How to guarantee big ROI on big data


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?

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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.

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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.

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