[mwetzler]

I'm a data scientist that works with companies on their analytics problems every day. This article is spot on.

By far the biggest factor influencing the success of an analytics project is that the company has a human who has the time and inclination to think and reason about the business. They figure out what questions are important to ask and then go look at the data to see what they find. Collecting the data is the easy part. There is no analytics product that asks & answers your most important business questions for you.

I enjoyed the jab at predictive modeling; it's almost comical how many companies dream about predictive when they haven't yet got basic tracking in place for what's _already_ happening in their business.

Love the post, thanks for sharing.

[mathattack]

Exactly - the human with domain knowledge is vital. I get scared when I see people trump up black boxes. Black boxes don't help with "Which questions should we be asking?" and "What are the missing variables?"

[dagw]

Domain knowledge is also really useful for spotting bugs. I recently worked on a project where I had very little domain knowledge. So anyway I wrote my code, ran my tests, crunched the data, double checked that all the results seemed reasonable, produced the pretty pictures and everything looked spot on. However once I started showing the results to a domain expert it took him 30 seconds to point to one of the outputs and go "that's impossible, you have a bug in your code". Sure enough I did. As a generalist the results looked fine to me (right size, seemingly reasonable relationship to surrounding values etc.), but to a domain expert the error stuck out like sore thumb.

[mathattack]

True. The ability to sanity check is very important.

[igrekel]

Not doing data analytics but selling software that has forecasting with a model that we build and calibrate. We have fairly good performance, recalibrating the same model tales a few seconds but building the model or changing it is never quick.

The effect of these marketing campaigns on would be clients is terrible. They start going after crazy crackpot solutions to gain revenue while they haven't addressed the simplest easy to reach low risk revenue gains. In a a lot of cases integrating complex side effect data costs a lot and provides only marginal revenue gains.