In 1958 Hans Peter Lunh (IBM Computer Scientist) defined Business Intelligence (BI) in it's simplest terms as "the ability to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal."
Prior to the days of sophisticated computing, BI was still important but based around an entirely manual process and decision support tools were limited to a purely human process. It was often impossible to properly analyze the data thus meaning most decisions were based on instinct rather than fact or truth; all having a detrimental effect on the business.
The early 80's were a prominent age in the data collection and analysing era. With the introduction of spreadsheets like Lotus 123 and SuperCalc, then multidimensional spreadsheets in the form of a product called Compete, the first to come into the scene. It was the introduction of Online Analytical Processing (OLAP) that made the biggest impact in the BI arena, OLAP are in essence small data cubes (3 dimensional) obtained from a large data set (or even multiple data sets). The information generated in reporting with OLAP cubes is then downloaded to the PC for processing, analysis and potentially querying.
In the current day we have a vast amount of data available, whether it be sales out, inventory, legislation, etc. Places to store the data are essential and in the larger forms data warehouses would be set up. With all this data being created and stored we then pose the question on how to access the data effectively as we now face the risk of too much information and not being able to access it in a costly or timely manor. With too much pressure put on the IT departments to generate reports that only meet some of the business needs for some of the people. This is where BI comes into play.