Data vs Information – Differences and Similarities

‘Data’ and ‘Information’ are two terms that are often considered similar and interchanged incorrectly. Though the two terms seem to be serving a similar purpose, they have their meanings and thus differ. Both Data and Information are crucial aspects of any business or enterprise and have a very thin margin between them.

With the rise of domains like Big Data, the importance of such terms has increased manifold. The Big Data sector is expected to cross $61.42 billion value by 2026. This would be a rise from a mere $4.99 billion in 2019, thus growing at a Cumulative Annual Growth Rate (CAGR) of 36.9%. Therefore you should keep your basics clear if aiming for this sector. 


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Such a rapid increase in domain value has attracted many candidates looking for job roles like Data Scientist and Data Analyst. An interestingly large number of students are now looking for such online courses, especially Free Data Analytics Courses. So you must know better about your job profile and the tasks on hand well in advance.

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What is Data and What is Information?

To clear the confusion, given below is detailed information regarding both Data and Information, later followed by their Differences and Similarities.


  • It is the term used for plain facts and statistics collected during the operations of a business. They are used to record a wide range of business activities, like customer data, number of jobs, number of inquiries, income, expenses, and anything else.
  • It is a raw and unorganized detail that needs to be interpreted to make it meaningful. They are usually in the form of numbers, characters, symbols, words, statements, observations, etc.
  • A data set describes facts and figures and consists of either one entry or a collection of different values. The data is collected for various purposes, mainly for scientific research.
  • If they are not put into context, these individual pieces of data are often of insignificant value.


  • Contrary to data, information is an output of a processed data set that allows businesses to make an informed decision.
  • For example, “customer information” is simply data, whereas that same data can be processed to get information that could be useful in providing metrics helpful for improving customer engagement.
  • Information is interpreting, organizing, and structuring data to derive meaningful inferences from the set. It gives data a purpose and also improves its reliability.
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Though they have different implications, the two terms are very closely related and interdependent. The chart below gives a better understanding of Data and Information.

Data and Information: What are the Similarities?

The two terms are commonly considered similar mainly because of grammatical reasons. In a literary sense, Data and Information are considered equivalent to being synonyms of each other and thus misused.

Still, directly or indirectly, they both serve a similar purpose. The points below show some of the common similarities between Data and Information.

  • Data and information both contain a piece or a collection of knowledge hidden in them. In data, they are in an unorganized form and in information, they are clearly expressed.
  • Data and information are both used for research purposes. 
  • They both are collected for the organization’s benefit, to strategize the business, or make better decisions.

Data and Information: What are the Differences?

Though the two terms are used in the same fashion in literary works, there is a huge difference between Data and Information, especially in the world of Big Data. Some of the key differences regarding these are explained in detail below.


  • Simply put, information is significant while data is not.
  • Any data set alone is of no use unless an inference like an “information” is drawn from it.
  • The raw data does not derive any meaning and cannot be utilized anywhere. Whereas information has some context and provides meaning.
  • Information is also a far more significant asset because a business or an entity can take actions or make decisions based on a piece or set of information.
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  • The commonly adapted methods of representing data sets are visualizations in the forms of Tables, Graphs, Flowcharts, Data Trees, etc.
  • Information on the other hand is a set of inferences drawn from the data set. Thus, it is commonly expressed in forms of language, ideas, thoughts, and other text materials that are based on the data.
  • Hypothesis writing for a given set of data can also be considered as 1 form of this, where inferences are written in verbal form.


  • Data is in a raw form that includes numbers, letters, symbols, a set of characters, pictures, or audio data.
  • They are scattered and not aligned with any context.
  • Information, otherwise, is in a form of ideas, conclusions, or inferences based on data.
  • They are organized with some context, have only the necessary data, and the rest are discarded.


  • Information is considered to be more reliable than data. A set of information conveys some meaning that is dedicated to a single context.
  • Whereas the data is raw-set and can be presented in any context. Also with every context, the meaning of the output will differ, thus data is unreliable.


  • Data is independent while information is not. Data is raw and can include anything as there are no specific requirements.
  • However, information is completely dependent on data and draws its value from it completely.

Decision Making

  • A decision cannot be made based on data, but it can be based on information.
  • Data is raw and unstructured and thus it is a meaningless set, not useful in most decision-making cases.
  • Information contains analytical coherence and provides insights, and thus helps make decisions.

The two terms play important and separate roles in fields like Big Data, where deep and wider arenas are covered. This is a chief reason why there are more differences between the two terms than the similarities. Though both Data and Information are essential for businesses as they can help solve problems, it depends completely upon how the organization interprets the two sets. Therefore, it’s always better to equip yourself with good skills to fit in the best role whenever the job calls.