Data analytics is the process of collecting, organizing, and analyzing raw data from different sources. You can then gain insights that'll help organizations make important predictions and decisions.

Data analytics mostly involves studying data trends over a given period, and then extracting useful information from these trends.

Why Is Data Analytics Important?

More precise decision making process: Data analytics helps organizations make more accurate decisions based on the insights gotten from data trends over time.

For example, a company selling different products can figure out what time of the year different products sell higher. This will enable them boost production of such products at the required time.

A better decision making process will eliminate the need for guess work, and minimize losses and avoidable risks.

Improved customer satisfaction: When you're able to serve customers, you retain them and keep business going. Insights gotten from data analytics can help you understand exactly what your customers want and when to act.

Data analytics also enables businesses to identify their target audience easily.

Improved business strategy: Data analytics helps organizations channel their resources towards the most efficient strategies.

Performance evaluation: Data analytics can help organizations evaluate how well or badly they've performed over a specified period. This will enable them make important decisions for the future of the organization.

Although the points listed above seem to be from the business point of view, that's not the only industry where data analytics is important.

You can see data analytics being used in healthcare, education, agriculture, and so on.

Types of Data Analytics

There are mainly four different types of data analytics:

  • Descriptive analytics: This type of analytics has to do with what happened with analyzed data over a specified period of time.
  • Diagnostic analytics: Diagnostic data analytics shows the "why" in a data trend. This involves having a deeper look into why certain patterns were present in the data.
  • Predictive analytics: The goal here is to foretell what is expected to happen in the future based on the outcomes of analyzed data over time.
  • Prescriptive analytics: In prescriptive analytics, the results from data analysis is used to make recommendations on what to do next.

What Is the Difference Between Data Analysis and Data Analytics?

You'll come across different definitions of data analytics and data analysis.

Some sources would define data analytics and data analysis as the same. Others would use them interchangeably.

Although, they are closely related, these terms have slightly different meanings. They are similar because they aid in the decision making process.

What Is Data Analysis?

Data analysis is the process of studying what has happened in the past in a dataset. There is no need to extend this definition.

Data analysis studies the why and how of data trends. Yes, it involves data collection, organization, and "analysis".

"How did the users respond to a new feature?".

"Why did the rate of purchase of a product fall during a particular period?".

Data analysts can make use of programming languages when analyzing data or data visualization tools.

What Is Data Analytics?

Data analytics is the process of taking insights gained from the analysis of past data trends, and making predictions or decisions for the future.

In the beginning of the article, we defined data analytics to include both analysis and analytics. This is mainly as a convention.

Analytics is used to proffer solutions or make recommendations.


There is data everywhere. We create them on a daily basis. But data in its raw form has no real meaning.

In order to understand the behavior of data over time, we have to group the data together, study them, and derive useful insights.

This article explained what data analytics is, the importance of data analytics, and the types of data analytics.

We also explained the difference between data analysis and data analytics.

Thank you for reading!