‘Business Analytics’ is often portrayed as the latest miracle cure for managers wanting to improve corporate performance. But like most IT-based capabilities, the hype is often in the realms of fantasy, which can never be realised. However, analytics is a capability that can bring tremendous value to those organisations who understand how and when it can be applied.

The concept behind Business Analytics is nothing new.  In the modern era, the use of analytics can be traced back to World War 2.  Gordon Welchman (1906-1985) worked at Bletchley Park with Alun Turing on breaking the enigma code.  He devised a method known as "traffic analysis" of encrypted messages, which looked at, among other things, message origination, message destination, time/date information, and so on. By looking at the patterns in this data he was able to identify which messages were important and worth deciphering, as well as an indication of what they might contain.  This turned out to be key in the success of the code breakers.

In the same way, modern analytics looks for patterns in data that can be used as the basis for understanding the past in order to predict future events. The advent of business computers back in the 1970s led to a range of applications tailored for corporate use. Gartner, the IT research and advisory group coined the name ‘Business intelligence (BI)’ as, in their words ‘… an umbrella term that includes the applications, infrastructure and tools, and best practices that enable access to and analysis of information to improve and optimize decisions and performance’. 

To begin with, business analytics was confined to on-line queries and the multi-dimensional analysis of data, such as that delivered by PowerPivot within Excel spreadsheets. These ‘early waves’ of analytics were soon enhanced with more forward-looking capabilities. This probably led Gartner to define ‘Business analytics’ as a range of solutions ‘used to build analysis models and simulations to create scenarios, understand realities and predict future states’. These solutions include the application of statistics for data mining (turning data into useful information), and predictive analytics (using past data to make predictions about the future).

Over the past few years, great improvements have been made in analytic capabilities, in particular in dealing with large volumes of data and in making dynamic ‘real-time’ interpretation of data, such as that used by credit card companies to detect on-line fraud as it happens, or in providing tailored responses during a telephone sales call.

Advancements in both hardware and software mean that the most powerful analytic capabilities are within the grasp of every business professional. Which is just as well as there is now an enormous wealth of data, from detailed market information to the minute by minute search habits of millions of potential customers, just waiting to be analysed.  Analyses that could reveal the ‘hidden’ code of what is most likely to happen in the future.

First was published on prevero Blog