A common assumption about data-driven marketing is it’s a discipline built on dry land: Statistics, mathematics, programming, data capture, data mining, data hygiene and alignment. Yes, of course as data scientists our eyes light up at the thought of solving problems leveraging data as our foundations.

But we have more going on than that: There is an art to the science isn’t what’s in dispute. What’s still up for discussion, however, is how art is applied to the science. The real problem solving value of a data scientist lies in the ability not only to see patterns in the data that may elude others but also to draw conclusions (they’re called insights but that’s not really what they are) from correlating a variety of data sets. In other words, where are the commonalities and variances and what can we say about them? There’s deduction and inference to that.

Where many marketers stumble is trying to stick themselves into one bucket or the other. Of course, marketers work hard to craft campaigns that will deliver the right message to the target audience at precisely the right time. That’s the science.

Still, much of that science involves forecasting when demand might rise (in a snowstorm in New York City people might want to know about winter coats right then and there). Timing, now there’s the art.

By Curtis Thornhill

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