Last year was bad for numerous reasons, with data practitioners among the most suffering, as trust in statistics hit its lowest point since the second half of the 17th century and the beginning of the enlightenment. Polls around Trump and Brexit were proved dramatically wrong, while everyone seemed to be constantly condemning facts and expert opinion as outdated. It felt at times like the drive for empirical evidence that began with the age of enlightenment had reached its zenith and was on its way out.
While politicians may have e, however, business knows that facts mean profits and data means facts. This year should see data analytics on the rise again, with companies now reaching a level of maturity that means they are at a point where they are truly practicing advanced analytics - and seeing tremendous results as a consequence.
It used to be that only the largest companies, like IBM and Amazon, had the data and expertise required to use it, but the technology is now available at a price where it is more affordable to companies of all sizes, and insights can be garnered by business staff without the same degree of expert knowledge.
The logical consequence of companies’ increased maturity in predictive analytics is automation of many processes in data science and analytics. Many are even now looking to adopt AI, having seen its potential as a game changing technology and believing that if they do not get in early they will lose ground on their rivals. There is, and always has been, a tendency in business to chase the next big thing in the quest not to fall behind. While this is understandable, many companies still lack the data and analytics foundation necessary to adopt it successfully, even if they think they are ready. In a recent survey of almost 1,000 digital professionals, 42% of businesses who responded said they don’t have a framework for structuring their measurement requirements. Many of these very companies will be looking at AI and thinking they can implement that, but that simply isn’t possible if they do not have the data in place to train machine learning algorithms. These companies need to act fast, to implement data and analytics best practises as soon as possible, or they risk missing out even more than they have now.