What Is Data Interpretation?

Data interpretation is a process of extracting insight from data, a critical step between collecting data and taking action based on that data. Companies in all industries collect a massive amount of data—like customer information, sales data, financial records, and more—and unlocking strategic insight from that information is crucial in competitive markets and modern industries. While analyzing data, you must understand and explore the information before using data interpretation to assign meaning and actionable insight to the data. Data interpretation is an essential part of the data analysis process. 

Explore the role of data interpretation within data analytics and learn how to use it to make data-driven strategic decisions in your company or organization. 

What is data interpretation? 

Data interpretation is a step of the data analysis process where you assign meaning to data. For example, if you were conducting an environmental study about rivers, you might gather historical data about samples collected from rivers in the past. You could analyze that data to look for trends using various methods. After understanding, visualizing, or otherwise engaging with the data, you can interpret the data and draw conclusions based on your analysis, such as whether the river is currently meeting environmental standards, why, and what can be done to change the current course. 

Data interpretation often includes presenting your findings to decision-makers and making recommendations. This can include creating visualizations or presentations to illustrate your interpretations to stakeholders in your company, scientific colleagues, or the greater public. 

What is data interpretation important?

Data interpretation is important because it bridges the gap between raw data and actions you can take to improve your organization or advance your research. It helps you identify patterns, predict what could happen, and apply a data-driven decision-making process to reach your organization's goals. You can use data analysis to understand your market, industry, and customers on a deeper level to position your brand or product, customize your marketing, and make strategic decisions. Data interpretation can also help you evaluate new ideas before taking them to market to analyze risk and project customer enthusiasm. As modern organizations increasingly rely on data, data interpretation skills become increasingly valuable. 

Types of analysis and data interpretation

You can explore different types of analysis to draw different conclusions through data interpretation. A few common types of analysis include descriptive, diagnostic, predictive, prescriptive, inferential, causal, exploratory, and mechanistic. Each type of analysis can offer different insights: 

• Descriptive analysis: Descriptive analysis helps explain what happened in the past, such as the mean, median, and range of the historical data. This analysis is foundational to other forms of analysis, which use historical data as the factual basis. 

• Diagnostic analysis: Diagnostic analysis considers descriptive analysis and interprets why the events that occurred happened. 

• Predictive analysis: After understanding how and why things happened, predictive analysis can help you project what might happen. 

• Prescriptive analysis: Prescriptive analysis takes the predictive analysis one step further and provides insight into what an organization should do to make its predictive analysis closer to representing the organizational goals. 

• Inferential analysis: Inferential analysis is a strategy for identifying patterns in descriptive analysis and closely analyzing a small segment of a larger group to draw inferences about the larger group. 

• Causal analysis: Causal analysis looks at the cause-and-effect relationship between data points.

• Exploratory data analysis (EDA )is a more open-ended form of analysis that looks for patterns and relationships in the data before drawing conclusions. This can help identify interesting associations or rule out anomalies before proceeding with other analyses. 

• Mechanistic analysis: Mechanistic analysis is a method of examining how different variables change in response to other variables in the data. It is used in pharmaceutical trials, where researchers study how one variable causes others to change. 

Who uses data interpretation?

Data interpretation is a key skill for professionals such as data analysts, marketing managers, and financial analysts. All industries can use data to gain insight into making their operational processes more efficient, so professionals with data interpretation skills can work in many different industries. If you want to start a career interpreting data, consider these careers: 

Data analysts

Average annual salary in the US: $86,051 [1]

Job outlook (projected growth from 2032 to 2033): 36 percent [2]

Data analysts help solve problems by collecting, storing, cleaning, analyzing, and interpreting data. You will use statistical and visualization tools to look for patterns in data and prepare reports to communicate your findings with stakeholders and decision-makers in your company or organization. 

Marketing managers

Average annual salary in the US: $81,251 [3]

Job outlook (projected growth from 2032 to 2033): 8 percent [4]

As a marketing manager, you will be responsible for planning and executing marketing campaigns, including conducting market research, designing visual advertising, developing marketing strategy, and working with other department heads to create a cohesive brand story. In this role, you may also be responsible for hiring and managing a team of marketing professionals. 

Financial analysts

Average annual salary in the US (Glassdoor): $78,669 [5]

Job outlook (projected growth from 2032 to 2033): 9 percent [6] 

As a financial analyst, you will help companies make financial decisions, either helping companies determine which financial investments to purchase or helping companies who offer investments (such as banks) for sale. In this role, you may also specialize in financial services or products, such as helping companies manage risk, or you may provide a service such as investment strategies, securities analysis, or portfolio management. 

Mathematical tools for data interpretation

The exact tools you need for data interpretation will depend on the problem you're trying to solve and what kind of data you're considering. A few examples of mathematical tools you can use for data interpretation include: 

• Linear algebra: Linear algebra is a type of math that solves linear equations with a finite number of solutions. 

• Descriptive statistics: Descriptive statistics is a form of math that uses formulas to describe a data set, such as average, mean, or median. 

• Probability: Probability uses math to predict the likelihood that events will happen in the future based on data from the past and present. 

• Univariate statistics: Univariate statistics is a form of math that helps analysts determine what one variable will do to many other variables. 

• Multivariate analysis: Multivariate analysis considers a more complex relationship between multiple variables than univariate statistics. 

• Convex optimization: Convex optimization is a type of computer science optimization that minimizes convex functions over convex sets. 

• Fourier analysis: Fourier analysis is a technique for simplifying a data set by identifying and removing time-based cycles on a data set to account for the way things happen periodically. 

Learn more about data interpretation on Coursera.

Data interpretation is an integral part of the data analytics process and can help companies turn a mountain of data into actionable insight. If you want to learn more about data interpretation or start a career as a data analyst, take the next step with Coursera. For example, you could explore Data Visualization in Excel in a course offered by Macquarie University or complete a Data Literacy Specialization offered by Johns Hopkins University. Alternatively, you can prepare for a career as a data analyst with the Google Data Analytics Professional Certificate.