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What is Business Intelligence (BI)? A Definitive Guide

It’s no longer news that the world of business has been transformed by various technological innovations and has made data collection, processing, and analyzing critical to business success.

Data rules the world, from customer service to sales, finance management, corporate communications, PR, marketing, and so much more, businesses rely on data from their customers, competitors, and industry to thrive, compete and expand their resources.

The growing need for businesses to gain access to valuable business data and use them to support their business growth is where business intelligence (BI) comes in.

Although existing research says almost 50% of businesses are already using business intelligence (BI) tools, this guide will share interesting insights on business intelligence, how it works and how businesses can leverage BI to grow.

Table of Content

  • What is business intelligence (BI)?
  • Applications of Business Intelligence
  • Business Intelligence Vs. Data Analytics, What’s the Difference?
  • Benefits of Business Intelligence
  • Business Intelligence Techniques
  • Types of Business Intelligence Tools
  • Best Practices for BI

What is business intelligence (BI)?

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Business intelligence is a tech-driven process that combines business analytics, data mining, data visualization, data tools and infrastructure, and best practices to help organizations make more data-driven decisions.

It refers to the business practice of collecting, analyzing, structuring and converting raw data into valuable insights that can support business growth. Business intelligence methods and tools break down complex information or data and compile them into simple, easy-to-understand forms that top-level executives and stakeholders can easily comprehend.

Business intelligence provides organizations with a comprehensive view of their business data – customer behavior, market trends, and business changes – to help them drive change, reduce inefficiencies and adapt effectively to market changes.

Business owners, data analysts, business users, and IT leaders leverage business intelligence techniques and tools to manage large historical and current data to identify the ways past trends impacted business performance and forecast future events or outcomes to benefit from the opportunities and avoid future losses.

Applications of Business Intelligence

Business intelligence is an integral practice that modern-day businesses use to stay ahead of market trends, understand customer needs, and leverage for business growth. Several businesses, including enterprises, use BI for the following:


Business intelligence (BI) tools are widely used in measurement applications. A lot of these tools can capture and process data from customer relationship management (CRM) systems, sensors, web traffic, ticketing systems, etc., to measure business key performance indicators (KPIs).

For example, a maintenance team at an e-commerce company opting for a solution that uses sensors to measure the temperature of vital equipment to optimize maintenance schedules.


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Analytics is the process of studying and processing data to find valuable insights and trends that can support business growth. It is one of the popular applications of business intelligence (BI) tools considering how it enables businesses to gain a better understanding of their data and efficiently propel business value by enabling data-driven decisions.

For example, a customer service firm could use analytics to identify persistent customer problems and use valuable business data to proffer solutions to the problem.


Reporting making is a common application of business intelligence tools and software. The growing need for businesses and enterprises to measure their KPIs and identify and monitor market trends has influenced the rate of data visualization and reporting framework. Many BI software can now generate detailed business reports to provide information for internal stakeholders, automate complex data analytics tasks and replace spreadsheets and word-processing programs.

For example, a sales manager could use a BI tool to create the sales team monthly report showing the sales growth, decline, and improvement to the business executives and stakeholders.


Collaboration has become one of the best uses of business intelligence software among enterprises presently. The collaboration feature enables users within the same team or in different teams to work on the same data and file together in real-time. Collaborations in business intelligence platforms give room for business growth and development and can help to create new reports or dashboards.

For example, in a situation where a business executive needs a personalized report on a product development progress, different team leads – product managers, data analysts, business executives, and quality assurance testers – can use a collaborative BI software or tool to fill in their data on the report.

Business Intelligence Vs. Data Analytics, What’s the Difference?

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The term “business intelligence” was coined in 1989 by business researcher and analyst Howard Dresner to use valuable data rather than facts to improve the business decision-making process.

In simple terms, business intelligence processes data to provide business executives and stakeholders with valuable business information in a simple, easily digestible way to help them make better business decisions.

While data analytics is a broad and advanced process of analyzing data with sophisticated technologies like big data analytics, machine learning, artificial intelligence (AI), predictive analytics, data mining, etc.

Business intelligence (BI) and advanced analytics differ in the following ways:

  1. BI answers questions like “what happened,” “when,” “who,” and “how many” while Analytics answers questions like “why did it happen,” “will it happen again,” and “what will happen if we change this to that,” and “what more can get from the data…”
  2. BI reports KPIs, metrics, automated monitoring and alerting, dashboard, scorecards, online analytical processing (OLAP), and operational and real-time BI, while advanced analytics uses statistical or quantitative data analysis, data mining, predictive analytics, and text analytics, and big data analytics.

Benefits of Business Intelligence

A successful BI program offers organizations many business benefits by analyzing customer data and business changes. It enables top-level executives and team leaders to monitor business performance on an ongoing basis so they can act accordingly when issues or opportunities arise.

Business intelligence also enables marketing, sales, supply chain, manufacturing, and customer service teams to monitor business and customer data to make their processes more effective, improve productivity, reduce labor costs, and many more.

Overall, the key benefits that businesses can get from BI applications are the following:

  1. Help to speed up and improve decision-making.
  2. Enhance internal business processes.
  3. Increase business operational productivity and efficiency.
  4. Identify business problems that need urgent attention.
  5. Discover emerging business and market trends and practices.
  6. Contribute to higher sales and revenues.
  7. Help businesses gain a competitive advantage over competitors.
  8. Effectively using data mining in the Internet of things (IoT) systems.
  9. Make data easier to understand and use.
  10. Improve customer experience
  11. Enhance employee satisfaction.

Business Intelligence Techniques

The BI process is cyclical rather than linear because of the continuous activities that go on in the process. It uses various techniques to accurately conduct business analytics, detect market trends and changes and effectively create forecasts. BI uses the following functions:

Data Mining: is the process of arranging, processing, and analyzing a large set of data, metrics, and statistics to identify trends and patterns and discern the connections between different variables. It is widely used by businesses to learn about their customer’s desires and wants and to detect fraud and spam.

Querying is a request for specific data or information from a database table or combination of tables. This data could be generated as pictorials, graphs, complex results, or as results processed by SQL (Structured Query Language).

Data Preparation: is the process of cleaning, aggregating, and structuring raw data before they are processed and analyzed. It is a crucial step that must be taken before processing, reformatting data, or making correcting data.

Reporting: is the process of using BI software or tool to prepare and analyze data and sharing actionable insights from the process and analyzed data and information with business executives and stakeholders to enable better decision-making and improve business performance.

Benchmarking: is the process of comparing and measuring key business metrics, practices, and internal processes against a competitor, industry colleagues, or other companies across the world to gain a better understanding of how and where the organization needs to change or improve to boost business performance.

Descriptive analysis: is the type of data analysis that describes, shows, or summarizes data points in a way that makes it easy to identify trends or patterns that is important to the business. The descriptive analysis set the premise for conducting statistical analysis. In simple terms, it references past data to explain changes in a business.

Statistical Analysis is a form of data analytics that oversees the collection and interpretation of data to discover patterns and trends. It is the collection of results from descriptive analytics and the application of key metrics that helps to gather research interpretations, design surveys, and studies or statistical modeling.

Data Visualization is the presentation of data in a comprehensive and visually appealing way using graphics like graphics, charts, infographics, plots, or animations to make it easy for the receivers to easily understand. Data visualization software is used to display and communicate complex data relationships and data-driven insights in an easily digestible way. Check out the sales metrics which are much needed for businesses.

Types of Business Intelligence Tools

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Several organizations use diverse BI tools to perform various tasks to enhance their business operations. Some of these tools are supported by self-service business intelligence (BI) software and platforms. The most common BI tools are as follows:

Ad-hoc Analysis

The ad-hoc analysis also known as Ad-hoc querying is one of the fundamental elements of modern BI applications and a key feature of BI self-service tools. It is the process of creating and running queries to identify and evaluate specific business problems.

Ad-hoc analysis is mostly run regularly, or for unexpected events, it can be used to generate reports.

Online Analytical Processing (OLAP)

Online analytical processing (OLAP) is one of the earliest BI technologies that enable users to analyze data across multiple dimensions or software layers. It is best suited for simplifying complete queries and calculations.

In the past, data were extracted from a data warehouse and stored in different OLAP cubes, however, presently, OLAP analyses are run alongside various databases for fast data processing.

Real-time Business Intelligence (BI)

Real-time BI applications analyze data as they are being created, collected, and processed to provide users with up-to-date information on business operations, customer interest and behavior, market trends, and other essential areas.

Real-time analytics can be used to check and monitor sales, inventory levels, credit scores, stock trading, etc., that management teams can view to make better business decisions.

Mobile Business Intelligence (BI)

Mobile BI enables users to access and use BI applications and dashboards on their mobile devices such as smartphones and tablets. They are mostly designed to increase data access and enhance ease of use with more emphasis on a user-friendly interface and easy data viewing.

For example, instead of showing the entire database, mobile BI applications could show a few visualization and key KPIs to make it easy to view and interact with them.

Operational Intelligence

Operational Intelligence (OI) otherwise known as Operational BI is a form of real-time analytics that streamlines and shares data with business managers and frontline workers. OI tools are specifically designed to enhance operational decision-making and call for faster actions to business issues.

Software as a Service (SaaS)

SaaS applications also known as cloud BI, are cloud-based programs or systems hosted by vendors that offer data analysis applications to various users as a subscription. SaaS mostly offers multi-cloud support that enables various businesses to launch their BI applications on different cloud platforms to meet their user’s needs. Check out the guide about SaaS sales.

Open Source Business Intelligence (OSBI)

This business software is mostly offered in two versions – the community edition, which is free of charge, and the subscription-based product, which includes customer support from the vendor/provider. Some open-source BI providers offer free editions for individual users.

Embedded BI

Embedded business intelligence tools combine and implement BI and data visualization functionalities into business applications to enable employees to analyze data within their primary software. Although embedded BI is integrated into software by the providers, it can also be programmed into customized applications.

Collaborative Business Intelligence (BI)

Collaborative BI combines several BI systems to enable multiple users to work or collaborate simultaneously on the same data. It streamlines data analysis, sharing, and workflow on team projects.

Location Intelligence (LI)

LI is a specialized type of business intelligence (BI) that enables users to evaluate locations and geospatial data with map-based visualization features. It offers insights into location-based operations.

Best Practices for BI

Business Intelligence initiatives can only succeed and yield good results if an organization commits to executing the following factors efficiently:

  1. Have a strong business support system in place.
  2. Understand all the business needs across various levels.
  3. Use the right amount of high-quality data.
  4. Identify all available critical business data.
  5. Encourage buy-ins
  6. Enhance requirement gathering for better transparency.
  7. Encourage training
  8. Call for support


Business intelligence (BI) is a set of processes and technologies that transforms raw data into meaningful business information that can help you make better decisions and business actions.

In today’s business world, many organizations use BI systems and technologies to identify market trends and spot business problems that need urgent attention and solutions. BI systems are widely used by data analysts, IT professionals, business users, and top-level executives to improve productivity, accountability, and visibility.

Although most BI systems are known to be time-consuming and complex to use, you can solve these problems by identifying critical data, monitoring and improving data sets, and calling for training and support among others.