Business Intelligence vs. Data Analytics: Key Differences

By Indeed Editorial Team

Published November 9, 2021

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

Data-driven decisions are an important part of operating a business. Whether it's sales receipts, buying trends, production timelines or geographic locations of your customers, collecting data on your business is crucial for strategic planning. Business intelligence and data analytics both use your company's data to chart financial opportunities, but they do so in different ways. In this article, we discuss the differences and similarities between business intelligence and data analytics, including how they differ within three different types of data analysis.

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What is business intelligence?

Business intelligence, sometimes abbreviated as BI, describes how a business collects data, transforms the data into useful metrics and ultimately applies those metrics to make more informed decisions. The purpose of business intelligence is to use figures and statistics to identify areas of success and improvement. By collecting data on how the company has historically operated, business owners are able to apply analytical models that visually represent a company's performance. Viewing data in a variety of forms can lead to deeper understanding and insight. Having different data models available can also make it easier to share information with external stakeholders.

Business intelligence represents the actions a business takes to capitalize on its data, but it can also refer to the specific tools a business uses to gather that data. Collecting and transforming raw data into a meaningful form is a high priority for many businesses, so there is a variety of software tools available to aid in the process. These tools often provide user-friendly interfaces and dashboards that allow you to easily monitor productivity and trends in your data.

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What is data analytics?

Data analytics is a term that covers a range of practices involved in the translation of data into actionable information. In the business world, professionals may use the terms "data analytics" and "business analytics" interchangeably. Business analytics is simply a category of data analytics that refers specifically to how businesses use data to create predictive models. Companies that apply data analytics do so to plan out their future.

Data analytics is primarily a practice for predicting the future needs of a company. An example of how a company applies data analytics is identifying trends in past data to plan how many materials it needs to keep up with demand in the next quarter. For example, a company that sells winter clothing might notice how sales increase every year during December and January. It could use that data to predict how much inventory It needs for the current year.

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Business intelligence vs. data analytics

Both business intelligence and data analytics represent a company's practices of using data to make informed decisions. While the two terms are similar, there are important distinctions between them. The primary difference between the two terms is that BI shows what has already happened, whereas data analytics helps you understand why something happened.

Since both terms refer to different methods of analyzing data, here are some examples of how they differ in three of the most common ways of looking at data:

Descriptive analytics

Descriptive analytics describes how companies look at historical data. Data from past events is valuable for a variety of reasons, so many businesses archive all their relevant historical information. Within descriptive analytics, a business intelligence approach focuses on interpreting trends and performance metrics to show what has happened. For example, a business intelligence approach might look at a sales report to see which models sold well and which sold poorly.

Under descriptive analytics, a data analytics approach takes the data and seeks to learn why a certain model sold well. Rather than sorting and categorizing the information, data analytics tries to understand why the numbers appear the way they do. This helps the user understand the reasons behind historical data.

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Predictive analytics

Predictive analytics is the process of interpreting data to provide a forecast of a business's future. Predictive analytics is often the next step in the evaluation process after descriptive analytics. A business intelligence approach to predictive analytics extrapolates on current sets of data to chart a likely continuation of present trends. BI might combine recent data with more historical data to present a graph of how seasonal trends change from year to year and predict the business's needs in the coming months.

If you were to apply a data analytics approach to predictive analysis, you would be trying to answer the same questions as someone using a business intelligence approach. The key difference between the two concepts is in how they answer the question. Data analytics provides more in-depth mathematical models based on complex algorithms and simulations. While business intelligence may only examine a few examples of past data to predict the future, data analytics combines multiple sets of data with advanced artificial intelligence software.

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Prescriptive analytics

The differences between business intelligence and data analytics are most apparent when performing prescriptive analysis, which is the final step in the three-part analytics process. This form of analysis focuses on designing a plan of action for your business based on historical data as well as predictive analysis. Because business intelligence is concerned primarily with identifying past and ongoing trends, a BI approach typically does not perform any prescriptive analysis. Instead, business intelligence provides a fundamentally important framework of data with which you can perform effective data analysis.

Conversely, the primary function of data analytics is performing prescriptive analysis. A prescriptive analysis combines the information you gather through descriptive analytics with the predictive analysis forecast to create a proposal for a business's future. When you apply data analytics to the information you gather through business intelligence, you are able to create an effective business plan for the future, understand where trends in data come from and anticipate future trends with more accuracy.

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