What is Business Intelligence?
Business intelligence combines business analytics, data mining, data visualization, data infrastructure and tools, and best practices to help organizations make data-driven decisions. In fact, you know you have advanced business intelligence when you have a comprehensive view of your organization’s data and can use that data to drive change, eliminate inefficiencies, and adapt to market or supply changes. Modern BI solutions prioritize flexible self-service analytics, governed data on trusted platforms, empowering business users, and rapid time to insight.
It’s worth noting that this is a very modern definition of BI, which has a turbulent history. Traditional business intelligence, capitalization, etc. first appeared in the 1960s as a system for sharing information in an organization. The term business intelligence was coined in 1989 in conjunction with computer models used for decision making. These programs evolved further to transform data into information, which then became a niche offering for BI teams with IT-dependent service solutions. This article will serve as an introduction to BI and is the tip of the iceberg.
How does business intelligence work?
A looping flowchart of a modern BI workflow.
Businesses and organizations have problems and goals. To answer these questions and track performance against those goals, they collect the necessary data, analyze it, and determine what steps to take to achieve their goals.
On the technical side, raw data is collected from business systems. Data is processed and then stored in data storage, cloud, applications and files. Once stored, users can access the data to begin the analysis process to answer business questions.
BI platforms also provide data visualization tools to convert data into tables or graphs and present them to any key stakeholder or decision maker.
Business intelligence is not just a specific “thing”, it is a general term that covers the methods and practices of collecting, storing and analyzing data from business operations or activities to improve performance. All of this comes together to create a holistic view of the business, helping people make better, more actionable decisions. In recent years, business intelligence has evolved to include more processes and activities to help improve performance. These processes include:
Discover trends in large data sets using databases, statistics, and machine learning (ML)
Reporting: Share data analysis with stakeholders so they can draw conclusions and make decisions
Performance Metrics and Benchmarking: Compare current performance data with historical data to track performance against goals, often using custom dashboards.
Descriptive Analytics: Use preliminary data analysis to find out what happened.
Query: Ask questions about data and extract answers from BI datasets.
Take results from descriptive analysis and use statistics to further explore data, such as how and why trends occur.
Transform data analysis into visual representations such as charts, graphs, and histograms, making data easier to consume.
Explore data through visual storytelling, share insights instantly and stay in the analysis pipeline
Collect multiple data sources, identify dimensions and measures, and prepare for data analysis.
How BI, data analytics, and business analytics work together
Business intelligence includes data analysis and business analysis, but only uses them as part of the overall process. BI helps users draw conclusions from data analysis. Data scientists mine the details of data using advanced statistical and predictive analytics to discover patterns and predict future patterns.
Data analytics asks, “Why did this happen, and what might happen next?” Business intelligence takes those models and algorithms and breaks down the results into actionable language. According to the Gartner IT dictionary, “Business analytics includes data mining, predictive analytics, applied analytics, and statistics.” In short, organizations use business analytics as