The word "analytics" is everywhere. Its methods now appear quite often in business talk, lots of articles distribute the analytical values, there are best-selling books that describe the analytical framework and even some movies with major Hollywood stars. I believe we are witnessing levels of work occupations with some "analyst" in its titles never known before. Big data, statistics and modeling are the buzz words. However, application of analytic methods is very often far from the most-productive way to do that. There are lots of ways preventing analytics not to deliver true value..
I have spent lots of thinking on the keys for a successful application of analytics and what makes an analytical project to achieve its goals. Recently I have came across an article that illustrates some key points that I would like to share with you. The article is Big Data in the Big Apple published in Slate. Take few minute to read it - it is a good read and good story (as if you could expect something else from Slate).
The first point is that analytics should provide actionable insight. The insights have to be such that the organization can directly relate to and see what needs action. Producing a very sophisticated index of parameter that identifies something important is not enough. The insights have to speak directly to the involved parties and should not need additional explanation on what is what.
Another well made point is about the complexity of the models. Sometimes simple models are good enough and deliver the results with enough accuracy, and sometimes even the most complex models struggle to solve a tough problem. There is a good enough model for every problem. Eager to apply everything they know, analysts tend to jump into making complex models – a strategy which is often not well justified.
Application of soft skills is also important. Analysts should tap the wisdom and experience of the people on the field. Important details are often invisible to the by-stander and no matter how bright the analyst, she sees only what she knows. On the other hand, talking to the people on the field puts a good perspective on all the modeling and is essential for extracting some purposes of the model are not well articulated.
There is much more to add and I will be discussing the topics of analytical success further in this blog. In the meantime, don’t forget to check out the article.