Public Affairs Forum
July 20, 2016
An interview with Prasanna (Sonny) Tambe, Associate Professor, New York University Stern School of Business

Prasanna Tambe: I'm Prasanna Tambe, from New York University. I study technology and labor markets.

What is big data?
When most people think about big data, I think they think about scale. I like to think about granularity, especially when it comes to this kind of HR decision making. So it's the ability to...I like the analogy of the microscope—it's the ability to turn up the power of that microscope, so to speak, to get deeper and deeper into what people are doing inside the organization, what people are doing in terms of training, classes, courses, and so on.

What is the value of big data?
What you can do with the data, so to speak, is the exciting part. You start with the measurement, of course, to help you get a handle on what's going on. But when it comes to the value of the data, it's really about, okay, how do we develop a new science around the data—that basically now that we can measure what people do, what their incentives are, what compensation needs to look like, what the process is and practices and algorithms we put in place to kind of let us make a science out of how we've been thinking about workforce development.

How do firms use big data?
So a few of the ways in which firms tend to use big data these days are going to be using big data for HR purposes include understanding their own skills needs, especially large firms that maybe have lots of locations. Labor markets can look really different from one another—regionally, geographically—in terms of what supply and demand look like, so getting a deeper understanding of where they should be hiring, how much they should be paying. At one level, that's just a really new window that firms can use to look at their own skill needs. There's also a category of measurement that is going to go on inside the firm, and I think a lot of this data's already being collected. There's a lot of instrumentation inside the firm that allows firms to digitally collect how employees produce on a day-to-day basis.

So the ability to use that kind of data to improve our understanding of what information worker productivity looks like is going to be, I think, a fast-growth area.

And then, of course, using data to source the candidates, to understand who it is they should be looking at. So [for] a lot of firms, that's one of the first-order problems: finding good talent and then sorting through all the resumes. So being able to use new tools and techniques to find where—not just in the U.S., but where in the world—talented people are is going to be, I think, a great use of the data.

Can big data be used in soft skill assessment?
So, clearly we see employers talking more and more these days, a lot of them, about soft skills, the difficulty of finding soft skills in new employees. And so I think we're certainly going to see, or certainly have seen, a number of tools and techniques emerge that promise to maybe do a better job of either filtering or training employees in the kinds of skills they would need that we might consider softer: things like face-to-face communication, collaboration, team-building, and so on. But we're seeing more and more emphasis, essentially, on trying to get beyond just picking up [on] individual skills and trying to get a picture of what that whole person, or whole package, is going to look like if they're working at that firm.

Can nonquantifiable data produce operational insights?
So in terms of quantifying information that they can't, they haven't been able to see before, I think when it comes to how production happens in many information-intensive environments, we really don't have a very good idea of what the key drivers are. And so, I think where some of the biggest opportunities for improvements are is being able to—coming back to the microscope analogy—turn up the microscope in organizations like these: consulting firms, accounting firms, firms that aren't as easy to measure outcomes as a manufacturing floor. Understand what the problems are, understand what the solutions are, understand how individuals contribute to the outcome. That's going to, I think, open up a whole range of decision-making possibilities; not just about employees, but also things like where to position people—even physically, in the building, right? How they communicate, how they talk, how to reward people, what motivates people, and who should be talking to who, who gets along with who, on teams and thing like that. All that, I think, is going to be enabled in a sense by some of these new data collection techniques.

What are the challenges of using big data?
For firms, there's at least three big issues here. One is the skills. It's going to continue to be hard to get the people in place to kind of digitize this kind of thinking. The labor market's tight, it's going to be tight for a while. There's also cultural barriers, which are significant, right? People are used to thinking a certain way; it's not an overnight thing moving to more datafication of decisions. We're talking about a major cultural shift, perhaps a shift generationally, in a sense.

So when you start to move towards analytics, moving [towards] greater use of analytics for decision making, I think we're thinking about, or looking at, some significant culture clash in the organization in terms of power and so on.

And then, of course, policy. From a policy perspective, there's so many questions that emerge when we think about using more data for HR. On the consumer side, for instance, there's so many privacy issues that emerge. I think this is going to be just as worrisome, if not more so, on the HR side. So there's going to be a lot of discussion, I think, in the upcoming years about some of this HR tack and what it implies for workers overall, as well as for worker privacy in particular.

Are there generational differences in HR data analytics?
It's a good question. In terms of generational differences, I ask my students this sometimes, and their claim is that it's not about age, it's about stage of life. So they tell me that when they get to have kids and so on, they're going to be just as concerned about privacy as their parents are.

You have a lot of people who know a lot of HR, right? Who know a lot about HR, who have spent years and years developing the right intuition, the right heuristics to know when a person's going to fit. It's not the smart thing to do, necessarily, to displace that kind of thinking.

So, how these things blend together, though—how you take analytics and marry it to all this experience and intuition—is going to be a big challenge. It doesn't always happen easily inside organizations.