The Manager's Guide to Building Strong Relationships Between Marketing & Data Analysts

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

Big data marketing Both marketing and data science teams should be held accountable 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.

ReachForce helps marketers increase revenue contribution by solving some of their toughest data management problems. We understand the challenges of results-driven marketers and provide solutions to make initiatives like marketing automation, personalization and predictive marketing better. Whether you have an acute pain to solve today or prefer to grow your capabilities over time, ReachForce can unify, clean and enrich prospect and customer lifecycle data in your business, and do it at your own pace.

To learn more about how ReachForce can help you optimize demand generation and your impact on revenue, get a free data assessment and get a demo today.

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