The term ābusiness intelligenceā was first coined by IBM researcher Hans Luhn in 1958, and then used in its modern sense in 1989 by then-Gartner analyst Howard Dresner.
The term ābusiness intelligenceā was first coined by IBM researcher Hans Luhn in 1958, and then used in its modern sense in 1989 by then-Gartner analyst Howard Dresner.
Earlier this year, Gartner joined other analyst firms such as IDC and started using ābusiness analyticsā as an umbrella term for solutions for turning data into value :
Business analytics is comprised of solutions used to build analysis models and simulations to create scenarios, understand realities and predict future states. Business analytics includes data mining, predictive analytics, applied analytics and statistics, and is delivered as an application suitable for a business user. These analytics solutions often come with prebuilt industry content that is targeted at an industry business process (for example, claims, underwriting or a specific regulatory requirement).
Thereās still lots of disagreement about the differences between the terms ābusiness intelligenceā vs ābusiness analyticsā (read the comments), but it now increasingly looks like the battle of the semantics has been lost to a newcomer: ābig dataā.
Google Trends lets us take a look at the dramatic rise of the term in searches, while ābusiness intelligenceā continues its slow, steady decline, and ābusiness analyticsā, while still growing, doesnāt look like it will ever catch upā¦
The term ābig dataā is used just as nebulously as its rivals. The reality is that new terms are created because of new technology ā in this case ābig dataā was first used (this time around) to describe the new opportunities afforded by Hadoop, Map Reduce, etc.
But, of course, everybody in the market knows that they are supposed to talk about the business benefits not the features: āthe hole, not the drill bit!ā ā but the business benefits are, of course, the same as theyāve always been: better decisions, insight, lower costs, etc. So everybody, even those not using Hadoop etc, started using it in conjunction with whatever was new in the industry (technologies like in-memory, mobile, cloud, visualization, predictive; data sources such as sensors, social, etcā¦ )
This meant that the definition of ābig dataā quickly became another industry squabbling match that has generated liters of ink (and nostalgia: The Google Books Ngram ViewerĀ seems to show the term was first used in the 1930sĀ and that the term was clearly used Ā in roughly the same sense as today evenĀ in 1969: āāDatamec has made some headway outside the field of big data processorsā)
What will the future hold? Will we all end up using ābig dataā as the new umbrella term? Will something else come along to take its place?
Time will tell, but in the meantime, Iāll stick to my position in the BI vs analytics post: āreal peopleā shouldnāt care very much. Let the analysts and marketers bicker ā instead, you should concentrate on pragmatically solving the business needs you face with the best technology available, whatever itās calledā¦
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