The story of Business Intelligence (BI) goes way back. The term itself was first coined by Richard Millar Devens in his Cyclopaedia of Commercial and Business Anecdotes in 1865. He used it while describing how banker Sir Henry Furnese gained an edge over competitors by acting on knowledge ahead of them.
Fast forward to 1958, IBM computer scientist Hans Peter Luhn expanded the concept further. He published an article explaining how technology could be used to gather and process BI. Today, BI is all about using technology to collect and analyze data, turning it into meaningful insights that help businesses act “before the competition.” In short, modern BI is about making faster, smarter, and more efficient decisions using the right information at the right time.
The Early Days of BI
Back in 1968, only people with very specific technical expertise could turn raw data into useful insights. The problem was that data lived in silos across departments. Reports were often fragmented and difficult to interpret.
That’s when Edgar Codd stepped in. In 1970, he proposed the relational database model—a breakthrough that changed the way people thought about databases. His model became widely adopted and set the stage for modern BI systems.
Decision Support Systems (DSS)
The first major BI-related database management system was Decision Support Systems (DSS). Many historians believe today’s Business Intelligence systems evolved from DSS.
By the 1980s, companies started realizing how useful BI could be. As a result, a wave of BI-focused companies appeared, developing tools to make data access and organization easier. Out of this period came several technologies such as:
- OLAP (Online Analytical Processing)
- Executive Information Systems (EIS)
- Data Warehouses
OLAP (Online Analytical Processing)
OLAP allowed users to examine data from multiple sources while viewing it from different angles (or paradigms). Using multidimensional data models, OLAP made it possible to run advanced analysis and ad hoc queries.
Common OLAP applications included:
- Sales and business reporting
- Marketing analysis
- Executive management reporting
- Business process management (BPM)
- Budgeting and planning
- Financial reporting
- Even emerging areas like agriculture
OLAP became very popular because it helped organize and compile data in flexible ways. While it lost some ground with the rise of NoSQL systems, modern companies like Kyvos Insights, Platfora, and AtScale have built OLAP solutions on top of NoSQL foundations.
Core OLAP operations:
- Consolidation: Merging data for broader insights (e.g., summing all branch sales to predict trends).
- Drill-down: Exploring details, such as breaking auto sales down by color, model, or fuel usage.
- Slicing and dicing: Extracting a slice of data from an OLAP cube and analyzing it from different dimensions.
Executive Information Systems (EIS)
In the late ’70s, executives started poking around the internet to dig up business information on their own. Out of that came something called Executive Information Systems (EIS). The whole point was to give top management quick access to numbers and reports without going through a dozen people first.
These systems looked nice for their time—lots of graphs, dashboards, simple menus. The idea was that CEOs could check their own reports, schedules, even email, instead of waiting for middle managers to pass it along.
It sounded great, but the reality was different. EIS had limits. It couldn’t really keep up as businesses grew more complex, and eventually it faded away, replaced by better and more flexible tools.
The Rise of Data Warehouses
By the 1980s, companies had data scattered everywhere—different departments, different formats, and usually lots of duplication. If you wanted a report, it often meant running jobs at night or on weekends so the main systems didn’t crash.
Data Warehouses solved a big chunk of that headache. Instead of bits of data living in silos, everything could be stored in one central place. That made pulling information much faster and cleaner.
This wasn’t just about speed either—it opened the door to what we now call Big Data. Suddenly, businesses could pull together massive amounts of information, from emails to online activity (and later social media), and actually make sense of it.
The impact was huge: fraud got easier to spot, losses shrank, profits went up, and decision-makers had insights that were basically impossible to see before.
BI Goes High-Tech
Then came a big moment in 1988: the Multiway Data Analysis Consortium in Rome. It might not sound exciting, but that conference lit a fire under BI. The big idea was making BI simpler and easier for normal users.
After that, new BI companies started popping up, pushing fresh tools into the market. At that point, BI was still mostly about two things—getting the data, and then presenting it in a way that people could actually use.
The Modern Shift: Late 1990s and Early 2000s
By the late ’90s, things really started to change. BI tools became quicker, easier to use, and more hands-on. Managers and executives no longer had to wait for IT to pull reports for them—they could finally dig into the data themselves.
This “self-service BI” made decision-making faster and more flexible. Tools became less intimidating, and suddenly, data wasn’t locked behind technical barriers. It was the start of the data-driven culture we see in businesses today.
