WSW Logo Claim

Business Analytics (BA) & Business Intelligence (BI) - Definition and Differences

Table of contents

A manager analyzes a company's financial reports using a laptop.

Every day, large amounts of different data are generated in companies and their environment. Anyone who captures this data and converts it into business-useful insights creates considerable added value. To achieve this, solutions such as business analytics and business intelligence are used. But what is behind these terms and what are the differences between the two approaches?

What is Business Analytics?

Business analytics (BA) is a digital process in which mass data (big data) is collected, enriched, processed and analyzed using statistical and machine methods. Business analytics is used to enable data-driven decisions in the company.

As the business form of data analytics, BA follows the flow below to provide decision-relevant information:

  1. Extract data from various sources and business systems
  2. Clean data
  3. Integrate data into a central repository (e.g. data warehouse or data mart)
  4. Examine data for patterns and relationships using analytics tools (e.g., data mining applications, predictive modeling applications).
  5. Creation of forecasts using predictive models (optional)


What is Business Intelligence?

Business Intelligence (BI) is a technology-supported process for analyzing data in companies. In addition, BI has the task of visualizing information in such a way that managers, executives and business users can make informed business decisions.

Business Intelligence is composed of several applications, methods and tools. These aim to collect internal and external data, prepare it for analysis, aggregate it, perform queries, and create data visualizations, reports, and dashboards.

Business Analytics vs. Business Intelligence: What are the differences?

The terms business analytics and intelligence are often used interchangeably. However, this is not correct, as there are important differences between the two approaches. For example, business intelligence uses descriptive (descriptive) analysis methods to present historical and current data in a summarized form. BI thus answers the following questions:

  • What has happened or what is happening right now?
  • What has worked well and what needs to be changed?


In contrast, business analytics is focused on predictive analytics and insight generation. Unlike business intelligence, business analytics does not only summarize historical data. BA also aims to recognize patterns in data sets, identify trends and predict future development. Essentially, business analytics thus answers the following questions:

  • What will happen in the future?
  • Why will it happen?


Business intelligence and business analytics each have a different perspective (backward vs. forward). In practice, it is useful to combine these two perspectives, as this creates an even better overall picture. Most companies start with business intelligence to assess the past and present status of their business. In a second step, they then add business analytics to gain new (previously hidden) insights based on existing data and make even better business decisions with the help of forecasts.

Why is Business Intelligence important and what are its benefits?

Initially, BI tools were mainly used by IT experts to create analyses and reports for specialist departments. This was especially true for the areas of finance and controlling. Over time, however, BI software continued to evolve. It became increasingly user-friendly and intuitive, so that it could eventually be used by business users without specific IT knowledge. Today, this approach is also called self-service BI. It offers the great advantage that reports and visualizations can be created or modified by managers and employees on their own. This makes it possible to answer business questions directly without having to rely on IT support.

Once upon a time, BI reports were essentially based on a single data source, the existing enterprise software (usually an ERP system). Today, however, BI solutions are capable of analyzing Big Data - i.e. large volumes of diverse data from different sources - together. This means that many more aspects can be included in the analyses and reports. This in turn enables a shift from gut decisions to very sound, data-driven decisions.

In summary, a professional business intelligence strategy has the following benefits:

  • Acceleration and improvement of decision-making processes
  • Identification of optimization potentials to increase efficiency
  • Identification of measures that can generate competitive advantages
  • Identification of opportunities for cost reduction
  • Identification of measures to increase sales


Use cases for Business Intelligence: Examples

There are now numerous use cases for BI software in various specialist areas. One example is sales. Among other things, it uses BI solutions to display sales data in real time and to react quickly to changes. Likewise, BI in sales supports the monitoring of SALES pipelines, the distribution and performance of sales territories, profitability analysis or the monitoring of compensation.

There are also several use cases in procurement and supply chain management. Some examples are:

  • Identify shipping delays
  • Identify supply chain disruptions
  • Analyze and optimize material procurement
  • Monitor compliance


Business Intelligence is also frequently used in marketing. Here, among others, there are these use cases:

  • Track and analyze effectiveness of campaigns (in various target segments)
  • Identify customer preferences
  • Calculate customer lifetime value
  • Monitor profitability of measures


What are the key components of BI software?

From a technical perspective, BI systems are based on different components, such as a ETL process, datamarts, OLTP and OLAP.

ETL stands for Extract, Transform, Load. The ETL process is therefore a procedure for extracting data from multiple sources, transforming it into a uniform format, and finally loading it into a target system (e.g., data warehouse).

Data warehouses and data marts are a central location where prepared and aggregated data is held for business analysis and reporting.

OLAP (Online Analytical Processing) is a technology that extracts Big Data from relational tables and converts it into a multidimensional format. This allows large amounts of data to be processed and analyzed quickly.

In addition to these basic components, BI software also has various functions for presenting data. Common forms of data visualization are dashboards, tables and charts.

What are the deployment scenarios for business analytics?

Business analytics can be divided into the following categories, each supporting different deployment scenarios:

  • Descriptive Analytics (Descriptive Analytics)
  • Predictive analytics
  • Prescriptive Analytics (Prescriptive Analysis)

Descriptive Analytics is a procedure for the past-related evaluation of data. This classic business intelligence approach is used, for example, to create and evaluate key figures and to output reports.

Predictive Analytics is a forecasting method that uses various mathematical methods and artificial intelligence. Companies can use it to find out what is likely to happen in the future and why. For this purpose, the method uses both historical and current data from internal and external sources. One application example is the prediction of demand for certain products.

Prescriptive Analytics aims to provide comprehensive information and/or alternative courses of action for business decisions. The focus here is primarily on external data that influence processes or decisions in the company. Prescriptive analytics incorporates this data into simulations and optimization processes.

Future outlook: How will business intelligence evolve?

Today, many business analytics and intelligence applications are already in the cloud. This trend will continue and enable the (real-time) analysis of even larger data volumes. From a functional point of view, it can be assumed that BI tools will continue to become more collaborative and intuitive. Artificial intelligence will also have a particularly high relevance in the BI area. This is especially true in Big Data scenarios, where it is simply impossible to identify patterns, correlations and trends manually.

In short: In a business world in which decisions are increasingly made on the basis of data and, in a further stage of expansion, even automated, business analytics and intelligence will be essential tools in the future. Companies that have not yet deployed tools of this kind should therefore take a closer look at the introduction of BI and BA solutions. 

Get in contact with us
For those who want to know more.

You want to know more about our services, products or have questions that our website could not answer? Then we look forward to talking to you!

KPI Packages