What Is Data Analysis? (With Examples)

Key takeaways

• Data analysis is the process of collecting, analyzing, and interpreting data to answer questions and solve problems.

• Effective data analysis methods include descriptive, diagnostic, predictive, and prescriptive analysis, each of which can provide different types of answers, such as whether clinical trials in health care yield effective results or whether marketing efforts reach the target audience.

• Data analysts do many things as part of their objective to gather insights from data to help inform decisions, including identifying the question or challenge you need to answer or solve, collecting and preparing data, analyzing it, and interpreting and sharing the results.

Professionals across a range of industries, including health care, marketing, and finance, use data analysis to answer questions and support data-driven decision-making. Explore the different types of data analysis and how you can use them to your advantage. If you’re ready to begin, consider enrolling in the Data Analysis and Visualizations Specialization from IBM. This beginner-level, four-course series offers an opportunity to gain valuable skills in areas such as statistical analysis, spreadsheet software, data storytelling, data visualization, and more.

Why is data analysis important?

Extracting meaning from data empowers us to make better decisions, and we’re living in a time when we have more data than ever at our fingertips. Because of that, companies have been wising up to the benefits of leveraging data and turning to data analysis to find insights to further business goals. The World Economic Forum Future of Jobs Report 2025 listed data analysts and scientists as one of the fastest-growing jobs, alongside AI and machine learning specialists and big data specialists [1]

What does a data analyst do?

A data analyst gathers information (data), looks for meaningful patterns in it (analyzes it), and shares their findings with those they work with to help them make decisions. If you work for a hospital as a data analyst, you might gather data from patient records to determine the effectiveness of certain treatments. In a marketing setting, you might analyze website visitor demographics to determine if your company is reaching its target audience. As a data analyst in the financial sector, you might analyze a company's investment portfolio to identify trends and give advice about future investment decisions.

What are the 5 steps of data analysis?

Data analysts use data to solve problems. As such, the data analysis process typically moves through several iterative phases. Let’s take a closer look at each:

• Identify the business question you’d like to answer. What problem is the company trying to solve? What do you need to measure, and how will you measure it?

• Collect the raw data sets you’ll need to help you answer the identified question. Data collection might come from internal sources, like a company’s client relationship management (CRM) software, or from secondary sources, like government records or social media application programming interfaces (APIs).

• Clean the data to prepare it for analysis. This often involves purging duplicate and anomalous data, reconciling inconsistencies, standardizing data structure and format, and dealing with white spaces and other syntax errors.

• Analyze the data. By manipulating the data using various data analysis techniques and tools, you can begin to find trends, correlations, outliers, and variations that tell a story. During this stage, you might use data mining to discover patterns within databases or data visualization software to help transform data into an easy-to-understand graphical format.

• Interpret the results of your analysis to see how well the data answered your original question. What recommendations can you make based on the data? What are the limitations of your conclusions?

Read more: 15 Data Analysis Tools and When to Use Them

You can complete labs and projects to feature in your portfolio with the IBM Data Analyst Professional Certificate.

What is data analysis? 4 types of data analysis (with examples)

Data can be used to answer questions and support decisions in many different ways. To identify the best way to analyze your data, it's useful to familiarize yourself with the different types of analysis most commonly used in the field.

1. Descriptive analysis

Descriptive analysis tells us what happened. This type of analysis helps describe or summarize quantitative data by presenting statistics. For example, descriptive statistical analysis could show the distribution of sales across a group of employees and the average sales figure per employee.

Descriptive analysis answers the question: “What happened?”

2. Diagnostic analysis

If the descriptive analysis determines the “what,” diagnostic analysis determines the “why.” Let’s say a descriptive analysis shows an unusual influx of patients in a hospital. Drilling into the data further might reveal that many of these patients shared symptoms of a particular virus. This diagnostic analysis can help you determine that an infectious agent—the “why”—led to the influx of patients.

Diagnostic analysis answers the question: “Why did it happen?”

3. Predictive analysis

So far, we’ve looked at types of analysis that examine and draw conclusions about the past. Predictive analytics uses data to form projections about the future. Using predictive analysis, you might notice that a given product has had its best sales during the months of September and October each year, leading you to predict a similar high point during the upcoming year.

Predictive analysis answers the question: “What might happen in the future?”

4. Prescriptive analysis

Prescriptive analysis takes all the insights gathered from the first three types of analysis and uses them to form recommendations for how a company should act. Using our previous example, this type of analysis might suggest a market plan to build on the success of the high sales months and harness new growth opportunities in the slower months.

Prescriptive analysis answers the question: “What should we do about it?”

Explore our free data analysis resources

Subscribe to our weekly LinkedIn newsletter, Career Chat, for updates on popular tools and certifications, job search tips, and more. Then, check out some of our other free resources to keep learning more about data analysis:

• Watch on YouTube: Data Analyst vs. Business Analyst: Which Career Path Is Right for You?

• Take the quiz: Which Data Analysis Course Should You Take? Find Out in 1 Minute

• Gain expert insights: 6 Questions with a Microsoft Data Analytics Leader

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