Bob Hoffman’s Ad Contrarian blog also raises serious questions regarding what is vaguely called digital marketing. Moreover, there have been serious data breaches in recent years and growing concerns about privacy. There are data and statistical issues, and there are also ethical issues, and many of us are concerned that a few well-publicized foul ups will tarnish analytics as a whole. Those of us working in what is now often called data science need to be careful about what we promise. If your vender tells you need to build a gigantic data lake and warehouse first, consider looking for a new vender.
Often it makes sense to start small and proceed incrementally. Exploratory analysis of samples of data, external as well as internal, can provide clues as to potential value of the data. Unfortunately, some management teams have a very gung-ho mentality and, in essence, try to hit a grand slam home run before they’ve gotten a runner on base. Frequently, one reason for perceived failure is a blind assumption that if you build it (data infrastructure) they (insights useful for decision-making) will come. “Dinosaurs” talk to one another and some have had direct experience with an expensive data mining and predictive analytics project that flopped. Data dredging (also data fishing, data snooping, and p-hacking) is the use of data mining to uncover patterns in data that can be presented as statistically significant, without first devising a specific hypothesis as to the underlying causality.ĭata dredging, understandably, is very much frowned upon by the statistical community. I can remember when “data mining” was something you might accuse someone of but would never put on your resume!
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