Here is a brief excerpt from an article written by Matt Ariker and Nimal Manuel for the McKinsey Quarterly, published by McKinsey & Company. They note that most companies are inundated with data, know how they would like to use it, yet can’t get it to work. It’s time to step away from the dashboard and get personal. To read the complete article, check out other resources, learn more about the firm, obtain subscription information, and register to receive email alerts, please click here.

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The type of question we hear most is: How can I get the big data train moving? After all, there’s no shortage of rational reasons to get big data programs rolling: companies that use customer analytics extensively are more than twice as likely to generate above-average profits as those that don’t; an integrated analytic approach can free up to 20 percent of marketing spending; and injecting big data and analytics into operations can help companies outperform their peers by 5 percent in terms of productivity and 6 percent in profitability. So what’s the problem?

The challenge is that many of the obstacles derailing big data efforts aren’t rational. They’re emotional. For all the technical and procedural complexity around big data, the biggest hurdle is often human behavior. Recommendations based on advanced analytics can make a huge difference—if sales reps and customer service agents use them. But many simply don’t want to. Leaders charged with making big data programs work need to understand and acknowledge this reality and develop specific approaches to build trust that overcomes the emotional resistance. That means more than just training employees to use technology to better engage with customers. The best leaders develop examples of what most effectively addresses specific concerns, creating a clear path of action and adopting new approaches to reward new behavior. Here are three of the most common behavioral obstacles and some thoughts about minimizing their impact.

[Actually, here is the first of three they discuss.]

Obstacle 1: “It’s too hard and not worth the effort.”

Many sales reps believe these “new fangled” analytics are too complicated and won’t provide enough benefit for the effort required to understand how to use them. And they have good reason to be skeptical: many have hit “tool fatigue,” having seen one allegedly revolutionary approach after another come and go. That means even with a tool with excellent usability, leadership needs to work hard to convince reps that the analytics aren’t complicated and that it’s worth adopting.

This issue needs to be addressed in three ways. First, note that studies show that the additional time associated with working with recommendations from analytics is insignificant or nonexistent. Second, in many cases these systems can in fact save agents’ time by providing accurate recommendations for specific cases that, in the past, agents themselves had to do with outmoded software and little or no analytics support. One of the best ways to convince your reps is to get them to commit to investing a small amount of time (less than 30 minutes) to test run a recommendation or run a simple query. Finally, frontline agents need to know you value their input and are listening to them. The keys to a program’s success are having robust user acceptance and operational-performance testing to ensure analytic recommendations are being delivered in a timely and accurate manner in support of employees.

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Here is a direct link to the complete article.

Matt Ariker is the chief operating officer of McKinsey’s Consumer Marketing Analytics Center and is based in McKinsey’s San Francisco office; Nimal Manuel is a principal in the Kuala Lumpur office.