Today many data analysts and business leaders use the abbreviation BI to represent Business Intelligence. But what is its origins? And why would we want to know?
The term Business Intelligence came to widespread use in the late 1990s. It is often credited to Howard Dresner (later a Gartner analyst), who in 1989 used the term to describe “concepts and methods to improve business decision making by using fact-based support systems.” Today, the definition of BI can mean so much more.
The earliest known printed use of the term Business Intelligence is from 1865, in Richard Millar Devens’ Cyclopædia of Commercial and Business Anecdotes book. The term is used to describe how, a 17th Century banker in England, Sir Henry Furnese received and used information to increase his profit, in a manner that was quicker than his competitors.
Sir Furnese used a system of informants to learn the status of battles in Holland, France and Germany. He knew that the outcome of these battles would affect England’s economics. His allies provided him the information before any of his competitors or the public learned the results.
In a 1958 article, Hans Peter Luhn, a researcher at IBM, used the term Business Intelligence to represent the interrelationships between facts (information) often gleaned from computer data and actions business take to achieve a desired goal (often profit).
In the 1960s, businesses started to use DSS (decision support systems), which are computer-aided models created to assist in decision making and planning for achieving business goals. As computers became more prevalent in businesses, they also became more important in making business decisions regarding planning and forecasts. The DSS model continued strongly throughout the 1980s. BI has evolved from DSS.
The explosion of personal computer usage throughout the 1980s and 1990s, created a “Big Data” surge. Business leaders now had more data about their business, their customers, their competitors, and market forces than ever before. For many businesses it was too much data. Too much to process, too much to enter, too much to analyze. Data was ignored because it was too difficult to access all of it. Or businesses fell into “paralysis by analysis” and could not act swiftly on the information they received.
By the 2000s, efforts were made to have collect and store the surge of data in more efficient ways. Once data was stored, the next effort was to transform data from a myriad of sources into a uniform language, so it could be comparatively analyzed. This becomes the Business Intelligence that is still evolving today.
Huge amounts of data are extracted from multiple data sources, transformed, and loaded into reports, dashboards, and other visualizations to empower business leaders to make wise decisions based on facts. This is the present-day world of BI.
BI has gone from informants on battle fields in the 1600s to data lakes that are transformed into computer dashboards giving you real-time pie-charts of expenditures and profit. When we know our history, it can help us to predict the future.