Big data is big business, its constant media presence meaning it needs little introduction, its analytical potential highly sought for the ability to predict outcomes that not only shape the future but bringing the future into the present. From Bloomberg and Forbes to industry specific outlets such as GovTech.com and Stanford University’s Medical Science Department, updates on its development come thick and fast.
Such advances inspire enthusiasm and trepidation in equal measure, with the benefits of sophisticated research being weighed against the consequences of such a resource falling into the wrong hands. It is often seen, however, as the inevitable outcome of our historical drive towards increased efficiency, greater quality of life and enhanced communication, with one writer at Stanford University suggesting the insights afforded by such technology are as important as those yielded through the invention of the microscope and the advent of DNA sequencing.
One of the key innovations of big data analysis is the ability to respond in real-time to real-time consumer trends. Researchers such as Hinchcliffe and Kim have discovered that this is giving consumers unprecedented leverage in marketplace dynamics, allowing popular opinion to determine the direction of product development. Companies such as Viacom and Kia have utilised these tools to foster what Hinchcliffe and Kim call “social business”, a business model in which communications channels between consumer and producer are opened up to allow for the free-flow of marketplace data.
Stockbrokers have adopted this insight and are now successfully using it to predict the rise and fall of stocks. When the frequency of searches for the word ‘debt’ increases dramatically, for example, traders can feel confident that a crash is on the horizon. Similarly, if the percentage of searches for the name ‘Microsoft’ increases, traders will know that an increase in share prices is coming. The subtleties of the science behind these predictions are infinitely more complex, but the premise is simple: predicting behaviour based on available data. When the amount of data reaches a pitch at which multiple outcomes and variables are calculable, accurate predictions of probability can be given.
We are all exposed to the effects of big data every time Amazon makes a product recommendation, or a Google Ad targets us, yet it has implications that reach far beyond market research. The ability to monitor and respond to trends and predictions in real-time is already having significant impact on key industries in the development of medicines and the prevention of crime.
A report from GovTech.com highlights the unique insight available through big data in its application by police forces, which, in the past, have been able to determine crime-prone areas through historical data only, relying on information related to previous incidents. Using what is known as ‘Risk Terrain Modelling’, two professors at Rutgers University are pioneering research designed to predict future crime ‘hot-spots’ based on correlations of a wide range of seemingly disparate variables, including everything from geographical terrain to real-estate price fluctuations.
Police forces have shared data with other agencies, such as Child Protection Services, for a long time, and this is part of the reason why domestic violence and child abuse cases have steadily fallen. During this current time of recession and austerity, the emphasis within these services is always prevention, and this is something that big data offers. Police in Kent are already using these tools to direct officers to areas, sometimes as small as a football pitch, where high crime is predicted, while Bangor University reports that police in the US have seen reductions of 19 per cent in burglary, assault and similar crimes.