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March 21, 2023 - 7 minutes

Business Intelligence: Definition, Job Types, and Examples

Discover business intelligence and career options available in the industry.

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Business Intelligence: Definition, Job Types, and Examples

It's likely that you have heard of the term business intelligence or BI. You might have even learned about it after exploring the field of data analytics. However, one question remains—what exactly is business intelligence?

In this blog post, we will explore the definition of business intelligence, discuss some jobs within the field of business intelligence, and provide a few examples of how businesses can use BI in their operations. I'll also add some commonly asked questions about BI.

What is Business Intelligence?

Business intelligence is a broad term that refers to the collection, storage, analysis, and visualization of data from an organization's systems. It typically involves analyzing large amounts of structured and unstructured data to provide business insights for decision-making. For example, BI tools can help identify trends or patterns in sales numbers that may not be obvious otherwise. It can also be used to identify areas of the business that can be improved or optimized. Business intelligence is a powerful application of data that can help organizations make better decisions, reduce costs, and increase profits.

Jobs in the Business Intelligence Field

The business intelligence field is large and diverse and comprises several different jobs. Here are some of the most common business intelligence job roles:

  • Business Intelligence Analyst: business intelligence analysts often work closely with data engineers and statisticians to collect, store, analyze, and visualize data. They use the insights from their analysis to identify trends or patterns that can help a company make better decisions.

  • Business Intelligence Developer: business intelligence developers specialize in designing and implementing data warehouses, ETL (extract, transform, load) processes, and reporting solutions. They are responsible for the development of databases, dashboards, and interactive reports.

  • SQL Developer: SQL developers create and maintain databases, tables, views, and other data structures. They are also responsible for writing complex queries to extract data that can be used in business intelligence.

  • Business Intelligence Consultant: business intelligence consultants provide strategic advice and suggest best practices for their clients. They help companies make decisions about which data to collect, how to use it, and how to get the most out of their data.

  • Business Intelligence Specialists: business intelligence specialists are responsible for building and maintaining a company's business intelligence system. They also support departments such as sales and marketing in using the data they have collected.

  • Business Intelligence Manager: business intelligence managers develop and maintain the company's business intelligence strategy. They also oversee the development of data warehouses, ETL processes, and reporting solutions. They manage all BI analysts and BI developers within a team.

In my experience as a data analyst, I previously worked together with a BI specialist to build and maintain data workflows in the company. We also provided support to other departments in collecting the data they needed for making decisions and putting them into simple dashboards on Tableau for reference.

Business Intelligence Examples

Business intelligence has been around for a while and is starting to pick up over the past decade on a wider range of applications across industries. To get a fuller grasp on where business intelligence can be used, check out these examples below:

1. Business intelligence in healthcare

Applications for BI in healthcare organizations can span many applications. Insights from the BI analysis enable informed decision-making about patient care, research, and operations. Through data analysis of medical and clinical data sources such as medical histories and insurance claims, healthcare providers can identify patterns that lead to better diagnosis and treatment of patients.

2. Business intelligence in manufacturing

As one of the slower industries to adopt technology, BI provides huge potential for improvement and optimization in the manufacturing sector. Manufacturers are leveraging BI tools to analyze data from their production lines, supply chains, and customer interactions. Using this analysis, they can identify problems with product or component design, inventory levels, and the overall efficiency of their operations. This helps them make informed decisions about how best to optimize their production processes.

For example, Tesla uses business intelligence to measure and improve efficiencies in their car assembly. Using their MES (Manufacturing Execution System), they monitor assembly progress to track their overall production rate. Additionally, they use Tableau, a common BI tool, for sharing data and reports among stakeholders.

3. Business intelligence in retail

Retailers use business intelligence to improve product forecasting, customer segmentation, inventory management, and pricing strategies. They can use data analysis to better understand customer behavior, identify high-value customers, and target them with personalized offers. Also, they can track key performance indicators like sales rate, average order value, and customer lifetime value, which helps inform decisions about product assortment and pricing.

Common Business Intelligence Tools

Working in business intelligence comes with some general tools to help process and clean data. These tools may vary depending on the different BI roles.

Here are some common ones you'll encounter when working in business intelligence:

  • SQL (Structured Query Language): SQL is the standard language for accessing, manipulating, and querying data from relational databases. It’s often used in BI projects to query data from a data warehouse or other database sources.

  • Tableau: Tableau is one of the most popular BI reporting tools. It provides powerful visualizations to quickly reveal insights from data analysis.

  • T-SQL: T-SQL stands for Transact-SQL and is a Microsoft extension of SQL. It’s used for manipulating data on Microsoft SQL Server databases, as well as writing queries in stored procedures.

  • Oracle BI: Oracle Business Intelligence is designed to help organizations make better decisions by providing a unified view of their data. It’s used for creating reports, dashboard visualizations, and performing analytics on structured and unstructured data sources.

  • Python/R: These are two popular programming languages used by BI professionals. They help with scripting, cleaning, manipulating, and analyzing data for BI projects. Using Python or R code, BI analysts can automate workflows.

  • Amazon Redshift:  Redshift is a cloud-based data warehouse solution offered by Amazon for large datasets. It’s used for analyzing and querying structured data sources with SQL.

  • Power BI:  Microsoft Power BI is a cloud-based business intelligence and analytics tool. It helps organizations produce interactive data visualizations for making informed decisions.

  • Hadoop: Hadoop is an open-source framework for processing and analyzing large amounts of data. It’s used to store large datasets in a distributed manner, making it perfect for data analysis projects.

How to Get Started with Business Intelligence

Have any of the above jobs or tools excited you? Or are you keen on making a transition into a tech career in business intelligence?

Here are some simple steps to help you get started:

  1. Learn about data analysis and visualizations: start by familiarizing yourself with the concepts of data science, machine learning, and analytics. Research how to analyze data effectively and create meaningful visualizations from it.

  2. Strengthen your technical skills: focus on strengthening your technical skills like databases, programming languages (Python/R), data cleaning, and working with BI tools.

  3. Build your portfolio: start building a portfolio that showcases your technical abilities and projects you’ve worked on. This will help when applying for jobs in business intelligence.

  4. Network & look for opportunities: network with people in the industry to understand the job requirements and start searching for business intelligence roles. Build a LinkedIn profile to help with your networking online.

By following these steps, you can take the first step towards one of the entry roles within BI. For example, you'll be able to land a junior BI developer or BI analyst role.

Related Questions

Curious to learn more? Here are some additional questions you might find helpful.

What skills do you need in business intelligence?

Business intelligence professionals need to have a wide range of skills, including technical skills like databases and programming languages (Python/R), as well as soft and analytical skills. Additionally, problem-solving, communication, and business acumen are also important.

Is a career in business intelligence a good choice?

Yes, a career in business intelligence can be very rewarding. BI professionals are usually well-paid for their skills and knowledge of data analysis and reporting. Plus, the field is constantly evolving with new technologies, giving you plenty of opportunities to learn and grow. Business intelligence is also an expanding field in many traditional industries like healthcare, finance, and manufacturing. Therefore, job opportunities for BI roles will be plenty.

Final Thoughts

Business intelligence is an exciting and dynamic field where you can work with data to drive informed decisions. Job opportunities across industries and company sizes are plentiful, and with the right skills, you can successfully transition into an entry-level BI/data analytics role.

Author Bio

Hi, I’m Austin Chia! I previously worked as an analytics instructor, a data scientist for healthcare research, and a health-tech data analyst. With my years of experience in data, I now seek to help others learn more about data science and analytics through content at, where I write and share my learnings. 

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