Ever since Business Intelligence emerged as a technical and commercial practice, the promise has been that the right information, delivered at the right time, in the right format would help us, as users, to make better decisions.
Sadly, that has rarely been the case. The classic formats of BI – dashboards and reports – have been useful for monitoring business, but seldom insightful enough for proactive decision making. BI has always been retrospective, looking back over data in the wake of events that have already occurred. The technology barely guides the all-too-human user, giving just the facts and leaving the most stressful work to you.
But surely we have Predictive Analytics ? The very name describes its forward looking approach. Algorithms extract patterns, often deeply hidden, from existing data, and project those forward. Predictive Analytics successfully powers numerous business scenarios, calculating credit scores in finance, stock levels in manufacturing, special offers in retail and more.
So what is the problem? Simply that Predictive Analytics is still a specialised area, most often requiring tools that are not designed for regular business users. As a result, when algorithms and their outcomes have been integrated with BI, it is too often quite an effort for users to grasp the implications unfolding from the statistical methods involved.
Source : YellowFin