A good read, the main point about all advice is that it is what it is, advice. This means that you have to evaluate it and asses if it has any value for you or your brand. Because when it is all said and done, your the one responsible for it.
We’re now in the crossfire of a peaking hype cycle for big data and its inevitable backlash.
Excerpted and condensed...
I confess, I emphasize with his frustration. Of course, I believe that data — the right data, used in the right ways — is immensely powerful in modern marketing. As I wrote in a post about pragmatic marketing, “Relying solely on gut-based, experience-driven decision-making in marketing is foolish in the digital age.”
But I also concluded: “The sensible answer for most companies is a balance of data analytics and human judgement.”
In the spirit of a balanced approach, I heartily encourage you to embrace data in marketing — but here are 14 rules of thumb to keep that data in perspective, to support a pragmatic approach to data-driven marketing (and avoid data-drowned or data-deluded marketing).
#8. Experimentation is the gold standard of causation.
Correlation is not causation. Every data scientist worth their salt will tell you this. But as marketers, it’s usually causation that we’re after — we want to know what we can do that will cause more customers to do more business with us. So what do we do when data shows a correlation that may reveal such a cause? We run a controlled experiment. Keep all other variables constant (as much as is practically feasible) and test the alternatives to prove or disprove our hypothesis.
#13. The model is not reality.
Data is not the reality that it claims to represent. At best, it is a reflection of reality, but one that is susceptible to being warped (see all the rules above). Certainly we want to use data — and maps, for that matter. As the great statistician George E. P. Box said, “All models are wrong, but some are useful.” But it’s prudent to maintain a little healthy skepticism about the correctness of the representation. In particular, we want to be alert to other signs — outside of the data — that suggest that reality differs.
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