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Predictive Analytics Has a Scaling Problem, and Bad Data Is to Blame

June 21, 2016 | Justin Gray | No Comments |

The mentality behind predictive analytics is sound. When done right, it can help to cut down on wasteful content from your marketing team and to find ideal prospects for your product.

But even as some companies begin to embrace true data science by combining Big Data on top of traditional CRM information, we’re likely years away from most marketers catching up.

Why? Because true data science involves a lot of research and experimentation. There are no easy fixes or absolute outcomes.

Moreover, a true admission of unknowns is scary for most marketers.

Predictive analytics has a natural ceiling in terms of scale that will remain in place until data governance, collection, and hygiene becomes much, much better. In that same maturity curve, marketers must better understand how to use non-traditional information. In this article, originally published at Marketing Profs, learn how to do just that. 


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