefits: Elevated customer experiences, surging conversions, enhanced retention, and refined brand perception. Navigate budget allocation through intuitive bar graphs. Improve your strategy with data symphony, moving a step closer to success through informed choices. Download now!

Template 10: Data Analysis Process for Visualization and Presentation

Embark on a data-driven journey with this PPT Set. Learn the process of Data Analysis, Visualization, and Presentation to address complex business challenges. This PPT Design walks you through these stages, from issue identification and data preparation to exploratory analysis modeling. Witness raw data transform into insights through rigorous validation. Culminate in captivating visualizations and masterful presentations, setting new standards for impactful communication. Download now!

Bridging Numbers and Narratives: Your Journey Through Data Analysis

In a world where data weaves the fabric of progress, our journey through this blog comes to an inspiring end. As you venture into data analysis armed with our templates, remember that each graph, each layout, and each piece of information is a brushstroke on the canvas of understanding. With every mouse click, you’re not just navigating slides; you're charting the course for informed decisions, breakthrough discoveries, and transformative strategies.

FAQs on Data Analysis

What is data analysis.

Data analysis involves inspecting, cleansing, transforming, and modeling data to derive meaningful insights, draw conclusions, and support decision-making. It encompasses various techniques, including statistical methods, machine learning, and visualization, to uncover patterns, trends, and relationships within datasets.

What are the four types of data analysis?

There are four main types of data analysis:

• Descriptive Analysis: This type of analysis focuses on summarizing and describing the main features of a dataset. It involves statistical measures such as mean, median, mode, range, and standard deviation. Descriptive analysis aims to clearly understand the data's characteristics but doesn't involve drawing conclusions or making predictions.
• Diagnostic Analysis: Diagnostic analysis involves digging deeper into data to understand why certain patterns or outcomes occurred. It aims to identify the root causes of specific events or trends. Techniques used in diagnostic analysis often include data visualization, exploratory data analysis, and statistical tests to uncover relationships and correlations.
• Predictive Analysis: Predictive analysis involves using historical data to predict future events or outcomes. This type of analysis uses statistical models, machine learning algorithms, and data mining techniques to identify patterns and trends that can be used to forecast future trends. It's widely used in finance, marketing, and healthcare for making informed decisions.
• Prescriptive Analysis: Prescriptive analysis goes beyond predicting future outcomes. It provides recommendations or solutions for specific situations based on historical and current data analysis. This type of analysis considers different possible actions and their potential outcomes to guide decision-making. Prescriptive analysis is often used in complex scenarios involving multiple variables and options.

Where is data analysis used?

Data analysis is used in a wide range of fields and industries, including but not limited to:

• Business: Analyzing customer behavior, market trends, and financial performance.
• Healthcare: Analyzing patient records, medical research data, and disease trends.
• Science: Analyzing experimental results, simulations, and observations.
• Finance: Analyzing investment trends, risk assessment, and portfolio management.
• Marketing: Analyzing campaign effectiveness, consumer preferences, and market segmentation.
• Social Sciences: Analyzing survey data, demographic trends, and human behavior.
• Sports: Analyzing player performance, game statistics, and strategy optimization.

What is the main tool for data analysis?

There isn't a single "main" tool for data analysis, as the choice of tools depends on the specific tasks and the preferences of the analyst. However, some widely used tools for data analysis include:

• Spreadsheet Software: Like Microsoft Excel or Google Sheets, used for basic data manipulation and visualization.
• Statistical Software: Such as R and Python's libraries (e.g., pandas, numpy, scipy), used for in-depth statistical analysis and modeling.
• Data Visualization Tools: Like Tableau, Power BI, or matplotlib/seaborn in Python, used to create visual representations of data.
• Database Management Systems (DBMS): Such as SQL-based systems for querying and managing large datasets.
• Machine Learning Libraries: Such as scikit-learn, TensorFlow, and PyTorch for building predictive models.

Why is data analysis important?

Data analysis is crucial for several reasons:

• Informed Decision-Making: It provides insights that help individuals and organizations make informed decisions based on evidence rather than intuition.
• Identifying Patterns and Trends: It helps to uncover hidden patterns, trends, and correlations in large datasets that might not be apparent on the surface.
• Problem Solving: Data analysis aids in solving complex problems by providing a structured approach to understanding and addressing issues.
• Improving Efficiency and Performance: It allows businesses to optimize processes, improve efficiency, and enhance performance based on data-driven insights.
• Innovation and Research: Data analysis is essential in scientific research and innovation, helping to validate hypotheses and drive discoveries.
• Competitive Advantage: Organizations that effectively use data analysis gain a competitive edge by better understanding their customers, markets, and internal operations.
• Risk Management: Data analysis enables better risk assessment and management by identifying potential issues or anomalies early on.
• Resource Allocation: It helps allocate resources effectively by understanding where investments are most likely to yield positive outcomes.

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A Step-by-Step Guide to the Data Analysis Process

Like any scientific discipline, data analysis follows a rigorous step-by-step process. Each stage requires different skills and know-how. To get meaningful insights, though, it’s important to understand the process as a whole. An underlying framework is invaluable for producing results that stand up to scrutiny.

In this post, we’ll explore the main steps in the data analysis process. This will cover how to define your goal, collect data, and carry out an analysis. Where applicable, we’ll also use examples and highlight a few tools to make the journey easier. When you’re done, you’ll have a much better understanding of the basics. This will help you tweak the process to fit your own needs.

Here are the steps we’ll take you through:

• Defining the question
• Collecting the data
• Cleaning the data
• Analyzing the data
• Sharing your results
• Embracing failure

On popular request, we’ve also developed a video based on this article. Scroll further along this article to watch that.

Ready? Let’s get started with step one.

1. Step one: Defining the question

The first step in any data analysis process is to define your objective. In data analytics jargon, this is sometimes called the ‘problem statement’.

Defining your objective means coming up with a hypothesis and figuring how to test it. Start by asking: What business problem am I trying to solve? While this might sound straightforward, it can be trickier than it seems. For instance, your organization’s senior management might pose an issue, such as: “Why are we losing customers?” It’s possible, though, that this doesn’t get to the core of the problem. A data analyst’s job is to understand the business and its goals in enough depth that they can frame the problem the right way.

Let’s say you work for a fictional company called TopNotch Learning. TopNotch creates custom training software for its clients. While it is excellent at securing new clients, it has much lower repeat business. As such, your question might not be, “Why are we losing customers?” but, “Which factors are negatively impacting the customer experience?” or better yet: “How can we boost customer retention while minimizing costs?”

Now you’ve defined a problem, you need to determine which sources of data will best help you solve it. This is where your business acumen comes in again. For instance, perhaps you’ve noticed that the sales process for new clients is very slick, but that the production team is inefficient. Knowing this, you could hypothesize that the sales process wins lots of new clients, but the subsequent customer experience is lacking. Could this be why customers don’t come back? Which sources of data will help you answer this question?

Tools to help define your objective

Defining your objective is mostly about soft skills, business knowledge, and lateral thinking. But you’ll also need to keep track of business metrics and key performance indicators (KPIs). Monthly reports can allow you to track problem points in the business. Some KPI dashboards come with a fee, like Databox and DashThis . However, you’ll also find open-source software like Grafana , Freeboard , and Dashbuilder . These are great for producing simple dashboards, both at the beginning and the end of the data analysis process.

2. Step two: Collecting the data

Once you’ve established your objective, you’ll need to create a strategy for collecting and aggregating the appropriate data. A key part of this is determining which data you need. This might be quantitative (numeric) data, e.g. sales figures, or qualitative (descriptive) data, such as customer reviews. All data fit into one of three categories: first-party, second-party, and third-party data. Let’s explore each one.

What is first-party data?

First-party data are data that you, or your company, have directly collected from customers. It might come in the form of transactional tracking data or information from your company’s customer relationship management (CRM) system. Whatever its source, first-party data is usually structured and organized in a clear, defined way. Other sources of first-party data might include customer satisfaction surveys, focus groups, interviews, or direct observation.

What is second-party data?

To enrich your analysis, you might want to secure a secondary data source. Second-party data is the first-party data of other organizations. This might be available directly from the company or through a private marketplace. The main benefit of second-party data is that they are usually structured, and although they will be less relevant than first-party data, they also tend to be quite reliable. Examples of second-party data include website, app or social media activity, like online purchase histories, or shipping data.

What is third-party data?

Third-party data is data that has been collected and aggregated from numerous sources by a third-party organization. Often (though not always) third-party data contains a vast amount of unstructured data points (big data). Many organizations collect big data to create industry reports or to conduct market research. The research and advisory firm Gartner is a good real-world example of an organization that collects big data and sells it on to other companies. Open data repositories and government portals are also sources of third-party data .

Tools to help you collect data

Once you’ve devised a data strategy (i.e. you’ve identified which data you need, and how best to go about collecting them) there are many tools you can use to help you. One thing you’ll need, regardless of industry or area of expertise, is a data management platform (DMP). A DMP is a piece of software that allows you to identify and aggregate data from numerous sources, before manipulating them, segmenting them, and so on. There are many DMPs available. Some well-known enterprise DMPs include Salesforce DMP , SAS , and the data integration platform, Xplenty . If you want to play around, you can also try some open-source platforms like Pimcore or D:Swarm .

Want to learn more about what data analytics is and the process a data analyst follows? We cover this topic (and more) in our free introductory short course for beginners. Check out tutorial one: An introduction to data analytics .

3. Step three: Cleaning the data

Once you’ve collected your data, the next step is to get it ready for analysis. This means cleaning, or ‘scrubbing’ it, and is crucial in making sure that you’re working with high-quality data . Key data cleaning tasks include:

• Removing major errors, duplicates, and outliers —all of which are inevitable problems when aggregating data from numerous sources.
• Removing unwanted data points —extracting irrelevant observations that have no bearing on your intended analysis.
• Bringing structure to your data —general ‘housekeeping’, i.e. fixing typos or layout issues, which will help you map and manipulate your data more easily.
• Filling in major gaps —as you’re tidying up, you might notice that important data are missing. Once you’ve identified gaps, you can go about filling them.

A good data analyst will spend around 70-90% of their time cleaning their data. This might sound excessive. But focusing on the wrong data points (or analyzing erroneous data) will severely impact your results. It might even send you back to square one…so don’t rush it! You’ll find a step-by-step guide to data cleaning here . You may be interested in this introductory tutorial to data cleaning, hosted by Dr. Humera Noor Minhas.

Carrying out an exploratory analysis

Another thing many data analysts do (alongside cleaning data) is to carry out an exploratory analysis. This helps identify initial trends and characteristics, and can even refine your hypothesis. Let’s use our fictional learning company as an example again. Carrying out an exploratory analysis, perhaps you notice a correlation between how much TopNotch Learning’s clients pay and how quickly they move on to new suppliers. This might suggest that a low-quality customer experience (the assumption in your initial hypothesis) is actually less of an issue than cost. You might, therefore, take this into account.

Tools to help you clean your data

Cleaning datasets manually—especially large ones—can be daunting. Luckily, there are many tools available to streamline the process. Open-source tools, such as OpenRefine , are excellent for basic data cleaning, as well as high-level exploration. However, free tools offer limited functionality for very large datasets. Python libraries (e.g. Pandas) and some R packages are better suited for heavy data scrubbing. You will, of course, need to be familiar with the languages. Alternatively, enterprise tools are also available. For example, Data Ladder , which is one of the highest-rated data-matching tools in the industry. There are many more. Why not see which free data cleaning tools you can find to play around with?

4. Step four: Analyzing the data

Finally, you’ve cleaned your data. Now comes the fun bit—analyzing it! The type of data analysis you carry out largely depends on what your goal is. But there are many techniques available. Univariate or bivariate analysis, time-series analysis, and regression analysis are just a few you might have heard of. More important than the different types, though, is how you apply them. This depends on what insights you’re hoping to gain. Broadly speaking, all types of data analysis fit into one of the following four categories.

Descriptive analysis

Descriptive analysis identifies what has already happened . It is a common first step that companies carry out before proceeding with deeper explorations. As an example, let’s refer back to our fictional learning provider once more. TopNotch Learning might use descriptive analytics to analyze course completion rates for their customers. Or they might identify how many users access their products during a particular period. Perhaps they’ll use it to measure sales figures over the last five years. While the company might not draw firm conclusions from any of these insights, summarizing and describing the data will help them to determine how to proceed.

Learn more: What is descriptive analytics?

Diagnostic analysis

Diagnostic analytics focuses on understanding why something has happened . It is literally the diagnosis of a problem, just as a doctor uses a patient’s symptoms to diagnose a disease. Remember TopNotch Learning’s business problem? ‘Which factors are negatively impacting the customer experience?’ A diagnostic analysis would help answer this. For instance, it could help the company draw correlations between the issue (struggling to gain repeat business) and factors that might be causing it (e.g. project costs, speed of delivery, customer sector, etc.) Let’s imagine that, using diagnostic analytics, TopNotch realizes its clients in the retail sector are departing at a faster rate than other clients. This might suggest that they’re losing customers because they lack expertise in this sector. And that’s a useful insight!

Predictive analysis

Predictive analysis allows you to identify future trends based on historical data . In business, predictive analysis is commonly used to forecast future growth, for example. But it doesn’t stop there. Predictive analysis has grown increasingly sophisticated in recent years. The speedy evolution of machine learning allows organizations to make surprisingly accurate forecasts. Take the insurance industry. Insurance providers commonly use past data to predict which customer groups are more likely to get into accidents. As a result, they’ll hike up customer insurance premiums for those groups. Likewise, the retail industry often uses transaction data to predict where future trends lie, or to determine seasonal buying habits to inform their strategies. These are just a few simple examples, but the untapped potential of predictive analysis is pretty compelling.

Prescriptive analysis

Prescriptive analysis allows you to make recommendations for the future. This is the final step in the analytics part of the process. It’s also the most complex. This is because it incorporates aspects of all the other analyses we’ve described. A great example of prescriptive analytics is the algorithms that guide Google’s self-driving cars. Every second, these algorithms make countless decisions based on past and present data, ensuring a smooth, safe ride. Prescriptive analytics also helps companies decide on new products or areas of business to invest in.

Learn more:  What are the different types of data analysis?

5. Step five: Sharing your results

You’ve finished carrying out your analyses. You have your insights. The final step of the data analytics process is to share these insights with the wider world (or at least with your organization’s stakeholders!) This is more complex than simply sharing the raw results of your work—it involves interpreting the outcomes, and presenting them in a manner that’s digestible for all types of audiences. Since you’ll often present information to decision-makers, it’s very important that the insights you present are 100% clear and unambiguous. For this reason, data analysts commonly use reports, dashboards, and interactive visualizations to support their findings.

How you interpret and present results will often influence the direction of a business. Depending on what you share, your organization might decide to restructure, to launch a high-risk product, or even to close an entire division. That’s why it’s very important to provide all the evidence that you’ve gathered, and not to cherry-pick data. Ensuring that you cover everything in a clear, concise way will prove that your conclusions are scientifically sound and based on the facts. On the flip side, it’s important to highlight any gaps in the data or to flag any insights that might be open to interpretation. Honest communication is the most important part of the process. It will help the business, while also helping you to excel at your job!

Tools for interpreting and sharing your findings

There are tons of data visualization tools available, suited to different experience levels. Popular tools requiring little or no coding skills include Google Charts , Tableau , Datawrapper , and Infogram . If you’re familiar with Python and R, there are also many data visualization libraries and packages available. For instance, check out the Python libraries Plotly , Seaborn , and Matplotlib . Whichever data visualization tools you use, make sure you polish up your presentation skills, too. Remember: Visualization is great, but communication is key!

You can learn more about storytelling with data in this free, hands-on tutorial .  We show you how to craft a compelling narrative for a real dataset, resulting in a presentation to share with key stakeholders. This is an excellent insight into what it’s really like to work as a data analyst!

6. Step six: Embrace your failures

The last ‘step’ in the data analytics process is to embrace your failures. The path we’ve described above is more of an iterative process than a one-way street. Data analytics is inherently messy, and the process you follow will be different for every project. For instance, while cleaning data, you might spot patterns that spark a whole new set of questions. This could send you back to step one (to redefine your objective). Equally, an exploratory analysis might highlight a set of data points you’d never considered using before. Or maybe you find that the results of your core analyses are misleading or erroneous. This might be caused by mistakes in the data, or human error earlier in the process.

While these pitfalls can feel like failures, don’t be disheartened if they happen. Data analysis is inherently chaotic, and mistakes occur. What’s important is to hone your ability to spot and rectify errors. If data analytics was straightforward, it might be easier, but it certainly wouldn’t be as interesting. Use the steps we’ve outlined as a framework, stay open-minded, and be creative. If you lose your way, you can refer back to the process to keep yourself on track.

In this post, we’ve covered the main steps of the data analytics process. These core steps can be amended, re-ordered and re-used as you deem fit, but they underpin every data analyst’s work:

• Define the question —What business problem are you trying to solve? Frame it as a question to help you focus on finding a clear answer.
• Collect data —Create a strategy for collecting data. Which data sources are most likely to help you solve your business problem?
• Clean the data —Explore, scrub, tidy, de-dupe, and structure your data as needed. Do whatever you have to! But don’t rush…take your time!
• Analyze the data —Carry out various analyses to obtain insights. Focus on the four types of data analysis: descriptive, diagnostic, predictive, and prescriptive.
• Share your results —How best can you share your insights and recommendations? A combination of visualization tools and communication is key.
• Embrace your mistakes —Mistakes happen. Learn from them. This is what transforms a good data analyst into a great one.

What next? From here, we strongly encourage you to explore the topic on your own. Get creative with the steps in the data analysis process, and see what tools you can find. As long as you stick to the core principles we’ve described, you can create a tailored technique that works for you.

To learn more, check out our free, 5-day data analytics short course . You might also be interested in the following:

• These are the top 9 data analytics tools
• 10 great places to find free datasets for your next project
• How to build a data analytics portfolio

Last updated on September 7, 2023

How to Build an Effective Data Analytics Team

Running a data-driven organization requires more than technology and processes—you need an effective data analytics team in place to ensure the right technology and processes are adopted, and that best practices are used to get the most value out of your data..

According to Gartner, the shortage of skilled talent is the biggest barrier to emerging technologies adoption and business transformation . Businesses are dealing with all sorts of disruptions, but none is evermore present than that of the strained workforce. It’s difficult to find individuals who are trained and experienced in data and analytics —and the last thing you want to do is hire the wrong person. You need to build your data analytics team in a strategic way so that you are set up for long-term success.

But how do you build the right team? What skills do you need? Where do you start? First, you need to identify how data and analytics fit into your overall business operations and then learn the common roles and functions of a data and analytics team. This will help you understand where the talent gaps lie and build an effective and efficient team.

In this blog, we’ll discuss how your operating model will shape your staffing needs and the different roles and functions your data analytics team needs in order to reach your business goals.

Specifically:

• What are the Different Operating Models?↵
• What are the Key Roles and Functions in A Data Analytics Team?↵
• What are the Roles and Functions Focused on Analyzing, Interpreting, and Communicating Data? ↵
• What are the Roles and Functions Focused on Preparing, Integrating, Transforming, and Managing Data? ↵

First, Determine Your Operating Model

How your business chooses to work with data and analytics—your data and analytics operating model—will determine a lot of the staffing and roles necessary to reach your goals and how to best tap into the value of your people.

What Are the Different Operating Models for a Data-Driven Organization?

There are three types of operating models—decentralized, centralized, and a hybrid of both. One isn’t better than the other—they’re usually determined by the size of your organization and its data analytics needs. As you scale—or if you’re looking to get more value out of your data—you can change operating models as necessary.

• Decentralized operating model distributes data and analytics responsibilities across different lines of business, as well as IT. There isn’t one centralized authority, but rather a more collaborative approach across the organization to things like data management , data strategy , and business intelligence . A decentralized operating model can lead to strong collaboration and faster time to value, but it can also lead to lack of consistency, data silos, and higher costs across the board. This model is typical for a smaller organization with limited resources.
• Centralized operating model is more structured with everything data and analytics related falling under the responsibility of a specific executive function. A centralized operating model allows for easier decision-making and less redundancy and can make data governance easier , but it can also lead to rigidness and delays with data and analytics initiatives. This model is typical for a more analytically mature organization.
• Hybrid   operating model has the best of both worlds—decentralized and centralized—where there is one central authority for data management with decentralized business unit groups across the organization. A hybrid operating model allows for consistent data management and data governance and freedom for each line of business to take charge of their data and analytics initiatives. This model is ideal for organizations that want to have advanced data operations without having to create a dedicated data and analytics business unit for the organization.

What Are the Key Roles and Functions in A Data Analytics Team?

Within each of the operating models, you’ll have roles and functions assigned to data and analytics responsibilities. Some functions and roles can be interchangeable depending on your organization and its needs.

• A role is defined as a job assigned to a person in a particular situation. For example, a senior sales rep in your organization can also be assigned the role of business analyst because of their experience and understanding of the business unit. One person can have multiple roles within an organization. This may occur in all three operating models but is critical for the decentralized and hybrid models to work. Assigning people more than one role can also provide an organization flexibility as they grow their team and/or change operating models.
• A function is the sole duty of someone, and it often requires having a specialized skill.

Identify the gaps in your data analytics team .

Below is a list of key roles and functions—and their definitions ( according to the DAMA International )—to consider when building an effective data and analytics team for your organization.

Address Each Stage of the Data Lifecycle

It’s important to ensure you have the proper roles and functions that address problems and opportunities across the entire data lifecycle . If, for example, you focus most of your investment only up front on data acquisition and at the end on analysis, you will miss out many opportunities for data enhancement, efficiency, integration, and more along the way. Ensure you are maximizing the value of your data across the entire lifecycle by assigning responsibilities at each stage.

When building a data analytics team, make sure you have assigned roles and functions that will address each stage of the data lifecycle.

Roles and Functions Focused on Analyzing, Interpreting, and Communicating Data:

• Business Analyst: Responsible for being the liaison between IT and the business unit. This role identifies and articulates known problems that data analytics can solve. They assess processes, determine requirements, and deliver insights and recommendations to executives and stakeholders.
• Business   Intelligence Architect/Administrator: Responsible for supporting effective use of data by business professionals and for the design, maintenance, and performance of the business intelligence user environment. The individual in this function is a senior level engineer who uses business intelligence software to make data accessible to the business in meaningful and appropriate ways . This role is important to improving the self-service capacity of an organization by ensuring the structure supports each type of business user from dashboard consumers to hands-on power users.
• Data Visualization Analyst/Analytics Report Developer: Responsible for creating reporting, dashboards, and analytical application solutions. The individual in this function works to create visual depictions of data that reveals the patterns, trends, or correlations between different points. This role enables business users to have data insights at their fingertips. Having reusable dashboards or analytics already prepared means business stakeholders can spend more time on their business function, interpreting the data and putting data insights into action in their day-to-day business decisions.
• Data Scientist: Responsible for analyzing and interpreting complex data by combining domain expertise, programming skills, and knowledge of mathematics and statistics. The individual in this function requires analytical data expertise as well as technical skills to clean, transform, and explore data so that they can create value from it and work with stakeholders to make sure they are helping to solve real business problems.

Roles and functions focused on analyzing, interpreting and communicating data.

Roles and Functions Focused on Preparing, Integrating, Transforming, and Managing Data:

• Data Architect: Responsible for data architecture and data modeling. The individual in this function is senior level and may work at the enterprise level. The person should be skilled in data modeling and have a good understanding of performing detailed data analysis. A strong data model designed according to best practices improves performance, flexibility, and accuracy when used for analytics and reporting. A skilled data architect can ensure a business is getting answers quickly, supports ad-hoc questions—and helps to promote self-service.
• Data Engineer/Data Integration Specialist: Responsible for designing and developing data infrastructure to ensure broad availability of data throughout an organization, as well as for implementing systems to integrate (replicate, extract, transform, load) data assets in batch or near-real-time. This function is designed to build systems for collecting, storing, and analyzing data at scale. The data engineer (or ETL developer) works with the business analysts on the source to target mappings to populate a data warehouse and then write the code to transform the data and load it into the target data model. Centralizing data integration and preparation and having a dedicated role for it means data analysts and business users can focus on analyzing data and using insights rather than spending large chunks of time manually combining data sources repeatedly, redundantly, and sometimes inaccurately.
• Data Governance Administrator: Responsible for defining processes and facilitating the identification and documentation of data definitions, business rules, data quality and security requirements, and data stewards. This role or function oversees an organization’s data management goals, standards, practices, and process, and ensures it is aligned with business strategy.
• Database Administrator: Responsible for the design, implementation, and support of structured data assets and the performance of the technology that makes data accessible. This function within an organization manages, maintains, and secures data in more than one system so that business users can perform analysis.
• Quality Assurance Analyst/ Data Quality Analyst: Responsible for determining the fitness of data for use and monitoring the ongoing condition of the data. This function within an organization contributes to root cause analysis of data issues and helps the organization identify business process and technical improvements that contribute to higher quality data.

Roles and functions focused on preparing, integrating, transforming, and managing data.

Filling In the Gaps with Your Data Analytics Team

There is no one-size-fits-all approach to building an effective data analytics team, just as there isn’t one operating model that’s better than another. It will always come back to your organization’s specific data and analytics needs, as well as your resources and capabilities at the time.

What our Chief People Officer thinks it takes to retain good talent

As you look to scale your analytics maturity and get more value out of your data, you can either build an effective data analytics team in-house, or you can use consulting services to help fill the gaps in the meantime. You should also consider examining your data strategy to make sure it’s up to date, assess the capabilities of your data stack , and think about what it will take to get to your desired future state. These activities are essential to transforming your business with data and analytics .

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How to assemble a highly effective analytics team

Data-driven success hinges on strong, diverse, cross-functional data teams. it leaders offer tips on creating and maintaining teams tuned for delivering keen data insights..

What happens when an organization deploys the latest and greatest data analytics tools but fails to assemble a top-notch analytics team? Lost opportunities and a lot of wasted time and money.

A stellar analytics team can make the difference between lackluster insights and a giant leap on competitors. But you can’t slap together an analytics team overnight. It takes hard work and diligence to bring together the right people and the right mix of skills.

“One of the biggest challenges for organizations is not the collection of the data itself but developing a team that will apply the data and drive change throughout the organization,” says Laura Smith, CIO of healthcare provider UnityPoint Health.

“Building and sustaining a successful team has never been more challenging than the last 18 months — especially in healthcare,” Smith says. “For me, my greatest career success is the team I’ve built at UnityPoint Health. It has been no easy task; the analytics market is highly competitive.”

It can be done, however. Here are several best practices to keep in mind.

Provide modern, effective tools and meaningful work

Top-notch data analysts must have equipment and access to data that makes it possible for them to succeed.

Laura Smith, CIO, UnityPoint Health

“I’ve seen many an analyst become frustrated and leave a company because their laptop was five years old and couldn’t manage the amount of data that they needed to process,” says Theresa Kushner, data and analytics practice lead at NTT Data Services, a global IT consulting firm. “Or they were denied access to the data that they need to build the right algorithms. Making sure that your analysts have up-to-date hardware, current software, and access to data are basics to the success of data analysts.”

So is providing meaningful assignments. “No data analyst wants to be part of a team whose work isn’t making a difference to the overall business,” Kushner says. “That means selecting projects that have impact. This is easier said than done but is so crucial to building a credible data analyst team.”

Team members need to be shown how their work is meaningful to the world at large, Smith says. “I think we all want to know how our work contributes to the greater good,” she says. “In the healthcare industry, we liken this to having a calling. We all come to this calling with our unique skills and talents.”

For the analytics team at UnityPoint, identifying the positive impact someone can have on communities is key, Smith says. “I put it front and center whenever I’m recruiting team members,” she says. “People want to know how what they’re doing contributes to the greater good.”

Theresa Kushner, data and analytics practice lead, NTT Data Services

For example, the analytics team has played a critical role in ensuring that patients and employees have adequate personal protective equipment (PPE) throughout the pandemic. The team created a dashboard that pulled PPE data together to display meaningful information for leaders, Smith says. “With actionable data in hand, leaders could confidently make data-driven supply decisions in real-time, ensuring the health and safety of our patients and team members.”

Build talent through internal training programs

The shortage of data analytics professionals is well documented, and competition for these skills is fierce. Companies that have the resources should consider offering training and continuous learning programs that help generate in-house talent. This can include internal programs or external courses.

These training programs can also take the form of mentorships or bringing together cross-functional teams to share experiences and knowledge.

James Rinaldi, chief IT advisor, research and development center, NASA’s Jet Propulsion Laboratory

“Develop data analytics early career hires by pairing them with an experienced analytics type leader,” says James Rinaldi, chief information technology advisor at NASA’s Jet Propulsion Laboratory. “They will grow fast, but give them projects that allow them to move at their speed. Let them learn how the data architecture and culture work.”

Projects have the opportunity to bring cross-functional teams together, Rinaldi says. “It is worth the cost to add a few junior members, to allow them to experience and get to know how things work outside their own organization,” he says.

It’s also a good idea to move people around to various projects, Rinaldi says. “Don’t let people become stale or comfortable in just one area,” he says.

When selecting team members, start strong

In professional sports what often draws free-agent signings — aside from the money — is the chance to be on a winning team. Likewise, with an analytics team it might be easier to lure great talent when great talent already exists on the team.

“Excellence attracts excellence,” Kushner says. “If you are building a data analytics team, it really pays to make your first hire a genuine superstar.”

That doesn’t mean the individual has to be the top graduate from one of the most prestigious universities, Kushner says. But the person should

have a proven track record of using data to make a difference in a business.

“And don’t think that you need a PhD in data science,” Kushner says. “That’s nice, but sometimes the key person on a data analysis team is the person who knows the most about your business. It also means that you hire someone who really wants to be part of the team. They must be aligned in spirit as well as understanding with the goals of the overall team.”

Make diversity a priority

Diversity in the workforce is a focal point for many organizations today, and data analytics teams should be part of this effort. That includes diverse work histories.

Jessica Lachs, vice president of analytics and data science, DoorDash

“Assemble a team of people who have different professional backgrounds,” says Jessica Lachs, vice president of analytics and data science at DoorDash, which provides an online food ordering and delivery platform.

“I’m often asked what my team’s standard candidate profile looks like — and people are surprised when I tell them that we don’t have one,” Lachs says. “Having entered this field without previous data experience — and built up the entire function at DoorDash — I believe that building a team of people with diverse backgrounds makes your team better, overall.”

While the company expects candidates for the analytics team to have coding skills and statistics proficiency, “we have found success hiring people from a range of backgrounds, including finance, consulting, and economics in addition to more comparable technology data science backgrounds,” Lachs says.

This approach creates a team that has all the skills needed to solve a variety of problems, Lachs says. “Even if each individual on the team can’t solve every problem on their own, the result is a stronger team with people who can learn from one another and tackle a wider set of challenges together,” she says.

Keep team members happy

It’s not just a matter of building a strong team, but retaining it as well. Given the demand for data analysts today, if organizations fail to keep analytics team members happy they might leave for other positions.

As such, team leaders should reward milestones and allow analysts to promote themselves and continue to learn new skills. “Data analysts want to create a brand for themselves and their companies,” Kushner says. To do this, they need the time to write articles that promote their work; and to gain certifications on new software, new processes, new approaches, she says.

“When they have created articles, encourage their presentation of their papers at conferences as well as internally,” Kushner says. “Make their achievements very public and ensure that there is a steady stream of information on what they are accomplishing.”

Oftentimes managers think visibility for the team should just be internal, but that’s only going halfway, Kushner says. “Visibility needs to be industrywide,” she says. “Your top analysts should be visible to other top analysts. Providing a venue for your analysts to shine ensures loyalty and casts a glow on your company as well as your organization.”

It’s a good idea to build into every analyst’s calendar time to think about what needs to happen next, to document the projects they are working on, and to collaborate with those in the business and IT who can contribute vital information, Kushner says. “The tendency when working with data analysts is to drive projects, and as a result, managers often drive people as well,” she says. “That’s a formula for frustration and turnover.”

Engage with people across the organization

The analytics team isn’t meant to work in a vacuum. Interaction with others throughout the enterprise helps the team keep abreast of business goals and gain an understanding of what’s important to a variety of coworkers. It also enables team members to share the importance of analytics with others in the organization.

Michael Mayta, CIO, City of Wichita, Kan.

“Incorporate business leaders into the process,” says Michael Mayta, CIO for the City of Wichita, Kan. “This is a critical aspect, as these are the people who understand the data and more importantly understand what questions need to be answered using the data.”

Partnering analysts with business users “creates a learning experience while enhancing the business process and expediting the outcomes,” Mayta says. “If an analyst understands the data in its raw form but does not understand the business needs or the specific data sets required to arrive at a solution, then a great deal of time can be wasted in communication or trial-and-error development.”

When UnityPoint Health built its analytics team, “we started by engaging with physicians and employees across the entire health system,” Smith says. “We brought together team members from across all the different care settings to hear their needs and help them understand the importance of using analytics to improve patient care.”

The opportunity to engage extends beyond a specific problem the team is trying to solve, Smith says. “Other opportunities can include engaging with peers and encouraging personal development through mentorship programs.”

The model of engagement with the business has been effective, Smith says. “We build strong relationships with our business, [creating] an environment where team members are able to deliver amazing solutions,” she says. “They can see directly why they are valued and the contributions they make to the organization. This is huge for both individual and team satisfaction.”

Create a ‘data-informed’ culture

An organization that places a high priority on everything data will fuel the growth and enhancement of the analytics team. That’s been the approach at the City of Long Beach, Calif.

In 2018, the city’s Department of Technology & Innovation (TID) and Office of Civic Innovation launched a Data Committee that involved staff from 90% of city departments. A year later, the city hosted a Citywide Data Challenge, a four-month “datathon” where staff from various departments teamed up to solve challenges using data analysis tools.

Lea Eriksen, director of technology and innovation, City of Long Beach, Calif.

“The Data Challenge allowed city employees to pitch challenges or problem statements that could benefit from the use of data analysis and visualization,” says Lea Eriksen, director of technology and innovation for the City of Long Beach. “Four challenges were selected and then teams formed and worked cross-departmentally on the different challenges.”

One example of a successful challenge was the evaluation and mapping of where CERT-trained residents live to assess communities’ resiliency in case of an emergency. There were lessons learned from both the Data Challenge and the operation of the Data Committee, “which we used to restructure our data efforts,” Eriksen says.

In early 2021, TID launched a Citywide Data Learning Community for city employees. “This is a fun, learning-focused space for staff from across all city departments to ask questions and share with one another how they are embedding data within their teams and departments’ projects,” Eriksen says. “Every other month we invite a different city team to present on the tools, techniques, and resources they are using to build data into our DNA here in Long Beach.”

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Ideas Made to Matter

How to build an effective analytics practice: 7 insights from MIT experts

Tracy Mayor

Jan 23, 2023

Faculty members in MIT Sloan’s Master of Business Analytics program don’t just teach courses like Hands-On Deep Learning and Econometrics for Managers.

They also have deep experience applying the tools of modern data science, optimization, and machine learning to solve real-world business problems for organizations like Facebook, Salesforce, Booz Allen Hamilton, and the intelligence wing of the Israeli Defense Forces.

Here, they share insights about mistakes to avoid, strategies to adopt, and the developments in analytics and data that excite them most.

Let business decisions drive data strategy

  Y. Karen Zheng, Associate Professor, Operations Management

One of the biggest mistakes companies make about analytics is the disconnect between the technology and real business decisions. Companies tend to collect data for the sake of having data, and to develop analytics for the sake of having analytics, without thinking about how they are going to use the data and analytics capabilities to inform business decisions.

Successful analytics organizations are always decision-driven. They start by asking what business decisions they need data and analytics for, then investing resources to collect the right data and build the right analytics.

It is equally important to have the right talents in the organization that speak both the language of analytics and business, so that they can be the bridge between the tech and the business decision-makers. These employees understand the business needs and how analytics can be utilized to satisfy those needs; at the same time, they’re able to communicate the tech solution to business decision-makers in an understandable, intuitive way, as opposed to delivering a “black-box solution” that is hardly adopted by humans. 

Use deep learning to get value from unstructured data

Rama Ramakrishnan, Professor of the Practice, Data Science and Applied Machine Learning

I am personally most excited by deep learning. Traditional analytics methods are very effective for structured data, but we weren’t previously able to get value from unstructured data — images, audio, video, natural language, and so on — without a lot of labor-intensive preprocessing.

With deep learning, this limitation is effectively gone. We can now leverage unstructured and structured data together in a single, flexible, and powerful framework and achieve significant gains relative to what we could do earlier. This is possibly the most significant analytics breakthrough that I have witnessed in my professional career.

Pick use cases that deliver value

Jordan Levine, Lecturer, Operations Research and Statistics

The strategy to build an analytics practice is simple. First, identify three sources of use cases and start to build them. The three sources include:

• Use cases that support C-level metrics (think revenue, cost, and risk).
• Business processes that can be supported by self-serve analytics and dashboards.
• Compliance must-do activities. 

I use these three sources because they will be looked at differently by the ultimate scorekeepers — the finance function.

The second important activity is to staff the bench to meet demand once these use cases start driving value. Companies will often erroneously hire for a variety of roles and think the work is done. Given the fluidity of the post-COVID work environment and often short tenures of scarce analytics talent, companies must not only staff up but establish pipelines of talent.

One way companies do this is to partner with a higher-ed organization like the MIT Sloan Master of Business Analytics program , work with students on capstone projects, hire those students when they graduate, and then ask them to work with a new crop of students. This virtuous cycle ensures a happy, competent, and staffed bench of analytics talent.

Develop a new organizational language based on data-enabled models

Retsef Levi, Professor, Operations Management

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Data and analytics technologies are a critical enabler to create intelligent workflow and decision processes and systems. That said, many companies think about this through a technical lens and miss the fact that this is an end-to-end organizational challenge. 

The opportunity to design intelligent decision processes emerges from the ability to sense the organizational environment better than ever. It requires a new organizational language based on data-enabled models. Organizations must deeply understand their existing decision processes and the data they generate, and then develop layers of data-enabled models to allow the design of innovative intelligent decision processes. To be successful, it’s critical that organizations understand and manage required changes in decision rights and workforce role definitions.

Embrace the full analytics pipeline, upstream and downstream

Alexandre Jacquillat, Assistant Professor, Operations Research and Statistics

Most analytics projects in practice are focused on the development of deep learning and artificial intelligence tools. This is the shiny object that any analytics team is trying to build, improve, and deploy, with an emphasis on technical performance indicators — “My accuracy is 87%,” and so forth.

However, these represent only a narrow subset of the full analytics pipeline, which spans data management, descriptive analytics (such as data visualization and pattern recognition), predictive analytics (using machine learning tools, including but not restricted to deep learning), prescriptive analytics (using optimization), and business impact.

Time and time again, analytics projects take shortcuts across that pipeline — upstream and downstream.

At the upstream level, many analytics teams forgo critical steps to ensure the quality of their data, the representativeness of their data, and their own understanding of their data. One remedy for that is systematic exploratory data analysis baked into the analytics pipeline.

At the downstream level, analytics teams oftentimes fail to address the challenges associated with complex, large-scale decision-making in complex systems. This is where analytics projects could gain an additional edge by systematically embedding predictive tools into prescriptive analytics pipelines and decision-support systems.

Address specific business use cases to improve decision-making

Daniel Freund, Assistant Professor, Operations Management

With all the hype around machine learning, it is easy to forget that predictions are most useful when they inform decision-making. I’ve seen organizations roll out predictive models that weren’t going to inform actual decisions at all.

But even if a predictive model directly feeds into decision-making, improving predictions doesn’t always improve the decisions. Instead, new analytics capabilities are most powerful when they’re done to address specific business use cases to improve decision-making.

To ensure successful outcomes, it’s best for companies to measure these capabilities by the quality of the decisions they produce rather than just the accuracy of the predictions feeding into them.

Establish a centralized system for randomized experiments

Dean Eckles, Associate Professor, Marketing

Firms building an analytics process must have consistent definitions and practices. The foundation for trustworthy analytics is consensus on how basic metrics are defined and how common analyses are conducted.

This is sometimes one more indirect benefit of setting up a centralized system for randomized experiments (A/B tests and beyond): It often requires figuring out what metrics will show up when analyzing a given test, and this requires getting teams to agree on just how particular metrics are defined — be it number of days active, time spent on site, or even ad revenue per user.

These benefits are on top of the more direct benefits of making it easier to run experiments and making their results standardized and trustworthy.

Read next: In-demand data and analytics skills to hire for now

6.894 : Interactive Data Visualization

Assignment 2: exploratory data analysis.

In this assignment, you will identify a dataset of interest and perform an exploratory analysis to better understand the shape & structure of the data, investigate initial questions, and develop preliminary insights & hypotheses. Your final submission will take the form of a report consisting of captioned visualizations that convey key insights gained during your analysis.

Step 1: Data Selection

First, you will pick a topic area of interest to you and find a dataset that can provide insights into that topic. To streamline the assignment, we've pre-selected a number of datasets for you to choose from.

However, if you would like to investigate a different topic and dataset, you are free to do so. If working with a self-selected dataset, please check with the course staff to ensure it is appropriate for the course. Be advised that data collection and preparation (also known as data wrangling ) can be a very tedious and time-consuming process. Be sure you have sufficient time to conduct exploratory analysis, after preparing the data.

After selecting a topic and dataset – but prior to analysis – you should write down an initial set of at least three questions you'd like to investigate.

Part 2: Exploratory Visual Analysis

Next, you will perform an exploratory analysis of your dataset using a visualization tool such as Tableau. You should consider two different phases of exploration.

In the first phase, you should seek to gain an overview of the shape & stucture of your dataset. What variables does the dataset contain? How are they distributed? Are there any notable data quality issues? Are there any surprising relationships among the variables? Be sure to also perform "sanity checks" for patterns you expect to see!

In the second phase, you should investigate your initial questions, as well as any new questions that arise during your exploration. For each question, start by creating a visualization that might provide a useful answer. Then refine the visualization (by adding additional variables, changing sorting or axis scales, filtering or subsetting data, etc. ) to develop better perspectives, explore unexpected observations, or sanity check your assumptions. You should repeat this process for each of your questions, but feel free to revise your questions or branch off to explore new questions if the data warrants.

• Final Deliverable

Your final submission should take the form of a Google Docs report – similar to a slide show or comic book – that consists of 10 or more captioned visualizations detailing your most important insights. Your "insights" can include important surprises or issues (such as data quality problems affecting your analysis) as well as responses to your analysis questions. To help you gauge the scope of this assignment, see this example report analyzing data about motion pictures . We've annotated and graded this example to help you calibrate for the breadth and depth of exploration we're looking for.

Each visualization image should be a screenshot exported from a visualization tool, accompanied with a title and descriptive caption (1-4 sentences long) describing the insight(s) learned from that view. Provide sufficient detail for each caption such that anyone could read through your report and understand what you've learned. You are free, but not required, to annotate your images to draw attention to specific features of the data. You may perform highlighting within the visualization tool itself, or draw annotations on the exported image. To easily export images from Tableau, use the Worksheet > Export > Image... menu item.

The end of your report should include a brief summary of main lessons learned.

Recommended Data Sources

To get up and running quickly with this assignment, we recommend exploring one of the following provided datasets:

World Bank Indicators, 1960–2017 . The World Bank has tracked global human developed by indicators such as climate change, economy, education, environment, gender equality, health, and science and technology since 1960. The linked repository contains indicators that have been formatted to facilitate use with Tableau and other data visualization tools. However, you're also welcome to browse and use the original data by indicator or by country . Click on an indicator category or country to download the CSV file.

Chicago Crimes, 2001–present (click Export to download a CSV file). This dataset reflects reported incidents of crime (with the exception of murders where data exists for each victim) that occurred in the City of Chicago from 2001 to present, minus the most recent seven days. Data is extracted from the Chicago Police Department's CLEAR (Citizen Law Enforcement Analysis and Reporting) system.

Daily Weather in the U.S., 2017 . This dataset contains daily U.S. weather measurements in 2017, provided by the NOAA Daily Global Historical Climatology Network . This data has been transformed: some weather stations with only sparse measurements have been filtered out. See the accompanying weather.txt for descriptions of each column .

Social mobility in the U.S. . Raj Chetty's group at Harvard studies the factors that contribute to (or hinder) upward mobility in the United States (i.e., will our children earn more than we will). Their work has been extensively featured in The New York Times. This page lists data from all of their papers, broken down by geographic level or by topic. We recommend downloading data in the CSV/Excel format, and encourage you to consider joining multiple datasets from the same paper (under the same heading on the page) for a sufficiently rich exploratory process.

The Yelp Open Dataset provides information about businesses, user reviews, and more from Yelp's database. The data is split into separate files ( business , checkin , photos , review , tip , and user ), and is available in either JSON or SQL format. You might use this to investigate the distributions of scores on Yelp, look at how many reviews users typically leave, or look for regional trends about restaurants. Note that this is a large, structured dataset and you don't need to look at all of the data to answer interesting questions. In order to download the data you will need to enter your email and agree to Yelp's Dataset License .

Additional Data Sources

If you want to investigate datasets other than those recommended above, here are some possible sources to consider. You are also free to use data from a source different from those included here. If you have any questions on whether your dataset is appropriate, please ask the course staff ASAP!

• data.boston.gov - City of Boston Open Data
• MassData - State of Masachussets Open Data
• data.gov - U.S. Government Open Datasets
• U.S. Census Bureau - Census Datasets
• IPUMS.org - Integrated Census & Survey Data from around the World
• Federal Elections Commission - Campaign Finance & Expenditures
• Federal Aviation Administration - FAA Data & Research
• fivethirtyeight.com - Data and Code behind the Stories and Interactives
• Buzzfeed News
• Socrata Open Data
• 17 places to find datasets for data science projects

Visualization Tools

You are free to use one or more visualization tools in this assignment. However, in the interest of time and for a friendlier learning curve, we strongly encourage you to use Tableau . Tableau provides a graphical interface focused on the task of visual data exploration. You will (with rare exceptions) be able to complete an initial data exploration more quickly and comprehensively than with a programming-based tool.

• Tableau - Desktop visual analysis software . Available for both Windows and MacOS; register for a free student license.
• Data Transforms in Vega-Lite . A tutorial on the various built-in data transformation operators available in Vega-Lite.
• Data Voyager , a research prototype from the UW Interactive Data Lab, combines a Tableau-style interface with visualization recommendations. Use at your own risk!
• R , using the ggplot2 library or with R's built-in plotting functions.
• Jupyter Notebooks (Python) , using libraries such as Altair or Matplotlib .

Data Wrangling Tools

The data you choose may require reformatting, transformation or cleaning prior to visualization. Here are tools you can use for data preparation. We recommend first trying to import and process your data in the same tool you intend to use for visualization. If that fails, pick the most appropriate option among the tools below. Contact the course staff if you are unsure what might be the best option for your data!

Graphical Tools

• Tableau Prep - Tableau provides basic facilities for data import, transformation & blending. Tableau prep is a more sophisticated data preparation tool
• Trifacta Wrangler - Interactive tool for data transformation & visual profiling.
• OpenRefine - A free, open source tool for working with messy data.

Programming Tools

• JavaScript data utilities and/or the Datalib JS library .
• Pandas - Data table and manipulation utilites for Python.
• dplyr - A library for data manipulation in R.
• Or, the programming language and tools of your choice...

The assignment score is out of a maximum of 10 points. Submissions that squarely meet the requirements will receive a score of 8. We will determine scores by judging the breadth and depth of your analysis, whether visualizations meet the expressivenes and effectiveness principles, and how well-written and synthesized your insights are.

We will use the following rubric to grade your assignment. Note, rubric cells may not map exactly to specific point scores.

Submission Details

This is an individual assignment. You may not work in groups.

Your completed exploratory analysis report is due by noon on Wednesday 2/19 . Submit a link to your Google Doc report using this submission form . Please double check your link to ensure it is viewable by others (e.g., try it in an incognito window).

Resubmissions. Resubmissions will be regraded by teaching staff, and you may earn back up to 50% of the points lost in the original submission. To resubmit this assignment, please use this form and follow the same submission process described above. Include a short 1 paragraph description summarizing the changes from the initial submission. Resubmissions without this summary will not be regraded. Resubmissions will be due by 11:59pm on Saturday, 3/14. Slack days may not be applied to extend the resubmission deadline. The teaching staff will only begin to regrade assignments once the Final Project phase begins, so please be patient.

• Due: 12pm, Wed 2/19
• Recommended Datasets
• Example Report
• Visualization & Data Wrangling Tools
• Submission form

Your Data Won’t Speak Unless You Ask It The Right Data Analysis Questions

In our increasingly competitive digital age, setting the right data analysis and critical thinking questions is essential to the ongoing growth and evolution of your business. It is not only important to gather your business’s existing information but you should also consider how to prepare your data to extract the most valuable insights possible.

That said, with endless rafts of data to sift through, arranging your insights for success isn’t always a simple process. Organizations may spend millions of dollars on collecting and analyzing information with various data analysis tools , but many fall flat when it comes to actually using that data in actionable, profitable ways.

Here we’re going to explore how asking the right data analysis and interpretation questions will give your analytical efforts a clear-cut direction. We’re also going to explore the everyday data questions you should ask yourself to connect with the insights that will drive your business forward with full force.

Let’s get started.

Data Is Only As Good As The Questions You Ask

The truth is that no matter how advanced your IT infrastructure is, your data will not provide you with a ready-made solution unless you ask it specific questions regarding data analysis.

To help transform data into business decisions, you should start preparing the pain points you want to gain insights into before you even start data gathering. Based on your company’s strategy, goals, budget, and target customers you should prepare a set of questions that will smoothly walk you through the online data analysis and enable you to arrive at relevant insights.

For example, you need to develop a sales strategy and increase revenue. By asking the right questions, and utilizing sales analytics software that will enable you to mine, manipulate and manage voluminous sets of data, generating insights will become much easier. An average business user and cross-departmental communication will increase its effectiveness, decreasing the time to make actionable decisions and, consequently, providing a cost-effective solution.

Before starting any business venture, you need to take the most crucial step: prepare your data for any type of serious analysis. By doing so, people in your organization will become empowered with clear systems that can ultimately be converted into actionable insights. This can include a multitude of processes, like data profiling, data quality management, or data cleaning, but we will focus on tips and questions to ask when analyzing data to gain the most cost-effective solution for an effective business strategy.

 “Today, big data is about business disruption. Organizations are embarking on a battle not just for success but for survival. If you want to survive, you need to act.” – Capgemini and EMC² in their study Big & Fast Data: The Rise of Insight-Driven Business .

This quote might sound a little dramatic. However, consider the following statistics pulled from research developed by Forrester Consulting and Collibra:

• 84% of correspondents report that data at the center stage of developing business strategies is critical
• 81% of correspondents realized an advantage in growing revenue
• 8% admit an advantage in improving customers' trust
• 58% of "data intelligent" organizations are more likely to exceed revenue goals

Based on this survey, it seems that business professionals believe that data is the ultimate cure for all their business ills. And that's not a surprise considering the results of the survey and the potential that data itself brings to companies that decide to utilize it properly. Here we will take a look at data analysis questions examples and explain each in detail.

19 Data Analysis Questions To Improve Your Business Performance In The Long Run

What are data analysis questions, exactly? Let’s find out. While considering the industry you’re in, and competitors your business is trying to outperform, data questions should be clearly defined. Poor identification can result in faulty interpretation, which can directly affect business efficiency, and general results, and cause problems.

Here at datapine, we have helped solve hundreds of analytical problems for our clients by asking big data questions. All of our experience has taught us that data analysis is only as good as the questions you ask. Additionally, you want to clarify these questions regarding analytics now or as soon as possible – which will make your future business intelligence much clearer. Additionally, incorporating a decision support system software can save a lot of the company’s time – combining information from raw data, documents, personal knowledge, and business models will provide a solid foundation for solving business problems.

That’s why we’ve prepared this list of data analysis questions examples – to be sure you won’t fall into the trap of futile, “after the fact” data processing, and to help you start with the right mindset for proper data-driven decision-making while gaining actionable business insights.

1) What exactly do you want to find out?

It’s good to evaluate the well-being of your business first. Agree company-wide on what KPIs are most relevant for your business and how they already develop. Research different KPI examples and compare them to your own. Think about what way you want them to develop further. Can you influence this development? Identify where changes can be made. If nothing can be changed, there is no point in analyzing data. But if you find a development opportunity, and see that your business performance can be significantly improved, then a KPI dashboard software could be a smart investment to monitor your key performance indicators and provide a transparent overview of your company’s data.

The next step is to consider what your goal is and what decision-making it will facilitate. What outcome from the analysis you would deem a success? These introductory examples of analytical questions are necessary to guide you through the process and focus on key insights. You can start broad, by brainstorming and drafting a guideline for specific questions about the data you want to uncover. This framework can enable you to delve deeper into the more specific insights you want to achieve.

Let’s see this through an example and have fun with a little imaginative exercise.

Let’s say that you have access to an all-knowing business genie who can see into the future. This genie (who we’ll call Data Dan) embodies the idea of a perfect data analytics platform through his magic powers.

Now, with Data Dan, you only get to ask him three questions. Don’t ask us why – we didn’t invent the rules! Given that you’ll get exactly the right answer to each of them, what are you going to ask it?  Let’s see….

Talking With A Data Genie

You: Data Dan! Nice to meet you, my friend. Didn’t know you were real.

Data Dan: Well, I’m not actually. Anyways – what’s your first data analysis question?

You: Well, I was hoping you could tell me how we can raise more revenue in our business.

Data Dan: (Rolls eyes). That’s a pretty lame question, but I guess I’ll answer it. How can you raise revenue? You can do partnerships with some key influencers, you can create some sales incentives, and you can try to do add-on services to your most existing clients. You can do a lot of things. Ok, that’s it. You have two questions left.

You: (Panicking) Uhhh, I mean – you didn’t answer well! You just gave me a bunch of hypotheticals!

Data Dan: I exactly answered your question. Maybe you should ask for better ones.

You: (Sweating) My boss is going to be so mad at me if I waste my questions with a magic business genie. Only two left, only two left… OK, I know! Genie – what should I ask you to make my business the most successful?

Data Dan: OK, you’re still not good at this, but I’ll be nice since you only have one data question left.  Listen up buddy – I’m only going to say this once.

The Key To Asking Good Analytical Questions

Data Dan: First of all, you want your questions to be extremely specific. The more specific it is, the more valuable (and actionable) the answer is going to be. So, instead of asking, “How can I raise revenue?”, you should ask: “What are the channels we should focus more on in order to raise revenue while not raising costs very much, leading to bigger profit margins?”. Or even better: “Which marketing campaign that I did this quarter got the best ROI, and how can I replicate its success?”

These key questions to ask when analyzing data can define your next strategy in developing your organization. We have used a marketing example, but every department and industry can benefit from proper data preparation. By using a multivariate analysis, different aspects can be covered and specific inquiries defined.

2) What standard KPIs will you use that can help?

OK, let’s move on from the whole genie thing. Sorry, Data Dan! It’s crucial to know what data analysis questions you want to ask from the get-go. They form the bedrock for the rest of this process.

Think about it like this: your goal with business intelligence is to see reality clearly so that you can make profitable decisions to help your company thrive. The questions to ask when analyzing data will be the framework, the lens, that allows you to focus on specific aspects of your business reality.

Once you have your data analytics questions, you need to have some standard KPIs that you can use to measure them. For example, let’s say you want to see which of your PPC campaigns last quarter did the best. As Data Dan reminded us, “did the best” is too vague to be useful. Did the best according to what? Driving revenue? Driving profit? Giving the most ROI? Giving the cheapest email subscribers?

All of these KPI examples can be valid choices. You just need to pick the right ones first and have them in agreement company-wide (or at least within your department).

Let’s see this through a straightforward example.

You are a retail company and want to know what you sell, where, and when – remember the specific questions for analyzing data? In the example above, it is clear that the amount of sales performed over a set period tells you when the demand is higher or lower – you got your specific KPI answer. Then you can dig deeper into the insights and establish additional sales opportunities, and identify underperforming areas that affect the overall sales of products.

It is important to note that the number of KPIs you choose should be limited as monitoring too many can make your analysis confusing and less efficient. As the old analytics saying goes, just because you can measure something, it doesn't mean you should. We recommended sticking to a careful selection of 3-6 KPIs per business goal, this way, you'll avoid getting distracted by meaningless data.

The criteria to pick your KPIs is they should be attainable, realistic, measurable in time, and directly linked to your business goals. It is also a good practice to set KPI targets to measure the progress of your efforts.

Now let’s proceed to one of the most important data questions to ask – the data source.

3) Where will your data come from?

Our next step is to identify data sources you need to dig into all your data, pick the fields that you’ll need, leave some space for data you might potentially need in the future, and gather all the information in one place. Be open-minded about your data sources in this step – all departments in your company, sales, finance, IT, etc., have the potential to provide insights.

Don’t worry if you feel like the abundance of data sources makes things seem complicated. Our next step is to “edit” these sources and make sure their data quality is up to par, which will get rid of some of them as useful choices.

Right now, though, we’re just creating the rough draft. You can use CRM data, data from things like Facebook and Google Analytics, or financial data from your company – let your imagination go wild (as long as the data source is relevant to the questions you’ve identified in steps 1 and It could also make sense to utilize business intelligence software , especially since datasets in recent years have expanded in so much volume that spreadsheets can no longer provide quick and intelligent solutions needed to acquire a higher quality of data.

Another key aspect of controlling where your data comes from and how to interpret it effectively boils down to connectivity. To develop a fluent data analytics environment, using data connectors is the way forward.

Digital data connectors will empower you to work with significant amounts of data from several sources with a few simple clicks. By doing so, you will grant everyone in the business access to valuable insights that will improve collaboration and enhance productivity.

3.5) Which scales apply to your different datasets?

WARNING: This is a bit of a “data nerd out” section. You can skip this part if you like or if it doesn’t make much sense to you.

You’ll want to be mindful of the level of measurement for your different variables, as this will affect the statistical techniques you will be able to apply in your analysis.

There are basically 4 types of scales:

*Statistics Level Measurement Table*

• Nominal – you organize your data in non-numeric categories that cannot be ranked or compared quantitatively.

Examples: – Different colors of shirts – Different types of fruits – Different genres of music

• Ordinal – GraphPad gives this useful explanation of ordinal data:

“You might ask patients to express the amount of pain they are feeling on a scale of 1 to 10. A score of 7 means more pain than a score of 5, and that is more than a score of 3. But the difference between the 7 and the 5 may not be the same as that between 5 and 3. The values simply express an order. Another example would be movie ratings, from 0 to 5 stars.”

• Interval – in this type of scale, data is grouped into categories with order and equal distance between these categories.

Direct comparison is possible. Adding and subtracting is possible, but you cannot multiply or divide the variables. Example: Temperature ratings. An interval scale is used for both Fahrenheit and Celsius.

Again, GraphPad has a ready explanation: “The difference between a temperature of 100 degrees and 90 degrees is the same difference as between 90 degrees and 80 degrees.”

• Ratio –  has the features of all three earlier scales.

Like a nominal scale, it provides a category for each item, items are ordered like on an ordinal scale and the distances between items (intervals) are equal and carry the same meaning.

With ratio scales, you can add, subtract, divide, multiply… all the fun stuff you need to create averages and get some cool, useful data. Examples: height, weight, revenue numbers, leads, and client meetings.

4) Will you use market and industry benchmarks?  

In the previous point, we discussed the process of defining the data sources you’ll need for your analysis as well as different methods and techniques to collect them. While all of those internal sources of information are invaluable, it can also be a useful practice to gather some industry data to use as benchmarks for your future findings and strategies. 

To do so, it is necessary to collect data from external sources such as industry reports, research papers, government studies, or even focus groups and surveys performed on your targeted customer as a market research study to extract valuable information regarding the state of the industry in general but also the position each competitor occupies in the market. 

In doing so, you’ll not only be able to set accurate benchmarks for what your company should be achieving but also identify areas in which competitors are not strong enough and exploit them as a competitive advantage. For example, you can perform a market research survey to analyze the perception customers have about your brand and your competitors and generate a report to analyze the findings, as seen in the image below. 

**click to enlarge**

This market research dashboard is displaying the results of a survey on brand perception for 8 outdoor brands. Respondents were asked different questions to analyze how each brand is recognized within the industry. With these answers, decision-makers are able to complement their strategies and exploit areas where there is potential. 

5) Is the data in need of cleaning?

Insights and analytics based on a shaky “data foundation” will give you… well, poor insights and analytics. As mentioned earlier, information comes from various sources, and they can be good or bad. All sources within a business have a motivation for providing data, so the identification of which information to use and from which source it is coming should be one of the top questions to ask about data analytics.

Remember – your data analysis questions are designed to get a clear view of reality as it relates to your business being more profitable. If your data is incorrect, you’re going to be seeing a distorted view of reality.

That’s why your next step is to “clean” your data sets in order to discard wrong, duplicated, or outdated information. This is also an appropriate time to add more fields to your data to make it more complete and useful. That can be done by a data scientist or individually, depending on the size of the company.

An interesting survey comes from CrowdFlower , a provider or a data enrichment platform among data scientists. They have found out that most data scientists spend:

• 60% of their time organizing and cleaning data (!).
• 19% is spent on collecting datasets.
• 9% is spent mining the data to draw patterns.
• 3% is spent on training the datasets.
• 4% is spent refining the algorithms.
• 5% of the time is spent on other tasks.

57% of them consider the data cleaning process the most boring and least enjoyable task. If you are a small business owner, you probably don’t need a data scientist, but you will need to clean your data and ensure a proper standard of information.

Yes, this is annoying, but so are many things in life that are very important.

When you’ve done the legwork to ensure your data quality, you’ll have built yourself the useful asset of accurate data sets that can be transformed, joined, and measured with statistical methods. But, cleaning is not the only thing you need to do to ensure data quality, there are more things to consider which we’ll discuss in the next question. 

6) How can you ensure data quality?

Did you know that poor data quality costs the US economy up to $3.1 trillion yearly? Taking those numbers into account it is impossible to ignore the importance of this matter. Now, you might be wondering, what do I do to ensure data quality?

We already mentioned making sure data is cleaned and prepared to be analyzed is a critical part of it, but there is more. If you want to be successful on this matter, it is necessary to implement a carefully planned data quality management system that involves every relevant data user in the organization as well as data-related processes from acquisition to distribution and analysis.  

Some best practices and key elements of a successful data quality management process include: 

• Carefully clean data with the right tools. 
• Tracking data quality metrics such as the rate of errors, data validity, and consistency, among others. 
• Implement data governance initiatives to clearly define the roles and responsibilities for data access and manipulation 
• Ensure security standards for data storage and privacy are being implemented 
• Rely on automation tools to clean and update data to avoid the risk of manual human error 

These are only a couple of the many actions you can take to ensure you are working with the correct data and processes. Ensuring data quality across the board will save your business a lot of money by avoiding costly mistakes and bad-informed strategies and decisions. 

7) Which statistical analysis techniques do you want to apply?

There are dozens of statistical analysis techniques that you can use. However, in our experience, these 3 statistical techniques are most widely used for business:

• Regression Analysis – a statistical process for estimating the relationships and correlations among variables.

More specifically, regression helps understand how the typical value of the dependent variable changes when any of the independent variables is varied, while the other independent variables are held fixed.

In this way, regression analysis shows which among the independent variables are related to the dependent variable, and explores the forms of these relationships. Usually, regression analysis is based on past data, allowing you to learn from the past for better decisions about the future.

• Cohort Analysis – it enables you to easily compare how different groups, or cohorts, of customers, behave over time.

For example, you can create a cohort of customers based on the date when they made their first purchase. Subsequently, you can study the spending trends of cohorts from different periods in time to determine whether the quality of the average acquired customer is increasing or decreasing over time.

Cohort analysis tools give you quick and clear insight into customer retention trends and the perspectives of your business.

• Predictive & Prescriptive Analysis – in short, it is based on analyzing current and historical datasets to predict future possibilities, including alternative scenarios and risk assessment.

Methods like artificial neural networks (ANN) and autoregressive integrated moving average (ARIMA), time series, seasonal naïve approach, and data mining find wide application in data analytics nowadays.

• Conjoint analysis: Conjoint analytics is a form of statistical analysis that firms use in market research to understand how customers value different components or features of their products or services.

This type of analytics is incredibly valuable, as it will give you the insight required to see how your business’s products are really perceived by your audience, giving you the tools to make targeted improvements that will offer a competitive advantage.

• Cluster analysis: Cluster or 'clustering' refers to the process of grouping a set of objects or datasets. With this type of analysis, objects are placed into groups (known as a cluster) based on their values, attributes, or similarities.

This branch of analytics is often seen when working with autonomous applications or trying to identify particular trends or patterns.

We’ve already explained them and recognized them among the biggest business intelligence trends for 2022. Your choice of method should depend on the type of data you’ve collected, your team’s skills, and your resources.

8) What ETL procedures need to be developed (if any)?

One of the crucial questions to ask when analyzing data is if and how to set up the ETL process. ETL stands for Extract-Transform-Load, a technology used to read data from a database, transform it into another form and load it into another database. Although it sounds complicated for an average business user, it is quite simple for a data scientist. You don’t have to do all the database work, but an ETL service does it for you; it provides a useful tool to pull your data from external sources, conform it to demanded standards, and convert it into a destination data warehouse. These tools provide an effective solution since IT departments or data scientists don’t have to manually extract information from various sources, or you don’t have to become an IT specialist to perform complex tasks.

*ETL data warehouse*

If you have large data sets, and today most businesses do, it would be wise to set up an ETL service that brings all the information your organization is using and can optimize the handling of data.

9) What limitations will your analysis process have (if any)?

This next question is fundamental to ensure success in your analytical efforts. It requires you to put yourself in all the potential worst-case scenarios so you can prepare in advance and tackle them immediately with a solution. Some common limitations can be related to the data itself such as not enough sample size in a survey or research, lack of access to necessary technologies, and insufficient statistical power, among many others, or they can be related to the audience and users of the analysis such as lack of technical knowledge to understand the data. 

No matter which of these limitations you might face, identifying them in advance will help you be ready for anything. Plus, it will prevent you from losing time trying to find a solution for an issue, something that is especially valuable in a business context in which decisions need to be made as fast as possible.   

10) Who are the final users of your analysis results?

Another of the significant data analytics questions refers to the end-users of our analysis. Who are they? How will they apply your reports? You must get to know your final users, including:

• What they expect to learn from the data
• What their needs are
• Their technical skills
• How much time they can spend analyzing data?

Knowing the answers will allow you to decide how detailed your data report will be and what data you should focus on.

Remember that internal and external users have diverse needs. If the reports are designed for your own company, you more or less know what insights will be useful for your staff and what level of data complexity they can struggle through.

However, if your reports will also be used by external parties, remember to stick to your corporate identity. The visual reports you provide them with should be easy-to-use and actionable. Your final users should be able to read and understand them independently, with no IT support needed.

Also: think about the status of the final users. Are they junior members of the staff or part of the governing body? Every type of user has diverse needs and expectations.

11) How will the analysis be used?

Following on the latest point, after asking yourself who will use your analysis, you also need to ask yourself how you’re actually going to put everything into practice. This will enable you to arrange your reports in a way that transforms insight into action.

Knowing which questions to ask when analyzing data is crucial, but without a plan of informational action, your wonderfully curated mix of insights may as well be collecting dust on the virtual shelf. Here, we essentially refer to the end-use of your analysis. For example, when building reports, will you use it once as a standalone tool, or will you embed it for continual analytical use?

Embedded analytics is essentially a branch of BI technology that integrates professional dashboards or platforms into your business's existing applications to enhance its analytical scope and abilities. By leveraging the power of embedded dashboards , you can squeeze the juice out of every informational touchpoint available to your organization, for instance, by delivering external reports and dashboard portals to your external stakeholders to share essential information with them in a way that is interactive and easy to understand. 

Another key aspect of considering how you’re going to use your reports is to understand which mediums will work best for different kinds of users. In addition to embedded reports, you should also consider whether you want to review your data on a mobile device, as a file export, or even printed to mull through your newfound insights on paper. Considering and having these options at your disposal will ensure your analytical efforts are dynamic, flexible, and ultimately more valuable.

The bottom line? Decide how you’re going to use your insights in a practical sense, and you will set yourself on the path to data enlightenment. 

12) What data visualizations should you choose?

Your data is clean and your calculations are done, but you are not finished yet. You can have the most valuable insights in the world, but if they’re presented poorly, your target audience won’t receive the impact from them that you’re hoping for.

And we don’t live in a world where simply having the right data is the end-all, be-all. You have to convince other decision-makers within your company that this data is:

• Urgent to act upon

Effective presentation aids in all of these areas. There are dozens of data charts to choose from and you can either thwart all your data-crunching efforts by picking the wrong data visualization (like displaying a time evolution on a pie chart) or give it an additional boost by choosing the right types of graphs .

There are a number of online data visualization tools that can get the hard work done for you. These tools can effectively prepare the data and interpret the outcome. Their ease of use and self-service application in testing theories, analyzing changes in consumer buying behavior, leverage data for analytical purposes without the assistance of analysts or IT professionals have become an invaluable resource in today’s data management practice.

By being flexible enough to personalize its features to the end-user and adjust to your prepared questions for analyzing data, the tools enable a voluminous analysis that can help you not to overlook any significant issue of the day or the overall business strategy.

Dynamic modern dashboards are far more powerful than their static counterparts. You can reach out and interact with the information before you while gaining access to accurate real-time data at a glance. With interactive dashboards, you can also access your insights via mobile devices with the swipe of a screen or the click of a button 24/7. This will give you access to every single piece of analytical data you will ever need.

13) What kind of software will help?

Continuing on our previous point, there are some basic and advanced tools that you can utilize. Spreadsheets can help you if you prefer a more traditional, static approach, but if you need to tinker with the data on your own, perform basic and advanced analysis on a regular basis, and have real-time insights plus automated reports, then modern and professional tools are the way to go.

With the expansion of business intelligence solutions , data analytics questions to ask have never been easier. Powerful features such as basic and advanced analysis, countless chart types, quick and easy data source connection, and endless possibilities to interact with the data as questions arise, enable users to simplify oftentimes complex processes. No matter the analysis type you need to perform, the designated software will play an essential part in making your data alive and "able to speak."

Moreover, modern software will not require continuous manual updates of the data but it will automatically provide real-time insights that will help you answer critical questions and provide a stable foundation and prerequisites for good analysis.

14) What advanced technologies do you have at your disposal?

When you're deciding on which analysis question to focus on, considering which advanced or emerging technologies you have at your disposal is always essential.

By working with the likes of artificial intelligence (AI), machine learning (ML), and predictive analytics, you will streamline your data questions analysis strategies while gaining an additional layer of depth from your information.

The above three emerging technologies are interlinked in the sense that they are autonomous and aid business intelligence (BI) across the board. Using AI technology, it’s possible to automate certain data curation and analytics processes to boost productivity and hone in on better-quality insights.

By applying ML innovations, you can make your data analysis dashboards smarter with every single action or interaction, creating a self-improving ecosystem where you consistently boost the efficiency as well as the informational value of your analytical efforts with minimal human intervention.

From this ecosystem will emerge the ability to utilize predictive analytics to make accurate projections and develop organizational strategies that push you ahead of the competition. Armed with the ability to spot visual trends and patterns, you can nip any emerging issues or inefficiencies in the bud while playing on your current strengths for future gain.

With datapine, you can leverage the power of autonomous technologies by setting up data alerts that will notify you of a variety of functions - the kind that will help you exceed your business goals, as well as identify emerging patterns and particular numeric or data-driven thresholds. These BI features armed with cutting-edge technology will optimize your analytical activities in a way that will foster innovation and efficiency across the business.

15) How regularly should you check your data? 

Once you’ve answered all of the previous questions you should be 80% on the right track to be successful with your analytical efforts. That being said, data analytics is a never-ending process that requires constant monitoring and optimization. This leads us to our next question: how regularly should you check your data? 

There is no correct answer to this question as the frequency will depend on the goals of your analysis and the type of data you are tracking. In a business setting, there will be reports that contain data that you’ll need to track on a daily basis and in real-time since they influence the immediate performance of your organization for example, the marketing department might want to track the performance of their paid campaigns on a daily basis to optimize them and make the most out of their marketing budget. 

Likewise, there are other areas that can benefit from monthly tracking to extract more in-depth conclusions. For example, the customer service team might want to track the number of issues by channel on a monthly basis to identify patterns that can help them optimize their service. 

Modern data analysis tools provide users with the ability to automatically update their data as soon as it is generated. This alleviates the pain of having to manually check the data for new insights while significantly reducing the risk of human error. That said, no matter what frequency of monitoring you choose, it is also important to constantly check your data and analytical strategies to see if they still make sense for the current situation of the business. More on this in the next question. 

16) What else do you need to know?

Before finishing up, one of the crucial questions to ask about data analytics is how to verify the results. Remember that statistical information is always uncertain even if it is not reported in that way. Thinking about which information is missing and how you would use more information if you had it could be one point to consider. That way you can identify potential information that could help you make better decisions. Keep also in mind that by using simple bullet points or spreadsheets, you can overlook valuable information that is already established in your business strategy.

Always go back to the original objectives and make sure you look at your results in a holistic way. You will want to make sure your end result is accurate and that you haven’t made any mistakes along the way. In this step, important questions for analyzing data should be focused on:

• Does is it make sense on a general level?
• Are the measures I’m seeing in line with what I already know about the business?

Your end result is equally important as your process beforehand. You need to be certain that the results are accurate, verify the data, and ensure that there is no space for big mistakes. In this case, there are some data analysis types of questions to ask such as the ones we mentioned above. These types of questions will enable you to look at the bigger picture of your analytical efforts and identify any points that need more adjustments or additional details to work on.

You can also test your analytical environment against manual calculations and compare the results. If there are extreme discrepancies, there is something clearly wrong, but if the results turn accurate, then you have established a healthy data environment. Doing such a full-sweep check is definitely not easy, but in the long term, it will bring only positive results. Additionally, if you never stop questioning the integrity of your data, your analytical audits will be much healthier in the long run.

17) How can you create a data-driven culture?

Dirty data is costing you.

Whether you are a small business or a large enterprise, the data tell its story, and you should be able to listen. Preparing questions to ask about data analytics will provide a valuable resource and a roadmap to improved business strategies. It will also enable employees to make better departmental decisions and, consequently, create a cost-effective business environment that can help your company grow. Dashboards are a great way to establish such a culture, like in our financial dashboard example below:

In order to truly incorporate this data-driven approach to running the business, all individuals in the organization, regardless of the department they work in, need to know how to start asking the right data analytics questions.

They need to understand why it is important to conduct data analysis in the first place.

However, simply wishing and hoping that others will conduct data analysis is a strategy doomed to fail. Frankly, asking them to use data analysis (without showing them the benefits first) is also unlikely to succeed.

Instead, lead by example. Show your internal users that the habit of regular data analysis is a priceless aid for optimizing your business performance. Try to create a beneficial dashboard culture in your company.

Data analysis isn’t a means to discipline your employees and find who is responsible for failures, but to empower them to improve their performance and self-improve.

18) Are you missing anything, and is the data meaningful enough?

Once you’ve got your data analytics efforts off the ground and started to gain momentum, you should take the time to explore all of your reports and visualizations to see if there are any informational gaps you can fill.

Hold collaborative meetings with department heads and senior stakeholders to vet the value of your KPIs, visualizations, and data reports. You might find that there is a particular function you’ve brushed over or that a certain piece of data might be better displayed in a different format for greater insight or clarity.

Making an effort to keep track of your return on investment (ROI) and rates of improvements in different areas will help you paint a panoramic picture that will ultimately let you spot any potential analytical holes or data that is less meaningful than you originally thought.

For example, if you’re tracking sales targets and individual rep performance, you will have enough information to make improvements to the department. But with a collaborative conversation and a check on your departmental growth or performance, you might find that also throwing customer lifetime value and acquisition costs into the mix will offer greater context while providing additional insight. 

While this is one of the most vital ongoing data analysis questions to ask, you would be amazed at how many decision-makers overlook it: look at the bigger picture, and you will gain an edge on the competition.

19) How can you keep improving the analysis strategy?

When it comes to business questions for analytics, it’s essential to consider how you can keep improving your reports, processes, or visualizations to adapt to the landscape around you.

Regardless of your niche or sector, in the digital age, everything is in constant motion. What works today may become obsolete tomorrow. So, when prioritizing which questions to ask for analysis, it’s vital to decide how you’re going to continually evolve your reporting efforts.

If you’ve paid attention to business questions for data analysis number 18 (“Am I missing anything?” and “Is my data meaningful enough?”), you already have a framework for identifying potential gaps or weaknesses in your data analysis efforts. To take this one step further, you should explore every one of your KPIs or visualizations across departments and decide where you might need to update particular targets, modify your alerts, or customize your visualizations to return insights that are more relevant to your current situation.

You might, for instance, decide that your warehouse KPI dashboard needs to be customized to drill down further into total on-time shipment rates due to recent surges in customer order rates or operational growth. 

There is a multitude of reasons you will need to tweak or update your analytical processes or reports. By working with the right BI technology while asking yourself the right questions for analyzing data, you will come out on top time after time.

Start Your Analysis Today!

We just outlined a 19-step process you can use to set up your company for success through the use of the right data analysis questions.

With this information, you can outline questions that will help you to make important business decisions and then set up your infrastructure (and culture) to address them on a consistent basis through accurate data insights. These are good data analysis questions and answers to ask when looking at a data set but not only, as you can develop a good and complete data strategy if you utilize them as a whole. Moreover, if you rely on your data, you can only reap benefits in the long run and become a data-driven individual, and company.

To sum it up, here are the most important data questions to ask:

• What exactly do you want to find out? 
• What standard KPIs will you use that can help? 
• Where will your data come from? 
• Will you use market benchmarks?
• Is your data in need of cleaning?
• How can you ensure data quality? 
• Which statistical analysis techniques do you want to apply? 
• What ETL procedures need to be developed (if any?) 
• What limitations will your analysis process have (if any)?
• Who are the final users of your analysis results? 
• How will your analysis be used? 
• What data visualization should you choose? 
• What kind of software will help? 
• What advanced technologies do you have at your disposal? 
• What else do you need to know?
• How regularly should you check your data?
• How can you create a data-driven culture? 
• Are you missing anything, and is the data meaningful enough? 
• How can you keep improving the analysis strategy? 

Weave these essential data analysis question examples into your strategy, and you will propel your business to exciting new heights.

To start your own analysis, you can try our software for a 14-day trial - completely free!

DATA 275 Introduction to Data Analytics

• Getting Started with SPSS
• Variable View
• Option Suggestions
• SPSS Viewer
• Entering Data
• Cleaning & Checking Your SPSS Database
• Recoding Data: Collapsing Continuous Data
• Constructing Scales and Checking Their Reliability
• Formatting Tables in APA style
• Creating a syntax
• Public Data Sources

Data Analytics Project Assignment

• Literature Review This link opens in a new window

For your research project you will conduct data analysis and right a report summarizing your analysis and the findings from your analysis. You will accomplish this by completing a series of assignments. 

Data 275 Research Project Assignment

In this week’s assignment, you are required accomplish the following tasks:

1. Propose a topic for you project

The topic you select for your capstone depends on your interest and the data problem you want to address. Try to pick a topic that you would enjoy researching and writing about.

Your topic selection will also be influenced by data availability. Because, this is a data analytics project, you will need to have access to data. If you have access to your organization’s data, you are free to use it. If you choose to do so, all information presented must be in secure form because Davenport University does not assume any responsibility for the security of corporate data. Otherwise, you can select a topic that is amenable to publicly available data.

Click the link for some useful suggestions: Project Proposal Suggestions 

2. Find a data set of your interest and download it

There are many publicly available data sets that you can use for your project. The library has compiled a list of many possible sources of data. Click on the link below to explore these sources. 

Public Data Sources 

The data set you select must have:

At least 50 observations (50 rows) and at least 4 variables (columns) excluding identification variables At least one dependent variable

You must provide:

A proper citation of the data source using APA style format A discussion on how the data was collected and by whom The number of variables in the data set The number of observations/subjects in the data set A description of each variable together with an explanation of how it is measured (e.g. the unit of measurement).

Deliverable

A minimum of one page description of your data analytics project which must include the following:

A title for your project A brief description of the project Major stakeholders who would use the information that would be generated from your analysis and how they would use/benefit from that information A description of the dataset you will use for your project

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How to Structure a Data Science Team: Key Models and Roles to Consider

• 15 min read
• Data Science
• Last updated: 30 Jun, 2020
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Data-driven teams

Watch our video for a quick overview of data science roles

Data science team roles

Skillset of a data scientist

Role of a Chief Analytics Officer

How data preparation works in machine learning

ML engineer role, explained in 12 minutes or less

What is data engineering?

Team assembly and scaling

How to integrate a data science team into your company, decentralized.

Decentralized implementation.

• This model often leads to silos striving, lack of analytics standardization, and – you guessed it – decentralized reporting.
• The hiring process is an issue. When managers hire a data scientist for their team, it’s a challenge for them to hold a proper interview. They clearly understand, say, a typical software engineer’s roles, responsibilities, and skills, while being unfamiliar with those of a data scientist. So, putting it all together is a challenge for them.
• Managing a data scientist career path is also problematic. While team managers are totally clear on how to promote a software engineer, further steps for data scientists may raise questions. The same problem haunts building an individual development plan.
• Lower quality standards and underestimated best practices are often the case. The point is that data scientists must gain knowledge from other mentoring data scientists. As such an option is not provided in this model, data scientists may end up left on their own. This usually leads to no improvements of best practices, which usually reduces data quality and the quality of a product as a whole.

Functional implementation

• Keeping off from the global company’s pains. The approach entails that analytical activities are mostly focused on functional needs rather than on all enterprise necessities. Such unawareness may result in analytics isolation and staying out of context.
• Weak cohesion due to the absence of a data manager. As an analytical team here is placed under a particular business unit, it submits reports directly to the head of this unit. In this way, there may not be a direct data science manager who understands the specifics of their team.

Consulting implementation

• First of all, poor data quality can become a fundamental flaw of the model. As data scientists can’t adhere to their best practices for every task, they have to sacrifice quality to business needs that demand quick solutions.
• Also, there’s the low-motivation trap. As data scientists are not fully involved in product building and decision-making, they have little to no interest in the outcome.
• A serious drawback of a consulting model is uncertainty. Deadlines are not clear as data scientists are not clearly familiar with data sources and the context of their appearance. Long-term and complex projects are hardly accessible because sometimes specialists work for years over the same set of problems to achieve great results.
• The prioritization method is also unclear. It’s still hard to identify how a data science manager prioritizes and allocates tasks for data scientists and what objectives to favor first.

Centralized

Centralized implementation

• There’s a high chance of becoming isolated and facing the disconnect between a data analytics team and business lines. As the data analytics team doesn’t participate in regular activities of actual business value units, they might not be closely familiar with the latter’s needs and pains. This may lead to the narrow relevance of recommendations that can be left unused and ignored.
• This leads to challenges in meaningful cooperation with a product team. Once the analytics group has found a way to tackle a problem, it suggests a solution to a product team. The biggest problem is that this solution may not fit into a product roadmap . And, conflict may appear. The only way out here is to create a team that would assess, design, and implement the suggested solution. This alternative, however, takes much effort, time, and money.

Center of Excellence (CoE)

Center of Excellence implementation

• While this approach is balanced, there’s no single centralized group that would focus on enterprise-level problems. Each analytical group would be solving problems inside their units.
• Another drawback is that there’s no innovation unit, a group of specialists that primarily focus on state-of-the-art solutions and long-term data initiatives rather than day-to-day needs.

Federated implementation

• Expenses for talent acquisition and retention. As this model suggests a separate specialist for each product team and central data management, this may cost you a penny. Thus, the approach in its pure form isn’t the best choice for companies when they are in their earliest stages of analytics adoption.
• Cross-functionality may create a conflict environment. It can lack a power parity between all team lead positions and cause late deliveries or questionable results due to constant conflicts between unit team leads and CoE management.

• The company that integrates such a model usually invests a lot into data science infrastructure, tooling, and training.
• You simply need more people to avoid tales of a data engineer being occupied with tweaking a BI dashboard for another sales representative, instead of doing actual data engineering work.

More recommendations for creating a high-performance data science team

The critical thing to be aware of.

CSE 163, Summer 2020: Homework 3: Data Analysis

In this assignment, you will apply what you've learned so far in a more extensive "real-world" dataset using more powerful features of the Pandas library. As in HW2, this dataset is provided in CSV format. We have cleaned up the data some, but you will need to handle more edge cases common to real-world datasets, including null cells to represent unknown information.

Note that there is no graded testing portion of this assignment. We still recommend writing tests to verify the correctness of the methods that you write in Part 0, but it will be difficult to write tests for Part 1 and 2. We've provided tips in those sections to help you gain confidence about the correctness of your solutions without writing formal test functions!

This assignment is supposed to introduce you to various parts of the data science process involving being able to answer questions about your data, how to visualize your data, and how to use your data to make predictions for new data. To help prepare for your final project, this assignment has been designed to be wide in scope so you can get practice with many different aspects of data analysis. While this assignment might look large because there are many parts, each individual part is relatively small.

Learning Objectives

After this homework, students will be able to:

• Work with basic Python data structures.
• Handle edge cases appropriately, including addressing missing values/data.
• Practice user-friendly error-handling.
• Read plotting library documentation and use example plotting code to figure out how to create more complex Seaborn plots.
• Train a machine learning model and use it to make a prediction about the future using the scikit-learn library.

Expectations

Here are some baseline expectations we expect you to meet:

Follow the course collaboration policies

If you are developing on Ed, all the files are there. The files included are:

• hw3-nces-ed-attainment.csv : A CSV file that contains data from the National Center for Education Statistics. This is described in more detail below.
• hw3.py : The file for you to put solutions to Part 0, Part 1, and Part 2. You are required to add a main method that parses the provided dataset and calls all of the functions you are to write for this homework.
• hw3-written.txt : The file for you to put your answers to the questions in Part 3.
• cse163_utils.py : Provides utility functions for this assignment. You probably don't need to use anything inside this file except importing it if you have a Mac (see comment in hw3.py )

If you are developing locally, you should navigate to Ed and in the assignment view open the file explorer (on the left). Once there, you can right-click to select the option to "Download All" to download a zip and open it as the project in Visual Studio Code.

The dataset you will be processing comes from the National Center for Education Statistics. You can find the original dataset here . We have cleaned it a bit to make it easier to process in the context of this assignment. You must use our provided CSV file in this assignment.

The original dataset is titled: Percentage of persons 25 to 29 years old with selected levels of educational attainment, by race/ethnicity and sex: Selected years, 1920 through 2018 . The cleaned version you will be working with has columns for Year, Sex, Educational Attainment, and race/ethnicity categories considered in the dataset. Note that not all columns will have data starting at 1920.

Our provided hw3-nces-ed-attainment.csv looks like: (⋮ represents omitted rows):

Column Descriptions

• Year: The year this row represents. Note there may be more than one row for the same year to show the percent breakdowns by sex.
• Sex: The sex of the students this row pertains to, one of "F" for female, "M" for male, or "A" for all students.
• Min degree: The degree this row pertains to. One of "high school", "associate's", "bachelor's", or "master's".
• Total: The total percent of students of the specified gender to reach at least the minimum level of educational attainment in this year.
• White / Black / Hispanic / Asian / Pacific Islander / American Indian or Alaska Native / Two or more races: The percent of students of this race and the specified gender to reach at least the minimum level of educational attainment in this year.

Interactive Development

When using data science libraries like pandas , seaborn , or scikit-learn it's extremely helpful to actually interact with the tools your using so you can have a better idea about the shape of your data. The preferred practice by people in industry is to use a Jupyter Notebook, like we have been in lecture, to play around with the dataset to help figure out how to answer the questions you want to answer. This is incredibly helpful when you're first learning a tool as you can actually experiment and get real-time feedback if the code you wrote does what you want.

We recommend that you try figuring out how to solve these problems in a Jupyter Notebook so you can actually interact with the data. We have made a Playground Jupyter Notebook for you that has the data uploaded. At the top-right of this page in Ed is a "Fork" button (looks like a fork in the road). This will make your own copy of this Notebook so you can run the code and experiment with anything there! When you open the Workspace, you should see a list of notebooks and CSV files. You can always access this launch page by clicking the Jupyter logo.

Part 0: Statistical Functions with Pandas

In this part of the homework, you will write code to perform various analytical operations on data parsed from a file.

Part 0 Expectations

• All functions for this part of the assignment should be written in hw3.py .
• For this part of the assignment, you may import and use the math and pandas modules, but you may not use any other imports to solve these problems.
• For all of the problems below, you should not use ANY loops or list/dictionary comprehensions. The goal of this part of the assignment is to use pandas as a tool to help answer questions about your dataset.

Problem 0: Parse data

In your main method, parse the data from the CSV file using pandas. Note that the file uses '---' as the entry to represent missing data. You do NOT need to anything fancy like set a datetime index.

The function to read a CSV file in pandas takes a parameter called na_values that takes a str to specify which values are NaN values in the file. It will replace all occurrences of those characters with NaN. You should specify this parameter to make sure the data parses correctly.

Problem 1: compare_bachelors_1980

What were the percentages for women vs. men having earned a Bachelor's Degree in 1980? Call this method compare_bachelors_1980 and return the result as a DataFrame with a row for men and a row for women with the columns "Sex" and "Total".

The index of the DataFrame is shown as the left-most column above.

Problem 2: top_2_2000s

What were the two most commonly awarded levels of educational attainment awarded between 2000-2010 (inclusive)? Use the mean percent over the years to compare the education levels in order to find the two largest. For this computation, you should use the rows for the 'A' sex. Call this method top_2_2000s and return a Series with the top two values (the index should be the degree names and the values should be the percent).

For example, assuming we have parsed hw3-nces-ed-attainment.csv and stored it in a variable called data , then top_2_2000s(data) will return the following Series (shows the index on the left, then the value on the right)

Hint: The Series class also has a method nlargest that behaves similarly to the one for the DataFrame , but does not take a column parameter (as Series objects don't have columns).

Our assert_equals only checks that floating point numbers are within 0.001 of each other, so your floats do not have to match exactly.

Optional: Why 0.001?

Whenever you work with floating point numbers, it is very likely you will run into imprecision of floating point arithmetic . You have probably run into this with your every day calculator! If you take 1, divide by 3, and then multiply by 3 again you could get something like 0.99999999 instead of 1 like you would expect.

This is due to the fact that there is only a finite number of bits to represent floats so we will at some point lose some precision. Below, we show some example Python expressions that give imprecise results.

Because of this, you can never safely check if one float is == to another. Instead, we only check that the numbers match within some small delta that is permissible by the application. We kind of arbitrarily chose 0.001, and if you need really high accuracy you would want to only allow for smaller deviations, but equality is never guaranteed.

Problem 3: percent_change_bachelors_2000s

What is the difference between total percent of bachelor's degrees received in 2000 as compared to 2010? Take a sex parameter so the client can specify 'M', 'F', or 'A' for evaluating. If a call does not specify the sex to evaluate, you should evaluate the percent change for all students (sex = ‘A’). Call this method percent_change_bachelors_2000s and return the difference (the percent in 2010 minus the percent in 2000) as a float.

For example, assuming we have parsed hw3-nces-ed-attainment.csv and stored it in a variable called data , then the call percent_change_bachelors_2000s(data) will return 2.599999999999998 . Our assert_equals only checks that floating point numbers are within 0.001 of each other, so your floats do not have to match exactly.

Hint: For this problem you will need to use the squeeze() function on a Series to get a single value from a Series of length 1.

Part 1: Plotting with Seaborn

Next, you will write functions to generate data visualizations using the Seaborn library. For each of the functions save the generated graph with the specified name. These methods should only take the pandas DataFrame as a parameter. For each problem, only drop rows that have missing data in the columns that are necessary for plotting that problem ( do not drop any additional rows ).

Part 1 Expectations

• When submitting on Ed, you DO NOT need to specify the absolute path (e.g. /home/FILE_NAME ) for the output file name. If you specify absolute paths for this assignment your code will not pass the tests!
• You will want to pass the parameter value bbox_inches='tight' to the call to savefig to make sure edges of the image look correct!
• For this part of the assignment, you may import the math , pandas , seaborn , and matplotlib modules, but you may not use any other imports to solve these problems.
• For all of the problems below, you should not use ANY loops or list/dictionary comprehensions.
• Do not use any of the other seaborn plotting functions for this assignment besides the ones we showed in the reference box below. For example, even though the documentation for relplot links to another method called scatterplot , you should not call scatterplot . Instead use relplot(..., kind='scatter') like we showed in class. This is not an issue of stylistic preference, but these functions behave slightly differently. If you use these other functions, your output might look different than the expected picture. You don't yet have the tools necessary to use scatterplot correctly! We will see these extra tools later in the quarter.

Part 1 Development Strategy

• Print your filtered DataFrame before creating the graph to ensure you’re selecting the correct data.
• Call the DataFrame describe() method to see some statistical information about the data you've selected. This can sometimes help you determine what to expect in your generated graph.
• Re-read the problem statement to make sure your generated graph is answering the correct question.
• Compare the data on your graph to the values in hw3-nces-ed-attainment.csv. For example, for problem 0 you could check that the generated line goes through the point (2005, 28.8) because of this row in the dataset: 2005,A,bachelor's,28.8,34.5,17.6,11.2,62.1,17.0,16.4,28.0

Seaborn Reference

Of all the libraries we will learn this quarter, Seaborn is by far the best documented. We want to give you experience reading real world documentation to learn how to use a library so we will not be providing a specialized cheat-sheet for this assignment. What we will do to make sure you don't have to look through pages and pages of documentation is link you to some key pages you might find helpful for this assignment; you do not have to use every page we link, so part of the challenge here is figuring out which of these pages you need. As a data scientist, a huge part of solving a problem is learning how to skim lots of documentation for a tool that you might be able to leverage to solve your problem.

We recommend to read the documentation in the following order:

• Start by skimming the examples to see the possible things the function can do. Don't spend too much time trying to figure out what the code is doing yet, but you can quickly look at it to see how much work is involved.
• Then read the top paragraph(s) that give a general overview of what the function does.
• Now that you have a better idea of what the function is doing, go look back at the examples and look at the code much more carefully. When you see an example like the one you want to generate, look carefully at the parameters it passes and go check the parameter list near the top for documentation on those parameters.
• It sometimes (but not always), helps to skim the other parameters in the list just so you have an idea what this function is capable of doing

As a reminder, you will want to refer to the lecture/section material to see the additional matplotlib calls you might need in order to display/save the plots. You'll also need to call the set function on seaborn to get everything set up initially.

Here are the seaborn functions you might need for this assignment:

• Bar/Violin Plot ( catplot )
• Plot a Discrete Distribution ( distplot ) or Continuous Distribution ( kdeplot )
• Scatter/Line Plot ( relplot )
• Linear Regression Plot ( regplot )
• Compare Two Variables ( jointplot )
• Heatmap ( heatmap )

Make sure you read the bullet point at the top of the page warning you to only use these functions!

Problem 0: Line Chart

Plot the total percentages of all people of bachelor's degree as minimal completion with a line chart over years. To select all people, you should filter to rows where sex is 'A'. Label the x-axis "Year", the y-axis "Percentage", and title the plot "Percentage Earning Bachelor's over Time". Name your method line_plot_bachelors and save your generated graph as line_plot_bachelors.png .

Problem 1: Bar Chart

Plot the total percentages of women, men, and total people with a minimum education of high school degrees in the year 2009. Label the x-axis "Sex", the y-axis "Percentage", and title the plot "Percentage Completed High School by Sex". Name your method bar_chart_high_school and save your generated graph as bar_chart_high_school.png .

Do you think this bar chart is an effective data visualization? Include your reasoning in hw3-written.txt as described in Part 3.

Problem 2: Custom Plot

Plot the results of how the percent of Hispanic individuals with degrees has changed between 1990 and 2010 (inclusive) for high school and bachelor's degrees with a chart of your choice. Make sure you label your axes with descriptive names and give a title to the graph. Name your method plot_hispanic_min_degree and save your visualization as plot_hispanic_min_degree.png .

Include a justification of your choice of data visualization in hw3-written.txt , as described in Part 3.

Part 2: Machine Learning using scikit-learn

Now you will be making a simple machine learning model for the provided education data using scikit-learn . Complete this in a function called fit_and_predict_degrees that takes the data as a parameter and returns the test mean squared error as a float. This may sound like a lot, so we've broken it down into steps for you:

• Filter the DataFrame to only include the columns for year, degree type, sex, and total.
• Do the following pre-processing: Drop rows that have missing data for just the columns we are using; do not drop any additional rows . Convert string values to their one-hot encoding. Split the columns as needed into input features and labels.
• Randomly split the dataset into 80% for training and 20% for testing.
• Train a decision tree regressor model to take in year, degree type, and sex to predict the percent of individuals of the specified sex to achieve that degree type in the specified year.
• Use your model to predict on the test set. Calculate the accuracy of your predictions using the mean squared error of the test dataset.

You do not need to anything fancy like find the optimal settings for parameters to maximize performance. We just want you to start simple and train a model from scratch! The reference below has all the methods you will need for this section!

scikit-learn Reference

You can find our reference sheet for machine learning with scikit-learn ScikitLearnReference . This reference sheet has information about general scikit-learn calls that are helpful, as well as how to train the tree models we talked about in class. At the top-right of this page in Ed is a "Fork" button (looks like a fork in the road). This will make your own copy of this Notebook so you can run the code and experiment with anything there! When you open the Workspace, you should see a list of notebooks and CSV files. You can always access this launch page by clikcing the Jupyter logo.

Part 2 Development Strategy

Like in Part 1, it can be difficult to write tests for this section. Machine Learning is all about uncertainty, and it's often difficult to write tests to know what is right. This requires diligence and making sure you are very careful with the method calls you make. To help you with this, we've provided some alternative ways to gain confidence in your result:

• Print your test y values and your predictions to compare them manually. They won't be exactly the same, but you should notice that they have some correlation. For example, I might be concerned if my test y values were [2, 755, …] and my predicted values were [1022, 5...] because they seem to not correlate at all.
• Calculate your mean squared error on your training data as well as your test data. The error should be lower on your training data than on your testing data.

Optional: ML for Time Series

Since this is technically time series data, we should point out that our method for assessing the model's accuracy is slightly wrong (but we will keep it simple for our HW). When working with time series, it is common to use the last rows for your test set rather than random sampling (assuming your data is sorted chronologically). The reason is when working with time series data in machine learning, it's common that our goal is to make a model to help predict the future. By randomly sampling a test set, we are assessing the model on its ability to predict in the past! This is because it might have trained on rows that came after some rows in the test set chronologically. However, this is not a task we particularly care that the model does well at. Instead, by using the last section of the dataset (the most recent in terms of time), we are now assessing its ability to predict into the future from the perspective of its training set.

Even though it's not the best approach to randomly sample here, we ask you to do it anyways. This is because random sampling is the most common method for all other data types.

Part 3: Written Responses

Review the source of the dataset here . For the following reflection questions consider the accuracy of data collected, and how it's used as a public dataset (e.g. presentation of data, publishing in media, etc.). All of your answers should be complete sentences and show thoughtful responses. "No" or "I don't know" or any response like that are not valid responses for any questions. There is not one particularly right answer to these questions, instead, we are looking to see you use your critical thinking and justify your answers!

• Do you think the bar chart from part 1b is an effective data visualization? Explain in 1-2 sentences why or why not.
• Why did you choose the type of plot that you did in part 1c? Explain in a few sentences why you chose this type of plot.
• Datasets can be biased. Bias in data means it might be skewed away from or portray a wrong picture of reality. The data might contain inaccuracies or the methods used to collect the data may have been flawed. Describe a possible bias present in this dataset and why it might have occurred. Your answer should be about 2 or 3 sentences long.

Context : Later in the quarter we will talk about ethics and data science. This question is supposed to be a warm-up to get you thinking about our responsibilities having this power to process data. We are not trying to train to misuse your powers for evil here! Most misuses of data analysis that result in ethical concerns happen unintentionally. As preparation to understand these unintentional consequences, we thought it would be a good exercise to think about a theoretical world where you would willingly try to misuse data.

Congrats! You just got an internship at Evil Corp! Your first task is to come up with an application or analysis that uses this dataset to do something unethical or nefarious. Describe a way that this dataset could be misused in some application or an analysis (potentially using the bias you identified for the last question). Regardless of what nefarious act you choose, evil still has rules: You need to justify why using the data in this is a misuse and why a regular person who is not evil (like you in the real world outside of this problem) would think using the data in this way would be wrong. There are no right answers here of what defines something as unethical, this is why you need to justify your answer! Your response should be 2 to 4 sentences long.

Turn your answers to these question in by writing them in hw3-written.txt and submitting them on Ed

Your submission will be evaluated on the following dimensions:

• Your solution correctly implements the described behaviors. You will have access to some tests when you turn in your assignment, but we will withhold other tests to test your solution when grading. All behavior we test is completely described by the problem specification or shown in an example.
• No method should modify its input parameters.
• Your main method in hw3.py must call every one of the methods you implemented in this assignment. There are no requirements on the format of the output, besides that it should save the files for Part 1 with the proper names specified in Part 1.
• We can run your hw3.py without it crashing or causing any errors or warnings.
• When we run your code, it should produce no errors or warnings.
• All files submitted pass flake8
• All program files should be written with good programming style. This means your code should satisfy the requirements within the CSE 163 Code Quality Guide .
• Any expectations on this page or the sub-pages for the assignment are met as well as all requirements for each of the problems are met.

Make sure you carefully read the bullets above as they may or may not change from assignment to assignment!

A note on allowed material

A lot of students have been asking questions like "Can I use this method or can I use this language feature in this class?". The general answer to this question is it depends on what you want to use, what the problem is asking you to do and if there are any restrictions that problem places on your solution.

There is no automatic deduction for using some advanced feature or using material that we have not covered in class yet, but if it violates the restrictions of the assignment, it is possible you will lose points. It's not possible for us to list out every possible thing you can't use on the assignment, but we can say for sure that you are safe to use anything we have covered in class so far as long as it meets what the specification asks and you are appropriately using it as we showed in class.

For example, some things that are probably okay to use even though we didn't cover them:

• Using the update method on the set class even though I didn't show it in lecture. It was clear we talked about sets and that you are allowed to use them on future assignments and if you found a method on them that does what you need, it's probably fine as long as it isn't violating some explicit restriction on that assignment.
• Using something like a ternary operator in Python. This doesn't make a problem any easier, it's just syntax.

For example, some things that are probably not okay to use:

• Importing some random library that can solve the problem we ask you to solve in one line.
• If the problem says "don't use a loop" to solve it, it would not be appropriate to use some advanced programming concept like recursion to "get around" that restriction.

These are not allowed because they might make the problem trivially easy or violate what the learning objective of the problem is.

You should think about what the spec is asking you to do and as long as you are meeting those requirements, we will award credit. If you are concerned that an advanced feature you want to use falls in that second category above and might cost you points, then you should just not use it! These problems are designed to be solvable with the material we have learned so far so it's entirely not necessary to go look up a bunch of advanced material to solve them.

tl;dr; We will not be answering every question of "Can I use X" or "Will I lose points if I use Y" because the general answer is "You are not forbidden from using anything as long as it meets the spec requirements. If you're unsure if it violates a spec restriction, don't use it and just stick to what we learned before the assignment was released."

This assignment is due by Thursday, July 23 at 23:59 (PDT) .

You should submit your finished hw3.py , and hw3-written.txt on Ed .

You may submit your assignment as many times as you want before the late cutoff (remember submitting after the due date will cost late days). Recall on Ed, you submit by pressing the "Mark" button. You are welcome to develop the assignment on Ed or develop locally and then upload to Ed before marking.

University of Washington Information School

Msim students create dashboard to visualize health data.

As the largest state in New England and the most sparsely populated state on the East Coast, Maine has a population that is unique, hardy, and accustomed to the long winters and so-called “Northern Attitude” for which the region is known. Mainers also face many of the same health issues as the rest of the U.S. – some of which are pervasive and even lethal. 

To explore health discrepancies in Maine, a group of four  Master of Science in Information Management students at the University of Washington Information School set a goal for their 2023-2024 academic year: a  Capstone project that, when completed, would showcase an interactive visualization of Maine’s health data to better understand the gaps.

Online MSIM students Michael Ly, Vincent Kao, Nikhil Navkal and Divya Rajasekhar worked closely with project sponsor Sudhakar Kaushik of Jeeva Health to accomplish their shared goals.

Jeff Barland instructed the students as they worked on their Capstone project over three academic quarters. The online students navigated a change in project scope as well as life changes, including welcoming new members of their families. They collaborated with their team members and project sponsor in different time zones.

“The way that they worked as a team to meet these challenges, and these changes, was impressive,” Barland said. 

“They all put in significant effort. They really managed well together. They functioned as a cohort. And what they delivered was very professional. I was really impressed with their final product,” he said.

The MSIM students studied Maine’s health data to better understand how the state’s resources might be used to address health issues such as substance use disorder and mental health crises, and to learn how these findings could begin to be applied to the rest of the country.

“The primary data sources were from government organizations,” said Kao, a second-year MSIM student. “Official reports, United States-wide statistics that are shared commonly across most of the organizations. The secondary sources were definitely a little bit of a challenge for the team, where we started to drill down into some county-specific data.” 

Acknowledging that privacy is an important aspect of health data, Kao said, the team was still able to find data they could use to highlight local disparities and key issues. 

“We drilled into some of the specific issues, for example, the opioid crisis. We were able to find specific data sources that address that community problem,” Kao said.

“We also looked at changes in the number of mental health providers in Maine,” said Ly. The team had to organize data from different types of reports with different formats and metrics, and make the data directly comparable. 

The Power BI visualization tool the graduate student team created allows for the ability to filter by county, to see the data mapped onto the state to note geographic patterns, and to compare for each metric between state and national data for the same time period. The dashboard’s three sections allow users to compare other socioeconomic factors as well.

The data from Maine was also useful for honing in on urban-rural comparisons, said Navkal, who was a professional opera singer before launching his technical career.

The team learned more about the practices and privacy rules particular to health data as part of the project.

Rajasekhar, a second-year student, said she was surprised by some of the findings, such as the fact that in 2021 Maine had a higher rate of hospitalizations due to substance use (per 100,000 people) than in the United States on average. The statistic is in contrast, she said, to the maple trees and relative safety people might picture when they think of Maine.

“It was interesting to dive deep into a place that I hadn’t been familiar with, and realize that every place has its own trends, its own statistics that need to be investigated, so that we can reach for resolution,” Rajasekhar said. The team hopes the dashboard will be a launchpad for research into other regions’ health data and that it will make a positive impact.

“It’s nice that we were able to work with a company like Jeeva Health that actually wants to make a difference in communities like this going forward,” she said. 

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New York Yankees' Gerrit Cole begins injury rehab assignment

New York Yankees ace Gerrit Cole is set to begin his injury rehab assignment Tuesday in Somerset.

One of the final steps before returns to the team, the Yankees and their ace have been patiently waiting for Cole to make his comeback after he started the season on the 60-day injured list due to right elbow nerve inflammation.

The reigning Cy Young Award winner is set to start Tuesday night's game for Double-A Somerset as they host the Hartford Yard Goats.

For the best and latest New York Yankees news and analysis, check out our  team home page  every day.

How many rehab starts Cole will need isn't clear. It could be as few as two if his recovery is going well enough. He may need a few more.

With that said, Cole didn't want to rule out a return during the month of June, and his rehab has been going very well up to this point.

The Yankees starting rotation has been one of the pleasant surprises across MLB to begin the season. The unit has the best ERA (2.78) in the American League and the second-best in MLB behind the Philadelphia Phillies (2.71).

Still, the return of Cole to the best team in the American League cannot come soon enough.

For  more Yankees coverage , check out Kevin Hickey's work on  Sporting News .

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Gold price slightly up as key economic data on deck.

By  Jim Wyckoff

Kitco news the leading news source in precious metals.

Kitco NEWS has a diverse team of journalists reporting on the economy, stock markets, commodities, cryptocurrencies, mining and metals with accuracy and objectivity. Our goal is to help people make informed market decisions through in-depth reporting, daily market roundups, interviews with prominent industry figures, comprehensive coverage (often exclusive) of important industry events and analyses of market-affecting developments.

Jim Wyckoff

Jim Wyckoff has spent over 25 years involved with the stock, financial and commodity markets. He was a financial journalist with the FWN newswire service for many years, including stints as a reporter on the rough-and-tumble commodity futures trading floors in Chicago and New York. As a journalist, he has covered every futures market traded in the U.S., at one time or another.

Jim is the proprietor of the "Jim Wyckoff on the Markets" analytical, educational and trading advisory service. Jim also worked as a technical analyst for Dow Jones Newswires and as the senior market analyst with TraderPlanet.com. Jim is also a consultant with the highly respected "Pro Farmer" agricultural advisory service. Jim was also the head equities analyst at CapitalistEdge.com. He received his degree from Iowa State University in Ames, Iowa, where he studied journalism and economics.

Follow Jim daily on Kitco.com as he provides both AM and PM roundups and a daily Technical Special. 1 877 963-NEWS jwyckoff at kitco.com

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The code quality platform for teams

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Static Code Analysis for Spring: Run Analysis, Fix Critical Errors, Hit the Beach

Fun fact: 72% of our JVM users use Spring in their applications, especially in industries like manufacturing and finance. Why? Spring makes programming with Java and Kotlin quicker, easier, and safer for everybody – but it also comes with unique challenges.

Why is it so tricky to manage code quality in Spring?

Spring has a rich API interface with annotations and configuration files – a broad system with many specifics. It’s a framework that works like glue for your app, determining how you structure, build, and run code, freeing you up to think about which logic to apply. 

In a Spring Framework project, you can change one file, and everything will still look correct. However, this small change can easily affect your app’s configuration and lead to errors in other files. It’s possible that you won’t see these errors until you open those other files, which is too late. 

To confidently change files in such a project, you need safeguards to help you navigate code quality in such a complex framework. That’s why we originally built the Spring plugin for IntelliJ IDEA to include a wide range of Spring-specific inspections for everyday use.  Now, we’ve added Spring-specific inspections to Qodana.

Spring, Qodana, and IntelliJ IDEA: How do the three work together?

The out-of-the-box experience of IntelliJ IDEA gives you everything you need for typical Java and Kotlin development, including Spring. The plugin contributes various checks for Spring Framework and comes bundled with the Qodana JVM linter.

You can find the full list of inspections for Spring Framework in IntelliJ IDEA under Settings | Editor | Inspections by searching for “Spring” to filter out irrelevant inspections.

The same checks run in the IDE when you write code and even include server-side analysis. This means that if you missed some issue in the IDE, it will be detected by the Qodana linter in the CI pipeline.

The Qodana report will reveal the issues that snuck into the project, and everyone on the team will know. Qodana makes code reviews a team sport ahead of runtime, improving overall code quality and bringing enhanced collaboration to your team’s workflow.

Spring inspection groups

Spring inspections can be grouped by use case into checks for issues related to autowiring, configuration files, the data access layer, the web layer, and many more. Let’s take a closer look at some of these groups. 

Spring autowiring inspections

As Spring’s programming model relies on annotations, the various components are typically scattered across the project and often reside in different files and packages. It’s not immediately obvious whether the required dependencies exist, whether there are no conflicts, or whether the references to lifecycle methods are correct. These problems fall into the category of autowiring issues, and this is the most typical group of issues that developers face when working with the Spring Framework.

You can spot many autowiring issues in Spring’s context in the editor, highlighted as you type. These include minor recommendations, such as avoiding field injection, or bigger issues, such as a mistyped package name in the ComponentScan annotation.

Let’s look at an example of a situation in which changing one component in a Spring application may cause the failure of another component. In Spring applications, it’s possible to describe a dependency between components using the @DependsOn annotation, where a dependency name is specified as a string. The components might reside in different parts of the project, and a change in the dependency could potentially break the lifecycle of a dependent component.

In the screenshot above, the IDE reports that the dependency referenced in the @DependsOn annotation parameter cannot be resolved, even though the GeneralConfig class that implements the component is visible in the project tree.

The problem is a typo in the reference name: instead of “generalconfig” (all lowercase letters), the value should be “generalConfig” (with a capital letter “C”) – that is the convention used in Spring Framework for referencing components in the context of the application. 

Even though the issue is highlighted in the editor, it’s easy to miss this error if the change is made elsewhere without updating the reference value. The compilation process won’t detect this issue and simple unit tests won’t catch it either.

What we need is a minimal set of integration tests and additional static code checks. This is where Qodana comes in handy, as this type of issue is included in the report that JetBrains’ flagship code quality platform generates. 

Application configuration inspections

Configuration issues are annoying. A little typo in a configuration file can cause an application to misbehave or even crash.

Configuration inspections report unresolved and deprecated configuration keys and invalid values in the configuration files of Spring Boot application properties, which can lead to runtime errors. 

For instance, the configuration might include deprecated property names. This may occur when we upgrade the version of Spring Framework in the project but remember to update the configuration accordingly.

Another type of configuration issue involves the misspelling of property values. For instance, in the screenshot above, the value of the spring.devtools.restart.poll-interval property is highlighted in red. This value should be spelled as “5m” instead of “5min”.

Qodana inspections for Spring Data 

Spring Data relies on its method naming convention to generate database queries. Although this approach is very convenient, it’s easy to miss the convention unless you’re paying very close attention.

In the example above, the convention for the findByType method signature requires the PetType class as a parameter, but Integer was used as a parameter instead. To fix this, we can either extract a JPQL query into a Query annotation for the method or simply change the parameter type.

There are more checks that verify that the code using Spring Data follows the applicable conventions. For instance, you’ll be notified if the method name doesn’t match the property name of the entity.

Inspections for Spring MVC

Data access isn’t the only place where Spring Framework relies on naming conventions. Another example where you need to be aware of the conventions is the implementation of the web layer.

When working with Spring MVC, typically we refer to external resources or path variables by name. It’s easy to make a typo and then the external resource won’t be resolved.

The inspections for Spring MVC can also detect the unresolved references in the PathVariable annotation and missing resources for template engineers like Thymeleaf.

The powerful combination of Spring, Qodana and IntelliJ IDEA

Spring Framework is a popular yet complex framework where a lot of its functionality is based on naming conventions, or relies on component name references that are not validated at compile time.

IntelliJ IDEA validates Spring Framework conventions as you type in the editor, while Qodana builds on top of IntelliJ IDEA’s static analysis engine to provide visibility into the project for the whole team.

As a direct result, you can use Qodana to increase productivity, improve code quality, and provide learning opportunities to developers at various stages of seniority.

Got questions on code quality for the Qodana team?

Reach out to [email protected] – or follow us on X (formerly Twitter) and LinkedIn for code quality updates. You can also view the documentation to get started.

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