Reading the latest issue of Intelligence Quarterly magazine has really brought home to me the limits of our human brain and its selective processing.

Think about it: visualise a London bus in your mind. Even though you see them every day or have at least seen pictures, are you able to draw one in detail from your mind’s eye? Do you know how many wheels there are, how many windows, or where the bus numbers are located? I bet you’re surprised how little detail you actually remember! This is because the human brain is incapable of processing hundreds of thousands of variables at once, let alone millions and billions. Most of the time, our selective brain processes work brilliantly, as really all we need to recognise is the form of a bus and make sure it is the correct route – all other details are irrelevant.

It’s a different story in the business world though, where the limitations of our human brain and its selectivity are frequently restricting progress. We need to take into account all the available data – not just a sample. This is where analytics can help. Data scientists and other data handlers can use analytics to explore vast quantities of data, sifting through to identify the golden nuggets and work out the relationships between these data elements really fast. Billions of rows can be manipulated in seconds, and advanced analysis can be performed.

Yet people with the statistical and analytical skills needed to manipulate data in this way are few and far between. As a result, many UK businesses are facing a worrying skills shortage, especially as demand for big data specialists is set to increase by more than 90 per cent over the next five years according to e-skills UK.

Thanks to new tools like SAS Visual Analytics, this is no longer the stumbling block it would once have been. By applying visualisation to sophisticated analytics tools, they are now very easy to use, making insights drawn from big data accessible to a much broader range of individuals in any given organisation, from the expert to the amateur. Even a statistical novice won’t have to try and get their head around advanced statistics – phew!