What Is Data Analysis? (With Examples)

Data analysis is the practice of working with data to glean useful information, which can then be used to make informed decisions. 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 wisening 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].

In this article, you'll learn more about the data analysis process, different types of data analysis, and recommended courses to help you get started in this exciting field. Afterward, if you want to start working toward a data career by building job-relevant skills, consider enroling in the Google Data Analytics Professional Certificate, where you'll work on projects you can feature in your portfolio.

Data analysis process

As the data available to companies continues to grow both in amount and complexity, so too does the need for an effective and efficient process by which to harness the value of that data. 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, standardising data structure and format, and dealing with white spaces and other syntax errors.

• Analyse the data. By manipulating the data using various data analysis techniques and tools, you can 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 visualisation 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? 

Is coding required for a data analyst?

Yes, data analysts need coding knowledge to collect and manipulate data, perform complex analyses, and automate various tasks. However, the amount of coding you’ll do will likely depend on your exact role and the tools your company uses. At the very least, you should have basic programming skills in SQL to efficiently query and retrieve data from databases. Knowledge of Python and R can help increase your chances of getting hired or advancing in your role. You might also require less coding if you are well-versed with data visualisation tools such as Power BI and Tableau. 

Types of data analysis (with examples)

Data can be used to answer questions and support decisions in many different ways. Identifying the best way to analyse your data can help familiarise you with the four types of data analysis commonly used in the field. 

In this section, we’ll look at these data analysis methods and an example of how each might be applied in the real world.

Descriptive analysis

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

Descriptive analysis answers the question, “What happened?”

Diagnostic analysis

If the descriptive analysis determines the “what,” then diagnostic analysis determines the “why.” Let’s say a descriptive analysis shows an unusual influx of patients in a hospital. Drilling into the data 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?”

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 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?”

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?”

This last type is where the concept of data-driven decision-making comes into play.

What is data-driven decision-making (DDDM)?

Data-driven decision-making, sometimes abbreviated as DDDM, can be defined as the process of making strategic business decisions based on facts, data, and metrics instead of intuition, emotion, or observation.

Build your data analysis skills on Coursera

If you’re interested in a career in the high-growth field of data analytics, you can begin building job-ready skills with the Google Data Analytics Professional Certificate. Prepare yourself for an entry-level job as you learn from Google employees—no experience or degree is required.

Frequently asked questions (FAQ)