If I had an hour to solve a problem I'd spend 55 minutes thinking about the problem and 5 minutes thinking about solutions.
Albert Einstein
If you've been in marketing for more than five years, you can skim this chapter pretty quickly. Consider it a gentle reminder of all you hold dear. If you are a data scientist working with marketing people, then you must read this chapter.
Rohit Nagraj Rudrapatna, a senior data scientist at Blueocean Market Intelligence, understands the need. “The biggest challenge for the data scientist is to speak the language of the marketing department,” he says. “We may face a lot of data challenges but the marketing team and the analytics team work in silos. One does not understand the other's language and that's a big communication gap. If there's a way these two departments can make a bridge to speak the other language, I think that's where synergies can come into play.”
Understanding the problem is crucial in an era when new technologies are so often brought in because they are the shiny, new object. A cool new tool is fun and interesting, but it cannot be useful until we understand what we're trying to accomplish in using it. (See Figure 3.1.)
Figure 3.1 Shiny new tools have always been cool. Finding a use for them is hard.
When television was shiny and new, most of the television ads featured a live ...
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