Transcript
Mark:
Welcome to PeopleTech, the podcast of the HCM Technology Report. I’m Mark Feffer.
Today, I’m joined by ADP’s Chief Data Officer, Jack Berkowitz. Data’s been a topic of discussion in HR for some years now, and Jack spends a lot of time working with the practitioners and managers who use it. Among other things, he’s seen some interesting people numbers come out of the pandemic, has a handle on what businesses want out of data, and says HR, it turns out, really likes the numbers. We’re going to talk about all of that on this edition of PeopleTech.
Hi, Jack. Welcome back. So you’ve been talking a lot about data for social good lately, and I wondered if you could explain to me what you mean and what the concept is?
Jack:
Yeah. So, we’re a very big operation, and so when we look at all of the information that moves through our system in a given year, it’s a large proportion of the US population happens to move through the system. And so, we use that data to provide them benefits, to provide them really good pay, to provide them, to provide their company’s insights on how they can optimize their workforce. But at the same time, we have that information that can be used for greater purposes. So we started a couple of years ago, several years ago actually, building an asset for sort of the economic markets in the US. The National Employment Reporting gives a view of information about how people are being hired or moving around the world, or the country in terms of compensation and things like that.
We started to look, okay, how can we now do that sort of benefit, but for our clients and for the employees of our clients? One of the areas that we latched onto a few years ago, really in partnership with our clients because they were asking for it is, can we help them get a view of aspects around diversity, equity, and inclusion? We were able to, for example, build the first benchmarks, real benchmarks, real time benchmarks, monthly, about the diversity mix inside of companies, as well as the ability for people to advance inside of companies. We’re able to do that at a very fine grain level and give anonymized benchmarks.
Then we extended that into pay equity. So how can we close pay equity gaps, not just by looking at a company’s internal data, but combining their data with anonymized data from hundreds of thousands of other companies and see if there’s really pay equity gaps that companies can close? And thankfully now, we’ve seen well north of a billion dollars in pay equity gaps closed, but providing this data simply for a good purpose.
Mark:
So who’s the user here? Is it managers, is it executives, is it both?
Jack:
Yes. Yeah. So sometimes, it’s HR professionals, whether they’re in the recruiting function, whether they’re in compensation. For some of our medium businesses, that’s everything, right? They do everything. But oftentimes, it’s executive teams trying to take decisions about how to strategically move and engage their workforce. There’s a lot of activity around talent recruiting or sourcing or employee engagement, and these are all really good topics. But at the end of the day, people work to get paid, and we see a lot of executive teams focus in on that, right? Making sure that the pay is equal, that the pay is right for the job, and how they can shape their organization to serve their needs, whether it’s building new product or doing services or whatever it happens to be, by combining this information. So we see both sets of teams using it.
Mark:
And I’m trying to imagine what it is they see and what it is get when they’re using this. Can you sketch it out for me?
Jack:
Yeah. So, one of our biggest challenges with data, particularly in the HR domain is, and I don’t want to overstate this, right, but is to make it consumable for people, right? I can give somebody a pretty elaborate spreadsheet and the CFO will love that, or I can give somebody a simple diagram to show a pay equity gap, for example. And so, we’ve tried to package it in ways that’s consumable. We’ve built things, for example, called storyboards. They’re graphs and their charts and their reports, but they put it together in, what it sounds like, a story that somebody can look at, understand quickly, see the gap, and then take action off of that pay equity gap, for example.
Now, we also have all the standard reports, and we can give you incredibly detailed reporting out of it as well if you want to give it to a CFO. At the same time, some people want to consume this information into their downstream enterprise business intelligence system, or tie it into a downstream system that they might have like an ERP system or something else. So we have APIs that we provide, application programming interfaces, in other words, calls that other computer systems can make to our systems so that people can pull that data. And we see that data pool quite a bit by clients, and then increasingly that way. So again, visualizations, planners, and then data feeds.
Mark:
We’re talking about internal data here, but as more companies get involved with more outside issues and outside organizations, do you find any of them are bringing data from those organizations into their own house?
Jack:
Yeah. So it’s really an interesting question, and we always have this debate. How big do we make it? So we provide a couple of interesting products here at ADP. One of them is a people analytics product, one of them we call Enhanced Insights, which is really this benchmark information. And the question is, well, should we, where’s that line between providing something very targeted for the HR practitioner or the HR department versus something more enterprise scale? How do we provide combinations of information?
So we do have the ability, for example, for the HR team to bring in sales numbers, or bring in budget information so that they can do localized analysis inside the system. But this idea of combining our data, for example, with some survey data, with some productivity data that they might get from salesforce.com or something like that, that’s something we’re not doing today, but we love the fact people want to do it. So we’ve built connectors to some of the more popular BI systems, business intelligence systems, to do that.
So you can take our data and combine it on the fly in a Power BI, which is Microsoft’s tool. You can combine it on the fly with a plugin that our data then can be right there in inside your Power BI. You can bring your Salesforce data together there and see it, and just through point and click, and we have a lot of clients using that today.
Mark:
You were saying that your clients came to you about developing this because they had the need. Do you think that most companies out there, most employers out there are recognizing that need? Or are you having to go out and sell it, essentially?
Jack:
It’s a great point. What I’ve seen, and maybe I’m biased because I work in this space, but I’ve seen over the past five years a shift, a definite shift in the HR practice and in companies. And I think a lot of it was due to the pandemic, right? During the pandemic, the notion that data about people was super important, not just about running the teams, but actually running the company, became essential. Where are people? Are they healthy? Do we even have drivers once we restart our business? Where do we get drivers? So I think this sort of substantial moment for my little niche area, or our little niche area in people data, happened during the pandemic. And so, whereas I think four or five years ago, it was kind of a push and you would get this response from an HR practitioner, “Oh, well, I don’t know anything about data. I’m just here for HR.”
Today, it’s exactly the opposite. You get into these debates with, I just was at one of our company conferences in Orlando, and I couldn’t give the five practitioners I was having lunch with or the five HR people I was having lunch with enough detail. They knew more about not just our information or their information, but how other companies were using information to do predictions than I was able to answer. So, I think it’s really pivoted in the past five years, and I think it pivoted because, or at least the pandemic helped. Now obviously, there’s better tools, better capabilities, these all are enablers.
But I think the realization that people are the central portion of every company. Doesn’t matter if you’re a manufacturing company or a services company or a shipping company. The people are the thing that drives it, right? The reason why we have supply chain problems for eggs is because we don’t have enough people to farm and deliver the eggs. And people need to, right? This is the essential problem.
Mark:
So is this a generational thing, you think? Or is it just, data’s been around for a while and people are getting used to it?
Jack:
Yeah. I want to be biased and say, “Oh yeah, it’s generational.” But I look in the mirror and I realize I’m from the later generations, the aging generation. Obviously, people are learning tech technology at a faster rate, and there is some adoption of technology. People that can use Instagram and Spotify and everything else seem more likely to gravitate towards using data. So there is a generational thing there. However, I’m in my late 50s and everybody I know uses Spotify and Instagram, and so, and I work with people now, literally work with people in their 70s, and they’re able to use it just fine as well.
And so, I think that old argument, “Oh, well, it’s a younger person’s thing.” Or, “I don’t know about…” I think that’s just a stale argument these days. I think the challenge of any company in any team is to make the information and the data available in a way that people can consume it. And so, we’re always pushing for that, and you see this whole AI push happening in the community. It’s about that. It’s about making the information more consumable.
Mark:
We did touch on this a little bit before, but I’d like to ask you the specific question, which is, can you talk a bit about the tools that are involved here? What are the… How are people getting at this and what are they getting?
Jack:
Yeah, so it really depends on the type of person that we’re providing the information to, or the use case as to which tools get involved. If you’re an HR practitioner or an HR person sitting behind your desk, then we have a user interface built into ADP’s products that people can use. But just at the same time, for managers on the go, we have data and insights and all these benchmarks and everything I’ve been talking about, bundled as part of our mobile application. So ADP Mobile is used by something like 20, 30 million people every month to check in on their pay statements. Well, right inside there, there’s an insights button, and they can get these insights pushed to them. In fact, our system automatically analyzes all the data and says, “Hey, here’s two or three things you need to know.” And that’s great if you happen to be a people manager who’s not going to be in the HR system every day, but you still benefit from people data.
And so, we’ve seen, one of our big retail clients in the Midwest, they’ve used that. They rolled it out, they didn’t do a big test. They just had turned it on, and they were having a problem with turnover inside of their retail operations. So it’s a big thousands of stores type thing, convenience stores, gas stations. And they were able to cut their turnover rates and their overtime rates by something like 70%, by just getting the people data into the hands of the frontline managers, the people who actually need to run and manage the operations. And they did that all through the ADP Mobile app that everybody had on their phone to begin with.
Mark:
And what kind of data was that, that the managers were using?
Jack:
So, it was a combination of calculated things from their data, things like turnover rates, as well as benchmarks. So we have 30 benchmarks of HR transactions across the nation. So things like average time and job, and time to fill, and turnover rate, overtime rates. We have benchmarks on these things. And so they were comparing themselves not to a specific competitor, but to other retail operations in their local area. And they saw, they could see, for example, that their turnover rate was higher than everybody else’s. And they could then make local changes, whether it was in compensation, policy, scheduling, they had lots of solutioning.
We give insights as to what solutions may work, but it’s really up to the individual HR team and the individual local manager to make those, to decide what they’re going to do because every situation’s unique. But we just arm them with the ammunition, if you will, so that they can go and solve the problem. And the best example I heard was, they noticed that they had high overtime rates and turnover at a couple of locations, and it literally had to do with the school system coming out of Covid had changed the opening times of the schools and the closing times of the schools. So based on that, they were able to adjust the, by mandate, hour schedulings, shift them by an hour and a half away from the corporate standard for that local region, and boom, they cut their overtime rates. They cut their turnover rates just by being more aware of the situation.
Mark:
Well, Jack, thank you very much. It’s been great to talk to you and I hope you’ll come back.
Jack:
Thank you. It’s been my pleasure, Mark.
Mark:
Today I’ve been talking with Jack Berkowitz, the Chief Data Officer of ADP. And this has been PeopleTech, the podcast of the HCM Technology Report. We’re a publication of Recruiting Daily. We’re also a part of Evergreen Podcasts. To see all of their programs, visit www.evergreenpodcasts.com. And to keep up with HR technology, visit the HCM Technology Report every day, with the most trusted source of news in the HR tech industry. Find us www.hcmtechnologyreport.com. I’m Mark Feffer.
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