Welcome to PeopleTech, the podcast of the HCM Technology Report. I’m Mark Feffer. My guest today is Brent Weiss, the Senior Director of Product Management for DataCloud Analytics at ADP. DataCloud allows even users without analytics expertise to answer business questions, then share what they’ve learned with others. We’re going to talk about how data’s used today, the role of innovation in analytics and more on this edition of PeopleTech. Hi, Brent, thanks for joining us. So obviously, it’s been an interesting few years for the business world with COVID and all. How did the pandemic impact DataCloud?
No, that’s actually a good question. We were talking about this yesterday with some other colleagues. When COVID hit and everybody started going home, within analytics we were spending a lot of time thinking about the ways that we could help our clients. And so we came up with some tools around planning for returning people back to their homes and sort of keeping track of making sure that everybody’s sort of safe. We issued some analytics around PPP reports. Some analytics around tax incentives that they were offering businesses. But the most impactful one was the return-to-work analytics. So while we were in the middle of COVID and a lot of uncertainty, eventually, we knew people were going to want to come back to the office.
And so employers were going to have to work through that, because at that point, you had not only some people who might be sick and generally unavailable, but then you had other employees who would have extenuating life circumstances. You might have children or elderly parents who otherwise need some support. And if these other support systems, like schools, are closed, then that’s going to impact the employee’s ability to return to work. And so what we did was we came up with a return-to-work survey. And that was easily configurable, launched within our mobile devices where employers could launch those to their employees and just ask some basic questions around whether or not they’re available to return back to the office and provide some additional information around what might be kind of holding them back.
And so we’ve subsequently developed that and released it and gotten some pretty good usage out of that. We still have it. It’s still active today. It’s been extended with evidence of booster shots and vaccination status and things like that. So that’s really a testament of the role of analytics and how it’s helping businesses sort of work through these times in order to get back to normal.
Now, what you were just talking about with analytics being used more commonly, I guess, among business, do you think users have become more sophisticated in their use of data in DataCloud since it was first launched?
I think in general, yes. There are some power users who are becoming increasingly sophisticated. But there’s still a good number of clients who are still pretty basic, I think is how I would think about that. So within DataCloud, we have basic features like measuring your headcount or your turnover. Those are normally pretty straightforward. It’s just how many people are within your organization with a status equals active or how many people left your organization where the action code is termination. Those are usually pretty easy for users to wrap their heads around. It doesn’t require a lot of training or education.
But when you get into more advanced features, like pay equity, where it’s comparing same people, a couple people with the same job title, same location, and it’s comparing how much they’re making along the lines of gender or ethnicity, that starts to get a lot more complicated, because you might have a numerical difference. Is that statistically significant? Is that going to get us in legal hot water? Have I sort of fully accounted for all those other variables that could be explaining this, whether it’s performance or tenure? And so with those types of tools, you need a more sophisticated kind of user in order to work through those analytic.
Same with something like predictive analytics with predicting turnover. It’s one thing just to look at the user interface and get a bunch of predictions of who you think is going to leave. But it’s another thing to then kind of understand what the model’s doing, trust the model, be able to explain it to others to get them to trust it, to start making some decisions. Just because we say, “Hey, Mark’s going to leave within the next year,” the solution might be, “Well, let’s give Mark a pay raise.” “Okay, what confidence do I have that Mark’s really going to leave? Otherwise, I don’t want to just give him money for no other good reason. So if I’m confident that this model is accurate and money will fix that problem, then I’m willing to take that action. But how do I know that this model is accurate and that this is the right decision for me?”
So that kind of becomes some of the inner psychology of the analyst and the manager who’s got to make those decisions. And so you have to be a little bit more sophisticated as a user to be able to work through those, to demonstrate that it is something worth trusting, that this is the right decision to take and get that over the finish line to make the decision and drive the change. So ultimately, I’d say, on the whole, our users are very good with basic metrics like headcount, turnover, even managing overtime, where it’s a little bit cut and dry. Whereas when you get to the more advanced features, there are power users who use those tools well. But then there are others who need upskilling.
And I think that’s a challenge for us as a product, because, and even as an industry, because it’s the analysts analyzing that data that is really going to make the difference in terms of the adoption of the tool, that’s going to drive the change. And so if you can’t upskill those people, then you’re not going to drive the change.
Now, DataCloud, I would say is at this point, a pretty well-established product. And I’m wondering whether it’s evolved the way you expected it to. Has it taken any paths that you didn’t anticipate? And what were they? How did it happen? Or did it just sort of move along with the roadmap the way you thought everything would?
Nothing ever moves along the way you would think it does on a roadmap. You make a roadmap and that gives you a general sense of where you’re heading. But you always have to be agile. If you think about it, in software development, the key thing is agile software development, which means you’re taking a very iterative approach. You quickly build something. You release it. You get customer feedback. And then that feedback is what shapes the next step and the directions of where you need to go in order to make the feature usable. When you embrace agile, that kind of is a little bit of a conflict with this concept of a roadmap. A roadmap almost has more of a waterfall kind of a mindset, which presumes you know where you’re going, you know how everything’s going to play out. And you can plan that down to the details for what the whole year or next couple of years is going to look like.
And we do both of those things, for sure. We balance it. But the reality is we’ll declare the roadmap, which is very customer-driven. We engage our customers. We talk to them twice a year and ask them about our features and how well things are working. We ask about other ideas we have. And we get them to weigh in on which ones would be most valuable to their business. And that helps to set the direction for where we want to go for the year. But the reality is each quarter, because we’re developing in an agile way, we make those adjustments every quarter. So that as we’re learning from our customers, we know better what we need to do next. And then we make our adjustments on that roadmap.
And so the other reality is just those macro events that we were talking about earlier, like COVID. Nobody predicted it. It just kind of showed up. And all of a sudden, that’s a great opportunity for innovation, a great way to help add value to our customers. So we had to put a pause on our roadmap and say, “What is it that we can be doing right now to help our customers?” And so we did the same thing last year for recruiting. As the labor market started to get really tight and there was clearly more demand than there was supply, we were hearing a lot of pain from our customers around hiring. And so we hit the pause button and said, “We need to develop a tool that will help our customers recruit better in a tight labor market.”
And so what we did was we built a feature called Talent Market Insight, which helps with skills-based hiring. So what that does is it allows you to start, instead of starting with a job and saying, “I’m looking to fill a software developer job,” and then understanding what the supply-demand dynamics are in that labor market and where these people are and how to find them, we start with a skill. You say, “I’m looking for a Java programmer.” And so now, what that does is that allows you to search across multiple jobs that meet that skill need. And that expands the types of employees that you can pursue and go after to meet your hiring need. And so that was an example of some ways we were trying to get a little bit more creative in helping clients expand addressable labor pools in order to hire under tighter-than-normal market conditions.
I’m just giving you some examples where essentially, we’re listening to the macro economy. We’re listening to our customers and constantly making adjustments to prioritize in ways that is going to continue to deliver value to our customers.
One thing that strikes me is it seems more and more people are using analytics and that slope has been going up for a number of years. And what do you think, is it because the products are getting better and more sophisticated? Or are the users becoming more sophisticated? Or is a little of both?
We are seeing quite a bit of an uptick in utilization. I would say, without really disclosing numbers, from the time I’ve been here we’ve increased our utilization well over a hundred, 150%. Almost all of our clients are using the analytics product. Of those who buy it, almost all of them are using it in some way, shape or form. So that has been definitely a positive trend. Like I said, some of the actual analytics that they’re using, I think are attentive, gravitate towards more basic things versus valuable. I think as an example, we have the ability to upload a budget. So it’s one thing to try to manage your costs, manage your overtime, hire is just bad in general, so it’s relatively easy. But if you really want to add business value, like helping to preserve the margins of your company, then uploading something like a budget for the year and then having that merge with the streaming data off of your payroll is really the gold standard.
That’s a way to transform your business. But we don’t have a lot of people really using that functionality. And so that’s a bit of a vexing challenge for us, because we know we’re sitting on something that’s highly valuable to our clients, but yet we’re not getting them to adopt that technology. So we’re taking a look at it, but that’s kind of just a little bit of my reaction where we have more people using it, but there’s still a lot more opportunity for them to use the more sophisticated features that add more value. Then the other thing that’s on my mind is using things in a digital way. We still have a lot of people. They’re just downloading this stuff and dropping it into PowerPoint. And once you take it out of that digital format, it becomes a manual exercise of crossing that finish line. It makes it harder to refresh the data. And it makes it a higher effort to repeat the analysis. So you’re breaking the flow state of analytics.
And so if you want to get to this sort of Moneyball kind of scenario for the industry, where stuff’s just kind of there and you’re making decisions in real-time, or almost like a minority report, you’re getting real-time analytics, it’s like you have to stay in a digital format. You have to stay within the analytics ecosystem. And then share and distribute from there. So we believe that that is really the best way to deliver value for our customers. But we’re not seeing the adoption of those channels as much as we’d like. So those are areas where we’re focusing
Well, what’s holding it back, do you think?
I think part of it is the users themselves, and the sophistication, and the comfort with that. You’ve got a lot of users that they’re very comfortable in things like Excel. They can see the numbers. They can kind of manipulate them themselves. So they start with the tool. Then they try to pull it out. And then they kind of manually work the rest of it through. And so that’s where we’re really paying attention to a lot of our user journeys to introduce a lot more flexibility into the user experience so that we can meet their needs within the tool ecosystem to encourage them to stay.
And then the other piece may be that some people, this is how they add value. So for some clients they’re like, “Hey, I run a turnover report,” and they might conflate activity with value. And so they’re like, “I’m relied upon to generate this report. And these are the steps I do. And then I generate this report. And I get a nice pat on the back.” And so they can talk about the number of days that they’re spending on this high-value activity. And so because they’re involved and they’re investing that time, they feel like that translates into value. And so us coming along saying, “We can automate that and just sort of pass that right along to your manager,” now starts to undermine the role of them in that process. And that’s seen as a threat.
So that’s very real. And so we need to find ways to convince those analysts that there are other ways to add value beyond just simply clicking buttons and delivering reports. There are conversations that have to happen with the data, and what that data means, and what we should be doing about it. What are the solutions, the tactics, the next steps that we should be taking? And so I think that’s what I’m talking about in terms of the upskilling of the analyst is repositioning them from being calculators to more advisors around data. And so not everybody’s comfortable with that. But if we can start to offer a framework and a way of guiding people, then that’ll help to unlock this for some of those clients. And that’ll get them on the path of where we think everybody in the industry as a whole really needs to go.
Now, the analytics and this technology that we’re talking about, how has it impacted HR? Has it changed the skills that practitioners need, for example? Or are they starting to make different types of decisions? Or is it not touching HR? It’s touching the C-suite and that’s impacting HR?
I mean, it’s two ways, for sure. Definitely the C-suite is interested in these kinds of analytics. Your CFO, your CEO, they want to know what’s going on with the costs and are they being controlled, and do we have our turnover under control, and things like that. So it’s not uncommon for executives to ask for those kinds of insights. And then of course, HR is trying to add value to the business and be proactive with driving insights. So you have both a top-down and a bottom-up kind of movement. Now, that may vary from company to company, but both of those things definitely exist. With our clients, we’re seeing some positive impact. We’re very focused on business outcomes. And so what we started to do was analyze clients who are using our tools and looking at how their metrics are changing over time. Within analytics, you have a natural kind of ROI calculator. You’re measuring your turnover. Your goal is to reduce turnover.
Well, if you measure it at point A and then you start to take actions to reduce it, well, now we can look at that same metric at point B. And did the turnover go down between point A and point B? And if so, then that activity worked. And that’s your return value. You reduced your turnover by this. And then by the way, we have a cost-of-turnover calculator. We can dollarize that for you and tell you how much you, by virtue of retaining those people, here’s how fewer hires you had to make, which reduced your hiring costs by X. Here’s the fewer training classes you had to run. Here’s the less productivity you lost during that downtime between the exit and the replacement. We can dollarize that. And we can quantify it in terms of lost labor hours, depending on how you want to think about it. So we do that for turnover. We’re finding that over half of customers who are actively using those turnover features are reducing their turnover.
We’re finding clients who are using our overtime metrics. They’re reducing that as well. I forget the exact numbers, but I could get those to you offline. But we’re looking at stuff like reduction of overtime costs, reduction of turnover, and the turnover costs, reduction of time to fill. We also look at DEI. So if you’re using our DEI features as your diverse representation, improving over time. If you’re using our pay equity features, are you seeing fewer pay equity gaps exist within your organization? That’s correlated with your activity of using the tool. So are our tools making a difference in your business? That’s something that we are now trying. We have measures on it. And we’re going to continue to repeat those measurements to make sure that our tools are making a difference.
Well, are there any particular areas that are particularly interesting, where businesses, are and HR in particular, are putting data and analytics to use?
So that’s a good question. Some of the more interesting use cases that we’re seeing right now, well, certainly I think the Talent Market Insight feature that I mentioned earlier is a way. You hear about skills. You hear about how skills are important. If you look at something like the Gartner hype cycle or whatever, it’s on there in some of those earlier stages. And I think that we, as an industry, intuitively know that skills is the way to manage workforce, to think about your hiring, because that’s really the thing you need. That’s the capability that you need. And so going a little bit more granular than jobs gets you a better level of precision that gives you better guidance on who you need to hire, creates more flexibility in terms of career opportunities for employees, things like that.
The industry, as a whole though, hasn’t given a lot of tactics for how to actually do things like skills-based hiring. And so with Talent Market Insight, that’s our attempt to try to operationalize some of these ideas and give users ways of playing with this intersection of jobs and skills to understand how that touches specific employees in that external labor market to give you a sense of, if I’m looking for somebody with Java skills and agile skills, how much does that reduce my addressable labor market versus just looking for somebody with Java skills? So it allows you to kind of analyze the more specific I get with my skill requirements, how much am I reducing how many people might have this thing that I could go after?
So it encourages this concept of play, where you can play with the user interface, adding skills, removing skills, looking at jobs, comparing jobs, to kind of understand the trad-offs of what you’re looking for and what you need relative to how difficult it’s going to be to find them in that labor market, as well as how expensive they’re going to be. And so that will allow customers to create possibilities, which hopefully, unlock new ways of doing business. So I think that’s pretty interesting from our point of view. I guess, as far as other analytics that we’re sort of seeing out there, we’re kind of focused right now on workforce planning.
So what we’re trying to do here is take a traditional finance concept and we’re trying to make it more approachable for HR, more visual rather than numerical, per se. So we’re trying to introduce an idea around org charts as the primary design visual or user interface, so that you start with your org as people, you got pictures of people. And within that, you can click buttons, and you can see what their skills are, and you can compare their skills against a skill profile to understand gaps. Or you can analyze the span of control of your organization. And if you want to start to think about different ways of designing your organization, you can just kind of start to add some jobs, move some people around. And by virtue of doing that, it just starts to create these planning scenarios. Nothing is binding. You’re just sort of playing around on the screen, exploring possibilities, different ways of running your business.
And so we want to make that very visual and simple, because we think it’ll facilitate conversations between HR and the business. But it’s also a very simple and easy interface that is less intimidating. When you give HR people a big spreadsheet, they start to shut down and say, “That’s kind of the finance domain.” So that’s where we’re trying to simplify it so that it becomes more approachable. And then that allows our users to add more strategic value to the business.
And at the end of the day, do you think that all this technology and all of these efforts that you are talking about, are they helping people make better decisions?
Yeah. I mean, that’s the hope, for sure. I think with scenario planning as we were… I think anytime that you’re in a planning phase, you should be exploring as much as possible. It shouldn’t be go quickly find the fastest solution you can think of. But instead, you should be sitting down with different stakeholders and getting their inputs and just thinking about, what are different ways that we could reimagine the way that we’re operating? And giving you tools that are very easy just to kind of drag and drop and make some of these changes that you can then easily compare one scenario to another scenario, et cetera, to then start to have that conversation, well, let’s talk about the pros, and the cons, and the trade-offs of these different options. So through that iterative process is usually where the most creative solutions come through.
Anybody who’s done any kind of innovation work knows that the initial ideas are usually the worst. Those are usually the most obvious. And it doesn’t get fun and good until you get to idea 10 through 20. And so we’re trying to apply that same mindset to concepts like workforce planning, where it’s like, go ahead and draw up the first simple solution and hurry up and get that one out of your head so that you can move to the next one, because the next one’s probably going to be the creative one. But again, also in addition to that, as you’re making these scenarios, make them comparable, but also shareable. Sharing is a very important part of analytics. No decisions are really made in a vacuum. Unless you’re the CEO or the head of a business unit, the person who’s doing the analysis probably needs to convince somebody else to take an action. And so that means you have to share it.
And so this idea of sharing as a critical step within the decision-making process is the pathway to better decision-making. So those are the kinds of things that we’re really sort of focused on, with the main goal of better improved decision-making that drives business outcomes.
Brent, thanks so much for talking today. I really appreciate it.
Absolutely, Mark. This has been fun. Thanks for having me.
My guest today has been Brent Weiss, the Senior Director of Product Management for DataCloud Analytics at ADP. And this has been PeopleTech, the podcast of the HCM Technology Report. We’re 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.