Business Analytics vs Business Intelligence (BI): Differences

business analytics vs business intelligence

If you work in the field of data analysis, you’ve probably heard the terms “business analytics” and “business intelligence” used interchangeably. However, although they are similar, there are some important differences between the two concepts. In this blog post, we’ll take a closer look at business analytics and business intelligence and explore the key ways in which they differ.

What is Business Analytics?

Business analytics is a set of analytical methods and tools / technologies for analyzing and solving business problems by gathering and analyzing data from disparate data sources, and, understanding, discovering and communicating significant patterns in the data. In other words, it is a process or set of methods / steps for exploring and uncovering significant patterns from data with the goal of optimizing overall business processes leading to realizing business objectives. Business analytics typically makes use of some of the following tools to generate and communicate insights from data.

  • Reports (excel-based reports) primarily for understanding past, present and future state of business. They mostly form a part of descriptive analytics.
  • Dashboards (Tableau, Qliksense, etc) primarily for visualizing the past, present and future state of business. They mostly form part of descriptive analytics.
  • Predictive modeling (using AI / machine learning algorithms) to assess / evaluate future state of business. Both excel sheet and dashboards can be used to view and analyze the predictions. 

Business analytics methodologies & data analytics

Breaking down complex problems into manageable parts is a crucial aspect of problem-solving in business. Here are some of the most common methodologies and related data and analytics tools that can be used:

  • Root Cause Analysis (RCA): This is a method used to identify the underlying cause of a problem, rather than just addressing the symptoms. Techniques under RCA include the “5 Whys” approach (asking “Why?” repeatedly until the root cause is identified) and the Fishbone Diagram, also known as Ishikawa or Cause and Effect diagram (a visual way to map out potential causes of a problem).
    • Data required: Historical data related to the problem, process data, performance data, qualitative data from stakeholders.
    • Tools: Mind mapping tools like Lucidchart, visualization tools like Microsoft Visio and Lucid chart, or even whiteboards for brainstorming sessions.
  • Problem Decomposition: Also known as “Divide and Conquer,” this technique involves breaking down a large problem into smaller, manageable sub-problems. Each sub-problem can then be solved independently. This approach is especially effective when dealing with complex problems, making them easier to understand and solve.
    • Data required: Comprehensive data about the problem. This can be qualitative or quantitative data depending on the problem at hand.
    • Tools: Flowchart and diagram tools like Microsoft Visio, Lucidchart, or Google Drawings can be useful.
  • Hypothesis Testing: This is a structured method used to test if a change in a business process results in improving the current situation. It involves the formulation of a null and an alternate hypothesis and then testing it using statistical analysis.
    • Data required: Data related to the key metrics that your hypothesis is expected to impact. This can include sales data, customer behavior data, process performance data, etc.
    • Tools: Statistical software like R, Python’s SciPy library, Excel or SPSS.
  • Scenario Analysis: This method involves predicting a range of possible future outcomes based on techniques such as What-if. This can help businesses plan for different possible scenarios, helping to uncover any potential problems before they arise.
    • Data required: Data about possible factors influencing different outcomes, which can include economic indicators, financial data, market research data.
    • Tools: Spreadsheet software for creating models (Microsoft Excel, Google Sheets), data visualization tools like Tableau for representing outcomes visually.

What is Business Intelligence?

Business intelligence (BI) is a process for extracting and communicating intelligence / insights from data that can be used to make better business decisions. In simple words, BI is the practice of gathering data from various sources inside and outside of an organization to identify patterns and trends that can be used to make better business decisions.

Business intelligence can be understood as the subset of business analytics. The “intelligence” keyword represents insights which get discovered from the dataset. 

The following are some of the popular BI tools which can be used to extract intelligence from the dataset:

  • Tableau
  • QlikView
  • Microsoft Power BI
  • Google sheets
  • Microsoft Excel
  • SAP Lumira
  • SAP Crystal Reports
  • Spotfire
  • SAP BusinessObjects

The tools for BI can also be used as tools for business analytics. 

Key Differences & Similarities: Business Analytics & Business Intelligence

The following are some of the key differences between business analytics and business intelligence:

  • Business analytics can be represented as a set of methods and tools for solving business problems by discovering patterns from data. Business intelligence can said to be a set of methods and tools which can be used to extract insights / intelligence from the data.
  • Business intelligence can be said as subset of business analytics.
  • A BI developer would most likely work with dashboards, reports etc while a business analytics personnel would work in analyzing business problems and solving them by leveraging data / insights using analytical tools & techniques. 
  • Tools used for BI can also be used for business analytics.


As you can see, although business analytics and business intelligence are similar, there are some important differences between the two concepts. When choosing a solution for your organization, it’s important to understand these differences so that you can select the right tool for your needs.

Ajitesh Kumar

Ajitesh Kumar

I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. I would love to connect with you on Linkedin. Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking.
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