What Is the Definition of Data?

With the advent of the internet, people and organizations now have an overwhelming amount of data at their fingertips. To make sense of this data, new methods to collect, analyze, and create value from the high volume of information have emerged. With an immense amount of data available, value comes from the ability to turn it into information that can help organizations make informed decisions. Today, businesses can use data to learn more about their customers and increase revenue. 

The creation of big data also led to various data-related careers across many industries, like data analyst, data scientist, and data engineer. To know the best ways to interpret your data, you need to understand what data is and how it’s used. Learn more about data, the various types, and how you can start a career working with it.  

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. 

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 can 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].

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 bootcamps and certifications. Bootcamps 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, offered on Coursera. With this program, you can learn essential analytical skills used daily by data analysts to help you become job-ready. 

Next steps

The term “data”' refers to any factual information you can use for calculation or reasoning. Data careers are on the rise because of the mass amount of data collected daily. If you are passionate about using data, you might enjoy working as a data scientist, data analyst, big data engineer, or another related role. 

Learn more about different data career options by enrolling in one of many highly-rated courses available on Coursera. Start with the Big Data Specialization from the University of San Diego, where you can learn big data fundamentals. Those interested in pursuing a career in data science should consider developing data science-specific programming skills with the University of Michigan's Applied Data Science with Python Specialization. This course covers topics from data visualization and statistical analysis to machine learning.