Marketing has been among the earliest adopters of data and analytical technologies, and a hefty chunk of the extremely limited pool of qualified data scientists are working within the ranks of marketing departments.
Data scientists are hailed for their strong mathematical and technical skills, often standing in stark contrast to their creative counterparts in marketing. Traditionally, the creative artsy types and the hard-science folks tend to butt heads. If this is your experience as a manager, don't fret! There are some simple ways to get your creative butterflies and your scientific machines to work together quite nicely for excellent results with big data marketing. Here you go.
Build Bridges Between the Art & Science
Don't make the mistake of assuming that none of your marketing folks are capable of analytics or that none of your data gurus have a creative side. The majority of marketing people are capable of doing a lot of basic analytics for themselves, and most of your data scientists are creative enough to learn to put their big data marketing analytics into a nice format that tells a story. Let them. Don't draw an un-crossable line between the "creatives" and the "intellectuals."
Value All Parts of the Process Equally
This is much like your chicken-and-egg argument. Which is most vital -- the big data marketing analytics behind the marketing messages, or the brilliantly completed marketing campaigns that were built atop the data analytics? Chickens versus eggs. Both are vital, and both need equal (and enthusiastic) recognition from their managers.
Teach Marketers to be Specific About What They Want
Some of the strife can be eliminated when the two teams learn to communicate better. Problems pop up when communication breaks down. For example, if marketers ask whether customers prefer the term cloud-based or SaaS in the marketing messages, data scientists have a good sense of what to ask the data and how to ask it. But they can't ask non-specific or nebulous questions, like, "How do customers want us to brand our products?" because big data marketing can't answer those unspecified, ill-defined types of questions.
Both Teams Must Share Responsibility for Data Quality
Another roadblock to solid big data marketing strategies is data quality. Instead of pointing fingers, place quality control squarely on both teams' shoulders. Marketers should be responsible for the data they enter, while data scientists should be charged with regular cleansing of outdated or corrupted data and data de-duplication. Neither team should be exempt, yet neither team should be 100 percent responsible for the quality of your big data marketing database.
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