What Is Computer Data? Types of Data Explained

Data is a part of nearly everything we do in the modern world. Whether we're sending a text message to a friend, participating in a video call, logging into a banking app, or playing video games, we're constantly using and creating it. But what exactly is data? In this article, learn what the data definition is, the different types of computer data, and how you can protect or improve your own data security.

What is data?

At a basic level, data represents raw facts, statistics, and information. It can be basic or as complex as your genetic code that sets you apart from other individuals. But in the world of computer data, we represent information in a more structured form - the binary language of 1s and 0s. Referred to as bits, the 1s and 0s are the building blocks of computer data.

Bits are the smallest data unit and represent either a 1 or a 0. Combining multiple bits creates bytes, which can then combine to create kilobytes, megabytes, gigabytes, and so on. This binary language is the building block of all information we store and process with computer systems.

Data types

Data comes in many shapes and sizes. Everything you see, watch, listen to, post and play online is data. But broadly speaking, we can categorize all data as either structured or unstructured.

What is structured data?

Structured data is organized and well-defined, usually stored in a database. With structured data, think of spreadsheets or tables with each piece of data organized into rows or columns.

There are several subtypes of structured computer data.

Numerical data. Numerical data represents quantitative measurements or counts of information. However, there are many types of numeric data; discrete data, continuous data, and ratio data are all numerical forms.
Discrete data. Discrete data can only take specific values within a closed range and usually consists of integers.
Continuous data. Continuous data can take any numeric value, including fractions and decimal points.
Ratio data. Ratio data is measured variables on a continuous scale. Unlike other types of data, ratio data has a "true zero," meaning a value of zero means a lack of value for whatever is being measured. A good example of ratio data is test scores.
Categorical data. Categorical data is information that represents the characteristics or qualities of a group of things. Because of this, it's usually represented by labels or names.

What is unstructured data?

Unlike structured data, unstructured computer data is not confined to predefined rules and is much harder to organize. This includes long text, multimedia files, social media posts, transcripts, etc. Every day, people all over the world generate trillions of bytes of unstructured data.

Note that this list of data types is not comprehensive, as there are many additional types of data out there, many of which are more complex than those discussed in this article.

Where does data come from?

What are the various sources of data? It comes from many places, but there are two main sources of data - primary sources and secondary sources.

Primary data is the data that is collected firsthand for a specific purpose. This can include surveys, research experiments, or direct observation.

Secondary data, on the other hand, consists of all of the data out there that was collected not for any specific purpose, but that was still repurposed and used for analysis. This includes information you can find in research papers, Internet searches, or databases. This is the most common source of data, as billions of us are constantly generating computer data around the world. Think of audio and video recordings, location data, texts, emails and other documents, transaction data, biometric data, social media data, and browsing activity.

Data storage and management

So now we know where data comes from. But how is data stored and managed? As discussed, the amount of computer data that we create every day continues to grow exponentially.
This requires technologies to store and organize all this data. Consider these ways to store and manage data.

Databases and data warehouses. These structured systems store large volumes of data sets. They typically organize data in tables for analytics, with data processing, reporting, visualization, and business intelligence features built-in.
Cloud storage. Cloud computing allows us to store and access data remotely without needing to be in proximity to a physical server.
Data lakes. These databases work for much larger, vast amounts of raw, unstructured data. If you've ever heard the term "big data," this is where that concept comes in.
Think of huge amounts of data from many different sources, representing all different types of data generated and stored every millisecond. Data lakes store, manage, retrieve, and analyze all this data.

How we use computer data

The amount of computer data present in our world may seem overwhelming. But it serves a purpose; data helps organizations and researchers gain valuable insights about our behavior to make data-driven decisions to improve services and hopefully make the world a better place. Data can help organizations across all industries, including business, healthcare, banking, marketing, higher education, and beyond.

Understanding and analyzing computer data

To learn about all of this data, data scientists use statistical methods to analyze the data and gain valuable information so that business leaders can make informed decisions.

Data analysis

Data analysis involves reviewing, cleaning, filtering, and visualizing data to discover useful information and also to draw conclusions about the state of reality. Here are the most common forms of computer data analysis:

Descriptive analysis. This form of analysis summarizes the main aspects of a dataset, providing a snapshot of what a dataset means.
Predictive analysis. This form of analysis concludes and makes predictions based on a sample of data. For example, it could be to say something like, "This happened given these circumstances and these variables. Given the same circumstances and variables, it's likely to happen again in the future."
Machine learning. This form uses algorithms to teach systems to learn from a dataset and make predictions or decisions, but without needing to program every single decision. Think of social media algorithms that give you recommendations based on your previous behavior.
Visualization. Visualization is a great way, no matter what type of analysis, to easily understand patterns or trends in data. It's one of the best ways to communicate findings to audiences who are unfamiliar with the raw dataset.

Data privacy and security

With all of this data out there, and with an understanding of how valuable data can be, it's easy to see why bad actors would love to get their hands on as much data as they possibly can. That's why it's more important than ever to ensure data privacy and security. Some effective data privacy measures include firewalls, antivirus software, encryption, and virtual private networks.

As we continue to generate unprecedented amounts of information, being able to secure, harness, and analyze data will be a driving force in shaping the future. With data being ubiquitous in everyday life, there's no sense in trying to escape it. Data can allow us to learn more about ourselves so we can pave the way for a better future.