By Kate Pavao
Organizations face many challenges when it comes to managing their talent in today’s market, from how to recruit the best candidates to how to incorporate new organizational models that drive innovation and profit. Here’s another challenge that might feel familiar: lacking the tools to do the job right.
In Talent Wins (Harvard Business Review Press, March 2018), authors Dominic Barton, global managing partner at McKinsey & Company; business adviser Ram Charan; and Dennis Carey, vice chairman at Korn Ferry, point to a recent survey that shows that only 8 percent of human resources departments report having the software they need to analyze employee performance effectively.
The investment in HR analytics is accelerating as are the insights people are getting, but I wouldn't just sort of blindly flip over to the technology without understanding how it works.”
It can all be pretty overwhelming for business leaders to figure out which moves to make first. That’s why in Talent Wins, the authors examine 60 companies—such as Amgen, BlackRock, and PepsiCo—to create what they are calling a “playbook” for CEOs. Here, Profit talks to McKinsey’s Barton about who should drive the revolution, how to surface the best people, and opportunities to deploy technology to move beyond bias and transform the way you manage talent.
On Leadership: The CEO needs to drive a top-down revolution to make sure talent is the priority. When you look at organizations that perform well over time, they’re the ones that reallocate both their capital and people well. The two are intimately linked and they come together with the CEO. Business leaders have focused a lot more on the capital, not the people, but it’s people that drive the results.
On the Critical 2 Percent: There are actually only a very small number of people who can drive an incredible amount of value in an organization, and they’re often not at the top of the house.
A lot of leaders think, “Here’s a profit opportunity. Let’s get everyone to do it.” Instead, you’ve got to translate those financial opportunities to the positions and the people that will actually make them happen. Secondly, you need to provide opportunities for younger or untested people. When you give them chances beyond their day jobs, you’re going to surface up the best people.
On Data: Sometimes the 2 percent are people that might not be high-performing yet, but they’ve got intrinsic attributes. When you’re dealing with thousands, you can’t go interview them all. But if you’re regularly doing culture surveys, you can get data that will give a sense of their drive, resilience, or attitude.
On Metrics Versus Gut: The investment in HR analytics is accelerating as are the insights people are getting, but I wouldn’t just sort of blindly flip over to the technology without understanding how it works. You want to experiment with metrics. At McKinsey, we get about 750,000 applications a year for 4,300 roles, so we are experimenting with how to use the best combination of people and machines to figure out which people to put through.
Dominic Barton, Global Managing Partner at McKinsey & Company and co-author of Talent Wins
We do machine learning on 100,000 of those CVs, but we also have people doing it at the same time. There are not wildly different answers, which is good, but the machine is about 10 percent less biased against women than the recruiters. Now we feel comfortable scaling up technology, saying “Maybe we can search CVs of people who had not applied to us, going into LinkedIn or other places, and identify people that we would like to hire.”
On Building for the Future: At McKinsey, we also did predictive analytics about why women leave. It turned out—surprisingly—that if the first project was not a good experience, there was a very high probability that that woman would leave, irrespective of flex program, sponsorship, and so on. This was not the case for men. We wouldn’t have come up with that insight without technology, and it’s helped shift what we do.
Also, consider succession planning. If you think about the number of promotions that are made, not just the big ones, that’s a lot of data that you actually get on what worked or didn’t. The key is you’ve got to be able to collect the data on the discussions and the input that went into the decision. Every organization has biases—you promote who you know or who went to the right schools. But when you use analytics, you can measure your success rate and discover the biases that you put in place that you shouldn’t have.
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