McKinsey & Company/Bryan Hancock and Bill Schaninger: Why you should apply analytics to your people strategy

McKinsey & Company
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Why you should apply analytics to your people strategy
April 2019 | Podcast

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Why you should apply analytics to your people strategy

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Bringing advanced computing power and analytics capabilities to bear on people decisions in an organization is crucial to driving lasting and effective change.

In this episode of the McKinsey Podcast, Simon London speaks with McKinsey partner Bryan Hancock and senior partner Bill Schaninger about why people analytics matters even more in a world awash with data and more advanced computing and analytics capabilities.
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Simon London: Hello, and welcome to this episode of the McKinsey Podcast, with me, Simon London. A certain breed of executive has always looked down on the people side of management. They see it as soft, squishy, and lacking in hard data. But while that may have been somewhat true 20 or 30 years ago, it certainly isn’t true today. There really is a revolution in progress as companies start to apply big data and advanced analytics to the human side of the enterprise. To talk about the promise and pitfalls of people analytics, I sat down in Philadelphia with McKinsey partners Bryan Hancock and Bill Schaninger. As we’ll hear, Bryan and Bill are optimistic, with the caveat that getting real value from people analytics requires not only technical smarts but also a solid understanding of organizational behavior and a pretty good grasp on how the business actually makes money.

So Bryan and Bill, welcome back to the podcast.

Bryan Hancock: Thank you.

Bill Schaninger: Thanks for having us.

Simon London: So an obvious first question for a nonspecialist is: When we’re talking about people analytics, what are we talking about?

Bryan Hancock: What we’re talking about is bringing data on people to specific business decisions. It can be a decision to hire. It can be a decision on how to configure a team. It can be a decision on where to source people. But it is bringing a data set to people decisions. It’s as simple as that.

People analytics has existed as a concept for a long time. It’s not that there’s anything radical about the idea of people analytics. What’s cool is that now there are new sources of data and advanced computing powers that allow you to do more with the data. But the underlying idea of using data to inform people-related business decisions is not necessarily a new thing.

Bill Schaninger: We make decisions every day about who we hire, how we deploy them, what teams we put them in, what we have them working on. Then we sit in judgment of their performances. Every one of those decisions can be made better with data. Not all those decisions are equally important, so you don’t have to bring it to bear in all of them, but you should probably bring it to bear more than we are doing today.

Simon London: As you say, it’s not an entirely new idea. But what’s the opportunity today? What makes this a particularly important and interesting topic?

Bryan Hancock: I think what makes it a particularly interesting and exciting topic today is a combination of a few factors.

One, there really are new advanced computing capabilities that allow you to factor in more variables and determine more of what really matters. Of course, you can’t just do that independently. It has to be linked to good research in what matters, but the advanced computing power does matter.

There are new and different sources of the data—super exciting and interesting. Those matter.

Also, there is the acceptance, more broadly, of advanced analytics—be it from marketing to sports. That is making people think, “Oh, if I can do this in understanding my consumer or understanding the quality of my first baseman, why can’t I do this for understanding what makes a good salesperson tick?”

Simon London: Talk a little bit about the sources and the categories of data. What kind of data are we talking about here?

Bill Schaninger: There are some interesting pools that I think, in many cases, we hadn’t really connected. Maybe it’s just because we were isolating around the individual employee in a way that wasn’t helpful. So there’s the obvious. There’s the information about the employee: where they went to school, and where they’ve worked. That’s basic.

But then you can also get into things about their attributes, their traits, their personalities. And then you think, “OK, well, what other data do we have?” We can collect data about performance. We can also collect data about the environments they’re in: perceptions of the bo