Business Intelligence

Business Intelligence: Turn data into actionable insights

Summary

Business Intelligence (BI) is the process of analysing and transforming data to extract valuable business insights to enable decision-making and reveal insights that help executives, managers, and decision-makers make strategically aligned business decisions.

In general, the process for business intelligence include the steps: identify and collect data, organise data, run models and analytical queries, data visualisations of the results, and finally, the decisions and insights based on the results

What is business intelligence?

Business Intelligence, often referred to as just BI, is the practice of turning data into actionable insights. This means that business intelligence helps companies analyze historical and current data to quickly discover actionable information for better strategic business decisions

Business intelligence tools allow companies to process large amounts of data from multiple sources and display results in visual formats that are easy to understand and share. Utilising business intelligence enables business decision-makers to make more informed, and therefore, hopefully, better decisions about how to operate and manage their companies.

I like to think that business intelligence is about:

  • Using the data a company has
  • Presenting the data visually and easy to understand 
  • Make it accessible for multiple people, teams, and departments in the company 
  • Finally, try to find influential insights (the “intelligence” part) that the company can take action on
Business Intelligence

Why use business intelligence?

Companies today have tremendous opportunities – and challenges – with the rapidly growing amount of data that they can get from numerous sources. Examples can be from the various digital sales channels that the company has, such as their website, social media, etc. 

With the use of business intelligence systems, companies can get a comprehensive view of their data more efficiently and detailed. Based on these views, companies can then translate it into insights into their business processes, which (hopefully) leads to improved business decisions.

Use cases for business intelligence

To give some examples

  • Analyse large amounts of data
  • Identify and eliminate business problems and inefficiencies
  • Find new opportunities for revenues and increased profitability
  • Track various implementations and tests to measure results 
  • Accelerate and improve decision-making
  • Market analysis and identify market trends and patterns
  • Analyse customer behaviours
  • Competitor analysis

How to use business intelligence?

In general, the process for business intelligence include the following five steps:

1. Identify and collect data

Data from various sources are identified and collected by integrating into data repository or data warehouse

2. Organise data

The data is prepared and structured to be ready for data analysis

3. Run models

Analyst run analytical queries on the data

4. Data visualisations

The results of queries are used to create visualisations in the form of charts, graphs, tables, or other visual representations, along with BI dashboards and reports
Example of data visualisations in Microsoft Power BI
Microsoft Power BI Business Intelligence Tool Data Visualization
Image source: Microsoft Power BI

5. Decisions and insights

Stakeholders can use the data visualisations and reports to support making decisions; they may also use their BI dashboard to explore the data for more information and insights

Business Intelligence 5 step process

Business intelligence and Data analytics: What’s the difference?

Even though business intelligence and data analytics can seem closely related and both terms are used interchangeably, with business intelligence being the generalized term that encompasses analytics, there are some differences. 

Some differentiating factors between business intelligence and data analytics: 

1. Operations vs Future predictions and trends

In general, the main difference between business intelligence and data analytics is that analytics is inclined more toward future forecasts and trends, while business intelligence helps individuals make decisions based on past data.

Business Intelligence may have a clearer focus on operation and ongoing processes and work, while data analytics is more inclined towards innovation by focusing on converting raw data and analyzing it to set future trends and identify patterns.

2. Tools and techniques

In general, the main difference between business intelligence and data analytics is that analytics is, as we have briefly already mentioned, various tools and techniques enable the procedures of Business Intelligence and Data Analytics, respectively.

Business Intelligence consists of tools that support data collection, data cleaning, and producing visualizations and reports. At the same time, data analytics utilizes tools and techniques, such as R, Python, SAS, Apache Spark, and many more, to create and execute analytic processes.

3. Data types

Business intelligence mainly utilizes structured data; with some unstructured data, the data analytics can use large sets of structured and unstructured data. In short, Structured data is organized and follows the same format making it easily searchable and managed.

On the other hand, unstructured data has no predefined format or organization, making it much more challenging to collect, process, and analyze. Our article on structured and unstructured data has gone through the differences if you are interested in learning more.

4. Users

If being a bit general here, business intelligence can be used by a wider set of people in a company as it’s targeted towards the decision makers. In other words, the business users of the data that has been visualized in the business intelligence dashboard.

In contrast, data analysts require, again a bit general here, a more technical knowledge and expertise in programming languages, statistical concepts and subject matter expertise. Often a data scientist.  

Note: These are just examples and may not be true in all cases; as said, business intelligence and data analytics are closely related with no clear distinction and boundaries.

Business Intelligence and Machine Learning

Machine learning (ML) is an application of artificial intelligence (AI) that allows systems to automatically learn and improve from experience without being explicitly programmed for the task.

Machine learning can be broadly defined as the capability of a machine to imitate intelligent human behavior.

What this means is that machine learning focuses on developing programs that can access data and use it to learn for themselves.

Machine learning can help in developing predictive models to extract insights from the vast amount of data collected. By integrating machine learning into BI, organizations can gain a deeper understanding of their business and improve decision-making.

Examples business intelligence

Let’s look at some real-world applications of business intelligence. I have put together some of my favourite examples – there are a lot of good ones.

Example 1. How Miami Heat uses Microsoft Power BI to transform their customer interactions and business operations

Since I love sports, this is one of my favorite examples and one I refer to often, how the NBA team the Miami Heat used the analysis and visualization features to transform its customer interactions and business operations completely.

By using business intelligence across almost the entire organization, with an adoption of 70% across the organization, the Miami Heat increased season ticket sales by 30% and saves approximately $1 million on operations.

Business Intelligence example Power Bi

Image source: Microsoft Power BI

How they did it? Well, the main thing was that they got a much better understanding of the different customer needs and how they could fulfill and exceed these needs. 

If you want to see the full example I recommend watching the video on the Microsoft website in the link: Microsoft Docs Power BI

Curious to learn more about Sports Analytics? Then check out our post on Sports Analytics: How Technology Is Changing The Game

Example 2. HelloFresh use Tableau to upgrade their digital marketing campaigns and increase conversion rates

HelloFresh is a subscription service that deliverers fresh food and recipes directly to consumer households. The service is actually really great, and I can recommend that you try it. 

The problem HelloFresh was facing was that digital marketing reporting was time-intensive, manual, and inefficient. As a solution, HelloFresh used Tableau to centralize global performance reporting, save about 10-20 work hours per day, and provide regional sales and marketing teams with real-time data for fast and flexible decision making.

Business Intelligence example tableau

Image source: Tableau

Wanna see how they did it? check out the video on the Tableau website

Example 3. Coca Cola used Tableau to improve operational efficiency

Final example, I don’t think this company needs an introduction. The problem that coca cola was facing was that manual reporting processes took valuable resources and restricted access to real-time sales and operations data

With their business intelligence platform, coca cola managed to automate manual reporting processes, saving over 260 hours a year—more than six 40-hour work weeks. This made valuable time and resources available, and various people within coca-cola can focus on big-picture strategy and long-term innovations instead of doing manual reporting tasks. 

Business Intelligence example tableau coca cola

Image source: Tableau

Wanna see how it was done? Check out the video on the Tableau website

Business intelligence tools and platforms

According to Gartner’s Magic Quadrant for analytics business intelligence platforms, the five leading programs are:

  1. Microsoft Power BI 
  2. Tableau 
  3. Qlik Sense 
  4. Oracle Analytics Cloud
  5. SAP BusinessObjects

Let’s briefly look at them

Microsoft Power BI

1. Microsoft Power BI

Microsoft Power BI is a popular data visualization tool that is well known for its easy-to-use data preparation, data visualization features, and integration with a wide range of data sources.

Power BI is currently available in three versions that are all synchronized:

  • Power BI Desktop: Download and install the desktop version of the PowerBi
  • Power BI Service: Online based to be used in the web browser 
  • Power BI Mobile: Mobile application for your smartphone 
Microsoft Power BI Example Business Intelligence

Image source: SelectHub

Read more about Microsoft Power Bi

Tableau

2. Tableau

Tableau is a data visualization and analytics solution that helps companies make data-driven business decisions and utilize business intelligence. It combines information from various sources to deliver actionable, real-time insights.

Tableau allows data exploration via intuitive means such as drag-and-drop filtering and natural language queries.

Tableau example business intelligence

Image source: SelectHub

Read more about Tableau

Qlik sense business intelligence

3. Qlik Sense

Qlik Sense is a business intelligence and visual analytics platform that supports a range of analytic use cases. The solution comes in three different editions – Qlik Sense Enterprise, Business, and Team. Qlik Sense can be deployed in the cloud or on-premises.

Qlik Sense is user-friendly and does not request strong coding knowledge. A non-IT person can easily take control and manage the solution and extract insights from the data.

Qlik sense dashboard example

Image source: SelectHub

Read more about Qlik Sense

Tableau

4. Oracle Analytics Cloud

Oracle Analytics Cloud is a Business Intelligence platform that allows companies to store and calculate data and display it in beautiful visualizations. Oracle Analytics Cloud has an AI-powered solution that provides powerful reporting and analytics features to businesses of all sizes.
Oracle Analytics Cloud

Image source: SelectHub

Read more about Oracle Analytics Cloud

Tableau

5. SAP BusinessObjects

SAP BusinessObjects offers a variety of packages to fit businesses of all industries and sizes. The platform has real-time analysis and connects to a range of data sources, including other SAP applications. It has intuitive visualization as you can create a variety of intuitive visualizations from data.
Oracle Analytics Cloud

Image source: Business Intelligence Software

Read more about SAP BusinessObjects business intelligence platform

Different Business Intelligence Job Types

There is a wide range of job titles within the field of Business Intelligence, each with its own unique set of responsibilities and skill sets.

Some popular BI job titles include business intelligence developer, business intelligence engineer, business intelligence manager, data analyst, and data scientist.

Business Intelligence offers a variety of exciting job opportunities for those with a passion for technology and data analysis. Whether you’re interested in becoming a business intelligence developer, business intelligence manager, data analyst, or data scientist, there is a role that is right for you.

Business Intelligence Project

Business intelligence projects are structured efforts to gather, process, and analyze data for the purpose of informing business decisions

BI projects can involve a wide range of activities, such as extracting data from various sourcescleaning and transforming the data, creating dashboards and visualizations, and performing statistical analyses. 

The goal of a BI project is to turn raw data into actionable insights that can help organizations make better decisions and achieve their strategic objectives.

FAQ: Business intelligence

What is business intelligence?

Business Intelligence (BI) is the process of analyzing and transforming data to extract valuable business insights to enable decision-making and reveal insights that help executives, managers, and decision-makers make strategically aligned business decisions.

How is business intelligence being used?

Business Intelligence (BI) is the practice of turning data into actionable insights. In general, the process for business intelligence include the following five steps: 
1. Identify and collect data:
2. Organize data:
3. Run models and analytical queries
4. Data visualisations of the results
5. Decisions and insights based on the results

Business intelligence vs Data analytics: What’s the difference?

Even though business intelligence and data analytics can seem closely related and both terms are used interchangeably, there are some differences, including the type of data being used, user skill sets, the purpose of the analysis, and the tools and techniques used.

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Eric J.
Eric J.

Meet Eric, the data "guru" behind Datarundown. When he's not crunching numbers, you can find him running marathons, playing video games, and trying to win the Fantasy Premier League using his predictions model (not going so well).

Eric passionate about helping businesses make sense of their data and turning it into actionable insights. Follow along on Datarundown for all the latest insights and analysis from the data world.