What Is Data Analysis? (With Examples)

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 2023 listed data analysts and scientists as one of the most in-demand 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 enrolling in the IBM Data Analyst Professional Certificate, where you'll work on projects you can feature in your portfolio.

What is 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 to your conclusions? 

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

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?”

Learn more about data analysis on Coursera

If you’re interested in a career in the high-growth field of data analytics, consider the following programs from industry leader IBM.

Begin building job-ready skills with the IBM Data Analytics Professional Certificate. Develop a working knowledge of Python as well as how to visualize data and present your findings. No prior experience necessary.

Practice working with data with the IBM Data Analytics with Excel and R Professional Certificate. Learn how to use Microsoft Excel to analyze data and make data-informed business decisions.

Frequently asked questions (FAQ)