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Differences between Business Intelligence and Data Analytics

Many data-driven organizations throw around the terms Business Intelligence and data intelligence often, and there have been many occasions where the two terms are used interchangeably. But it is important to remember that though they share some similarities, they are not the same.

Hence, this post will define Business Intelligence and Data Analytics before we delve into their differences and features. If you’re unfamiliar with this topic, we strongly suggest you read the article from start to end.

What is Business Intelligence?

In Business Intelligence (BI), business data is gathered, studied, and worked on to improve performance. Business data is usually used to explain the past performance of the business as well as reflect on its overall growth.

BI promotes errorless, accurate operations concerning data. It also provides insights about past business transactions, which subsequently aid business officials in evaluating the business journey, especially regarding its economic progress and other aspects.

There are several Business Intelligence tactics. They are:

  • Monitoring of data in real-time
  • Benchmarking
  • Development and reporting dashboards
  • Integration of Business Intelligence software tools
  • Data and text mining
  • Performance Analytics

Although the list provided above is not comprehensive, it also sheds light on the various processes and tasks involved in Business Intelligence.

It has been assumed that improving an enterprise’s strategy and decision-making process is one of the main goals of Business Intelligence. Although this is true, another Business Intelligence goal is profit growth through improved operations.

What is Data Analytics?

In Data Analytics, data is collected, cleaned, inspected, transformed, stored, modeled, queried, and transformed (along with several other tasks). Business, science, government, and education, as well as other domains, benefit from its insights to inform decision-making.

An analytics process is based on nitty-gritty details, which is where Data Analytics excels. Business Intelligence tools are not exclusively used in business contexts.

BI (such as dashboards and custom reporting) are often incorporated into Data Analytics, yet many of these features are not fundamental to the process. It is more convenient to treat them like add-ons.

If one were to consider Data Analytics as a technical discipline, Data Analytics could then be divided into four categories. They are:

  • In descriptive analytics, the past is described objectively, based on facts, i.e., ‘A’ happened.
  • It aims at understanding why something happened, not just what happened in the past.
  • In predictive analytics, we predict future trends based on past data, i.e., because A happened, we predict C will happen.
  • The purpose of predictive analytics is to provide actionable steps toward a specified goal, such as To achieve goal X, we must take action Y.

All the activities taken up by data analysis, from collecting and parsing data to building databases and carrying out various analyses, focused on achieving one of the mentioned goals.

What’s the difference between Business Intelligence and Data Analytics?

Some differences between Business Intelligence and Data Analytics include:

Using insights vs. creating insights

  • The primary purpose of Business Intelligence is to support the decision-making process by using actionable insights obtained through Data Analytics.
  • The primary goal of Data Analytics is to convert and clean the raw data sets and transform them into actionable insights that can be used for various purposes, including Business Intelligence.

Backward-looking vs. Forward-looking

  • Business Intelligence aims to look back at the past and use it to inform future decisions.
  • Additionally, to identify past patterns, Data Analytics often predicts what will happen in the future (see ‘predictive analytics’ in section 2).

Structured vs. unstructured data

  • Structured data, such as data stored in warehouses, tabular databases, or other systems, are used in Business Intelligence. The data gathered can be used to produce dashboards and reports.
  • Structured data is also used in analytics but is usually derived from real-time, unstructured data. Data analysts are responsible for cleaning and organizing these data before storing them.

Non-technical users vs. Technical users

  • Leadership teams and non-technical personnel like chief executives, financial directors, and chief information officers primarily use Business Intelligence.
  • Analysts, data scientists, or computer programmers engaged in technical activities usually use Data Analytics.

Neat vs. Messy

  • Business Intelligence relies on dashboards, reports, and other monitoring techniques to relay insights in a clear, easily consumable manner.
  • Data Analytics involves analyzing data, developing algorithms, modeling, and simulating to obtain insights.

Conclusion

As pointed out above, there are some stark differences between Business Intelligence and Data Analytics. However, despite these differences, it should be mentioned that Business Intelligence relies on Data Analytics as it cannot function independently.

Likewise, Data Analytics, although used heavily in business operations, can function independently without relying on business data. Even though Business Intelligence is one of the primary ways Data Analytics is used, it can also be applied to many other fields.


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