What is Data? (Definition, Types)


How many times have you heard the word “data”? If you are a business owner or employed at any company, you must have heard the term data regularly. Your manager, colleagues, or other employees must have used this term before you. So what does this term mean? What exactly is data that everyone talks about? Let’s learn some interesting things about data in this article.

What is Data?

Data is a piece of information that efficiently provides value to its users. Data has different meanings in different fields. For a science student, data refers to information containing facts, formulas, or other information that provides valuable knowledge. On the other hand, when it comes to a lawyer, data might refer to case studies, shreds of evidence, or additional similar details.

In computing, data is information converted into binary data (digital form). The digital form, unlike the analog form, consists of ones(1) and zeros(0)—all computer data (post-1960) became digital.

The earliest record of using data to help a business dates back to 7000 years ago in Mesopotamia when accounting was introduced to record the growth of crops. The first large data project was initiated in 1937 by Franklin D. Roosevelt’s administration in the United States. The government had to keep track of contributions from 26 million Americans, and over 3 million employers after the Social Security Act was passed in 1937. IBM was awarded the contract to construct a punch card-reading system for this large bookkeeping operation.

Types of Data

Data is primarily of two types, namely quantitative and qualitative.

1. Quantitative Data

Quantitative data plays with numbers. It makes the information countable. The main characteristic of quantitative data is that it can be added, subtracted, divided, or multiplied and still gives valuable information. 

Quantitative data is of two types: Discrete and Continuous.

a. Discrete

This category of data has a discrete value. Discrete data are integers or whole numbers. The number of runs scored by a batsman in a match can be 30, 35, 70, 100, etc. This type of data can never be fractional, which makes it discrete data.

b. Continuous

This data consists of numbers that can be fractional. For example, the average number of runs scored by a batsman (per match) after playing any number of games can be fractional. The velocity of a vehicle, distance, and temperature are all types of data that come under this category.

2. Qualitative Data

Also known as categorical data, qualitative data can not be easily counted or quantified. To add more details to this data, we can categorize this data. The gender of a person (male or female) and eye color are examples of qualitative data. 

Qualitative data is also of two types: Nominal and Ordinal.

a. Nominal

Nominal data is data that can not be compared easily. We can not compare the eye color of two human beings. None can say that green eye color is better or greater than blue eye color. This data gives information about a thing. But such information is likely to be unmeasurable.

b. Ordinal

While keeping their class of values, certain types of values have a natural ordering. If we analyze a clothing brand’s size, we can simply categorize them into small, medium, and large. The grading method used to score candidates on an exam can also be considered an ordinal data type, with A+ being significantly better than B.

These classifications assist us in determining the encoding approach that is appropriate for a particular sort of data. Data encoding is crucial for qualitative data because machine learning models can’t handle these values directly and must be translated to numerical types as the methods are mathematical.

Data Management

With the increase in demand for accurate and meaningful data, data management has become a crucial process for businesses today. Data management is the process by which companies collect the required data from reliable sources to identify solutions for their customers. Operations are performed best when stakeholders can analyze data and use it effectively to stay at the top of their game. Efficient data management helps in better decision-making. There are plenty of ways for companies to manage data.

The most effective results are obtained by performing steps such as ensuring data quality, reducing duplication, and guaranteeing accurate records. The essential steps involved in data management are data cleansing, data modeling, data extraction, data transformation, and data loading to integrate data. There is another term called “data about data.” This helps the administrators and users understand the database.

As firms seek to profit from such data, data analytics that blend structured and unstructured data have proven helpful. To manage incoming data at high ingestion rates and analyze data streams for immediate application in operations, systems for such analytics are increasingly striving to deliver high performance.


Data is a valuable and vital term for any business because businesses want to extract meaningful information from data that can help them to make better decisions. Data also has plenty of meanings. It may mean something different to every professional. But to conclude what has been stated so far, we can say that taking good care of data in terms of managing, storing, and analyzing it always brings fruitful results.

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