Data Definition: What It Is, Types, and How It's Used

Key takeaways

• Data is a collection of facts that you can gather and analyze for calculation, discussion, or reasoning, and is either quantitative or qualitative.

• Quantitative data is expressed by numbers, such as measurements, while qualitative data is expressed by characteristics or categories, such as colors.

• When it comes to data versus information, data is raw facts, while information is the interpretation of those facts so you can use it in meaningful ways.

With an immense amount of data available, value lies in the ability to turn it into information that helps organizations make informed decisions. Learn more about data, the various types, and how you can start a career working with it. Afterward, if you're ready to get on the fast track to a career in data, explore the Google Data Analytics Professional Certificate. You’ll have the opportunity to learn in-demand skills, such as how to clean and organize data for analysis, how to visualize and present data findings, and how to use essential tools, including spreadsheets, SQL, Python, and Tableau, while getting AI training from Google experts.

Data definition: What is data?

Data is factual information you can process for reasoning, discussion, or calculation. Two subcategories you can use to define data are qualitative and quantitative.

Qualitative data isn’t something you can express with numbers. Instead, you describe it using categories or characteristics such as color. While this data can be subjective, it still has value as it helps develop hypotheses.

Quantitative data, however, is data you can express numerically, allowing you to use it for statistical data analysis and to answer hypotheses. Examples of quantitative data include measurements such as distance or time.

Data can be further divided into two categories when used for research: primary and secondary. Researchers generate primary data using experiments, surveys, and other methods. They use secondary data to extract it from files, such as the data generated by health care facilities, educational institutions, or government agencies.

What is data vs. information?

Data is raw facts, while information is the interpretation of those facts. Data is relatively meaningless by itself, but when it is analyzed and interpreted, it becomes information that is usable and meaningful. For instance, you might gather data on your customers' ages and locations. These will seem like a collection of numbers and addresses until you analyze them to determine which age groups are your most frequent customers and which states or countries they live in. This information helps you determine who your target market is so you can make more informed decisions about your marketing and product development efforts.

Data types

The four data types are nominal, ordinal, discrete, and continuous. Nominal and ordinal data are examples of qualitative data, while discrete and continuous data are quantitative.

Nominal data

Nominal or categorical data has no numerical reference. Because of this, you cannot apply calculations to it. Instead, nominal data represents characteristics such as someone's gender or race or to answer a simple yes or no question with a limit to the number of possible categories. You can gather statistics such as frequency from nominal data.

Ordinal data

Ordinal data is qualitative data you can organize by ranked categories based on their natural order or scale. For example, when collecting data on people’s highest education level, you could have categories ranging from high school to undergraduate degrees to advanced degrees. Like nominal data, ordinal data applies to gathering statistical insights such as frequency, median, and averages.

Discrete data

Discrete data has a finite number of values you can count and express as a whole number. You would use discrete data to represent the number of products sold or the points scored in a basketball game. Discrete data helps analyze things that have already happened to gain insights.

Continuous data

Continuous data represents measurable figures with infinite possibilities, such as the time it takes to complete an assignment or the square footage of office space. Since it's an exact measurement, you can express continuous data with decimal points. You can also analyze continuous data and choose to represent it visually to display change over a period of time.

Data sources

A data source is any place where data originates. It can be the place where the data was first born or where it is found after it has been refined and processed.

When collecting data for research purposes, you can rely on primary and secondary data sources. Primary data is gathered through interviews or surveys focused directly on your hypothesis. Secondary data is data that another person, group, or organization has already collected, such as previously completed studies.

Organizations today are collecting massive amounts of data, known as big data, that can be analyzed to extract meaningful insights. Big data sources include social media analytics, user activity, financial transaction history, and health care records, to name a few.

Who uses data, and how is it used?

Because of the depth of analysis organizations can perform using big data, many industries are utilizing this tool to improve their operations. Health care, banking, marketing, education, and government professionals rely on big data to provide insights into trends, guide decisions, and predict what may happen in the future.

Within these industries, key data-related roles include data scientists, data analysts, and big data engineers. With data use continuing to expand and data-related career opportunities expected to grow significantly over the next decade, according to the US Bureau of Labor Statistics, now is a great time to start in this space [1].

Read more: Big Data in Health Care: What It Is, Benefits, and Jobs

Data scientist

Data scientists collect, process, and analyze data to develop insights that you can use to create plans and make educated decisions and predictions. In this role, you might use machine learning to categorize data and make predictions about it. You can then use this analysis to make recommendations for business owners and other stakeholders.

Data analyst

Data analysts share similar responsibilities as data scientists, except they work to answer already-defined questions rather than build machine learning models. Instead, they explore data related to what has already happened to help facilitate the decision-making process, using many of the same tools as data scientists, such as data visualization.

Big data engineer

Big data engineers develop big data architecture, including databases and warehouses, to allow storage and access to large amounts of data. In this position, you will also look for opportunities to acquire big data to build upon your current resources.

Valuable skills for working with data

Working with data effectively means you will need to develop a robust set of technical skills. Some in-demand skills that will be greatly beneficial in your data-related career include:

• Machine learning

• Data visualization

• SQL and NoSQL

• Data cleaning

• Programming languages

• Probability and statistics

• Cloud computing

• Deep learning and neural networks

• Advanced math

• Data ethics

Data education options

You can learn the necessary skills to work with data by earning a bachelor’s degree, generally the minimum requirement for data analyst and data scientist positions. Your degree can be in data science or a related field like computer science. Other ways to gain skills specifically related to data include boot camps and certifications. Boot camps allow you to learn sought-after skills and tools that employers currently use.

You can gain skills and demonstrate your knowledge to employers by earning a Professional Certificate, such as the Google Data Analytics Professional Certificate on Coursera. With this program, you can learn essential analytical skills used daily by data analysts to help you become job-ready.

Learn more about data with our free resources

Explore career paths, assess your skills, and connect with resume guidance while browsing our Career Resources Hub. And if you want to learn more about data, check out these free resources:

• Watch on YouTube: Career Spotlight: Data Analyst

• Read our Career Chat issue: 4 Beginner-friendly Data Analytics Projects

• Hear from an expert: 6 Questions with an IBM Data Scientist and AI Engineer

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