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What is data analysis? Examples and how to get started

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Even with years of professional experience working with data, the term "data analysis" still sets off a panic button in my soul. And yes, when it comes to serious data analysis for your business, you'll eventually want data scientists on your side. But if you're just getting started, no panic attacks are required.

Table of contents:

Quick review: What is data analysis?

Why is data analysis important, types of data analysis (with examples), data analysis process: how to get started, frequently asked questions.

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Data analysis is the process of examining, filtering, adapting, and modeling data to help solve problems. Data analysis helps determine what is and isn't working, so you can make the changes needed to achieve your business goals. 

Keep in mind that data analysis includes analyzing both quantitative data (e.g., profits and sales) and qualitative data (e.g., surveys and case studies) to paint the whole picture. Here are two simple examples (of a nuanced topic) to show you what I mean.

An example of quantitative data analysis is an online jewelry store owner using inventory data to forecast and improve reordering accuracy. The owner looks at their sales from the past six months and sees that, on average, they sold 210 gold pieces and 105 silver pieces per month, but they only had 100 gold pieces and 100 silver pieces in stock. By collecting and analyzing inventory data on these SKUs, they're forecasting to improve reordering accuracy. The next time they order inventory, they order twice as many gold pieces as silver to meet customer demand.

An example of qualitative data analysis is a fitness studio owner collecting customer feedback to improve class offerings. The studio owner sends out an open-ended survey asking customers what types of exercises they enjoy the most. The owner then performs qualitative content analysis to identify the most frequently suggested exercises and incorporates these into future workout classes.

Here's why it's worth implementing data analysis for your business:

Understand your target audience: You might think you know how to best target your audience, but are your assumptions backed by data? Data analysis can help answer questions like, "What demographics define my target audience?" or "What is my audience motivated by?"

Inform decisions: You don't need to toss and turn over a decision when the data points clearly to the answer. For instance, a restaurant could analyze which dishes on the menu are selling the most, helping them decide which ones to keep and which ones to change.

Adjust budgets: Similarly, data analysis can highlight areas in your business that are performing well and are worth investing more in, as well as areas that aren't generating enough revenue and should be cut. For example, a B2B software company might discover their product for enterprises is thriving while their small business solution lags behind. This discovery could prompt them to allocate more budget toward the enterprise product, resulting in better resource utilization.

Identify and solve problems: Let's say a cell phone manufacturer notices data showing a lot of customers returning a certain model. When they investigate, they find that model also happens to have the highest number of crashes. Once they identify and solve the technical issue, they can reduce the number of returns.

There are five main types of data analysis—with increasingly scary-sounding names. Each one serves a different purpose, so take a look to see which makes the most sense for your situation. It's ok if you can't pronounce the one you choose. 

Types of data analysis including text analysis, statistical analysis, diagnostic analysis, predictive analysis, and prescriptive analysis.

Text analysis: What is happening?

Text analysis, AKA data mining , involves pulling insights from large amounts of unstructured, text-based data sources : emails, social media, support tickets, reviews, and so on. You would use text analysis when the volume of data is too large to sift through manually. 

Here are a few methods used to perform text analysis, to give you a sense of how it's different from a human reading through the text: 

Word frequency identifies the most frequently used words. For example, a restaurant monitors social media mentions and measures the frequency of positive and negative keywords like "delicious" or "expensive" to determine how customers feel about their experience. 

Language detection indicates the language of text. For example, a global software company may use language detection on support tickets to connect customers with the appropriate agent. 

Keyword extraction automatically identifies the most used terms. For example, instead of sifting through thousands of reviews, a popular brand uses a keyword extractor to summarize the words or phrases that are most relevant. 

Because text analysis is based on words, not numbers, it's a bit more subjective. Words can have multiple meanings, of course, and Gen Z makes things even tougher with constant coinage. Natural language processing (NLP) software will help you get the most accurate text analysis, but it's rarely as objective as numerical analysis. 

Statistical analysis: What happened?

Statistical analysis pulls past data to identify meaningful trends. Two primary categories of statistical analysis exist: descriptive and inferential.

Descriptive analysis

Descriptive analysis looks at numerical data and calculations to determine what happened in a business. Companies use descriptive analysis to determine customer satisfaction , track campaigns, generate reports, and evaluate performance. 

Here are a few methods used to perform descriptive analysis: 

Measures of frequency identify how frequently an event occurs. For example, a popular coffee chain sends out a survey asking customers what their favorite holiday drink is and uses measures of frequency to determine how often a particular drink is selected. 

Measures of central tendency use mean, median, and mode to identify results. For example, a dating app company might use measures of central tendency to determine the average age of its users.

Measures of dispersion measure how data is distributed across a range. For example, HR may use measures of dispersion to determine what salary to offer in a given field. 

Inferential analysis

Inferential analysis uses a sample of data to draw conclusions about a much larger population. This type of analysis is used when the population you're interested in analyzing is very large. 

Here are a few methods used when performing inferential analysis: 

Hypothesis testing identifies which variables impact a particular topic. For example, a business uses hypothesis testing to determine if increased sales were the result of a specific marketing campaign. 

Confidence intervals indicates how accurate an estimate is. For example, a company using market research to survey customers about a new product may want to determine how confident they are that the individuals surveyed make up their target market. 

Regression analysis shows the effect of independent variables on a dependent variable. For example, a rental car company may use regression analysis to determine the relationship between wait times and number of bad reviews. 

Diagnostic analysis: Why did it happen?

Diagnostic analysis, also referred to as root cause analysis, uncovers the causes of certain events or results. 

Here are a few methods used to perform diagnostic analysis: 

Time-series analysis analyzes data collected over a period of time. A retail store may use time-series analysis to determine that sales increase between October and December every year. 

Data drilling uses business intelligence (BI) to show a more detailed view of data. For example, a business owner could use data drilling to see a detailed view of sales by state to determine if certain regions are driving increased sales.

Correlation analysis determines the strength of the relationship between variables. For example, a local ice cream shop may determine that as the temperature in the area rises, so do ice cream sales. 

Predictive analysis: What is likely to happen?

Predictive analysis aims to anticipate future developments and events. By analyzing past data, companies can predict future scenarios and make strategic decisions.  

Here are a few methods used to perform predictive analysis: 

Machine learning uses AI and algorithms to predict outcomes. For example, search engines employ machine learning to recommend products to online shoppers that they are likely to buy based on their browsing history. 

Decision trees map out possible courses of action and outcomes. For example, a business may use a decision tree when deciding whether to downsize or expand. 

Prescriptive analysis: What action should we take?

The highest level of analysis, prescriptive analysis, aims to find the best action plan. Typically, AI tools model different outcomes to predict the best approach. While these tools serve to provide insight, they don't replace human consideration, so always use your human brain before going with the conclusion of your prescriptive analysis. Otherwise, your GPS might drive you into a lake.

Here are a few methods used to perform prescriptive analysis: 

Lead scoring is used in sales departments to assign values to leads based on their perceived interest. For example, a sales team uses lead scoring to rank leads on a scale of 1-100 depending on the actions they take (e.g., opening an email or downloading an eBook). They then prioritize the leads that are most likely to convert. 

Algorithms are used in technology to perform specific tasks. For example, banks use prescriptive algorithms to monitor customers' spending and recommend that they deactivate their credit card if fraud is suspected. 

The actual analysis is just one step in a much bigger process of using data to move your business forward. Here's a quick look at all the steps you need to take to make sure you're making informed decisions. 

Circle chart with data decision, data collection, data cleaning, data analysis, data interpretation, and data visualization.

Data decision

As with almost any project, the first step is to determine what problem you're trying to solve through data analysis. 

Make sure you get specific here. For example, a food delivery service may want to understand why customers are canceling their subscriptions. But to enable the most effective data analysis, they should pose a more targeted question, such as "How can we reduce customer churn without raising costs?" 

These questions will help you determine your KPIs and what type(s) of data analysis you'll conduct , so spend time honing the question—otherwise your analysis won't provide the actionable insights you want.

Data collection

Next, collect the required data from both internal and external sources. 

Internal data comes from within your business (think CRM software, internal reports, and archives), and helps you understand your business and processes.

External data originates from outside of the company (surveys, questionnaires, public data) and helps you understand your industry and your customers. 

You'll rely heavily on software for this part of the process. Your analytics or business dashboard tool, along with reports from any other internal tools like CRMs , will give you the internal data. For external data, you'll use survey apps and other data collection tools to get the information you need.

Data cleaning

Data can be seriously misleading if it's not clean. So before you analyze, make sure you review the data you collected.  Depending on the type of data you have, cleanup will look different, but it might include: 

Removing unnecessary information 

Addressing structural errors like misspellings

Deleting duplicates

Trimming whitespace

Human checking for accuracy 

You can use your spreadsheet's cleanup suggestions to quickly and effectively clean data, but a human review is always important.

Data analysis

Now that you've compiled and cleaned the data, use one or more of the above types of data analysis to find relationships, patterns, and trends. 

Data analysis tools can speed up the data analysis process and remove the risk of inevitable human error. Here are some examples.

Spreadsheets sort, filter, analyze, and visualize data. 

Business intelligence platforms model data and create dashboards. 

Structured query language (SQL) tools manage and extract data in relational databases. 

Data interpretation

After you analyze the data, you'll need to go back to the original question you posed and draw conclusions from your findings. Here are some common pitfalls to avoid:

Correlation vs. causation: Just because two variables are associated doesn't mean they're necessarily related or dependent on one another. 

Confirmation bias: This occurs when you interpret data in a way that confirms your own preconceived notions. To avoid this, have multiple people interpret the data. 

Small sample size: If your sample size is too small or doesn't represent the demographics of your customers, you may get misleading results. If you run into this, consider widening your sample size to give you a more accurate representation. 

Data visualization

Last but not least, visualizing the data in the form of graphs, maps, reports, charts, and dashboards can help you explain your findings to decision-makers and stakeholders. While it's not absolutely necessary, it will help tell the story of your data in a way that everyone in the business can understand and make decisions based on. 

Automate your data collection

Data doesn't live in one place. To make sure data is where it needs to be—and isn't duplicative or conflicting—make sure all your apps talk to each other. Zapier automates the process of moving data from one place to another, so you can focus on the work that matters to move your business forward.

Need a quick summary or still have a few nagging data analysis questions? I'm here for you.

What are the five types of data analysis?

The five types of data analysis are text analysis, statistical analysis, diagnostic analysis, predictive analysis, and prescriptive analysis. Each type offers a unique lens for understanding data: text analysis provides insights into text-based content, statistical analysis focuses on numerical trends, diagnostic analysis looks into problem causes, predictive analysis deals with what may happen in the future, and prescriptive analysis gives actionable recommendations.

What is the data analysis process?

The data analysis process involves data decision, collection, cleaning, analysis, interpretation, and visualization. Every stage comes together to transform raw data into meaningful insights. Decision determines what data to collect, collection gathers the relevant information, cleaning ensures accuracy, analysis uncovers patterns, interpretation assigns meaning, and visualization presents the insights.

What is the main purpose of data analysis?

In business, the main purpose of data analysis is to uncover patterns, trends, and anomalies, and then use that information to make decisions, solve problems, and reach your business goals.

Related reading: 

How to get started with data collection and analytics at your business

How to automatically pull data for reports, charts, and dashboards

The best survey apps

How to conduct your own market research survey

Automatically find and match related data across apps

How to build an analysis assistant with ChatGPT

This article was originally published in October 2022 and has since been updated with contributions from Cecilia Gillen. The most recent update was in September 2023.

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Shea Stevens

Shea is a content writer currently living in Charlotte, North Carolina. After graduating with a degree in Marketing from East Carolina University, she joined the digital marketing industry focusing on content and social media. In her free time, you can find Shea visiting her local farmers market, attending a country music concert, or planning her next adventure.

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Data Analysis: Techniques, Tools, and Processes

Big or small, companies now expect their decisions to be data-driven. The world is growing and relying more on data. There is a greater need for professionals who know data analysis techniques.

Data analysis is a valuable skill that empowers you to make better decisions. This skill serves as a powerful catalyst in your professional and personal life. From personal budgeting to analyzing customer experiences , data analysis is the stepping stone to your career advancement.

So, whether you’re looking to upskill at work or kickstart a career in data analytics, this article is for you. We will discuss the best data analysis techniques in detail. To put all that into perspective, we’ll also discuss the step-by-step data analysis process. 

Let’s begin.

What is Data Analysis?

Data analysis is collecting, cleansing, analyzing, presenting, and interpreting data to derive insights. This process aids decision-making by providing helpful insights and statistics. 

The history of data analysis dates back to the 1640s. John Grant, a hatmaker, started collecting the number of deaths in London. He was the first person to use data analysis to solve a problem. Also, Florence Nightingale, best known as a nurse from 1854, made significant contributions to medicine through data analysis, particularly in public health and sanitation.

This simple practice of data analysis has evolved and broadened over time. “ Data analytics ” is the bigger picture. It employs data, tools, and techniques (covered later in this article) to discover new insights and make predictions.  

Why is Data Analysis so Important Now?

How do businesses make better decisions, analyze trends, or invent better products and services ?

The simple answer: Data Analysis. The distinct methods of analysis reveal insights that would otherwise get lost in the mass of information. Big data analytics is getting even more prominent owing to the below reasons.

1. Informed Decision-making

The modern business world relies on facts rather than intuition. Data analysis serves as the foundation of informed decision-making. 

Consider the role of data analysis in UX design , specifically when dealing with non-numerical, subjective information. Qualitative research delves into the 'why' and 'how' behind user behavior , revealing nuanced insights. It provides a foundation for making well-informed decisions regarding color , layout, and typography . Applying these insights allows you to create visuals that deeply resonate with your target audience.

2. Better Customer Targeting and Predictive Capabilities

Data has become the lifeblood of successful marketing . Organizations rely on data science techniques to create targeted strategies and marketing campaigns. 

Big data analytics helps uncover deep insights about consumer behavior. For instance, Google collects and analyzes many different data types. It examines search history, geography, and trending topics to deduce what consumers want.

3. Improved Operational Efficiencies and Reduced Costs

Data analytics also brings the advantage of streamlining operations and reducing organizational costs. It makes it easier for businesses to identify bottlenecks and improvement opportunities. This enables them to optimize resource allocation and ultimately reduce costs.

Procter & Gamble (P&G) , a leading company, uses data analytics to optimize their supply chain and inventory management. Data analytics helps the company reduce excess inventory and stockouts, achieving cost savings.  

4. Better Customer Satisfaction and Retention

Customer behavior patterns enable you to understand how they feel about your products, services, and brand. Also, different data analysis models help uncover future trends. These trends allow you to personalize the customer experience and improve satisfaction.

The eCommerce giant Amazon learns from what each customer wants and likes. It then recommends the same or similar products when they return to the shopping app. Data analysis helps create personalized experiences for Amazon customers and improves user experience . 

Enhance your knowledge by understanding “when” and “why” to use data analytics.

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Types of Data Analysis Methods

“We are surrounded by data, but starved for insights.” — Jay Baer, Customer Experience Expert & Speaker

The above quote summarizes that strategic analysis must support data to produce meaningful insights. 

Before discussing the top data analytics techniques , let’s first understand the two types of data analysis methods.

1. Quantitative Data Analysis

As the name suggests, quantitative analysis involves looking at the complex data, the actual numbers, or the rows and columns. Let’s understand this with the help of a scenario.

Your e-commerce company wants to assess the sales team’s performance. You gather quantitative data on various key performance indicators (KPIs). These KPIs include

The number of units sold.

Sales revenue.

Conversion rates.

Customer acquisition costs.

By analyzing these numeric data points, the company can calculate:

Monthly sales growth.

Average order value.

Return on investment (ROI) for each sales representative.

How does it help?

The quantitative analysis can help you identify:

Top-performing sales reps

Best-selling products. 

Most cost-effective customer acquisition channels.

The above metrics help the company make data-driven decisions and improve its sales strategy.

2. Qualitative Data Analysis

There are situations where numbers in rows and columns are impossible to fit. This is where qualitative research can help you understand the data’s underlying factors, patterns, and meanings via non-numerical means. Let’s take an example to understand this.

Imagine you’re a product manager for an online shopping app. You want to improve the app’s user experience and boost user engagement. You have quantitative data that tells you what's going on but not why . Here’s what to do:

Collect customer feedback through interviews, open-ended questions, and online reviews. 

Conduct in-depth interviews to explore their experiences. 

Watch this instructional video to elevate your interview preparation to a more professional level.

By reading and summarizing the comments, you can identify issues, sentiments, and areas that need improvement. This qualitative insight can guide you to identify and work on areas of frustration or confusion. 

Learn more about quantitative and qualitative user research in this video.

10 Best Data Analysis and Modeling Techniques

We generate over 120 zettabytes daily. That’s about 120 billion copies of the entire Internet in 2020, daily . Without the best data analysis techniques, businesses of all sizes will never be able to collect, analyze, and interpret data into real, actionable insights .  

Now that you have an overarching picture of data analysis , let’s move on to the nitty-gritty: top data analysis methods .

An infographic showcasing the best quantitative and qualitative data analysis techniques.

© Interaction Design Foundation, CC BY-SA 4.0

Quantitative Methods

1. cluster analysis.

Also called segmentation or taxonomy analysis, this method identifies structures within a dataset. It’s like sorting objects into different boxes (clusters) based on their similarities. The data points within a similar group are similar to each other (homogeneous). Likewise, they’re dissimilar to data points in another cluster(heterogeneous).  

Cluster analysis aims to find hidden patterns in the data. It can be your go-to approach if you require additional context to a trend or dataset.

Let’s say you own a retail store. You want to understand your customers better to tailor your marketing strategies. You collect customer data, including their shopping behavior and preferences. 

Here, cluster analysis can help you group customers with similar behaviors and preferences. Customers who visit your store frequently and shop a lot may form one cluster. Customers who shop infrequently and spend less may form another cluster.

With the help of cluster analysis, you can target your marketing efforts more efficiently.

2. Regression Analysis

Regression analysis is a powerful data analysis technique. It is quite popular in economics, biology, biology, and psychology. This technique helps you understand how one thing (or more) influences another. 

Suppose you’re a manager trying to predict next month’s sales. Many factors, like the weather, promotions, or the buzz about a better product, can affect these figures.

In addition, some people in your organization might have their own theory on what might impact sales the most. For instance, one colleague might confidently say, “When winter starts, our sales go up.” And another insists, “Sales will spike two weeks after we launch a promotion.”

All the above factors are “variables.” Now, the “dependent variable” will always be the factor being measured. In our example—the monthly sales. 

Next, you have your independent variables. These are the factors that might impact your dependent variable.

Regression analysis can mathematically sort out which variables have an impact. This statistical analysis identifies trends and patterns to make predictions and forecast possible future directions. 

There are many types of regression analysis, including linear regression, non-linear regression, binary logistic regression, and more. The model you choose will highly depend upon the type of data you have

3. Monte Carlo Simulation

This mathematical technique is an excellent way to estimate an uncertain event’s possible outcomes. Interestingly, the method derives its name from the Monte Carlo Casino in Monaco. The casino is famous for its games of chance. 

Let’s say you want to know how much money you might make from your investments in the stock market. So, you make thousands of guesses instead of one guess. Then, you consider several scenarios . The scenarios can be a growing economy or an unprecedented catastrophe like Covid-19. 

The idea is to test many random situations to estimate the potential outcomes.

4. Time Series Analysis

The time series method analyzes data collected over time. You can identify trends and cycles over time with this technique. Here, one data set recorded at different intervals helps understand patterns and make forecasts. 

Industries like finance, retail, and economics leverage time-series analysis to predict trends. It is so because they deal with ever-changing currency exchange rates and sales data. 

Using time series analysis in the stock market is an excellent example of this technique in action. Many stocks exhibit recurring patterns in their underlying businesses due to seasonality or cyclicality. Time series analysis can uncover these patterns. Hence, investors can take advantage of seasonal trading opportunities or adjust their portfolios accordingly.

Time series analysis is part of predictive analytics . It can show likely changes in the data to provide a better understanding of data variables and better forecasting. 

5. Cohort Analysis

Cohort analysis also involves breaking down datasets into relative groups (or cohorts), like cluster analysis. However, in this method, you focus on studying the behavior of specific groups over time. This aims to understand different groups’ performance within a larger population.

This technique is popular amongst marketing, product development, and user experience research teams. 

Let’s say you’re an app developer and want to understand user engagement over time. Using this method, you define cohorts based on a familiar identifier. This identifier can be the demographics, app download date, or users making an in-app purchase. In this way, your cohort represents a group of users who had a similar starting point. 

With the data in hand, you analyze how each cohort behaves over time. Do users from the US use your app more frequently than people in the UK? Are there any in-app purchases from a specific cohort?

This iterative approach can reveal insights to refine your marketing strategies and improve user engagement. 

Qualitative Methods

6. content analysis.

When you think of “data” or “analysis,” do you think of text, audio, video, or images? Probably not, but these forms of communication are an excellent way to uncover patterns, themes, and insights. 

Widely used in marketing, content analysis can reveal public sentiment about a product or brand. For instance, analyzing customer reviews and social media mentions can help brands discover hidden insights. 

There are two further categories in this method:

Conceptual analysis: It focuses on explicit data. For example, the number of times a word repeats in a content. 

Relational analysis: It examines the relationship between different concepts or words and how they connect. It's not about counting but about understanding how things fit together. A user experience technique called card sorting can help with this.

This technique involves counting and measuring the frequency of categorical data. It also studies the meaning and context of the content. This is why content analysis can be both quantitative and qualitative. 

How to Improve Your Design with Task Analysis

7. Sentiment Analysis

Also known as opinion mining, this technique is a valuable business intelligence tool. It can assist you to enhance your products and services. The modern business landscape has substantial textual data, including emails, social media comments, website chats, and reviews. You often need to know whether this text data conveys a positive, negative, or neutral sentiment.

Sentiment Analysis tools help scan this text to determine the emotional tone of the message automatically. The insights from sentiment analysis are highly helpful in improving customer service and elevating brand reputation.

8. Thematic Analysis

Whether you’re an entrepreneur, a UX researcher, or a customer relationship manager— thematic analysis can help you better understand user behaviors and needs. 

The thematic technique analyzes large chunks of text data such as transcripts or interviews. It then groups them into themes or categories that come up frequently within the text. While this may sound similar to content analysis, it’s worth noting that the thematic method purely uses qualitative data. 

Moreover, it is a very subjective technique since it depends upon the researcher’s experience to derive insights. 

9. Grounded Theory Analysis

Think of grounded theory as something you, as a researcher, might do. Instead of starting with a hypothesis and trying to prove or disprove it, you gather information and construct a theory as you go along.

It's like a continuous loop. You collect and examine data and then create a theory based on your discovery. You keep repeating this process until you've squeezed out all the insights possible from the data. This method allows theories to emerge naturally from the information, making it a flexible and open way to explore new ideas.

Grounded theory is the basis of a popular user-experience research technique called contextual enquiry .

10. Discourse Analysis

Discourse analysis is popular in linguistics, sociology, and communication studies. It aims to understand the meaning behind written texts, spoken conversations, or visual and multimedia communication. It seeks to uncover:

How individuals structure a specific language

What lies behind it; and 

How social and cultural practices influence it

For instance, as a social media manager, if you analyze social media posts, you go beyond the text itself. You would consider the emojis, hashtags, and even the timing of the posts. You might find that a particular hashtag is used to mobilize a social movement. 

The Data Analysis Process: Step-by-Step Guide

You must follow a step-by-step data analytics process to derive meaningful conclusions from your data. Here is a rundown of five main data analysis steps :

A graphical representation of data analysis steps. 

© Interaction Design Foundation , CC BY-SA 4.0

1. Problem Identification

The first step in the data analysis process is “identification.” What problem are you trying to solve? In other words, what research question do you want to address with your data analysis?

Let’s say you’re an analyst working for an e-commerce company. There has been a recent decline in sales. Now, the company wants to understand why this is happening. Our problem statement is to find the reason for the decline in sales. 

2. Data Collection

The next step is to collect data. You can do this through various internal and external sources. For example, surveys , questionnaires, focus groups , interviews , etc.

Delve deeper into the intricacies of data collection with Ann Blandford in this video:

The key here is to collect and aggregate the appropriate statistical data. By “appropriate,” we mean the data that could help you understand the problem and build a forecasting model. The data can be quantitative (sales figures) or qualitative (customer reviews). 

All types of data can fit into one of three categories:

First-party data : Data that you, or your company, can collect directly from customers.

Second-party data : The first-party data of other organizations. For instance, sales figures of your competition company. 

Third-party data : Data that a third-party organization can collect and aggregate from numerous sources. For instance, government portals or open data repositories. 

3. Data Cleaning

Now that you have acquired the necessary data, the next step is to prepare it for analysis. That means you must clean or scrub it. This is essential since acquired data can be in different formats. Cleaning ensures you’re not dealing with bad data and your results are dependable. 

Here are some critical data-cleaning steps:

Remove white spaces, duplicates, and formatting errors.

Delete unwanted data points.

Bring structure to your data.

For survey data, you also need to do consistency analysis. Some of this relies on good questionnaire design, but you also need to ensure that:

Respondents are not “straight-lining” (all answers in a single column).

Similar questions are answered consistently.

Open-ended questions contain plausible responses.

4. Data Analysis

This is the stage where you’d be ready to leverage any one or more of the data analysis and research techniques mentioned above. The choice of technique depends upon the data you’re dealing with and the desired results. 

All types of data analysis fit into the following four categories:

An illustration depicting the four data analysis processes. These types further represent their respective objectives.

A. Descriptive Analysis

Descriptive analysis focuses on what happened. It is the starting point for any research before proceeding with deeper explorations. As the first step, it involves breaking down data and summarizing its key characteristics.   

B. Diagnostic Analysis

This analysis focuses on why something has happened. Just as a doctor uses a patient’s diagnosis to uncover a disease, you can use diagnostic analysis to understand the underlying cause of the problem. 

C. Predictive Analysis

This type of analysis allows you to identify future trends based on historical data. It generally uses the results from the above analysis, machine learning (ML), and artificial intelligence (AI) to forecast future growth. 

D. Prescriptive Analysis

Now you know what to do, you must also understand how you’ll do it. The prescriptive analysis aims to determine your research’s best course of action.  

5. Data Interpretation

The step is like connecting the dots in a puzzle. This is where you start making sense of all the data and analysis done in the previous steps. You dig deeper into your data analysis findings and visualize the data to present insights in meaningful and understandable ways. 

Explore this comprehensive video resource to understand the complete user research data analysis process:

The Best Tools and Resources to Use for Data Analysis in 2023

You’ve got data in hand, mastered the process, and understood all the ways to analyze data . So, what comes next?

Well, parsing large amounts of data inputs can make it increasingly challenging to uncover hidden insights. Data analysis tools can track and analyze data through various algorithms, allowing you to create actionable reports and dashboards.

We’ve compiled a handy list of the best tools for you with their pros and cons. 

1. Microsoft Excel

The world’s best and most user-friendly spreadsheet software features calculations and graphing functions. It is ideal for non-techies to perform basic data analysis and create charts and reports.

No coding is required.

User-friendly interface.

Runs slow with complex data analysis.

Less automation compared to specialized tools.

2. Google Sheets

Similar to Microsoft Excel, Google Sheets stands out as a remarkable and cost-effective tool for fundamental data analysis. It handles everyday data analysis tasks, including sorting, filtering, and simple calculations. Besides, it is known for its seamless collaboration capabilities. 

Easily accessible .

Compatible with Microsoft Excel.

Seamless integration with other Google Workspace tools.

Lacks advanced features such as in Microsoft Excel.

May not be able to handle large datasets.

3. Google Analytics

Widely used by digital marketers and web analysts, this tool helps businesses understand how people interact with their websites and apps. It provides insights into website traffic, user behavior, and performance to make data-driven business decisions .

Free version available.

Integrates with Google services.

Limited customization for specific business needs.

May not support non-web data sources.

4. RapidMiner

RapidMiner is ideal for data mining and model development. This platform offers remarkable machine learning and predictive analytics capabilities. It allows professionals to work with data at many stages, including preparation, information visualization , and analysis.

Excellent support for machine learning.

Large library of pre-built models.

Can be expensive for advanced features.

Limited data integration capabilities.

Being one of the best commercial data analysis tools, Tableau is famous for its interactive dashboards and data exploration capabilities. Data teams can create visually appealing and interactive data representations through its easy-to-use interface and powerful capabilities. 

Intuitive drag-and-drop interface.

Interactive and dynamic data visualization.

Backed by Salesforce.

Expensive than competition.

Steeper learning curve for advanced features.

6. Power BI

This is an excellent choice for creating insightful business dashboards. It boasts incredible data integration features and interactive reporting, making it ideal for enterprises. 

Short for Konstanz Information Miner, KNIME is an outstanding tool for data mining. Its user-friendly graphical interface makes it accessible even to non-technical users, enabling them to create data workflows easily. Additionally, KNIME is a cost-effective choice. Hence, it is ideal for small businesses operating on a limited budget. 

Visual workflow for data blending and automation.

Active community and user support.

Complex for beginners.

Limited real-time data processing.

8. Zoho Analytics

Fueled by artificial intelligence and machine learning, Zoho Analytics is a robust data analysis platform. Its data integration capabilities empower you to seamlessly connect and import data from diverse sources while offering an extensive array of analytical functions.

Affordable pricing options.

User-friendly interface

Limited scalability for very large datasets.

Not as widely adopted as some other tools.

9. Qlik Sense

Qlik Sense offers a wide range of augmented capabilities. It has everything from AI-generated analysis and insights to automated creation and data prep, machine learning, and predictive analytics. 

Impressive data exploration and visualization features.

Can handle large datasets.

Steep learning curve for new users.

How to Pick the Right Tool?

Consider the below factors to find the perfect data analysis tool for your organization:

Your organization’s business needs.

Who needs to use the data analysis tools?

The tool’s data modeling capabilities.

The tool’s pricing. 

Besides the above tools, additional resources like a Service Design certification can empower you to provide sustainable solutions and optimal customer experiences. 

How to Become a Data Analyst? 

Data analysts are in high demand owing to the soaring data boom across various sectors. As per the US Bureau of Labor Statistics , the demand for data analytics jobs will grow by 23% between 2021 and 2031. What’s more, roles offer excellent salaries and career progression. As you gain experience and climb the ranks, your pay scales up, making it one of the most competitive fields in the job market. 

Learning data analytics methodology can help you give an all-new boost to your career. Here are some tips to become a data analyst:

1. Take an Online Course

You do not necessarily need a degree to become a data analyst. A degree can give you solid foundational knowledge in relevant quantitative skills. But so can certificate programs or university courses. 

2. Gain the Necessary Technical Skills

Having a set of specific technical skills will help you deepen your analytical capabilities. You must explore and understand the data analysis tools to deal with large datasets and comprehend the analysis. 

3. Gain Practical Knowledge

You can work on data analysis projects to showcase your skills. Then, create a portfolio highlighting your ability to handle real-world data and provide insights. You can also seek internship opportunities that provide valuable exposure and networking opportunities. 

4. Keep Up to Date with the Trends

Since data analysis is rapidly evolving, keep pace with cutting-edge analytics tools, methods, and trends. You can do this through exploration, networking, and continuous learning.

5. Search for the Ideal Job

The job titles and responsibilities continue to change and expand in data analytics. Beyond “Data Analyst,” explore titles like Business Analyst, Data Scientist, Data Engineer, Data Architect, and Marketing Analyst. Your knowledge, education, and experience can guide your path to the right data job. 

The Take Away

Whether you’re eager to delve into a personal area of interest or upgrade your skills to advance your data career, we’ve covered all the relevant aspects in this article. 

Now that you have a clear understanding of what data analysis is, and a grasp of the best data analysis techniques , it’s time to roll up your sleeves and put your knowledge into practice.

We have designed The IxDF courses and certifications to align with your intellectual and professional objectives. If you haven’t already, take the initial step toward enriching your data analytics skills by signing up today. Your journey to expertise in data analysis awaits.

Where to Learn More

1. Learn the most sought-after tool, Microsoft Excel, from basic to advanced in this LinkedIn Microsoft Excel Online Training Course .

2. Ensure all the touchpoints of your service are perfect through this certification in Service Design .

3. Learn more about the analytics data types we encounter daily in this video.

Author: Stewart Cheifet. Appearance time: 0:22 - 0:24. Copyright license and terms: CC / Fair Use. Modified: Yes. Link: greatestgames

4. Read this free eBook, The Elements of Statistical Learning , to boost your statistical analysis skills.

5. Check out Python for Data Analysis to learn how to solve statistical problems with Python. 

6. Join this beginner-level course and launch your career in data analytics. Data-Driven Design: Quantitative UX Research Course

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How to Analyze a Dataset: 6 Steps

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  • 05 Apr 2017

In the modern world, vast amounts of data are created every day. The World Economic Forum estimates that by 2025, 463 exabytes of data will be created globally every day.

Rich data can be an incredibly powerful decision-making tool for organizations when harnessed effectively, but it can also be daunting to collect and analyze such large amounts of information.

Here’s a deeper look at the data analysis process and how to effectively analyze a dataset.

What Is a Dataset?

A dataset is a collection of data within a database.

Typically, datasets take on a tabular format consisting of rows and columns. Each column represents a specific variable, while each row corresponds to a specific value. Some datasets consisting of unstructured data are non-tabular, meaning they don’t fit the traditional row-column format.

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What Is Data Analysis?

Data analysis refers to the process of manipulating raw data to uncover useful insights and draw conclusions. During this process, a data analyst or data scientist will organize, transform, and model a dataset.

Organizations use data to solve business problems, make informed decisions, and effectively plan for the future. Data analysis ensures that this data is optimized and ready to use.

Some specific types of data analysis include:

  • Descriptive analysis
  • Diagnostic analysis
  • Predictive analysis
  • Prescriptive analysis

Regardless of your reason for analyzing data, there are six simple steps that you can follow to make the data analysis process more efficient.

6 Steps to Analyze a Dataset

1. clean up your data.

Data wrangling —also called data cleaning—is the process of uncovering and correcting, or eliminating inaccurate or repeat records from your dataset. During the data wrangling process, you’ll transform the raw data into a more useful format, preparing it for analysis.

It’s imperative to clean your data before beginning analysis. This is particularly important if you’ll be presenting your findings to business teams who may use the data for decision-making purposes . Teams need to have confidence that they’re acting on a reliable source of information.

2. Identify the Right Questions

Once you’ve completed the cleaning process, you may have a lot of questions about your final dataset. There’s so much potential that can be uncovered through analysis.

Identify the most important questions you hope to answer through your analysis. These questions should be easily measurable and closely related to a specific business problem. If the request for analysis is coming from a business team, ask them to provide explicit details about what they’re hoping to learn, what they expect to learn, and how they’ll use the information. You can use their input to determine which questions take priority in your analysis.

3. Break Down the Data Into Segments

It’s often helpful to break down your dataset into smaller, defined groups. Segmenting your data will not only make your analysis more manageable, but also keep it on track.

For example, if you’re attempting to answer questions about a specific department’s performance, you’ll want to segment your data by department. From there, you’ll be able to glean insights about the group that you’re concerned with and identify any relationships that might exist between each group.

4. Visualize the Data

One of the most important parts of data analysis is data visualization , which refers to the process of creating graphical representations of data. Visualizing the data will help you to easily identify any trends or patterns and obvious outliers.

By creating engaging visuals that represent the data, you’re also able to effectively communicate your findings to key stakeholders who can quickly draw conclusions from the visualizations.

There’s a variety of data visualization tools you can use to automatically generate visual representations of a dataset, such as Microsoft Excel, Tableau, and Google Charts.

5. Use the Data to Answer Your Questions

After cleaning, organizing, transforming, and visualizing your data, revisit the questions you outlined at the beginning of the data analysis process. Interpret your results and determine whether the data helps you answer your original questions.

If the results are inconclusive, try revisiting a previous step in the analysis process. Maybe your dataset was too large and should have been segmented further, or perhaps there’s a different type of visualization better suited to your data.

6. Supplement with Qualitative Data

Finally, as you near the conclusion of your analysis, remember that this dataset is only one piece of the puzzle.

It’s critical to pair your quantitative findings with qualitative information, which you may capture using questionnaires, interviews, or testimonials. While the dataset has the ability to tell you what’s happening, qualitative information can often help you understand why it’s happening.

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The Importance of Data Analysis

Virtually all business decisions made by organizations are informed by some type of data. Because of this, it’s crucial that businesses are able to leverage data that s available to them.

Businesses rely on the insights gained from data analysis to guide a myriad of activities, ranging from budgeting to strategy execution . The importance of data analysis for today’s organizations can't be understated.

Are you interested in improving your data science and analytical skills? Download our Beginner’s Guide to Data & Analytics to discover how you can use data to generate insights and tackle business decisions.

This post was updated on March 8, 2021. It was originally published on April 5, 2017.

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Best Data Analysis Examples to Effectively Use Data

The best data analysis examples are found in business operations that effectively use data across different industries. They typically incorporate data analysis in research, risk management, and improving customer experience. There are also plenty of examples of data analysis techniques that businesses use, with some carried out with the help of advanced technologies.

In this article, you’ll learn more about what data analysis is, why it is important, and see some real-world examples of data analytics in companies’ business operations. Keep reading to find out how to properly learn data analytics and the tools that you can employ.

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What is data analysis.

Data analysis is the systematic process of acquiring data, evaluating it, and drawing conclusions through visual tools like charts and graphs. It’s largely used in business, manufacturing, and technological industries to help in their daily operations. Research firms, universities, and laboratories also apply data analytics and statistical techniques in their academic and scientific endeavors.

Where Is Data Analysis Used?

  • Business Processes
  • Engineering

Why Is Data Analysis Important?

Data analysis is important because of the valuable insights that it provides through various data gathering techniques and examination. This helps organizations improve their business performance and provides an effective analysis of what should be their next move. Advanced analysis can predict patterns and define phenomena that are crucial in creating business strategies and making informed decisions.

Different Types of Data Analysis Techniques

  • Content Analysis. Content analysis is the systematic process of analyzing long-form text by identifying themes. Conceptual content analysis gives valuable insights by providing context and language evaluation.
  • Descriptive Analysis. Descriptive analytics is used to provide actionable insights to achieve business goals. It employs descriptive statistics to connect relationships between variables and events.
  • Diagnostic Analysis. Diagnostic analysis identifies the significance of patterns within a given data set. It correlates the data with other independent variables and postulates alternative hypotheses to further understand the data set.
  • Exploratory Analysis. Exploratory data analysis is a powerful tool that allows researchers to posit trends by producing statistical significance. It summarizes the fundamental characteristics of datasets and their underlying factors to yield the best approaches to handling data.
  • Predictive Analysis. The common methods of predictive analytics are mainly found in forecasting and projections. Hypothesis testing involves incorporating and analyzing past data and predictor variables and accounting for random factors to orient future decisions.
  • Prescriptive Analysis. Prescriptive analysis is a form of analytics that processes data to make strategic business decisions. It develops recommendations and courses of action using inferential statistics to pinpoint the best line of development.
  • Regression Analysis. There are different ways to create a regression analysis model, but it’s mainly used to showcase the relationship between independent and dependent variables in a linear regression line.
  • Relational Analysis. Relational analysis tools specify the key relationship between variables or specific types of samples using cross-referencing and analytical processes. The analysis process involves identifying important parameters and using correlation coefficients to determine the strength of the relationship.
  • Sentiment Analysis. This analytics technique is commonly used in textual data by determining the weight of the language being written. It helps determine whether certain words are positive, negative, or neutral.
  • Statistical Analysis. Statistical analysis harvests large amounts of quantitative data, usually through survey data collection, to detect patterns and trends. This statistical method draws valid conclusions to bring relevant insights that help inform business decisions.

Real-World Examples of Data Analysis Methods

Below are some real-world examples of data analysis in different sectors. Predominantly, data analysis is used in technological tools and business performance, as you’ll see in this section’s discussion. They harness data through data gathering and various types of analysis.

  • Artificial Intelligence
  • Customer Behaviors
  • Customer Experiences
  • Customer Retention
  • Data Protection
  • Marketing Campaigns
  • Product Development
  • Risk Management
  • Supply Chain Management

10 Great Examples of Data Analysis

Data analysis example 1: artificial intelligence (ai).

AI is used in conjunction with data analysis to create complex neural networks of information. Amazon, for example, uses AI and data analysis for product recommendations and to improve their website’s search functions. They implement machine learning algorithms that monitor website activity to keep track of consumer habits and trends.

Data Analysis Example 2: Customer Behavior

Marketing teams gather data on customer behavior and habits to form business strategies around them. A company like Starbucks keeps track of its customer base through its mobile app. The mobile app provides insight into consumer spending and buying behaviors, and the data is used in predictive analysis to orient future decisions.

Data Analysis Example 3: Customer Experience (CX)

Another aspect that companies improve by using data analytics is customer experience. CX is the engagement and interaction of customers with businesses. For example, McDonald’s stores customer data through their mobile app. These analytical efforts help them automatically send out promotions, discounts, and other updates.

Data Analysis Example 4: Customer Retention

Customer retention is the ability of a business to keep a customer for a long time. A great example of a company that uses data analytics to retain customers is Coca-Cola. This company uses professional data analysis software and metrics to continually increase brand awareness, so it’s always a top-of-mind brand for the broader population.

Data Analysis Example 5: Data Protection

Data protection is an essential facet of data analytics. Through predictive modeling and prescriptive analysis, data analytics can help safeguard customer information from any potential threats of hacking and security breaches. American Express, a financial corporation, uses data metrics to protect its consumers’ confidential information and prevent fraud.

Data Analysis Example 6: Marketing Campaigns

Marketing campaigns are usually prevalent on social media platforms for promotional information or brand awareness. Instagram helps businesses through paid advertisements by localizing campaigns to desired target markets. Marketing campaign analytics can also help with designing campaigns to cater and appeal to individual consumers.

Data Analysis Example 7: Medicine

Data is important for the healthcare industry for tracking medical processes and patient information. Hospitals use advanced statistical analysis techniques like statistical programming tools and Structured Query Language (SQL). These record empirical data and evaluate them through rigorous testing. Proper use of this information can impact clinical outcomes.

Data Analysis Example 8: Product Development

Product development can rely on data for key metrics that allow for maximum optimization and utility. Businesses may employ data analysis methods and conceptual analysis to streamline product development. This can also lead to improvements to an existing product or service that caters to a specific group of people.

Data Analysis Example 9: Risk Management

Risk management is an executive level of analysis that involves making informed business decisions using business intelligence tools. Business consultants and firms engage in risk management tactics to evaluate opportunity costs and resource expenditures. Data plays a big part in making these informed decisions to mitigate risk and properly allocate resources.

Data Analysis Example 10: Supply Chain Management

Logistics is heavily reliant on real-time data and correct projections of business analytics. Big companies like Unilever, Nestle, and Walmart all depend on data analytics to manage their supply chain infrastructure. This is critical for them to accurately operate considering their scale of operations.

Tips to Boost Your Data Analysis Skills

  • Take Online Courses . Research online data analytics courses that you can enroll in to develop an in-depth understanding of the subject.
  • Practice Your Math Skills . Practicing your mathematical abilities can sharpen your mind and help you in your data analysis learning journey. It can improve your analytical skills and your grasp of theoretical concepts.
  • Study Data Analytics Tools . Learn about the coding process of data by studying the different data analysis tools and their practical significance.

What Should Be the Next Step in My Data Analysis Learning Journey?

The next step in your learning journey is attending a data analytics bootcamp like General Assembly, devCodeCamp , or Thinkful to improve your skills through hands-on learning. Coding bootcamps are short and intensive educational programs that are great alternatives to traditional schooling. They offer comprehensive and digestible information through classes and mentorships.

Coding bootcamps are usually less expensive than a college education, but they still require a financial investment. You can first try free coding bootcamps to test out the waters and see if this is a suitable learning path for you.

Data Analysis Examples FAQ

Yes, a coding bootcamp can help you get a data analyst job. Furthermore, there are companies that hire coding bootcamp graduates , so you don’t have to worry about employment opportunities.

The data analysis industry has a positive job outlook. According to the US Bureau of Labor Statistics, market research analysts have a job outlook of around 22 percent between 2020 to 2030 . It’s also projected that there would be 96,000 new job openings each year in the next 10 years.

Yes, you may find that data analyst jobs are difficult. It can be mentally taxing to analyze numbers and troubleshoot data tools on a daily basis. Pressure from deadlines and the imperative for accurate measurements may also take a toll on your mental well-being. Keep these in mind and consider if these are challenges you can overcome as you pursue a career in data analytics.

Yes, data analyst jobs pay well. The average base salary of a data analyst is about $62,754 , according to Payscale. Moreover, entry-level data analyst jobs have total compensation of around $57,492, while an experienced data analyst can potentially earn about $71,879.

About us: Career Karma is a platform designed to help job seekers find, research, and connect with job training programs to advance their careers. Learn about the CK publication .

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

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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

Data Dan is our helpful 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.

The total volume of sales, a retail KPI showing the amount of sales over a period of time

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:

Table of the levels of measurements according to the type of descriptive statistic

*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. 

Market research dashboard example

**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

*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:

Data report example from the financial department

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!

What Does a Data Analyst Do? 2023 Career Guide

There’s no escaping it: data analytics is one of the hottest jobs of the 21st century! But what exactly is data analytics, and what does a data analyst do, actually?

There’s no end of discussion and commentary about data analytics online. However, it’s not always easy to find a no-frills description of what a data analyst does on a day-to-day basis. This is made even harder by the fact that data analytics is often lumped in with related fields like data science, machine learning, artificial intelligence, and business analytics. While data analytics plays a key role in all these fields, it is a distinct discipline in its own right.

In this article, we offer a clear, career-focused introduction to data analytics. We’ll cover all the need-to-know knowledge without the fuss, answering:

  • What is data analytics?
  • What does a data analyst do?
  • Data analyst vs. data scientist: what’s the difference?
  • What types of data analysts are there?
  • What tasks and processes does a data analyst follow?
  • What skills does a data analyst need?
  • What tools do data analysts use?
  • How much do data analysts earn?
  • Wrap-up and further reading

So, what does a data analyst do? Let’s find out.

1. What is data analytics?

Before diving into what a data analyst does, it’s necessary to answer: what is data analytics? And why is it important? Watch this video for an introduction to the field, or keep reading!

In its simplest form, data analytics is the process of drawing meaning from disordered information. By systematically exploring data for patterns and relationships, data analysts seek to find and communicate useful insights using those data. But what counts as data? Well, pretty much anything you can imagine. Often, data are numerical (quantitative data). But sounds, images, words, or anything else that can be interpreted in some way can also be classed as data (qualitative data).

An analyst’s job begins with what’s known as ‘raw data.’ Raw data are disordered and—without context—essentially meaningless. We can only obtain useful information from them once we have brought order to chaos. As such, collecting, cleaning, and organizing data are all parts of the data analytics process.

What’s more, effective data analytics incorporates many techniques to help the process along. These include statistics, programming, visualization, and more. Luckily, to streamline the process, many of these techniques have been automated. Some are even developing as fields in their own right. However, a good data analyst will have at least some knowledge of them all.

Why does data analytics matter?

There are two simple reasons why data analytics matters. Firstly, it’s useful for decision-making. Secondly, it’s evidence-based. Combine these two attributes, and data analytics becomes a potent tool. Basing decisions on empirical information (rather than relying on opinion or ‘gut feel’) is a much more scientific way of approaching problems. While this does not mean data analytics is always 100% accurate, it’s by far the best tool we have for predicting future trends and drawing conclusions about past events.

Data analytics also has a wide range of applications across society. Online, you’ll often find data analytics touted as a tool for business intelligence, e.g. predicting future sales or informing product development and marketing spend.

2. What does a data analyst do?

Now we know what data analytics is , let’s take a look at what the role of the data analyst actually entails.

As a data analyst, it’s your responsibility to turn raw data into meaningful insights. Following the data analysis process (which we’ll cover in the next section), you’ll solve specific problems or answer certain questions based on data and the insights it provides.

You’ll then take these insights and share them with key stakeholders and decision makers, who can take action or plan for the future accordingly. At the same time, data analysts may be responsible for overseeing the overall processes for collecting and storing data, as well as setting guidelines for data quality .

A great way to gauge what a data analyst actually does on a day-to-day basis is to look at the tasks and responsibilities that are typically listed in data analyst job descriptions . Based on actual job descriptions posted on , here’s what you can expect to do as a data analyst:

  • Develop and implement databases and data collection systems
  • Work closely with management to identify critical metrics and KPIs, and to prioritize business needs
  • Collect data from primary and / or secondary data sources
  • Filter and clean data
  • Identify, analyze, and interpret trends and patterns in complex data sets
  • Visualize and present findings to key stakeholders
  • Build and customize reports
  • Develop and maintain dashboards
  • Create and maintain documentation regarding data models, measures, and infrastructure as they are developed

So far, we’ve taken a rather high-level look at the work of a data analyst. Next, we’ll look at the difference in the job titles of  data analyst and  data scientist .

3. Data analyst vs. data scientist: What’s the difference?

So, you may have already done a bit of research into the role of the data analyst and come across some content which talks about data science. Despite the fact that these two terms are often used interchangeably, they are in fact two separate career paths, serving different purposes—and requiring a different skillset.

As we’ve already covered, data analysts use a company’s data and interpret it for those in charge of making business decisions. Their work is focused on answering questions and developing solutions by looking into data patterns and turning those into dashboards and visualizations for broader use.

In turn, a data scientist will work deeper within the data, identifying patterns using data mining and machine learning. They will set up experiments, then produce models and tests in order to prove or disprove their findings. Then, based on their findings, they’ll offer solutions as to how a company should act going forward.

In short: data analysts analyze the past, while data scientists are more concerned with the future. To look into this topic in more detail, check out this article: What’s The Difference Between A Data Scientist And A Data Analyst?

4. What types of data analysts are there?

As you might have been able to glean so far, the practice of data analysis has an important function with applications across many industries.

However, data analytics goes far beyond simply boosting a company’s bottom line. It’s also used in health settings to improve patient care . It’s currently being applied in agriculture to transform the way we feed the world. It’s even used by governments to tackle issues like human trafficking . So if you want to help improve the world—as well as business—a career in data analytics might be for you!

With regards to types of data analysts and job titles, here are some of the common titles you may see on job advertisements:

  • Business analyst
  • Business intelligence analyst
  • Business systems analyst
  • Medical and healthcare analyst
  • Market research analyst
  • Operations analyst
  • Intelligence analyst

Now let’s zoom in on some of the more specific tasks associated with the data analysis process.

5. What tasks and processes does a data analyst follow?

As a data analyst, your job is to carry out each step of the data analytics process to identify and solve a problem. As your career progresses, you may choose to specialize in a particular area, such as data visualization or data engineering. As a beginner, though, it’s important to learn the process as a whole.

So, what are the key tasks and processes that a data analyst should expect to follow? Although it’s not as straightforward as following one task directly after another (you may find yourself repeating steps, going back on yourself, and so on) the main tasks include:

Defining a question

Collecting data, data cleaning, conducting an analysis, communicating your results.

First up, you need to define your objective. In some ways, this is the hardest part of the process. This is because what seems like an obvious problem may not always get to the core of an issue.

For example, let’s say you work for a company that wants to boost its revenue. The senior management is set on doing this by launching a suite of new products. As a result, you spend lots of time and resources analyzing what products to create, which market to launch them in, and so on.

However, with a bit more probing upfront, you might discover that there’s nothing wrong with the company’s existing products: it’s simply that the sales process is poor, resulting in low customer satisfaction and less repeat business. With this insight, you might find that investing in sales training will boost revenue at a much lower cost.

While this is just a hypothetical case, it illustrates the importance of probing an issue from multiple angles before investing too much time in it. It also means not being afraid to speak truth to power (in this case, telling managers that their new product idea is wrong). Defining the question you want to answer involves obtaining a deep understanding of the needs and demands of the business, keeping track of metrics, KPIs, and so on. You’ll usually carry out some initial analyses at this stage, too.

Once you’ve identified the question, your next task is to figure out which data are best-suited to help you solve it. This can be quantitative data (such as marketing figures) or qualitative data (such as customer reviews). More specifically, data types can be divided into three categories: first-party data (collected directly by you or your organization), second-party data (the first-party data of another organization), and third-party data (which is aggregated from numerous sources by a third-party).

If you don’t already have access to these data, you’ll have to devise a strategy for collecting them. This might include carrying out surveys, social media monitoring, website analytics, online tracking, and so on. However you collect it, once you have the data at your fingertips, you’re ready to clean it.

Freshly collected data will usually be in a raw format. This means that it hasn’t yet been organized, checked for errors, and so on. To get it into a state that’s suitable for analysis, the data need cleaning. This involves a variety of tools and techniques (such as custom algorithms, generic software, and exploratory analyses) to get it into a more suitable state.

Data cleaning tasks include removing errors, duplicates, and outliers, eradicating unwanted data (i.e. those that don’t serve your analysis), structuring the data in a more useful way, filling in gaps, and so on. When this is done, you’ll validate the data. This involves checking that it meets your requirements. Often, you’ll find it doesn’t, which means you’ll have to go back a step.

For this reason, data cleaning is considered an iterative process. The combined process of collecting and cleaning data is sometimes referred to as data wrangling . You can learn more about data cleaning in this guide .

Once your dataset is clean and tidy, you are good to analyze! There are a great many types of data analysis , and part of the challenge is identifying which approach is best-suited to the task at hand. To keep things simple, we’ll offer a quick overview of the four main categories of data analytics.

The first is descriptive analytics. This involves summarizing (or describing) the features of a dataset to better understand it. It isn’t usually used to draw firm conclusions, but it’s a useful first step for deciding how to investigate the data further.

Next, diagnostic analytics focuses on understanding why something has happened (e.g. by exploring correlations between values in a dataset). This helps identify problems and is often used in the first stage of data analytics, i.e. defining the question.

Finally, we have predictive analysis (which helps to identify trends based on past data) and prescriptive analytics (which helps decide on a future course of action). The latter is sometimes carried out using machine learning techniques.

Once you’ve carried out an analysis and drawn some insights, the final step is to communicate these to those who commissioned them in the first place. This usually involves visualizing your data in some way—creating graphs and charts, for example.

It may also involve creating interactive dashboards, documents, reports, or presentations. It’s easy to overlook the artistry of this step, but it’s very important to get it right. Not only must you interpret your findings correctly, but you need to share them in a way that is clear for time-short, non-technical personnel. This is important as it ensures any decision-making is based on high-quality, well-understood insights.

6. What skills does a data analyst need?

In some ways, the skills a data analyst needs vary depending on their role. For instance, knowledge of the business you’re working in is very important. However, as a rule, this is something you can learn on the job.

Before nabbing that first opportunity, though, there’s a core set of skills that all beginner data analysts need. We can divide these into hard skills (or technical abilities) and soft skills (or useful personality traits that help you get the job done).

Technical skills for data analysts

Hard skills sometimes have a steep learning curve. However, with a little discipline, anyone can pick them up. Key hard skills for data analysts include:

  • Math and statistics: You’ll be mathematically minded. You may have an undergraduate or Master’s degree in an area like applied math, statistics, or computing. However, while qualifications can be useful, they’re not always necessary if you’re a newcomer to the field. As long as you have solid math skills, e.g. algebra and calculus, that could be sufficient.
  • Programming skills: To create or tweak algorithms that automate data analytics tasks (like parsing or re-structuring large datasets) an element of programming know-how is unavoidable. Scripting languages like Python or MATLAB and statistical computing languages like R and SAS are all popular in data analytics.
  • Database knowledge: As well as programming languages, you’ll need some understanding of database warehousing software, e.g. Hive, and analytics engines like Spark. You’ll also need to know database query languages like SQL.
  • Excel skills: Commonly used for transforming raw data into a readable format, or for automating complex calculations, MS Excel is core to any data analyst’s toolset. Be sure to familiarize yourself with its key analytical functions .
  • Visualization skills: A core aspect of data analytics is the ability to visualize data with charts and graphs. This helps us identify patterns, correlations, and trends. At the very least, you should be able to create plots using Python, or tables and charts using MS Excel.
  • Basic machine learning knowledge: As a beginner, nobody will expect you to be an expert in machine learning—it’s an entire discipline in its own right. Nevertheless, the tenets of machine learning underpin many data analytics tasks. You should be familiar with the theory, e.g. supervised learning versus unsupervised learning.

Non-technical skills for data analysts

While soft skills can be honed with practice, they are generally considered more inherent. You’ll need to have a natural flair for the following:

  • Communication: Communication is key in any job, but especially in data analytics. Obtaining accurate insights is the priority, but effectively communicating these to wider audiences is vital. You should have excellent interpersonal skills, be able to communicate complex concepts in straightforward terms, and be confident giving presentations and answering questions for non-technical personnel.
  • Critical thinking: Arguably the most important skill in data analytics, critical thinking is the ability to question what’s in front of you to better understand it. You’ll have a naturally inquisitive mindset, won’t take anything at face value, and will approach tasks using logical reasoning and deduction.
  • Creative problem-solving: Problem-solving involves applying your reflective way of seeing the world to specific data-related situations or problems. You’ll take a step-by-step approach when defining a problem, devise an approach for solving it, and carry out the necessary subsequent tasks. These tasks will be different every time, so you’ll need a creative mindset.
  • Ethics: You’ll understand the importance of data privacy, be aware of your personal biases, and be comfortable presenting outcomes—even when these are undesirable or are unlikely to win you any praise. Adhering to a strong ethical code is hugely important. Without it, data can be easily misused, which can have a real-world impact on individuals and groups affected by your work.

If you’re dipping a toe into data analytics for the first time, ask yourself: do these skills describe you? If not, don’t worry. While it’s important to appraise your strengths and weaknesses honestly, the most important thing is to be enthusiastic about the field and willing to develop the necessary skills. Nobody hiring a beginner will expect you to be an expert right away.

7. What tools do data analysts use?

So far, we’ve covered the skills a data analyst needs and the high-level process and tasks they need to carry out. As a beginner, this may feel a bit overwhelming. Fortunately, there’s a huge range of applications and software to help streamline the process. While these require a bit of technical know-how, once you’ve covered the basics, you should find the whole process a lot easier.

Common tools for data analysts include:

  • Databases and management systems

Let’s take a closer look at some of those now.

MS Excel for data analytics

A must-have for any data analyst is MS Excel. Excel allows you to sort data, break it into smaller subsets, and use a wide variety of functions to understand it better. These functions include pivot tables , search functions like XLOOKUP and VLOOKUP , the AVERAGE function (which gives you the average of a given range of numbers), and the SUMIF function (which lets you calculate the sum of different cells). These tools, along with a great many more, make Excel an invaluable piece of software for beginners and experts alike.

Python for data analytics

The general-purpose programming language, Python, has fast become the go-to programming tool for data analysts. This is partly because of its simple syntax, which makes it quick and easy to learn. However, its popularity is also down to the fact that the Python Package Index (PyPI) offers a massive range of software libraries.

Python can be used for almost any aspect of the data analytics process. For instance, Pandas is excellent for manipulating time-series and other quantitative data. Matplotlib is perfect for data visualization. And NumPy is popular for conducting a range of complex mathematical functions. These are just three of the many thousands of Python packages that are available.

R for data analytics

R, another programming language, is also common in data analytics. While R is generally considered more complex to learn than Python, it remains popular due to its historical use in statistical programming (which has benefits in a field like data analytics). While R doesn’t carry out things like image processing with the ease of Python, it has more data analytics functions built in. It’s also often used in scientific fields. Like Python, R also has a library of software, CRAN , with many additional packages available.

Databases and data management systems

As the variety of data we collect becomes more complex, the way we store and manage these data is also evolving. In data analytics, it’s vital to have an understanding of how databases and data warehouses work. For instance, MySQL is a relatively simple type of relational database management system that is commonly used.

Apache Hadoop , meanwhile, is a more complex framework, used to store, manage and process big data using distributed databases. Whether you’re using simple databases or complex infrastructures, they are ultimately unavoidable!

Structured Query Language (SQL)

SQL (sometimes pronounced ‘sequel’) is a programming language designed to communicate with relational databases. In a world where data is the main currency, this has obvious applications. While relational databases are built using a variety of languages, such as C or C++, SQL allows you to pull, add or edit data without needing knowledge of the database’s native language.

Since most organizations now have information stored digitally or online, SQL is becoming an important language to learn, even for non-analysts. It’s a must-have for those in the field.

Industry-specific data analytics tools

In addition to the tools already described, the industry is starting to produce ever-more sophisticated sector-specific applications to support data analytics. These tools range from general business intelligence software like Microsoft Power BI , to data visualization and dashboarding applications like Tableau .

They also include niche products that you’ll only be likely to learn if you work in a specific industry. For instance, Definitive Healthcare is an analytics platform designed specifically to manage tasks relating to health data.

8. How much do data analysts earn?

So, what kind of salary can you expect to get as a data analyst, then? Unfortunately, that’s not a question that’s easy to answer, as salaries will be dependent on job location, experience, and likely industry, too.

We’ve got an in-depth guide that covers the average salaries across these criteria in our guide .

9. Wrap-up and further reading

In this post, we’ve covered everything you need to know if you’re just starting in data analytics. We’ve explored what a data analyst does, what skills they need, and the basic tools that a beginner analyst should aim to learn.

Once you have all these skills at your fingertips, you’ll soon be ready to enter the field. Whether you’re interested in data analytics for e-commerce, finance, healthcare, government, the sciences, or any other area of your choosing, one of the great benefits of the field is its versatility. With a little experience under your belt, you can branch into broader data science , or specialize in areas like data engineering, data modeling, or machine learning.

For a deeper taste of what data analytics involves, try our free, 5-day data analytics short course . Want to learn more about a career in data? Take a look at the following:

  • Am I a good fit for a career as a data analyst?
  • What’s the typical data analyst career path?
  • A guide to the best data analytics certification programs 
  • Online Degree Explore Bachelor’s & Master’s degrees
  • MasterTrack™ Earn credit towards a Master’s degree
  • University Certificates Advance your career with graduate-level learning
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15 Data Analyst Interview Questions and Answers

Enter your data analyst interview with confidence by preparing with these 15 interview questions.

A smiling woman in a wheelchair interviews for a data analyst job with a hiring manager.

If you’re like many people, the job interview can be one of the most intimidating parts of the job search process. But it doesn’t have to be. With some advanced preparation, you can walk into your data analyst interview feeling calm and confident. 

In this article, we’ll review some of the most common interview questions you’ll likely encounter as you apply for an entry-level data analyst position. We’ll walk through what the interviewer is looking for and how best to answer each question. Finally, we’ll cover some tips and best practices for interviewing success. Let’s get started.

General data analyst interview questions

These questions cover data analysis from a high level and are more likely to appear early in an interview. 

1. Tell me about yourself.

What they’re really asking: What makes you the right fit for this job?

This question can sound broad and open-ended, but it’s really about your relationship with data analytics. Keep your answer focused on your journey toward becoming a data analyst. What sparked your interest in the field? What data analyst skills do you bring from previous jobs or coursework?

As you formulate your answer, try to answer these three questions:

What excites you about data analysis?

What excites you about this role?

What makes you the best candidate for the job?

An interviewer might also ask:

What made you want to become a data analyst?

What brought you here?

How would you describe yourself as a data analyst?

2. What do data analysts do?

What they’re really asking: Do you understand the role and its value to the company?

If you’re applying for a job as a data analyst, you likely know the basics of what data analysts do . Go beyond a simple dictionary definition to demonstrate your understanding of the role and its importance.

Outline the main tasks of a data analyst: identify, collect, clean, analyze, and interpret. Talk about how these tasks can lead to better business decisions, and be ready to explain the value of data-driven decision-making.

What is the process of data analysis?

What steps do you take to solve a business problem?

What is your process when you start a new project?

3. What was your most successful/most challenging data analysis project?

What they’re really asking: What are your strengths and weaknesses?

When an interviewer asks you this type of question, they’re often looking to evaluate your strengths and weaknesses as a data analyst. How do you overcome challenges, and how do you measure the success of a data project?

Getting asked about a project you’re proud of is your chance to highlight your skills and strengths. Do this by discussing your role in the project and what made it so successful. As you prepare your answer, take a look at the original job description. See if you can incorporate some of the skills and requirements listed.

If you get asked the negative version of the question (least successful or most challenging project), be honest as you focus your answer on lessons learned. Identify what went wrong—maybe your data was incomplete or your sample size was too small—and talk about what you’d do differently in the future to correct the error. We’re human, and mistakes are a part of life. What’s important here is your ability to learn from them. 

Walk me through your portfolio.

What is your greatest strength as a data analyst? How about your greatest weakness?

Tell me about a data problem that challenged you.

4. What’s the largest data set you’ve worked with?

What they’re really asking: Can you handle large data sets?

Many businesses have more data at their disposal than ever before. Hiring managers want to know you can work with large, complex data sets. Focus your answer on the size and type of data. How many entries and variables did you work with? What types of data were in the set?

The experience you highlight doesn't have to come from a job. You’ll often have the chance to work with data sets of varying sizes and types as a part of a data analysis course, bootcamp, certificate program, or degree. As you put together a portfolio, you may also complete some independent projects where you find and analyze a data set. All of this is valid material to build your answer. 

What type of data have you worked with in the past?

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Data analysis process questions

The work of a data analyst involves a range of tasks and skills. Interviewers will likely ask questions specific to various parts of the data analysis process to evaluate how well you perform each step.

5. Explain how you would estimate … ?

What they’re really asking: What’s your thought process? Are you an analytical thinker?

With this type of question (sometimes called a guesstimate), the interviewer presents you with a problem to solve. How would you estimate the best month to offer a discount on shoes? How would you estimate the weekly profit of your favorite restaurant?

The purpose here is to evaluate your problem-solving ability and overall comfort working with numbers. Since this is about how you think, think out loud as you work through your answer.

What types of data would you need?

Where might you find that data?

Once you have the data, how would you use it to calculate an estimate?

6. What is your process for cleaning data?

What they’re really asking: How do you handle missing data, outliers, duplicate data, etc.?

As a data analyst, data preparation, also known as data cleaning or data cleansing, will often account for the majority of your time. A potential employer will want to know that you’re familiar with the process and why it’s important.

In your answer, briefly describe what data cleaning is and why it’s important to the overall process. Then walk through the steps you typically take to clean a data set. Consider mentioning how you handle:

Missing data

Duplicate data

Data from different sources

Structural errors

How do you deal with messy data?

What is data cleaning?

7. How do you explain technical concepts to a non-technical audience?

What they’re really asking: How are your communication skills?

While drawing insights from data is a critical skill for a data analyst, communicating those insights to stakeholders, management, and non-technical co-workers is just as important. 

Your answer should include the types of audiences you’ve presented to in the past (size, background, context). If you don’t have a lot of experience presenting, you can still talk about how you’d present data findings differently depending on the audience. 

What is your experience conducting presentations?

Why are communication skills important to a data analyst?

How do you present your findings to management?

Tip: In some cases, your interviewer might not be involved in data analysis. The entire interview, then, is an opportunity to demonstrate your ability to communicate clearly. Consider practicing your answers on a non-technical friend or family member.

8. Tell me about a time when you got unexpected results.

What they’re really asking: Do you let the data or your expectations drive your analysis?

Effective data analysts let the data tell the story. After all, data-driven decisions are based on facts rather than intuition or gut feelings. When asking this question, an interviewer might be trying to determine:

How you validate results to ensure accuracy

How you overcome selection bias

If you’re able to find new business opportunities in surprising results

Be sure to describe the situation that surprised you and what you learned from it. This is your opportunity to demonstrate your natural curiosity and excitement to learn new things from data.

9. How would you go about measuring the performance of our company?

What they’re really asking: Have you done your research?

Before your interview, be sure to do some research on the company, its business goals, and the larger industry. Think about the types of business problems that could be solved through data analysis, and what types of data you’d need to perform that analysis. Read up on how data is used by competitors and in the industry.

Show that you can be business-minded by tying this back to the company. How would this analysis bring value to their business?

Technical skill questions

Interviewers will be looking for candidates who can leverage a wide range of technical data analyst skills . These questions are geared toward evaluating your competency across several skills.

10. What data analytics software are you familiar with?

What they’re really asking: Do you have basic competency with common tools? How much training will you need?

This is a good time to revisit the job listing to look for any software emphasized in the description. As you answer, explain how you’ve used that software (or something similar) in the past. Show your familiarity with the tool by using associated terminology.

Mention software solutions you’ve used for various stages of the data analysis process. You don’t need to go into great detail here. What you used and what you used it for should suffice.

What data software have you used in the past?

What data analytics software are you trained in?

Tip: Gain experience with data analytics software through a Guided Project on Coursera. Get hands-on learning in under two hours, without having to download or purchase software. You’ll be ready with something to talk about during your next interview for analysis tools like:

Power BI Desktop

Google Sheets

Microsoft Excel

11. What scripting languages are you trained in?

As a data analyst, you’ll likely have to use SQL and a statistical programming language like R or Python . If you’re already familiar with the language of choice at the company, you’re applying to, great. If not, you can take this time to show enthusiasm for learning. Point out that your experience with one (or more) languages has set you up for success in learning new ones. Talk about how you’re currently growing your skills.

Interviewer might also ask:

What functions in SQL do you like most?

Do you prefer R or Python?

Five SQL interview questions for data analysts

Knowledge of SQL is one of the most important skills you can have as a data analyst. Many interviews for data analyst jobs include an SQL screening where you’ll be asked to write code on a computer or whiteboard. Here are five SQL questions and tasks to prepare for:

1. Create an SQL query: Be ready to use JOIN and COUNT functions to show a query result from a given database.

2. Describe an SQL query: Given an SQL query, explain what data is being retrieved.

3. Modify a database: Insert new rows, modify existing records, or permanently delete records from a database.

4. Debug a query: Correct the errors in an existing query to make it functional.

5. Define an SQL term: Understand what terms like foreign and primary key, truncate, drop, union, union all, and left join and inner join mean (and when you’d use them).

Learn more: 5 SQL Certifications for Your Data Career

12. What statistical methods have you used in data analysis?

What they’re really asking: Do you have basic statistical knowledge?

Most entry-level data analyst roles will require at least a basic competency in statistics and an understanding of how statistical analysis ties into business goals. List the types of statistical calculations you’ve used in the past and what business insights those calculations yielded. 

If you’ve ever worked with or created statistical models, be sure to mention that as well. If you’re not already, familiarize yourself with the following statistical concepts:

Standard deviation

Sample size

Descriptive and inferential statistics

What is your knowledge of statistics?

How have you used statistics in your work as a data analyst?

13. How have you used Excel for data analysis in the past?

Spreadsheets rank among the most common tools used by data analysts. It’s common for interviews to include one or more questions meant to gauge your skill working with data in Microsoft Excel. 

Five Excel interview questions for data analysts

Here are five more questions specific to Excel that you might be asked during your interview:

1. What is a VLOOKUP, and what are its limitations?

2. What is a pivot table, and how do you make one?

3. How do you find and remove duplicate data?

4. What are INDEX and MATCH functions, and how do they work together?

5. What’s the difference between a function and a formula?

Need a quick refresher before your interview? Get a hands-on walkthrough of important functions and techniques in under 90 minutes with the Problem Solving Using Microsoft Excel .

14. Explain the term…

What they’re really asking: Are you familiar with the terminology of data analytics?

Throughout your interview, you may be asked to define a term or explain what it means. In most cases, the interviewer is trying to determine how well you know the field and how effective you are at communicating technical concepts in simple terms. While it’s impossible to know what exact terms you may be asked about, here are a few you should be familiar with:

Normal distribution

Data wrangling

KNN imputation method

Statistical model

15. Can you describe the difference between … ?

Similar to the last type of question, these interview questions help determine your knowledge of analytics concepts by asking you to compare two related terms. Some pairs you might want to be familiar with include:

Data mining vs. data profiling

Quantitative vs. qualitative data

Variance vs. covariance

Univariate vs. bivariate vs. multivariate analysis

Clustered vs. non-clustered index

1-sample T-test vs. 2-sample T-test in SQL

Joining vs. blending in Tableau

The final question: Do you have any questions?

Almost every interview, regardless of field, ends with some variation of this question. This process is about you evaluating the company as much as it is about the company evaluating you. Come prepared with a few questions for your interviewer, but don’t be afraid to ask any questions that came up during the interview as well. Some topics you can ask about include:

What a typical day is like

Expectations for your first 90 days

Company culture and goals

Your potential team and manager

The interviewer’s favorite part about the company

Tips for preparing for your interview

Set yourself up for success in your next data analyst interview by using these questions alongside the Coursera Interview Guide . Get tips on formatting your answers using the STAR framework, researching the company, and tailoring your answers to the job.

Practice data analysis with Coursera

Practicing the data analysis process can help you feel more prepared to talk about your experience using common data analysis tools. Before your next interview, try some of these top-rated courses:

To reinforce the data analysis process, try the Google Data Analytics Professional Certificate . Build the skills you need for an entry-level role while you learn how data analysts work with data using Google Sheets, SQL, and R programming.

To deepen your SQL skills, try the Learn SQL Basics for Data Science Specialization from the University of California, Davis. Go beyond simple queries and use SQL to complete four progressively more difficult SQL projects with data science applications.

To get hands-on experience with Power BI, try the Microsoft Power BI Data Analyst Professional Certificate . Learn how to use the tool to drive data-driven decision-making and prepare for the industry-recognized Microsoft PL-300 Certification exam. Plus, learners who complete this program will receive a 50 percent discount voucher to take the PL-300 Certification Exam.

Keep reading

This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

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15 Data Analysis Examples

data analysis examples and definition, explained below

Data analysis is the process of inspecting, cleaning, transforming, and modeling data to discover useful information, derive conclusions, and support decision-making (Upton & Brawn, 2023).

It encompasses a variety of techniques from statistics, mathematics, and computer science to interpret complex data structures and extract meaningful insights (Bekes & Kezdi, 2021).

We use data analysis to generate useful insights from data that can help in our decision-making and strategic planning in various realms. For example:

  • It can help businesses to develop a better understanding of market trends and customer preferences to inform marketing strategies.
  • We can develop a modeled understanding of risks and prevent issues before they escalate into larger problems.
  • Data analysis may reveal hidden or not easily identifiable insights and trends, empowering you to enrich your knowledge base and anticipate future needs (Naeem et al., 2020; Upton & Brawn, 2023).

Below are some common ways that data analysis is conducted.

Data Analysis Examples

1. Sales Trend Analysis This type of data analysis involves assessing sales data over various periods to identify trends and patterns. For instance, a retail company might monitor its quarterly sales data to identify peak buying times or popular products (Bihani & Patil, 2014). Such analysis allows businesses to adjust their sales strategies, inventory management, and marketing efforts to align with customer demands and seasonal trends, thereby enhancing profitability and operational efficiency (Kohavi, Rothleder & Simoudis, 2002).

2. Customer Segmentation In this data analysis example, businesses compartmentalize their customer base into different groups based on specific criteria such as purchasing behavior, demographics, or preferences (Kohavi, Rothleder & Simoudis, 2002). For example, an online shopping platform might segment its customers into categories like frequent buyers, seasonal shoppers, or budget buyers. This analysis helps tailor marketing campaigns and product offerings to appeal to each group specifically, enabling improved customer engagement and business growth.

3. Social Media Sentiment Analysis This is a popular use of data analysis in the digital age. Companies harness big data from social media platforms to analyze public sentiment towards their products or brand. By examining comments, likes, shares, and other interactions, they can gauge overall satisfaction and identify areas for improvement. This kind of scrutiny can significantly impact a business’s online reputation management and influence its marketing and public relations strategies.

4. Forecasting and Predictive Analysis Businesses often use data analysis to predict future trends or outcomes. For instance, an airline company might analyze past data on seat bookings, flight timings, and passenger preferences to forecast future travel trends. This predictive analysis allows the airline to optimize its flight schedules, plan for peak travel periods, and set competitive ticket prices, ultimately contributing to improved customer satisfaction and increased revenues.

5. Operational Efficiency Analysis This form of data analysis is focused on optimizing internal processes within an organization. For example, a manufacturing company might analyze data regarding machine performance, maintenance schedules, and production output to identify bottlenecks or inefficiencies (Bihani & Patil, 2014). By addressing these issues, the company can streamline its operations, improve productivity, and reduce costs, signifying the importance of data analysis in achieving operational excellence.

6. Risk Assessment Analysis This type of data analysis helps businesses identify potential risks that could adversely impact their operations or profits. An insurance company, for instance, might analyze customer data and historical claim information to estimate future claim risks. This supports more accurate premium setting and helps in proactively managing any potential financial hazards, underscoring the role of data analysis in sound risk management.

7. Recruitment and Talent Management Analysis In this example of data analysis, human resources departments scrutinize data concerning employee performance, retention rates, and skill sets. For example, a technology firm might conduct analysis to identify the skills and experience most prevalent among its top-performing employees (Chang, Wang & Hawamdeh, 2019). This enables the company to attract and retain high-caliber talent, tailor training programs, and improve overall workforce effectiveness.

8. Supply Chain Optimization Analysis This form of data analysis aims to enhance the efficiency of a business’s supply chain. For instance, a grocery store might examine sales data, warehouse inventory levels, and supplier delivery times to ensure the right products are in stock at the right time (Chang, Wang & Hawamdeh, 2019). This can reduce warehousing costs, minimize stockouts or overstocks, and increase customer satisfaction, marking data analysis’s role in streamlining supply chains.

9. Web Analytics In this digital age, businesses invest in data analysis to optimize their online presence and functionality. An ecommerce business, for example, might analyze website traffic data, bounce rates, conversion rates, and user engagement metrics. This analysis can guide website redesign, enhance user experience, and boost conversion rates, reflecting the importance of data analysis in digital marketing and web optimization.

10. Medical and Healthcare Analysis Data analysis plays a crucial role in the healthcare sector. A hospital might analyze patient data, disease patterns, treatment outcomes, and so forth. This can support evidence-based treatment plans, inform research on healthcare trends, and contribute to policy development (Islam et al., 2018). It can also enhance patient care by identifying efficient treatment paths and reducing hospitalization time, underlining the significance of data analysis in the medical field.

11. Fraud Detection Analysis In the financial and banking sector, data analysis plays a paramount role in identifying and mitigating fraudulent activities. Banks might analyze transaction data, account activity, and user behavior trends to detect abnormal patterns indicative of fraud. By alerting the concerned authorities about the suspicious activity, such analysis can prevent financial losses and protect customer assets, illustrating data analysis’s importance in ensuring financial security.

12. Energy Consumption Analysis Utilities and energy companies often use data analysis to optimize their energy distribution and consumption. By evaluating data on customer usage patterns, peak demand times, and grid performance, companies can enhance energy efficiency, optimize their grid operations, and develop more customer-centric services. It shows how data analysis can contribute to a more sustainable and efficient use of resources.

13. Market Research Analysis Many businesses rely on data analysis to gauge market dynamics and consumer behaviors. A cosmetic brand, for example, might analyze sales data, consumer feedback, and competitor information. Such analysis can provide useful insights about consumer preferences, popular trends, and competitive strategies, facilitating the development of products that align with market demands, showcasing how data analysis can drive business innovation.

14. Quality Control Analysis Manufacturing industries often use data analysis in their quality control processes. They may monitor operational data, machine performance, and product fault reports. By identifying causes of defects or inefficiencies, these industries can improve product quality, enhance manufacturing processes, and reduce waste, demonstrating the decisive role of data analysis in maintaining high-quality standards.

15. Economic and Policy Analysis Government agencies and think tanks utilize data analysis to inform policy decisions and societal strategies. They might analyze data relating to employment rates, GDP, public health, or educational attainment. These insights can inform policy development, assess the impact of existing policies, and guide strategies for societal improvement. This reveals that data analysis is a key tool in managing social and economic progression.

For more General Examples of Analysis, See Here

Data analysis, encompassing activities such as trend spotting, risk assessment, predictive modeling, customer segmentation, and much more, proves to be an indispensable tool in various fields.

From optimizing operations and making informed decisions to understanding customer behavior and predicting future trends, its applications are diverse and far-reaching. Through meticulous examination of relevant data and astute interpretation of patterns, businesses and organizations can extract actionable insights, enhance their strategic planning, and bolster their competitive advantage.

Furthermore, with the current growth in digital technology, the potency of data analysis in enhancing operational efficiency, facilitating innovation, and driving economic growth cannot be overstated. Therefore, mastery of data analysis techniques and methodologies is critical for anyone seeking to harness the full potential of their data.

Ultimately, data analysis seeks to turn raw data into valuable knowledge, enabling organizations and individuals to thrive in today’s data-driven world.

Bekes, G., & Kezdi, G. (2021). Data Analysis for Business, Economics, and Policy . Cambridge University Press.

Bihani, P., & Patil, S. T. (2014). A comparative study of data analysis techniques.  International journal of emerging trends & technology in computer science ,  3 (2), 95-101.

Chang, H. C., Wang, C. Y., & Hawamdeh, S. (2019). Emerging trends in data analytics and knowledge management job market: extending KSA framework.  Journal of Knowledge Management ,  23 (4), 664-686. doi:

Islam, M. S., Hasan, M. M., Wang, X., Germack, H. D., & Noor-E-Alam, M. (2018, May). A systematic review on healthcare analytics: application and theoretical perspective of data mining. In  Healthcare  (Vol. 6, No. 2, p. 54). doi:

Kohavi, R., Rothleder, N. J., & Simoudis, E. (2002). Emerging trends in business analytics .  Communications of the ACM ,  45 (8), 45-48.

Naeem, M., Jamal, T., Diaz-Martinez, J., Butt, S. A., Montesano, N., Tariq, M. I., … & De-La-Hoz-Valdiris, E. (2022). Trends and future perspective challenges in big data. In  Advances in Intelligent Data Analysis and Applications: Proceeding of the Sixth Euro-China Conference on Intelligent Data Analysis and Applications, 15–18 October 2019, Arad, Romania  (pp. 309-325). Springer Singapore.

Upton, G., & Brawn, D. (2023). Data Analysis: A Gentle Introduction for Future Data Scientists . Oxford: Oxford University Press.


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Home Market Research

Data Analysis in Research: Types & Methods


Content Index

Why analyze data in research?

Types of data in research, finding patterns in the qualitative data, methods used for data analysis in qualitative research, preparing data for analysis, methods used for data analysis in quantitative research, considerations in research data analysis, what is data analysis in research.

Definition of research in data analysis: According to LeCompte and Schensul, research data analysis is a process used by researchers to reduce data to a story and interpret it to derive insights. The data analysis process helps reduce a large chunk of data into smaller fragments, which makes sense. 

Three essential things occur during the data analysis process — the first is data organization . Summarization and categorization together contribute to becoming the second known method used for data reduction. It helps find patterns and themes in the data for easy identification and linking. The third and last way is data analysis – researchers do it in both top-down and bottom-up fashion.

LEARN ABOUT: Research Process Steps

On the other hand, Marshall and Rossman describe data analysis as a messy, ambiguous, and time-consuming but creative and fascinating process through which a mass of collected data is brought to order, structure and meaning.

We can say that “the data analysis and data interpretation is a process representing the application of deductive and inductive logic to the research and data analysis.”

Researchers rely heavily on data as they have a story to tell or research problems to solve. It starts with a question, and data is nothing but an answer to that question. But, what if there is no question to ask? Well! It is possible to explore data even without a problem – we call it ‘Data Mining’, which often reveals some interesting patterns within the data that are worth exploring.

Irrelevant to the type of data researchers explore, their mission and audiences’ vision guide them to find the patterns to shape the story they want to tell. One of the essential things expected from researchers while analyzing data is to stay open and remain unbiased toward unexpected patterns, expressions, and results. Remember, sometimes, data analysis tells the most unforeseen yet exciting stories that were not expected when initiating data analysis. Therefore, rely on the data you have at hand and enjoy the journey of exploratory research. 

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Every kind of data has a rare quality of describing things after assigning a specific value to it. For analysis, you need to organize these values, processed and presented in a given context, to make it useful. Data can be in different forms; here are the primary data types.

  • Qualitative data: When the data presented has words and descriptions, then we call it qualitative data . Although you can observe this data, it is subjective and harder to analyze data in research, especially for comparison. Example: Quality data represents everything describing taste, experience, texture, or an opinion that is considered quality data. This type of data is usually collected through focus groups, personal qualitative interviews , qualitative observation or using open-ended questions in surveys.
  • Quantitative data: Any data expressed in numbers of numerical figures are called quantitative data . This type of data can be distinguished into categories, grouped, measured, calculated, or ranked. Example: questions such as age, rank, cost, length, weight, scores, etc. everything comes under this type of data. You can present such data in graphical format, charts, or apply statistical analysis methods to this data. The (Outcomes Measurement Systems) OMS questionnaires in surveys are a significant source of collecting numeric data.
  • Categorical data: It is data presented in groups. However, an item included in the categorical data cannot belong to more than one group. Example: A person responding to a survey by telling his living style, marital status, smoking habit, or drinking habit comes under the categorical data. A chi-square test is a standard method used to analyze this data.

Learn More : Examples of Qualitative Data in Education

Data analysis in qualitative research

Data analysis and qualitative data research work a little differently from the numerical data as the quality data is made up of words, descriptions, images, objects, and sometimes symbols. Getting insight from such complicated information is a complicated process. Hence it is typically used for exploratory research and data analysis .

Although there are several ways to find patterns in the textual information, a word-based method is the most relied and widely used global technique for research and data analysis. Notably, the data analysis process in qualitative research is manual. Here the researchers usually read the available data and find repetitive or commonly used words. 

For example, while studying data collected from African countries to understand the most pressing issues people face, researchers might find  “food”  and  “hunger” are the most commonly used words and will highlight them for further analysis.

LEARN ABOUT: Level of Analysis

The keyword context is another widely used word-based technique. In this method, the researcher tries to understand the concept by analyzing the context in which the participants use a particular keyword.  

For example , researchers conducting research and data analysis for studying the concept of ‘diabetes’ amongst respondents might analyze the context of when and how the respondent has used or referred to the word ‘diabetes.’

The scrutiny-based technique is also one of the highly recommended  text analysis  methods used to identify a quality data pattern. Compare and contrast is the widely used method under this technique to differentiate how a specific text is similar or different from each other. 

For example: To find out the “importance of resident doctor in a company,” the collected data is divided into people who think it is necessary to hire a resident doctor and those who think it is unnecessary. Compare and contrast is the best method that can be used to analyze the polls having single-answer questions types .

Metaphors can be used to reduce the data pile and find patterns in it so that it becomes easier to connect data with theory.

Variable Partitioning is another technique used to split variables so that researchers can find more coherent descriptions and explanations from the enormous data.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

There are several techniques to analyze the data in qualitative research, but here are some commonly used methods,

  • Content Analysis:  It is widely accepted and the most frequently employed technique for data analysis in research methodology. It can be used to analyze the documented information from text, images, and sometimes from the physical items. It depends on the research questions to predict when and where to use this method.
  • Narrative Analysis: This method is used to analyze content gathered from various sources such as personal interviews, field observation, and  surveys . The majority of times, stories, or opinions shared by people are focused on finding answers to the research questions.
  • Discourse Analysis:  Similar to narrative analysis, discourse analysis is used to analyze the interactions with people. Nevertheless, this particular method considers the social context under which or within which the communication between the researcher and respondent takes place. In addition to that, discourse analysis also focuses on the lifestyle and day-to-day environment while deriving any conclusion.
  • Grounded Theory:  When you want to explain why a particular phenomenon happened, then using grounded theory for analyzing quality data is the best resort. Grounded theory is applied to study data about the host of similar cases occurring in different settings. When researchers are using this method, they might alter explanations or produce new ones until they arrive at some conclusion.

LEARN ABOUT: 12 Best Tools for Researchers

Data analysis in quantitative research

The first stage in research and data analysis is to make it for the analysis so that the nominal data can be converted into something meaningful. Data preparation consists of the below phases.

Phase I: Data Validation

Data validation is done to understand if the collected data sample is per the pre-set standards, or it is a biased data sample again divided into four different stages

  • Fraud: To ensure an actual human being records each response to the survey or the questionnaire
  • Screening: To make sure each participant or respondent is selected or chosen in compliance with the research criteria
  • Procedure: To ensure ethical standards were maintained while collecting the data sample
  • Completeness: To ensure that the respondent has answered all the questions in an online survey. Else, the interviewer had asked all the questions devised in the questionnaire.

Phase II: Data Editing

More often, an extensive research data sample comes loaded with errors. Respondents sometimes fill in some fields incorrectly or sometimes skip them accidentally. Data editing is a process wherein the researchers have to confirm that the provided data is free of such errors. They need to conduct necessary checks and outlier checks to edit the raw edit and make it ready for analysis.

Phase III: Data Coding

Out of all three, this is the most critical phase of data preparation associated with grouping and assigning values to the survey responses . If a survey is completed with a 1000 sample size, the researcher will create an age bracket to distinguish the respondents based on their age. Thus, it becomes easier to analyze small data buckets rather than deal with the massive data pile.

LEARN ABOUT: Steps in Qualitative Research

After the data is prepared for analysis, researchers are open to using different research and data analysis methods to derive meaningful insights. For sure, statistical analysis plans are the most favored to analyze numerical data. In statistical analysis, distinguishing between categorical data and numerical data is essential, as categorical data involves distinct categories or labels, while numerical data consists of measurable quantities. The method is again classified into two groups. First, ‘Descriptive Statistics’ used to describe data. Second, ‘Inferential statistics’ that helps in comparing the data .

Descriptive statistics

This method is used to describe the basic features of versatile types of data in research. It presents the data in such a meaningful way that pattern in the data starts making sense. Nevertheless, the descriptive analysis does not go beyond making conclusions. The conclusions are again based on the hypothesis researchers have formulated so far. Here are a few major types of descriptive analysis methods.

Measures of Frequency

  • Count, Percent, Frequency
  • It is used to denote home often a particular event occurs.
  • Researchers use it when they want to showcase how often a response is given.

Measures of Central Tendency

  • Mean, Median, Mode
  • The method is widely used to demonstrate distribution by various points.
  • Researchers use this method when they want to showcase the most commonly or averagely indicated response.

Measures of Dispersion or Variation

  • Range, Variance, Standard deviation
  • Here the field equals high/low points.
  • Variance standard deviation = difference between the observed score and mean
  • It is used to identify the spread of scores by stating intervals.
  • Researchers use this method to showcase data spread out. It helps them identify the depth until which the data is spread out that it directly affects the mean.

Measures of Position

  • Percentile ranks, Quartile ranks
  • It relies on standardized scores helping researchers to identify the relationship between different scores.
  • It is often used when researchers want to compare scores with the average count.

For quantitative research use of descriptive analysis often give absolute numbers, but the in-depth analysis is never sufficient to demonstrate the rationale behind those numbers. Nevertheless, it is necessary to think of the best method for research and data analysis suiting your survey questionnaire and what story researchers want to tell. For example, the mean is the best way to demonstrate the students’ average scores in schools. It is better to rely on the descriptive statistics when the researchers intend to keep the research or outcome limited to the provided  sample  without generalizing it. For example, when you want to compare average voting done in two different cities, differential statistics are enough.

Descriptive analysis is also called a ‘univariate analysis’ since it is commonly used to analyze a single variable.

Inferential statistics

Inferential statistics are used to make predictions about a larger population after research and data analysis of the representing population’s collected sample. For example, you can ask some odd 100 audiences at a movie theater if they like the movie they are watching. Researchers then use inferential statistics on the collected  sample  to reason that about 80-90% of people like the movie. 

Here are two significant areas of inferential statistics.

  • Estimating parameters: It takes statistics from the sample research data and demonstrates something about the population parameter.
  • Hypothesis test: I t’s about sampling research data to answer the survey research questions. For example, researchers might be interested to understand if the new shade of lipstick recently launched is good or not, or if the multivitamin capsules help children to perform better at games.

These are sophisticated analysis methods used to showcase the relationship between different variables instead of describing a single variable. It is often used when researchers want something beyond absolute numbers to understand the relationship between variables.

Here are some of the commonly used methods for data analysis in research.

  • Correlation: When researchers are not conducting experimental research or quasi-experimental research wherein the researchers are interested to understand the relationship between two or more variables, they opt for correlational research methods.
  • Cross-tabulation: Also called contingency tables,  cross-tabulation  is used to analyze the relationship between multiple variables.  Suppose provided data has age and gender categories presented in rows and columns. A two-dimensional cross-tabulation helps for seamless data analysis and research by showing the number of males and females in each age category.
  • Regression analysis: For understanding the strong relationship between two variables, researchers do not look beyond the primary and commonly used regression analysis method, which is also a type of predictive analysis used. In this method, you have an essential factor called the dependent variable. You also have multiple independent variables in regression analysis. You undertake efforts to find out the impact of independent variables on the dependent variable. The values of both independent and dependent variables are assumed as being ascertained in an error-free random manner.
  • Frequency tables: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
  • Analysis of variance: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
  • Researchers must have the necessary research skills to analyze and manipulation the data , Getting trained to demonstrate a high standard of research practice. Ideally, researchers must possess more than a basic understanding of the rationale of selecting one statistical method over the other to obtain better data insights.
  • Usually, research and data analytics projects differ by scientific discipline; therefore, getting statistical advice at the beginning of analysis helps design a survey questionnaire, select data collection  methods, and choose samples.

LEARN ABOUT: Best Data Collection Tools

  • The primary aim of data research and analysis is to derive ultimate insights that are unbiased. Any mistake in or keeping a biased mind to collect data, selecting an analysis method, or choosing  audience  sample il to draw a biased inference.
  • Irrelevant to the sophistication used in research data and analysis is enough to rectify the poorly defined objective outcome measurements. It does not matter if the design is at fault or intentions are not clear, but lack of clarity might mislead readers, so avoid the practice.
  • The motive behind data analysis in research is to present accurate and reliable data. As far as possible, avoid statistical errors, and find a way to deal with everyday challenges like outliers, missing data, data altering, data mining , or developing graphical representation.

LEARN MORE: Descriptive Research vs Correlational Research The sheer amount of data generated daily is frightening. Especially when data analysis has taken center stage. in 2018. In last year, the total data supply amounted to 2.8 trillion gigabytes. Hence, it is clear that the enterprises willing to survive in the hypercompetitive world must possess an excellent capability to analyze complex research data, derive actionable insights, and adapt to the new market needs.

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Analyzing Text Data

  • Overview of Text Analysis and Text Mining

Choosing a Method

How much data do you need, word frequency analysis, machine learning/natural language processing, sentiment analysis.

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Choosing the right text mining method is crucial because it significantly impacts the quality of insights and information you can extract from your text data. Each method provides different insights and requires different amounts of data, training, and iteration. Before you search for data, it is essential that you:

  • identify the goals of your analysis
  • determine the method you will use to meet those goals
  • identify how much data you need for that method
  • develop a sampling plan to build a data set that accurately represents your object of study.

Starting with this information in mind will make your project go more quickly and smoothly, and help you overcome a lot of hurdles such as incomplete data, too much or too little data, or problems with access to data.

More Resources:

  • Content Analysis Method and Examples, from Mailman School of Public Health, Columbia University
  • Qualitative Research Methods Overview, from Northeastern University

Before you start collecting data, think about how much data you really need. New researchers in text analysis often want to collect every source mentioning their topic, but this is usually not the best approach. Collecting so much data takes a lot of time, uses many computational resources, often goes against platform terms of service, and doesn't necessarily improve analysis.

In text analysis, an essential idea is saturation , where adding more data doesn't significantly improve performance. Saturation is when the model has learned as much as it can from the available data, and no new patterns are themes are emerging with additional data. Researchers often use experimentation and learning curves to determine when saturation occurs; you can start by analyzing a small or mid-sized dataset and see what happens if you add more data.

Once you know your research question, the next step is to create a sampling plan . In text analysis, sampling means selecting a representative subset of data from a larger dataset for analysis. This subset, called the sample, aims to capture the diversity of sentiments in the overall dataset. The goal is to analyze this smaller portion to draw conclusions about the information in the entire dataset.

For example, in a large collection of customer reviews, sampling may involve randomly selecting a subset for sentiment analysis instead of analyzing every single review. This approach saves computational resources and time while still providing insights into the overall sentiment distribution of the entire dataset. It's crucial to ensure that the sample accurately reflects the diversity of sentiments in the complete dataset for valid and reliable generalizations.

Example Sampling Plans

Sampling plans for text analysis involve selecting a subset of text data for analysis rather than analyzing the entire dataset. Here are two common sampling plans for text analysis:

Random Sampling:

  • Description: Randomly select a subset of text documents from the entire dataset.
  • Process: Assign each document a unique identifier and use a random number generator to choose documents for inclusion in the sample.

Stratified Sampling:

  • Description: Divide the dataset into distinct strata or categories based on certain characteristics (e.g., product types, genres, age groups, race or ethnicity). Then, randomly sample from each stratum.
  • Process: Divide the dataset into strata, and within each stratum, use random sampling to select a representative subset.

Remember, the choice of sampling plan depends on the specific goals of the analysis and the characteristics of the dataset. Random sampling is straightforward and commonly used when there's no need to account for specific characteristics in the dataset. Stratified sampling is useful when the dataset has distinct groups, and you want to ensure representation from each group in the sample.

Exactly How Many Sources do I need?

Determining the amount of data needed for text analysis involves a balance between having enough data to train a reliable model and avoiding unnecessary computational costs. The ideal dataset size depends on several factors, including the complexity of the task, the diversity of the data, and the specific algorithms or models being used.

  • Task Complexity:  If you are doing a simple task, like sentiment analysis or basic text classification, a few dozen articles might be enough. More complex tasks, like language translation or summarization, often require datasets on the scale of tens of thousands to millions.
  • Model Complexity:  Simple models like Naive Bayes often perform well with smaller datasets, whereas complex models, such as deep learning models with many parameters, will require larger datasets for effective training.
  • Data Diversity:  Ensure that the dataset is diverse and representative of the various scenarios the model will encounter. A more diverse dataset can lead to a more robust and generalizable model. A large dataset that is not diverse will yield worse results than a smaller, more diverse dataset.
  • Domain-Specific Considerations:  Sometimes there is not a lot of data available, and it is okay to make do with what you have!

Start by taking a look at articles in your field that have done a similar analysis. What approaches did they take? You can also schedule an appointment with a Data Services Librarian to get you started.

More Readings on Sampling Plans for Text Analysis:

  • How to Choose a Sample Size in Qualitative Research, from LinkedIn Learning  (members of the GW community have free access to LinkedIn Learning using their GW email account)
  • Sampling in Qualitative Research , from Saylor Academy
  • Lowe, A., Norris, A. C., Farris, A. J., & Babbage, D. R. (2018). Quantifying Thematic Saturation in Qualitative Data Analysis. Field Methods, 30(3), 191-207.

Word Frequency Analysis of Coffee and Tea from the HathiTrust Database. Coffee is more common than tea after 1907.

Software for Word Frequency Analysis

  • NVivo via GW's Virtual Computer Lab NVivo is a software package used for qualitative data analysis. It includes tools to support the analysis of textual data in a wide array of formats, as well as and audio, video, and image data. NVivo is available through the Virtual Computer Lab. Faculty and staff may find NVivo available for download from GW's Software Center.
  • Analyzing Word and Document Frequency in R This chapter explains how to use tidy to analyze word and document frequency using Tidy Data in R.
  • word clouds in R R programming functionality to create pretty word clouds, visualize differences and similarities between documents, and avoid over-plotting in scatter plots with text.
  • ATLAS.ti Trial version of qualitative analysis workbench for processing text, image, audio, and video data. (Note: Health science students may have access to full version through Himmelfarb Library)

Related Tools Available Online

  • Google ngram Viewer When you enter phrases into the Google Books Ngram Viewer, it displays a graph showing how those phrases have occurred in a corpus of books (e.g., "British English", "English Fiction", "French") over the selected years.
  • HathiTrust This link opens in a new window HathiTrust is a partnership of academic and research institutions, offering a collection of millions of titles digitized from libraries around the world. To log in, select The George Washington University as your institution, then log in with your UserID and regular GW password.
  • Voyant Voyant is an online point-and-click tool for text analysis. While the default graphics are impressive, it allows limited customizing of analysis and graphs and may be most useful for exploratory visualization.

Related Library Resources

  • HathiTrust and Text Mining at GWU HathiTrust is an international community of research libraries committed to the long-term curation and availability of the cultural record. Through their common efforts and deep commitment to the public good, the libraries support the teaching and learning activities of the faculty, students or researchers at their home institutions, and the scholarly needs of the broader public as well.
  • HathiTrust+Bookworn From the University of Illinois Library: HathiTrust+Bookworm is an online tool for visualizing trends in language over time. Developed by the HathiTrust Research Center using textual data from the HathiTrust Digital Library, it allows you to track changes in word use based on publication country, genre of works, and more.
  • Python for Natural Language Processing A workshop offered through GW Libraries on natural language processing using Python.
  • Text Mining Tutorials in R A collection of text mining course materials and tutorials developed for humanists and social scientists interested to learn R.
  • Oxford English Dictionary This link opens in a new window The Oxford English Dictionary database will provide a word frequency analysis over time, drawing both from Google ngrams and the OED's own databases.

Example Projects Using Word Frequency Analysis

Robinson, J. S. and D. (n.d.). 3 Analyzing word and document frequency: Tf-idf | Text Mining with R . Retrieved November 21, 2023, from

  • Zhang, Z. (n.d.). Text Mining for Social and Behavioral Research Using R . Retrieved November 21, 2023, from
  • Exploring Fascinating Insights with Word Frequency Analysis In the realm of data analysis, words hold immense power. They convey meaning, express ideas, and shape our understanding of the world. In this article, we’ll explore the fascinating world of textual data analysis by examining word frequencies. By counting the occurrence of words in a text, we can uncover interesting insights and gain a deeper understanding of the underlying themes and patterns. Join us on this word-centric journey as we dive into the realm of word frequency analysis using Python.

Machine learning for text analysis is a technology that teaches computers to understand and interpret written language by exposing them to examples. There are two types of machine learning for text analysis: supervised learning, in which a human helps to train the computer to detect patterns, and unsupervised learning, which enables computers to automatically categorize, analyze, and extract information from text without needing explicit programming.

One type of machine learning for text analysis is  Natural Language Processing (NLP).  NLP for text analysis is a field of artificial intelligence that involves the development and application of algorithms to automatically process, understand, and extract meaningful information from human language in textual form. NLP techniques are used to analyze and derive insights from large volumes of text data, enabling tasks such as sentiment analysis, named entity recognition, text classification, and language translation. The aim is to equip computers with the capability to comprehend and interpret written language, making it possible to automate various aspects of text-based information processing.

Software for Natural Language Processing

  • NLTK for Python NLTK is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning, wrappers for industrial-strength NLP libraries, and an active discussion forum.
  • scikit Simple and efficient tools for predictive data analysis, using Python.

Related Resources Available Online

  • [Large Language Models] LLMs in Scientific Research
  • HathiTrust and Text Mining at GWU Information on text data mining using HathiTrust
  • Social Feed Manager Social Feed Manager software was developed to support campus research about social media including Twitter, Tumblr, Flickr, and Sina Weibo platforms. It can be used to track mentions of you or your articles and other research products for the previous seven days and on into the future.. Email [email protected] to get started with Social Feed Manager or to schedule a consultation

Example Projects using Natural Language Processing

  • Redd D, Workman TE, Shao Y, Cheng Y, Tekle S, Garvin JH, Brandt CA, Zeng-Treitler Q. Patient Dietary Supplements Use: Do Results from Natural Language Processing of Clinical Notes Agree with Survey Data?  Medical Sciences . 2023; 11(2):37.
  • Nguyen D, Liakata M, DeDeo S, Eisenstein J, Mimno D, Tromble R, Winters J. How We Do Things With Words: Analyzing Text as Social and Cultural Data. Front Artif Intell. 2020 Aug 25;3:62. doi: 10.3389/frai.2020.00062. 

Sentiment analysis is a method of analyzing text to determine whether the emotional tone or sentiment expressed in a piece of text is positive, negative, or neutral. Sentiment analysis is commonly used in businesses to gauge customer feedback, social media monitoring, and market research.

Software for Sentiment Analysis

  • Sentiment Analysis using NLTK for Python NLTK is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning, wrappers for industrial-strength NLP libraries, and an active discussion forum.
  • Sentiment Analysis with TidyData in R This chapter shows how to implement sentiment analysis using tidy data principles in R.
  • Tableau Tableau works with numeric and categorical data to produce advanced graphics. Browse the Tableau public gallery to see examples of visuals and dashboards. Tableau offers free one-year Tableau licenses to students at accredited academic institutions, including GW. Visit for more about the program or to request a license.
  • Qualtrics Text iQ Qualtrics is a powerful tool for collecting and analyzing survey data. Qualtrics Text iQ automatically performs sentiment analysis on collected data.

Related Resources Available Online

  • finnstats. (2021, May 16). Sentiment analysis in R | R-bloggers.

Example Projects Using Sentiment Analysis

  • Duong, V., Luo, J., Pham, P., Yang, T., & Wang, Y. (2020). The Ivory Tower Lost: How College Students Respond Differently than the General Public to the COVID-19 Pandemic. 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 126–130.
  • Ali, R. H., Pinto, G., Lawrie, E., & Linstead, E. J. (2022). A large-scale sentiment analysis of tweets pertaining to the 2020 US presidential election. Journal of Big Data, 9(1).
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Title: metastore: high-performance metagenomic analysis via in-storage computing.

Abstract: Metagenomics has led to significant advancements in many fields. Metagenomic analysis commonly involves the key tasks of determining the species present in a sample and their relative abundances. These tasks require searching large metagenomic databases containing information on different species' genomes. Metagenomic analysis suffers from significant data movement overhead due to moving large amounts of low-reuse data from the storage system to the rest of the system. In-storage processing can be a fundamental solution for reducing data movement overhead. However, designing an in-storage processing system for metagenomics is challenging because none of the existing approaches can be directly implemented in storage effectively due to the hardware limitations of modern SSDs. We propose MetaStore, the first in-storage processing system designed to significantly reduce the data movement overhead of end-to-end metagenomic analysis. MetaStore is enabled by our lightweight and cooperative design that effectively leverages and orchestrates processing inside and outside the storage system. Through our detailed analysis of the end-to-end metagenomic analysis pipeline and careful hardware/software co-design, we address in-storage processing challenges for metagenomics via specialized and efficient 1) task partitioning, 2) data/computation flow coordination, 3) storage technology-aware algorithmic optimizations, 4) light-weight in-storage accelerators, and 5) data mapping. Our evaluation shows that MetaStore outperforms the state-of-the-art performance- and accuracy-optimized software metagenomic tools by 2.7-37.2$\times$ and 6.9-100.2$\times$, respectively, while matching the accuracy of the accuracy-optimized tool. MetaStore achieves 1.5-5.1$\times$ speedup compared to the state-of-the-art metagenomic hardware-accelerated tool, while achieving significantly higher accuracy.

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  25. [2311.12527] MetaStore: High-Performance Metagenomic Analysis via In

    Metagenomics has led to significant advancements in many fields. Metagenomic analysis commonly involves the key tasks of determining the species present in a sample and their relative abundances. These tasks require searching large metagenomic databases containing information on different species' genomes. Metagenomic analysis suffers from significant data movement overhead due to moving large ...