Differences between business intelligence vs analytics

By Indeed Editorial Team

Published 6 April 2022

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.

Business intelligence and analytics are similar and complementary practices. There can sometimes be confusion between the two, and people frequently use the terms interchangeably, but they are subtly different. Understanding business intelligence and analytics can help you appreciate their uses so you can utilise them to improve business practices. In this article, we clearly define business intelligence and analytics and discuss how they can help you make key decisions.

Business intelligence vs analytics

Business intelligence vs analytics has much in common, and some may argue that analytics is a subsection of BI, while others believe BI is a subsection of analytics. There is a strong correlation between them, so let's take a look at a simplified version of their similarities and differences:


Data and business intelligence involve harvesting, storing and analysing large quantities of data using software platforms, spreadsheets and other tools. Infographics, reports and charts can display the findings of these processes in a way that is easily understandable. This means that whilst both practices are highly technical, you needn't be an IT expert to reap the benefits of BI and analytics.


The key differences between BI and data analytics typically relate to the past, present and future. BI concerns the study of what has happened and what is happening. Whereas analytics focuses on why and how these things happened and what is likely to happen.

Therefore, BI helps you identify current market trends so that you can repeat what is going well and eliminate what is going wrong. Analytics uses AI and human intuition to identify patterns to predict future trends so you can prepare your business for what lies ahead. In conjunction, they can drive sales, broaden your reach, cut waste and grow your business.

Related: What does a business analyst do?

Specific roles for business analyst vs data analyst

Business intelligence and business analytics are subtly different from data analytics. They accommodate two different roles:

Data analyst

A Data analyst harvests data, profiles users and processes and stores the information they gather. Data analysis goes beyond business and might work in government, science, education or healthcare. Besides harvesting, data analytics involve the organisation of data. For example, when you mine the addresses and phone numbers of your users, the formatting might be inconsistent, particularly if you're gathering data globally. Cleaning data is the process of managing it so that it's consistent and usable.

Read more: How to become a data analyst

Business analyst

A business analyst uses data to improve the overall performance of a business. They work with the practical application of data to improve business, rather than evaluating the technical details. The key skills are innovation, enhancing technology and implementing strategy. Alongside drawing concepts for company-wide changes, a business analyst draft plans to ensure these changes transpire efficiently. They then continuously monitor and record the success of operations.

Related: How to become a business analyst (with roles and salaries)

Uses of BI and analytics

BI analytics can provide a variety of benefits and improvements for organisations. Here are some of the key advantages:

Profile your customers

Businesses typically gravitate towards speculation. A retailer might make assumptions about who is buying their products, which might influence key decisions, such as:

  • the tone and style of their marketing

  • the pricing of their products

  • their product range

Using BI, you can gather information for analysis, such as the demographic of your customers and their buying habits. Using analytics, you can then adjust your marketing strategy, identify times when you might require more stock and make other key changes. For example, you might find that you can charge more for your products if you emphasise that your company is carbon neutral.

Increase profits

Let's use a hypothetical company as an example. A sauce manufacturer is steadily growing its business and starts using BI and analytics to chart its growth and improve operations. Using BI, the company detects a spike in sales for their chilli sauce globally. They act on this by producing more chilli sauce and shipping it to their global distributors to cope with the demand and increase profits.

Using analytics, they are able to identify that the reason for this spike in sales is due to a travelling food vlogger using the sauce in one of their recipes. They then make the decision to send free samples to other vloggers and influencers to promote future sales. This improves their current operations and informs their future strategy.

Personalise your service

Gaining a better understanding of your customers means that you can tailor your services to their needs. Rather than assuming what they want and why they chose to deal with you, you have data as a foundation for your decision-making. You can then base your strategy on evidence rather than impulse.

Related: Decision-making skills: definition and examples

Conduct market research

Besides analysing your own customers, you can use BI and analytics to gather data on external factors, such as your competitors and market trends. This can assist you in tailoring your product to your customer. All of which can help streamline your strategy.

Make operational changes

Business intelligence vs analytics goes beyond market research. You can analyse your internal operations on a cross-company basis to increase efficiency, improve productivity and reduce waste. You can optimise and improve every aspect of your organisation to enhance workflow and drive return on investment (ROI).

Difference between reporting and analytics

The essence of BI lies in reporting, so it's important to explore the definition between reporting and analytics:


Reporting involves collecting and storing existing data and organising it in a way that is digestible. It helps to answer what happened when reviewing fluctuation charts for products and services. You can utilise reporting in any area of an organisation.


Analytics is the process of exploring and interpreting reports and data. You can then utilise them to improve decision-making and enhance operations. It typically asks:

  • Why did it happen?

  • How did it happen?

  • What happens next?

3 divisions of analytics

To better understand the distinction between BI and analytics, it's useful to explore three common ways of subdividing analytics:

1. Descriptive analytics

Descriptive analytics typically describe how companies look at historical data. Data from past events are 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 was performing well. Rather than sorting and categorising the information, data analytics try to understand why the numbers appear the way they do. This helps the user understand the reasons behind historical data.

2. 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 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' needs in the future.

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 provide more in-depth mathematical models via complex algorithms and simulations. While business intelligence may only examine a few examples of past data to predict the future, data analytics combine multiple sets of data with advanced artificial intelligence software.

3. 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 by reviewing historical data alongside predictive analysis. Since business intelligence is concerned primarily with identifying past and ongoing trends, a BI approach rarely performs 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' future. When you apply data analytics to the information you gather through business intelligence, you're able to create an effective business plan for the future, understand where trends in data come from and anticipate upcoming trends with more accuracy.


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