Welcome to PeopleTech, the podcast of the HCM Technology Report. I’m Mark Feffer.
My guest today is Bob Lockett, chief diversity and talent officer at ADP. He’s responsible for the company’s diversity and talent strategy and oversees performance management, leadership development, engagement and culture, among other things.
We’re going to talk a lot about data and its relationship with DEI, from helping determine where a company’s at, to initiating new programs. That’s on this edition of PeopleTech. Bob, welcome. It’s great to meet you.
How does one attack the task of leading on diversity for a company the size of ADP?
Well, Mark, the first thing I’ll tell you, it’s a very challenging task, because you have so many different constituents and everybody wants their own piece of the pie. What about us? What about us? What about us?
As you can imagine, DEI is a very emotional topic, for that reason. So, the approach that I’ve taken, that we’ve taken at ADP, is really tied to doing a couple of things.
Number one is using the scientific method. You know that thing, Mark, that we learned about back in middle school, that many of us did those experiments?
You would say, develop your hypothesis. Then from the hypothesis, you allow data to prove or disprove your beliefs. And then once you do that, then you really define the problem.
After you define that problem, then start to put plans in place to achieve the outcomes. You tweak as you go, as needed, based on feedback.
So what we’ve done is taking that exact approach and say, let’s take the emotion out of it as best we can. Let’s focus on the data. Let the data be our guiding light, to help us understand where we need to focus and what we need to do.
Now, this doesn’t just apply from a US standpoint. Think about it. This is a global opportunity that we’ve embarked upon. The way I view it is, there are needs everywhere, for people to feel like they are seen, valued and heard for all that they are.
So, not only do we think about diversity… You can measure diversity very easily. You can look at demographic data. How many of these do you have? How many of those do you have?
You can measure equity by looking at pay, but the key is also to measure inclusion. So, we take this holistic approach, all data driven.
The inclusion piece is all sentiment driven, but it’s really leveraging the scientific method and leveraging data, to help tell our story.
Can you expand a bit on how data is used in DEI work? I mean, you mentioned that this is a pretty emotional subject. It always strikes me as interesting when you apply data to an emotional subject. How do they work together? So can you talk about that?
Sure. I could tell you the stories of how we landed where we are, with some of our things.
The first thing that we did as an organization, when I took over the role, I wanted to understand how we looked, because I have a vision that our associate population in our company is reflective of the communities in which we operate and the clients that we serve. That’s very specific and very clear.
How do you test that, your hypothesis about that? How do you make it a realistic vision?
We looked at about three or four different datasets. One dataset was a census data. And as you know, the census data doesn’t mean that everybody’s working.
So, we looked at the census data and we say, “What’s the representation for African Americans, Hispanics, Asians, white women, everybody in our organization?” Let’s lay that out to understand it.
Then we looked at the Bureau of Labor statistics data. Of the people in the workforce, let’s take a look at how that compares and then let’s compare that against our information.
So, we compared it against our information, I’m talking specifically in the US and said, “Huh? Where do we have gaps?”
My hypothesis was that we didn’t look like the communities in America, but the reality of it was, we did. So, I was really impressed. I was like, wow, this is great news.
But as you look at the data, we also found that when you look up in the organization, you don’t have parity in representation for two populations in particular, which were African Americans and Hispanics.
We said, they represent 15% of the overall workforce in the US, for Hispanics. Let’s say it was 11% for African Americans.
Well, we noticed a gap in our company of about four percentage points each way, for African Americans and Hispanics.
We said, well, we should close that gap, because as you come to an organization, you also want to be able to see if there are opportunities for you to advance.
If you don’t see anyone that looks like you, in management level positions, then you start to wonder if you have a real future there. So, that was our quest.
This is how we use data to really understand and tell our story and to put plans in place to do it.
Now, notice the nuance here. Because again, if you go back to my original hypothesis, that we didn’t look like that, we did, but then we pivoted very quickly, because the data told us a different story. We said, that’s where we’re going to focus our efforts.
Now, some people use, Mark, data to try and boil the ocean. You can’t do everything. You can’t be all things to all people. That is a recipe for failure, particularly in DEI.
So, that’s why we have a very narrow focused approach. We have multiple initiatives that we work on, but suffice it to say, that was our main effort, for us to be able to say, we’re moving the needle when it comes to leadership representation in our company.
Now, do you think your company is an outlier in that, or do you think that more corporations are starting to get on board with the idea of using data in this regard?
Yeah. I think it’s a mixed bag, Mark, is probably the best way to describe it. Most organizations will take a look at their data. They’ll focus on where they think their opportunities are.
But it depends on where they are in their journey, their DEI journey, which I always talk about, that not everybody’s at the same place.
For us, I believe we’re an outlier. We’re an outlier because if you think about DEI, it’s one of our values. The things that really resonate in our organization, is that each person counts. In order for each person counts, by default, you have to have a DEI strategy.
Some organizations don’t put as much interest or effort into it, so there at varying stages.
It became a great corporate buzzword two years ago. Prior to that, many organizations weren’t making headway, with respect to that. So, my belief is, we’re certainly an outlier with our use of data.
Of course, Mark, that is our middle name. So, we use data to make sure that we can tell our story, to solve the problem, to understand all of those things. We’re all about measuring success. How do you measure the effectiveness of what you’re doing?
Having said that, I think we’re a bit of an outlier. I think there are other organizations that are doing great things, but I think there are some that are not doing anything because they don’t know where to start.
If that’s the challenge for them, then a great place to start is, understand your data at least. Then, think about where you want to have an impact.
Can you think of any particularly surprising things that you’ve learned from data?
I can give you a couple of examples of things that I think we’ve learned. Number one is that it’s never enough. Here’s what I mean. We had to put plans in place to do this.
I’ll just give you this example, Mark. We launched our talent task force. It was a specific focus on the African American and Hispanics/Latino community.
Well, as soon as we put that out, the first question that came was, hey, what about the Asian community? I said, “Huh? I’ve got a story for you. Asians represent 5% of our population, but yet they represent 8% of leadership.” So, there’s no problem there.
Then the next call came from the LGBTQ+ community. I said, “Huh? Tell me what the data says.”
The reason we couldn’t make a decision and put a plan in place to improve representation for that community, is because we didn’t have any data. So, that’s one of the things that will surprise you about that.
And when you don’t have enough of it, everyone wants to do these things, which is back to my point about, people get involved in this. They want to represent their constituents.
But at the same time, without the data, you can’t get involved and create corporate programs to improve something.
The second piece still ties to self-ID. If you take this to a global scale, so typically in numerous countries, they don’t collect the same data that we do in the US. They don’t collect it because their philosophies are different. It could vary, country to country.
However, there’s renewed emphasis on understanding your workforce and being inclusive. So, just imagine, you’re a multinational corporation and you don’t understand the dynamics that exist in operating in Tunisia or the dynamics that exist in operating in France or Italy and who the underrepresented groups are. So, we’re trying to capture new data.
That’s one of the surprising things, is that we’re beginning a journey globally, to do a self-ID approach.
It’s not just us, by the way. There are multiple companies now showing renewed interest in this, to say, how do we understand our workforce? How do we become more inclusive, so we can appeal to the needs of various communities where we operate?
Are you satisfied with the kind of data that’s available to you today? What could be better?
Yeah. I’m in a unique position, Mark. I tell people this all the time. At ADP, because we’re a data company… again, it’s in our middle name, I have the unique opportunity that we have our own department that does all of the analytics, pulls the data, does the comparative analysis, the sensitivity analysis to whatever we want to do.
Now, for companies that don’t have that, we do have a diversity dashboard, that gives them insights into their own information, that they may not have thought about before.
They may not have the luxury of having a large DEI department, like we do. They may not have the luxury of having the analytic capability, but we can provide them with some insights about how their organization looks, what their leadership makeup is. Oh, by the way, with pay equity too, we can take a look at that data as well.
So I think I’m in an enviable position. I’ve got all the data that I need. The key for me, is staying focused and executing, to ensure that we make a difference with our DEI efforts.
What are your overall goals for your DEI efforts? I mean, what kind of changes are you hoping to enable or enact? What has to happen for you to be able to get there?
Yeah, it’s a great question, Mark. I’ll go back to my vision. The vision that, we want our associate population to be reflective of the communities in which we operate and the clients that we serve.
That is the most important thing, because I believe that the efforts that we take to do that, will have a great cyclical impact on the environment.
Here’s what I mean. I’m not in the DEI business because I’m a social justice warrior. I’m in the DEI business because I believe that there are economic opportunities in a capitalistic society, that we can get everyone to participate in and grow the pie. I firmly believe that.
In many cases, it starts with employment. So, what do we do as part of our DEI, some of the work that we’re doing? Well, we want to hire in those various communities.
We have outreach efforts to every community, to make sure that we’re attracting the best and the brightest for our organization.
Then of course, once you get there, you have to walk the talk. So, culture is really important, Mark, in this space, to ensure that if you said you’re going to do it, then you have to do it.
My saying is, don’t talk about it. You have to be about it. So, if you’re about what you said you are, by bringing everybody together and giving everybody an opportunity, so they can be their true authentic selves, then that makes a tremendous difference.
So, that’s the talent piece of it. Getting them in, giving them the opportunities to grow and develop, and then seeing them get promoted and being able to contribute.
Now, I also talk about DEI from a business practice standpoint. Oftentimes in the past, organizations that I’ve worked for, DEI was all about some of the HR practices, which I just talked about briefly. It was all about talent practices,
But I also incorporate business practices. Business practices are really about, well, how do we tap into the ecosystem of businesses and communities?
Oftentimes, you have underserved communities, that don’t have the same opportunities to understand things.
Give you an example. We have a company that we partner with. What the founder shared with us, was the fact that for many minority-owned businesses, they only have one way to finance their business. That’s through loans from family members or debt.
So, they don’t get the full spectrum of how to do revenue-based financing for their business, or how to think about the debt market very differently, that others have had exposure and access to.
So, giving them exposure and access to the full gamut is really important, but that also requires some education. So, we partner with organizations, to do that, just so businesses can finance it.
Now, selfishly, because I am a capitalist, I believe that we should be able to capture some of that market.
We should be able to say, we’ll help them. There’s no guarantee that they’re going to come back and nor is there an expectation, but just imagine if we’re the ones that help them understand how to run payroll.
I said, “We want you to focus on your business. If you make pizzas or if you have a restaurant, we want you to focus on what you do best. Let us do what we do best, which is run payroll, help you do time and attendance and help you with all of those other things. That’s what we do”
So, I think it’s important for us to extend our reach into the underserved communities, such that we can help raise the tide for all boats. That’s really the impetus here.
Say, if we do this the right way, DEI becomes much more holistic, so it’s focused on the economic empowerment.
If you do that by getting people great jobs, what do they do? Well, they go spend money in their communities. If they spend money in their communities, businesses grow. And if businesses grow, for us it’s a great thing, because that means you have more people to pay from your payroll systems and the like.
So, this ecosystem approach that I think is really critical and important, when we think about DEI.
Now, the other piece, Mark, that I’ll share with you about DEI is, I’ll share two other avenues of this.
One is the environment. Our environmental practices now, have become relevant in the DEI equation.
Let me back up and give you the broader view. Most companies talk about ESG, environmental, social and governance. The environmental piece is really critical. That’s where you have, what are you going to do for greenhouse gas emission reduction?
This S is all DEI. The G is board governance or governance of whatever programs that you take a look at. So, that’s something else you have to consider as you think about DEI.
We have practices to reduce greenhouse gas emissions. The good news for us is that, we don’t manufacture anything. Probably, our facilities and employees driving to work are our largest contributors to this. But what we also focus on is, what can we do to meet target? We put together plans to do that.
The last thing I’ll mention is what we’re doing as an organization, to make a difference, as we think about DEI and the like.
We have the ADP Foundation. We make contributions to a variety of 501(c)(3)’s nonprofits, to help support them in the communities in which they operate. So, there’s this holistic view that we have about, we can do well and do good at the same time.
Bob, thanks very much. We appreciate your time today.
My guest today has been Bob Lockett, chief diversity and talent officer at ADP. This has been PeopleTech, the podcast of the HCM Technology Report.
We’re a publication recruiting daily. We’re also a part of the Evergreen Podcasts. To see all of their programs, visit www.EvergreenPodcasts.com.
To keep up with HR technology, visit the HCM Technology Report every day. We’re the most trusted source of news in the HR tech industry. Find us at www.HCMTechnologyReport.com. I’m Mark Feffer.
Welcome to PeopleTech, the podcast of the HCM Technology Report. I’m Mark Feffer. My guest today is Joe Kleinwaechter, the vice president of Global UX for ADP. Among other things, it’s his job to make data accessible and useful. So he’ll tell us about those efforts, about how you make use of tens of millions of records and whether analytics and HR deserves all the attention it gets, on this edition of People Tech.
Hey Joe, it’s good to see you again. Could you tell me what you’re working on right now? ADP’s a big company, deal with a lot of data. You are basically in charge of helping people get access to the information. So what’s that translate to on the ground right now?
On the ground, my job is a lot of questions. Asking lots of questions and trying to really understand. One of the greatest challenges with us as human beings is that we think we have a really good understanding of others and we only understand it through our lens. And so trying to dismiss that and constantly realize that people do things a lot differently than I do on a daily basis. So my job is to figure out when they need data, when they need access to something, why do they need it? What are they ultimately trying to do? Not necessarily, yeah, maybe they’re trying to get their pay slip, but why are they doing that? What’s the bigger picture?
Because it’s in that understanding of what they’re actually trying to do and those emotional states they have, that I can maybe get them there quicker to the end, rather than through a series of steps such as this is the way you always get your pay slip. So I really focus a lot on trying to listen for things that don’t make sense to me or are cognitive dissonance to the way we think about the world.
Do you have an example of that cognitive dissonance?
Yeah. It’s funny. You think that, listen, if I wanted to pay in the old days, if I wanted to pay somebody, I would have to go to my wallet, give them money because that’s where the money was, in my wallet. And it was only until you realize later that the money was just there because that’s all we had. People didn’t want to have a wallet. People didn’t want to have money. They wanted to ultimately give something in exchange for something else. They didn’t even want to spend money. They wanted to go get a cup of coffee at Starbucks.
And then Starbucks figures out guess what? If I have a card for you, I can keep on file for you, or I can know about you. I can maybe help you get there better, not just in paying for the money, but maybe there’s something else. Your favorites, your history. How you operate. Things that make you happier as a customer that maybe you didn’t think about when you pull out your wallet with your Starbucks case, $20 for my coffee. But at the same time, what were the other things along the way that maybe could have been easier for you? So in my job, it’s not just about how do I go and look at my pay. I got to figure out what are they really trying to do? Are they trying to figure out if they have enough money to pay something? Or better yet, maybe they have some ambitious goals to try to accomplish and I can help them along that way. And that’s exactly what we’re doing in wisely right now, in our wisely product line.
Now, obviously ADP has a ton of data and that’s kind of factored into your work, I would think. How does it factor in? How do you approach making all of this data digestible and useful?
By ignoring most of it. I know that sounds kind of contrarian, but you could get absolutely awash in all of the data. Data’s a really fascinating thing. They say from a mathematical sense, data never lies, no, but, reading it does, right? Somebody could say something perfectly legitimate, but you can interpret it a lot of different ways. So the danger you have with lots of data is that the more you read, the more you make it confused. And what you have to do is take the data and figure out, okay, what can I start with as a hypothesis? Does the data support this? Does it not? And if it’s not, how do I change and pivot on my hypothesis? Those pivots often come by taking that hypothesis and trying it out with people. Seeing does it resonate?
Okay, this says, this says this about the great resignation. This is what we know about it. Is that really what’s happening down there? And that’s where UX comes into play. Because we then go out and say, okay, we have this hypothesis, the data says this, what really is it? Is it really true or not? Maybe there’s other ways to interpret that data. And that’s probably one of our biggest challenges, there’s many ways to look at data and you can make data loo, however you want, right? The old statistics line, right? You’ve got to figure out a way that it’s unequivocally true for the people that you serve and localized to their needs. That’s the hard part about data.
Okay. Can you tell me a little bit about the technology that’s behind all this? What’s going on under the hood?
You mean gathering the data?
Not just gathering the data, but putting it together and presenting it in a way that’s usable.
Yeah, I think the biggest thing we have to focus on really is what are people actually doing versus what does the data? So the data gives us a starting spot, but really the really good data comes from what they’re actually doing as they’re using your software, for instance. How are they using it? What are they doing? So the best data is the one that actually follows them doing what they’re trying to do, rather than maybe some larger data set that gives you great demographics and breakdowns, but doesn’t really get personal enough. So what we typically do, I’ll give you a great example. In one of our latest products here called Intelligent Self-Service, we actually go back and look at all of our calls that come into the service center. And we find out which ones are the most plentiful, because those are the ones that are probably, our hypothesis is, nobody wants to call into a service center. Nobody wants to call cam Comcast, right? Or call Google. They don’t want to do that. So therefore, how can we subvert those calls ahead of time?
Well, okay, we go and look at the top 10, and this is what they’re calling in for. It doesn’t really tell me why or what their circumstance was, or other characteristics like do they really need a human, maybe they need some confidence. We then take that data and apply it in, let’s say hypothesis. We say, listen, people want to know, for instance, who their HR benefits person is whenever they do this. We then watch the way they behave using our software and say, okay, at this time we think they want this. And that combination will help them not call. So it’s a series of hypothesis driven design along the way that takes the data that we see in the call center, combined with the demographics of what we know from our products and how they use our products. Combined with what the user did at that moment, that triggers us wonderful little in, we use the AIML phrase, this black magic that happens with AIML that causes us to say, oh, these things when together have a high degree of confidence that what he’s trying to do is this. Give him this.
Now 20 years ago, I mean, we’ve been try AI for a real long time, right? For a very long time. And what makes it really good today is that the models have gotten so good that we’re right far more than we are wrong. Remember the old days of Clippy trying to figure out what you were trying to do? Hey, it looks like you’re trying to write a resume. Irritating as all get out. But now we know what you’re actually trying to do with some high degree of confidence, because we have so much data that built that model so great that we actually have a good idea that maybe not only can we tell you what you need, but maybe we can actually do it on your behalf if you want us to.
And that’s really where the state of experience is going to, can we be predictive? Can we be insightful? Can we be intuitive to what they’re trying to do and then be bold enough to offer to do it. And then when we find out that we’ve got really high degree of confidence, that we can do it every time, maybe recommend doing it on their behalf, without them knowing about it, if that’s what they want. That’s the model that the experience is going to.
Well, how does this all fit into ADP’s efforts overall?
In which respect? In terms of the UX, the experience model, this intuitive model?
So I would say right now we recognize that the big position that ADP has different than a lot of others in the industry and competitors, is that data, is the wealth of data. It would not be wise for us to ignore the fact that’s a competitive differentiator. So we use that data all over the place. So what’s really key? Our data sciences inside of ADP are pretty, pretty high level. And I say that with the great degree of confidence, because I’ve seen it operate on myself. Our AIML models that we have out there for telling where you’re going to go separates from everybody else. Now, since we have all that data, now the question is what’s the right thing to do with that data? What is the proper thing to do with the data?
And our view is really simple. If it helps our clients, our customers, our users out there to do something that they wanted to do or to make them aware of something that they want they should know, then that’s good. Right? So it’s the alignment of that big data through a good model to get into the data at the right time. That’s across the whole product line. That’s across everything ADP is trying to do. We’re trying to become, a little bit like a barista at Starbucks where we know you enough that maybe we have your coffee ready for you because you always do that. You come and say the usual. Okay, good. Here’s the thing that took you half an hour to spout out before, now happens as you get in line. And that’s what ADP is really trying to do, is to be there before even you are there.
I mean, obviously there’s a lot of technology behind this and that makes me wonder, how has the technology evolved over the last 10 years say. Which as the technology was evolving, it seemed also that the use of data was spreading. And I’m curious about, first how the technology became more of a foundation. But also how did the growing demand for it influence the technology and vice versa?
Yeah, there’s a couple of things. It’s funny having been in many industries that relied on data. There’s a good natural checks and balances with the using of data as we know. There’s good ways to use data. There’s bad ways to use data. And it’s different for every person. I used to, and I still do, refer to something called the creep factor. Something is creepy. Back in 2002, if somebody told you that you need to get in your car because your flight is going to leave in a half an hour and the roads are blocked, you’d be kind of like, well, that’s kind of creepy. How did it know all this stuff, right? And you go, well, that’s creepy. But there’s a point at which you say but that’s useful. Okay.
In the early days, we didn’t expect people to have all of that data. Now we’ve come to the point where we are growing up with societies where our kids and all others just assume you have that data, just assume that data is out there. It’s a different world about what we assume the data. Right or wrong, or whether you have that data, they make an assumption that data is there. Therefore, why wouldn’t you use it for me? How dare you not use it to help me become better? And that’s a far cry from where we were in the early 2000s, where how dare you use that data, to the point we said that data’s actually pretty useful. I kind of like the fact that you can do this for me. And then you start allowing a little bit more data, a little bit more data. And next thing we have data fields all over the place that are being mined for lots of different reasons.
First, it was just concrete data, physical data. Now it’s behavioral data. How you operate, where you move, where you go. And to the point that it’s useful, great. But there’s always this paranoia that it’s not being used in the right way. And that’s something that I think is really healthy. I think that’s a really healthy check on making sure that we are good ambassadors of that data.
What do you mean by paranoia around the data?
Well, I think anytime somebody knows something about you that you either A, didn’t want them to know or didn’t know that they know, there’s a natural paranoia in us that asks how are you going to use that? What are you going to do with that, right? And knowing that if this were a benevolent world where everybody was going to use it, right, we’d have no problem with it probably. Not everyone, but a higher majority. But now we’re in the place where we have to be very careful about those that want to use the data to harm us or to use it in a way that annoys us at the very least, right? The scam calls that you get all the time, all the phishing techniques that are being used, things like that. There’s a whole black science of UX out there to trick you to go do things because they have some data, right?
There’s reason HIPAA was set up, right? There’s a very valid reason why HIPAA was set up and needs to be needs to be respected and done because of the bad that you could do with that data they aren’t governed correctly. So we treat governance with data incredibly, incredibly important. It’s at the top of what we do in all of that governance. We know we have an ethics board. We have our chief data officers constantly making sure that we are using data in an ethical way. And that it really truly not only is just ethical, it’s got to be valuable. It’s got to be something valuable for our clients and our customers. Otherwise, it’s just data.
I’d like to shift gears a little bit for the last few questions. Delivering data in the flow of work, the whole notion of in the flow of work is gaining a lot of traction. A lot more vendors are exploring ways to present their products that way. Does that pose any particular challenges for a data service or is it better? What’s your response to it?
Yeah. There’s a fascinating thing that I learned, again back in the early 2000s, I worked at a company that we decided at the time Google had come up with Appliance, right? That you could put inside your internet and all of sudden you could use as a search engine localized to your internet. We put the Appliance up there and it didn’t perform well at all. We let it run. We let it run for a couple of months and it kept getting data. It could never, the finds were just not good. They weren’t even close to what you would get on the internet. And what we learned from the Google data scientist at the time was the reason that the internet is so valuable as a search tool and so accurate, is because it has so much heterogeneous dat. Data that doesn’t appear related but in a way is, and that heterogeneous data gives us a much greater chance of finding that needle in the haystack that you’re looking for.
Whereas inside of a company, it all looks like the same thing, give me the latest dev report, give me the latest financial report. It’s more of a monocosm of stuff, and therefore you couldn’t find things. As we start meeting people on the go, where they are, we now have the chances for other types of data to improve that. Now depending upon where you land on the privacy of knowing where you are, geofencing and things like that, there’s a lot that can be done by knowing where you are. The question is by knowing where you are could you also use that for nefarious means? Yes, I guess so. Sure.
So you’ve got, I think the real challenge is, as we learn all this new data, what’s right to keep and what’s right not? And that’s not necessarily our choice, right? That needs to be our client’s choice of what’s valuable because again, going back to the creep line, if I know where you are and I can offer you this new service, it should be your choice, whether you want to exchange that data for that service. Not we’re going to take this all from you.
Companies have gotten in trouble in the past. We’re going to take this data. We’re going to read where you are and not tell you, and we’re going to give you a great product. Even if it may benefit you, the fact that you took that without my knowledge makes me suspicious that you may take it to do something else. And I think we’ve got to be really, really careful that having an honest conversation, a full disclosure and a strong ethics policy behind your data is really going to make the difference. Now with that in place, now I can meet people where they are. I can see where they are. I can get a lot more information.
A great example. One of our customers has a lot of field workers, right? And they have their phones on, they got GPS on their phones. If they want to transmit their GPS information, great, they’ll be great. They can do it. We can tell when they’re going to clock in, when they’re going to clock out and maybe even clock them in automatically. So we get rid of the single biggest call, to most HR departments, is I forgot to clock in. Can you clock me in? I forgot to clock out. Can you clock me out? Something as simple as that, just by turning on GPS location. Is that valuable or not? Well, that’s kind of a client thing, isn’t it? You tell me. Is it something you want to exchange for that? Then I have put governance about what I’m not going to do with that data. That’s just as important. And maybe I’d say is even more important. Because just because I have the data doesn’t mean I can use it however I want. I’ve got to use it in a prescriptive way,
Joe. Thanks very much. Really appreciate it.
My pleasure, Mark. Thank you.
My guest today has been Joe Kleinwaechter, the vice president of global UX for ADP. And this has been PeopleTech, the podcast of the HCM Technology Report, a publication of RecruitingDaily. 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. We’re the most trusted source of news in the HR tech industry. Find us at www.hcmtechnologyreport.com. I’m Mark Feffer.