Business Intelligence Automation

Business Intelligence Automation: What Is It and How to Use It?


Business intelligence (BI) automation is the process of automating the collection, analysis, and distribution of business intelligence data. This data can be used to support decision-making in all levels of a company.

In this article, we will explore how business intelligence (BI) automation is used, what the benefits and challenges are, and finally, some tools that are being used for BI automation.

What is Business Intelligence Automation?

Business Intelligence (BI) automation is the process of automating the gathering, storing, and analysis of data to support business decision making.

 BI automation is usually a combination of artificial intelligence (AI), robotic process automation (RPA), and business process management (BPM) that together has the main goal of reducing the involvement of a data analyst or scientist in the process.

If you want to read more about business intelligence (BI), we recommend our post Introduction to business intelligence (BI), or check out all of our posts related to business intelligence

Automation can help businesses gain insight into their operations, identify trends, and make more informed decisions. Automation can also reduce the time and effort required to carry out these tasks, allowing businesses to focus more on strategic initiatives. 

There are many different types of automation tools available, such as data mining, predictive analytics, and machine learning.

5 Benefits With Business Intelligence Automation

BI automation can help organizations save time and money by reducing the need for manual work, such as data entry and analysis. Five top benefits with BI automation: 

  1. Enhanced and Automated Analytics
  2. Reduce cost
  3. Streamline reporting
  4. Reduce Bias
  5. Widespread Accessibility 

Let’s have a look at them a bit closer

Enhanced and Automated Analytics

Automating business intelligence can also enhance analytics capabilities. Automation can help to uncover hidden patterns and trends in data and identify correlations between different datasets. This can help businesses to gain a deeper understanding of their customers, markets, and operations.

Real-time Data for Business Intelligence

Reduce Cost

Automating business intelligence can also result in significant cost savings. By eliminating manual processes, businesses can reduce labor costs and free up resources for higher-value activities. Automation can also reduce the need for expensive software solutions and hardware. 

Real-time Data for Business Intelligence

Streamline Reporting

Automation can also streamline the reporting process. By eliminating manual processes, businesses can quickly generate reports and insights on a regular basis and make better, more informed decisions.

Artificial Intelligence in BI

Reduce Bias

Can be used to reduce bias in decision-making by providing an unbiased view of data. Automation can remove any potential biases that may be present in the data, such as age, gender, and race, allowing decisions to be made based on accurate and unbiased information. 

Artificial Intelligence in BI

Widespread Accessibility

Finally, automation can make business intelligence tools more accessible to a wider range of users, allowing the insights generated to be shared more widely.

Business intelligence automation can eliminate middlemen, minimize bottlenecks, and improve data accessibility across all departments. Data interpretation done by analysts may no longer be needed as end-users can access and interpret data themselves.

Data Democratization in Business Intelligence

Challenges With Business Intelligence Automation

BI automation can help us to improve efficiency and decision-making, but it also comes with some challenges. 

Quality Control

While BI automation can offer significant advantages, it also comes with some challenges. One of the biggest challenges is ensuring the quality of the data. This can lead to decision-makers making bad decisions based on bad data. Simply put, wrong (low-quality) data will lead to inaccurate results

To overcome this challenge, businesses need to put in place quality control measures to ensure that the data that is being used is accurate and complete. This can be a time-consuming and expensive process, but it is essential to know that the data we are using are correct. 

Loss of Data

With BI automation, businesses can collect and analyze large amounts of data very quickly. But if something goes wrong, that data can be lost just as quickly. 

However, on the other hand, When data is manually entered into BI tools, there is always the potential for human error. This can lead to data being entered incorrectly, or even not entered at all. 

To conclude, loss of data is prevalent both with and without BI automation, just be aware of it and treat it accordingly.

Starting Cost

Although business intelligence automation can save money and time, it can be a challenge to justify the up-front investment needed to get everything up and running. What you need is a thorough plan for justifying the needed resources to get decision-makers on board and see the long-term profits of automating 

Tools for BI Automation

There are many different tools available for automating business intelligence processes

Automation and Orchestration Tools

Automation and orchestration tools are designed to simplify and streamline the process of managing business intelligence systems. These tools automate the steps necessary to create, deploy, and maintain BI systems, often using a graphical user interface.

Popular automation and orchestration tools include IBM’s Cognos, Informatica’s PowerCenter, and Oracle’s ODI.

Data Integration Tools

Data integration tools are used to facilitate the transfer of data from multiple sources, such as databases, spreadsheets, and other applications. Popular data integration tools include IBM’s DataStage, Informatica’s Data Integration Platform, and Oracle’s GoldenGate.

Data Warehousing Tools

Data warehousing tools are used to store, organize, and analyze data. Popular data warehousing tools include Oracle’s Exadata and Teradata’s Warehouse, as well as open-source solutions such as Apache Hadoop.

Analytics Tools

Analytics tools are used to analyze data, uncover patterns, and make predictions. Popular analytics tools include IBM’s SPSS and SAS, as well as open-source solutions such as R.

Visualization Tools

Visualization tools are used to create interactive, graphical representations of data. Popular visualization tools include Tableau, Power BI, and Qlik sense.


Tableau is a powerful business intelligence automation tool that helps organizations visualize and explore their data. It offers an easy-to-use interface and powerful analytics capabilities to help businesses gain insights from their data.

Tableau Business Intelligence Logo

Microsoft Power BI

Microsoft Power BI is a business intelligence automation tool that allows users to explore their data and generate insights from it. It offers a wide range of features, such as data visualization, data modeling, and predictive analytics. 

Power BI Business Intelligence Logo

Qlik Sense

Qlik Sense is a business intelligence automation and analytics platform that helps organizations quickly uncover insights from their data. It provides users with interactive visualizations, natural language queries, and predictive analytics capabilities.

Power BI Business Intelligence Logo

Conclusion: Automation In Business Intelligence

Automation in business intelligence can be a powerful tool for reducing manual labor and improving efficiency. Automation can help streamline and simplify processes, reducing costs and increasing accuracy. Automation can help decrease the time it takes to generate reports, freeing up resources and allowing businesses to focus on more strategic tasks. 

Curious to see the most notable trends in BI that we expect to see in 2023 and beyond, then check-out our post on: Top 5 Business Intelligence Trends In 2023

FAQ: Business Intelligence Automation

What is Business Intelligence Automation? 

Business intelligence automation (BIA) is a type of software that helps organizations collect, store, and analyze data to make more informed decisions.

It automates the process of gathering, analyzing, and visualizing data from multiple sources, including databases, web services, and other sources.

What are the 4 concepts of business intelligence?

1. Data Warehousing: This is the practice of collecting and organizing data from multiple sources into a single, centralized repository. This allows for reporting and analysis of the data.

2. Data Mining: This is the process of discovering patterns and trends in large datasets. It is often used to develop predictive models and identify correlations.

3. Online Analytical Processing (OLAP): OLAP is a technology used to analyze multidimensional data from multiple angles. It is used to create interactive dashboards and visualize data in different ways.

4. Dashboards and Visualization: Dashboards and visualization help to provide a visual representation of data. They are used to quickly identify trends, outliers, and other insights.

What is an example of Business intelligence automation?

An example of business intelligence automation is a software system that can be used to automatically collect, store, and analyze data from various sources to generate meaningful insights.

This could include tracking customer purchase patterns, analyzing market trends, or identifying inefficiencies in operations.

What is an example of intelligent automation?

An example of intelligent automation is an automated chatbot that is able to provide customer support or answer customer inquiries. The chatbot is able to understand the customer’s request, respond to it with meaningful answers, and even suggest additional services the customer may be interested in.

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.