Big Data as a means of business intelligence

The term “business intelligence” 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. It has since been a term to go on and have a new kind of meaning of its own. 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).

There’s still lots of disagreement about the differences between the terms “business intelligence” vs “business analytics” (read the comments), but it now increasingly looks like the battle of the semantics has been lost to a newcomer: “big data”. The latter is a whole new area and a large area to be in the know of in the world of technology. 

If you try to understand your business data that is structured and not of huge volume or variety or velocity then you make use of typical business intelligence tools and technologies. If the data significant to your business is of very high volume, variety and velocity then you need different set of  software/hardware capabilities to process this data and get meaningful insights out of it. Data of this nature is called Big Data and the associated tools and technologies are called Big Data technologies.

Very large and complex sets of data come under the terminology of Big Data, sets of data so large and multifaceted it is extremely difficult to process using traditional data processing applications. The recognised challenges of analysing Big Data include analysis, capture, curation, search, sharing, storage, transfer, visualization, and privacy violations.