I will start this article with an analogy:
To put it differently, do you think it is worth spending $10,000,000 on a very expensive weather prediction system that is supposed to provide perfect predictions every 5 minutes, anywhere in US? After all, my own prediction system “tomorrow will be the same as today” works just fine for me (I live in Seattle – and this prediction beats many advanced algorithms). But recently, with my wife complaining that we can’t schedule a nice dinner on a terrace or a hike in the mountains until the very last minute (when restaurants are full booked), I am wondering if using a better prediction tool could make sense, even if it means spending money. And what about airline companies interested in reducing bumpy air and detecting micro-bursts to increase client satisfaction, diminish aircraft wear, and reduce plane crashes? How much great weather predictions are worth to airline companies, the military, or hotels?
One of the main reasons big data ROI seems so obscure to CEO’s is because CEO’s don’t use the right people to assess ROI on big data – they might indeed use nobody, but gut feelings instead. There is still a lot of confusion about what a data scientist is, and I believe this is one of the major bottlenecks against adopting big data. Executive people erroneously think that hiring a data scientist trained with a respected university degree is the solution, but many times it fails because they are hiring a fake data scientist. Professionals with very advanced statistical knowledge won’t help you much. You need a guy like me, who maybe is not as statistically or computer science savvy as my peers, yet has incredible business experience and both broad and deep business knowledge – including law, human resources, operations, product, accounting, sales, client relationships and analytics. In short, a vertical data scientist. Such a person is very hard to find. In my case, I would ask a salary well above $500K per year, making it a bad hire for several reasons (not just the salary). I would not accept a data science position in any company big or small, except with companies where I am the founder.
So what is the solution? Working with a guy like me for about 20 hours to help you jump-start your big data projects and assess expected ROI, in a role very similar to a management consultant.This is the real solution to the problem.