What You Need to Know About Data

Understanding data is a key step toward making better decisions—whether you're analyzing trends in a spreadsheet, optimizing business processes, or organizing personal information. Data forms the foundation of modern decision-making, guiding everything from corporate strategies to everyday choices.

This article explores what data is, how it can be classified, and why its quality matters. By understanding these concepts, you’ll gain insight into how to work with data effectively. For a deeper dive into these concepts and hands-on practice, explore the Defining Data lesson on Dataquest.

What Is Data?

Let’s start with a simple definition: data is the plural form of "datum," which refers to a single fact or observation. For example:

• Your height (e.g., 5 feet, 9 inches).
• A purchase amount (e.g., $23.50).
• A survey response (e.g., "Satisfied").

When these individual facts are recorded, measured, or collected, they become data. Importantly, if something isn't documented, it isn't considered data. Your verbal review of a restaurant, for instance, isn’t data unless it’s written down or recorded. Similarly, personal opinions and beliefs aren’t considered data unless they are supported by recorded evidence.

Types of Data

Data comes in many forms, and classifying it helps us understand how to work with it. Broadly, data can be categorized as:

• Quantitative data: Numerical and measurable, such as income, age, or temperature.
• Qualitative data: Descriptive and categorical, such as colors, names, or survey responses.

These classifications also intersect with:

• Structured data: Organized in a clear format, like rows and columns in a spreadsheet.
• Unstructured data: Freeform, such as images, text, or audio recordings.

For example, demographic information (like age or marital status) in a dataset about employees might be quantitative or qualitative, while satisfaction ratings from a survey would be structured data.

Confidence in Data

Not all data is equally reliable, so evaluating its quality is essential. Here are some questions to consider:

• Accuracy: Was the data recorded correctly?
• Completeness: Are there gaps in the dataset?
• Timeliness: Is the data up-to-date?

Taking a closer look at these factors can help you ensure that your analysis is built on a solid foundation.

Why Does It Matter?

Once data is classified and its quality assessed, it’s easier to determine how to analyze it effectively. For instance:

• Quantitative data can help us calculate trends or averages.
• Qualitative data can reveal patterns or preferences.

In the lesson’s case study, a fictional company, DQ Health, uses employee data to explore questions like:

• What’s the rate of staff attrition over time?
• How effective are bonus policies at retaining employees?
• Which employees are most likely to leave?

By combining quantitative measures (like income and years of experience) with qualitative insights (like satisfaction ratings), organizations can uncover actionable insights.

Ready to Explore More?

Data is everywhere, and learning how to define and classify it is the first step toward using it effectively. If you’re ready to deepen your understanding, the Defining Data lesson on Dataquest is free this week. It’s part of the Junior Data Analyst Career Path, where you can build the skills to confidently analyze and work with data every day.