Whether an Election or CRM: Nothing is More Frustrating Than Bad Data

Andrew Staples —  November 10, 2016 — Leave a comment

I read this passage in an article on CIO.com yesterday:

Most election prediction shops and public polls in recent days foresaw Republican Donald Trump losing the U.S. presidential race to Democrat Hillary Clinton.

They got it wrong, bigly. And the failed predictions could cast doubts on some hot technology sectors, including big data and customer relationship management.

Let me begin by saying this isn’t a political post. Instead, I’m going to focus on the value of clean data. No matter what side of the vote you come down on, I think we can agree something went terribly wrong with the almost all of the pre-election predictions. The reason, the forecasters were working with incomplete data. Which, led to a shockingly inaccurate (at least according to what the data predicted) result.

Relating this back to the CRM industry. If your CRM has inaccurate data, you will also get an inaccurate result. If you are dealing with dirty data, how can your CRM project be successful? How can your sales team be efficient and perform at an optimal level? Once dirty data enters the CRM, it can be painstaking to cleanse.

Luckily, intelligent CRM can help by automatically bringing in a broad range of accurate data for a more complete view of the customer. In so doing, the CRM solves a problem that has often been a stubborn obstacle to CRM adoption — the time-consuming effort of having to add, edit and update information in the system. Even more important than gathering a tremendous amount of accurate data, a product like Sugar Intelligence will use predictive analytics to sift through the data to enable the CRM to make intelligent recommendations for insights and best actions to take when engaging with customers.

It’s been said we are in the era of “big data politics.” Perhaps by 2020, we’ll have evolved to a political era of predictive analytics or even machine learning. But, I’ll save that for a future post.

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