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.

"It is a capital mistake to theorize before one has data. Insensibly, one begins to twist facts to suit theories, instead of theories to suit facts," Sherlock Holmes proclaims in Sir Arthur Conan Doyle's A Scandal in Bohemia. 

This idea lies at the root of data analysis. When we can extract meaning from data, it empowers us to make better decisions. And we’re living in a time when we have more data than ever at our fingertips.

Companies are wisening up to the benefits of leveraging data. Data analysis can help a bank personalize customer interactions, a health care system predict future health needs, or an entertainment company create the next big streaming hit.

The World Economic Forum Future of Jobs Report 2020 listed data analysts and scientists as the top emerging jobs, followed immediately by AI and machine learning specialists and big data specialists [1]. In this article, you'll learn more about the data analysis process and different types of data analysis, as well as recommended courses to help you get started in this exciting field.

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 needed 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 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? 

Watch this video to hear how Kevin, Director of Data Analytics at Google, defines data analysis.

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 analyze your data can help you familiarize yourself with the four types of data analysis commonly used in the field. 

In this section, we’ll take a look at each of these data analysis methods, along with 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 summarize quantitative data by presenting statistics. For example, descriptive statistical analysis could show the 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,” 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?”

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 to DDDM, can be defined as the process of making strategic business decisions based on facts, data, and metrics instead of intuition, emotion, or observation.

This might sound obvious, but in practice, not all organizations are as data-driven as they could be. According to the global management consulting firm McKinsey Global Institute, data-driven companies are better at acquiring new customers, maintaining customer loyalty, and achieving above-average profitability [2].

Get started with 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. Once you finish, you can apply directly to employers, including Google.

Frequently asked questions (FAQ)