Schon Parris – How to Start Making Data-Driven HR Decisions

Connect with Schon Parris:

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Connect with David:

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• david.turetsky@salary.com

Podcast Manager, Karissa Harris:

• HRDataLabs@salary.com

 

Production by Affogato Media

Resources:

• Salary.com's Consulting Practice
• HR Data Labs Season 6 Episode 22
• HR Data Labs Season 6 Episode 20

• “Somebody has to take it upon themselves to make [data analytics] important. . . . Unless you have at least one person that’s going to dive in and do the work, it’s not going to go anywhere.”
• “There’s no need for someone within HR to fear the evolution of AI stealing [their job].”

Announcer: 0:02

Here's an experiment for you. Take passionate experts in human resource technology, invite cross industry experts from inside and outside HR. Mix in what's happening in people analytics today. Give them the technology to connect, hit record for their discussions into a beaker, mix thoroughly. And voila, you get the HR Data Labs podcast, where we explore the impact of data and analytics to your business. We may get passionate, and even irreverent, that count on each episode challenging and enhancing your understanding of the way people data can be used to solve real world problems. Now, here's your host, David Turetsky.

David Turetsky: 0:46

Hello, and welcome to the HR Data Labs podcast. I'm your host, David Turetsky. Like always, we try and find you people who are fascinating, fun, and just brilliant. And today is a phenomenal example of that kind of person. We have with Schon Parris from ADP. Schon, how are you?

Schon Parris: 1:02

David, I'm doing fine. And I don't think I've ever had such a kind introduction. From the bottom of my heart, thank you very much.

David Turetsky: 1:09

Well, I appreciate it from the bottom of your heart. So Schon, why don't you explain to people who you are and what you do at ADP?

Schon Parris: 1:16

I would be happy to do so. I have been at ADP for a very long time now, I can't even believe it. It's been 23 years.

David Turetsky: 1:25

Wow.

Schon Parris: 1:25

I've done a lot, a lot of different things worked directly with clients, I've worked with our sales force. But my most recent assignment last three years, I have been focused on the data that we have here at ADP, specifically helping our clients use data, their own data, as well as all of the data we have here at ADP, really just to make better decisions.

David Turetsky: 1:47

And for those of you who are not familiar with ADP, ADP has over a million clients, and about 40 million employee records in that data set you were talking about.

Schon Parris: 1:56

It's true, true. And David, what's special about that data is that unlike many other sources of benchmarking

David Turetsky: 2:03

And what's brilliant about it, if I can data, this data comes from the aggregated transactions of our clients. So every month takes us about three weeks to aggregate payroll transactions, HR transactions, they go through a data normalization process, certainly anonymize that data. But then we make it available for our data customers to benchmark themselves against really to get a sense of what's going on in the market. pump it up a little bit, is it's not just about compensation benchmarking, it's also about benchmarking the types of metrics that make the ADP Data Cloud and ADP's technology special. Like for example, turnover, or recruiting metrics or payroll metrics. There's a lot of things you can learn about how you operate as a business by looking at those things. Right, Schon?

Schon Parris: 2:58

Well, thanks for the lead up, that's absolutely true. You know, people do think of us is certainly a payroll company, we are much more than that. We provide services across a whole host of disciplines, whether it's time management, you know, recruiting, benefits administration, yes, payroll, talent, et cetera, et cetera. To the greatest extent possible, we do, we do collect all of that data and make as much of it available as we can. So, you know, just another example, I think that would surprise your listeners is that we also have access to demographic data. So we can get a good sense for what the gender or race or ethnic makeup or what tenure looks like for workers in a certain geography or even a certain industry. You know, as as companies go about their their DEI strategies, it's always helpful to understand what the market looks like in which they operate. So get a good benchmark of, you know, do they represent those that they serve, those that they employ?

David Turetsky: 4:00

And so we're gonna get into that a little bit and how that drives business strategies and making decisions. But first, what is one fun thing that no one knows about Schon Parris?

Schon Parris: 4:11

Well your listeners probably don't know anything about me at all, so.

David Turetsky: 4:14

They do! They just got introduced you. So what's the one fun thing though?

Schon Parris: 4:19

The one fun thing. I am a very proud member of North Georgia's premier cover band, a collection of dads that are in their 50s just like myself, get together play rock and roll music for anybody that will listen. We just had a great event this past Wednesday at Dahlonega's 50 Something Gold Rush Festival, where we played for a collection of people that were just like ourselves.

David Turetsky: 4:50

Of course, because we only we can appreciate that kind of music.

Schon Parris: 4:54

Only we can. I agree.

David Turetsky: 4:56

That sounds awesome. I'm actually a bass player too, but I'm trying to Learn piano now. So chopsticks is about to happen.

Schon Parris: 5:04

You know, most people start with piano and switch over to bass, because it's a much more awesome instrument.

David Turetsky: 5:09

Awesome, I agree. Complex. Both are relatively complex. And people don't appreciate that as far as they go like, well it only has four strings. Well it's complex.

Schon Parris: 5:21

Understood.

David Turetsky: 5:23

That being said, now that we know about our friend, Schon Parris, now we're all friends with Schon Parris. Let's dive into our topic. Today, Schon, we're going to be talking about how does HR take a data driven approach to making decisions and where to get started? So Schon, our first question is, what gets in the way of HR actually using data successfully?

Schon Parris: 5:55

to guide decision making, have just begun using data to make good decisions and are not using it at all but trying. And what I can share with you working through a number of those, working with a number of our clients is that the reasons are across the board. And and if I can name one thing like, we don't have buy in from leadership to transform into a data driven organization, I would say that, but that's, that's not it. We hear things like, we don't have trust in the data that's being that's being pulled or accessed, or we do but our senior leaders don't trust the data, or we are light on technical resources and getting access to the data that we need to put them into visualizations that make sense for us to provide context around the the answers that we're looking to understand to a greater degree would would be a common reason that we see. We hear, I just don't have the time to get started. And you know, to be honest with you that that may be one of the most common obstacles to get the clients we work with started on that data journey. At the end of the day, I tell you what, at the beginning of the day, somebody has got to take it upon themselves, to make this important to sit down and say I am going to get our data ready to be used, I'm going to take the time to build the tools and I'm going to be an advocate at this organization to help transform us into a data driven organization. Unless you have at least one person that is going to dive in and do the work, it's not gonna go anywhere, it's gonna sit on the shelf unused. So you got to start certainly with an advocate, and then some of those other things can come into play. But if you have a serious advocate that is willing to do that, willing to roll up their sleeves willing to dive in, then those other things can be overcome.

David Turetsky: 8:17

In the past, when I've done what you're talking about, one of the things I found is, is that the people that I had been dealing with weren't exactly the owners of that process. So do you have to do any work actually to find the right people to talk to in order for them to understand the value of those types of insights that are available to them?

Schon Parris: 8:38

Well, for sure, you know, one of the things that that we see is, is we can get C suite very excited about the data that we can produce and publish and the way that we're able to show trends across the entire enterprise and, and tell stories about how that enterprise relates to the markets in which they operate. But then when it comes time to deploy to implement to begin using, we know the C suite's not going to be the one to actually do it. It's going to fall typically to someone within a Human Resources capacity. Whether it's a manager, or a generalist, or a VP of HR, that's where it's ultimately going to end up, at least to the extent that analytics are supporting human capital management type conversations. I would say it's more about the willingness of the individual, the it's not even willingness. It's something where I would say, almost passionate that the passion that the individual has to head in this direction is going to generate success more than just assigning it to someone who feels like it's yet another task they're gonna have to get through because it takes a lot of work. There's no, there's no way around that it takes work. It takes focus and if you've got passion to do it, it's going to carry you through.

David Turetsky: 9:59

But but you need to have the acumen then. If you're if you're having that person who's assigned to it, they have to have the acumen to understand not only the background of the data, but also the statistics they're looking at, and the complexities of how do I make sure it's telling the right story in order to be able to hand it to managers, and engaged them in a way in which it's not just an HR dashboard being shoved in their face again.

Schon Parris: 10:26

I couldn't agree more with that. You know, we've we've done a lot to make our tools as easy to understand as possible. What I mean by that is, you know, we've gone to a large extent, and delivered data within a narrative within a context. So instead of staring at, you know, a number 12.5, you know, if you don't have a high degree of acumen, it may take some work to understand the context of what that particular metric means to you. So where possible, we serve that up within a narrative, and we might say something like, your overtime has increased by 12.5%, over the last quarter. So that's part of it is delivering these visualizations, like I say, with context, and that's going to help a practitioner, somebody that doesn't have perhaps a, you know, a data scientist degree under their belt. But also, we certainly bring resources to bear. And that's, that's very much needed, I think, you know, And once they get started, then hopefully, that person can take there's a, there's a high degree of anxiety, especially with those that are going through this for the first time of understanding the kind of effort it's going to take to get your data in the appropriate shape that it needs to be in order to drive analytics and ultimately decision making for an organization. So we do find ourselves needing to jump in and help in those areas. it from there.

Announcer: 12:00

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David Turetsky: 12:11

So tell me about the success stories you have of those clients who are able to get access to the data, who can understand the data, and then take it from there. What are the commonalities with those clients?

Schon Parris: 12:24

So one of the things that, that I've seen work well with our clients is to start small, when they're getting going on a data journey, really, for the first time is to pick a single topic, something like turnover, a lot of organizations are focused on keeping turnover as low as possible. And so start off by making a hypothesis, you know, where's turnover coming from? Is it coming from a certain manager? Or coming from a certain department? And then think to yourself, you know, what kind of data do I need to confirm the hypothesis that I've made, you start to pull that data, and you may find out that your hypothesis is not correct. That's okay. That's just as valuable as proving that what you thought was true is true, because it's going to help you know where to look. Right? So turnovers, I wouldn't say that that's an easy problem to solve, but it's easier than you think to understand what's driving it. At the end of the day, turnovers coming from a particular job type, particular location, maybe a particular manager, you know, there are going to be some themes around turnover that as you start to pull data to support your hypothesis are going to come very clear to you. And that's really going to get you started on making an impact on on something that is very, very difficult for organizations to control. But once you know where it's coming from, what's driving it, you're going to hopefully be able to move the

David Turetsky: 13:51

Yeah but Schon. The other part about that is meter at that point. that it's not just about the data that you have, it's also about the data that you need to acquire, that becomes contextual for making that decision whether to accept or reject the hypothesis, right? So you could take all the data that you know that you have in the system, whether it's turnover rate data, whether it's termination reason data, and as you said, the, what we'll call the x axis for that, which is by manager, by function, by job, whatever. But we can also then go and talk to people, we can talk to the HR generalists, or we can talk to managers directly and say, you know, what do you think is causing these things? I have a bunch of hypotheses about it, but I want to hear from you. So you're collecting all that anecdotal data to help the online versus offline you know, real strength of your argument. That's not scalable for gigantic companies, unless you start driving other forms of data gathering like surveys and whatnot. Have you seen companies do that? Or is it more where they're using most of mostly the online data to drive their hypotheses and their conclusions?

Schon Parris: 14:58

Well, you know, I'd actually like to to kind of play off something you just mentioned was you said that that's not scalable for large organizations, I agree. But here's a way that it could work, you could actually allow the data to lead you to the proper department or place or people in a large organization that you could then talk to. And I'll just give you a really, really quick example. We had a, we had a client that was trying to solve this very problem with turnover, for example. And what they did was they pulled together some data that, you know, that assessed like, what does our turnover rate look like by by manager, position, and department and so forth. What they found was there was an individual manager that appeared to be driving more termination into the organization than any other. So before conducting any survey, you know, the thought in the HR department was like, Well, gosh, what's wrong with this guy? Driving all these people out of the door!

David Turetsky: 15:52

Right. And that call that was made to the manager to

Schon Parris: 15:53

So here's a situation where the data provided the channel to focus on and so when they picked up the phone, and they talked to this particular manager, what they found was, there was a company that had just opened up across the street that was poaching all the talent. So it wasn't the manager. The data suggested it might be the manager, but what the data did was actually provide the, the pathway to understanding what the real problem was. So that would be a way that even a larger organization could take the data and really begin that sort of investigative process. But yeah, it was coming from outside of the organization. say, what are you doing to your people? They had to call them back and say, sorry, we found out what the problem was. Yeah, sorry about that!

David Turetsky: 16:38

Sorry to get you all wound up. But yeah, our bad, we'll do it again. No but actually that does actually channel to, you know, the best practices here, which is, don't always assume, just because you were saying, because the data showed that it was that manager, that it's necessarily that manager's fault. There may be some other circumstances. That's why the, you're trying to bring all the context necessary to make the best business decision possible.

Schon Parris: 17:04

I mean, data doesn't the data is not solving problems, you know, the data is, is directing your attention.

David Turetsky: 17:11

Right.

Schon Parris: 17:11

Right? It's, it's still going to take an HR expert, it's still going to take a manager, it's still going to take people to solve the problems, the data is going to make that easier, it's going to help us know where to focus, it's going to reduce the time it takes to get from point A to point B. And to the degree that your data is good, and accurate and current, it's even going to get you there faster and better.

David Turetsky: 17:36

Hey, are you listening to this and thinking to yourself, Man, I wish I could talk to David about this? Well, you're in luck. We have a special offer for listeners of the HR Data Labs podcast, a free half hour call with me about any of the topics we cover on the podcast, or whatever is on your mind. Go to Salary.com/HRDLconsulting to schedule your FREE 30 minute call today. Well, that drives me to my next question, which is the robots being our overlords. How do we look forward and see that the tools around our data are going to change with regard to your favorite two letter word, AI, artificial intelligence and the ability for clients to adapt to that?

Schon Parris: 18:21

Well, you just came back from HR Tech, did you not?

David Turetsky: 18:25

I did!

Schon Parris: 18:26

I heard, I wasn't able to go myself, that that AI was one of the primary topics that was discussed at HR Tech, what would you say?

David Turetsky: 18:34

In the same way that Cloud was about 10 years ago? Five years ago? AI was this time. Yeah, it was on everybody's booth. Everybody except for ours. It was kind of amazing that everybody was trying to tout different features or functions that AI was going to be used to solve client problems. And so much so that I think it became just noise after a while. You expected the next booth to have some kind of artificial intelligence that was built into their technology. So I think, Schon, it's transitioning from the, yay, wow, cool to Oh, yeah, it's got to have that. So what do you think, from from that perspective, what do you think clients are kind of building into their model for how AI changes the equation in being able to interpret this data?

Schon Parris: 19:23

Well, I mean, it's definitely going to be interesting to see and, you know, when you when you mention AI or machine learning, you know, you can always, it's funny to watch the expression on the person's face that you're talking to to try and get a sense for how they're feeling about the topic because you see, faces and brows they carry excitement, or fear, or uncertainty, or a desire to want to talk more about the topic or please don't ever say that to me again. But, you know, just working with our product people the way that I do, working directly with our clients the way that I do, you know, some of the some of the things that that are already being done that leverages what unquote AI is things like, you know, probability models. You know, the ability to produce a list of individuals that might be at risk for leaving an organization. And days gone by, yeah. How would you know such a thing? Unless you had to talk to those those individual employees and, you know, water cooler conversation, and I'm not happy here. And I could make more somewhere else, and I hate my manager and all these things. But now to be able to look at data on employees and assess things like, how does their salary relate to their peers? How does their salary relate to our

David Turetsky: 20:39

Right. competitors, who are also in the area? When was the last time this individual received a promotion? What's the distance that this individual travels to get from their home to work? How much overtime is this individual working? Like, these are all things that we know can contribute to someone leaving their employer. And the good news is, if you're building a model, these are all data points that reside within HCM technology. So you can take a look at the data for all of your employees, and at least produce a list of people whose data tells us they may potentially be at risk for leaving. So that is that is that is artificial intelligence producing a list of individuals that may be ready to walk. Only the individuals know if they're going to leave, but we can show you that according to their data, they may be at that point. So if you value these individuals, as workers, they are great workers, they bring positive energy or what have you, then now just like we mentioned before with the turnover thing, data is not solving a problem, but it's helping you know where to focus. So now you can put your HR hats on again and have conversations with people about what it's going to take to keep them. Well it enables that HR generalist to have the strategic conversation with that manager to, as you say, focus in on those particular roles, or those particular people first, where you may not have focused on them at all, you might have glossed over them. Because you don't know who last got a promotion when and you don't know how long someone has a, you know, long distance to travel to get to the office, you don't know any of that stuff. But to your point, the algorithm can tell you based on all those factors, all those fields, you mentioned, hey, here's a list of the top 10 people you need to talk about.

Schon Parris: 22:28

The algorithm! You said it, I love the algorithm.

David Turetsky: 22:33

Well, you kind of boiling it down, I mean I'm a statistician, right? And I kind of love to boil things down to: its math. And you know, the AI generates a probabilistic conversation that has to get started about, you know, who are the people who might leave over the next 12 months? Well, boiling it down. That's math.

Schon Parris: 22:50

Yeah, absolutely. So, you know, and other ways. I mean, so that's a way that you know, it's being used today. I mean, as you look out, you can imagine coming in and having a conversation with an interface on your computer saying, you know, Siri, tell me, are the salaries in our research department competitive with those of, you know, with other organizations like us within the geography, like having a conversation? Or what type of benefits do we need to add here in order to be, you know, on par with our competitors and having data being brought forward to help address questions like that, that you may have, as, you know, as an HR leader.

David Turetsky: 23:31

But but that's an interesting concept you bring up because if I'm having that conversation, it needs to be a conversation, right? And I need to be able to drill down into the response. And that's not something that the current AIs, especially the consumer based AIs have been very good at. Whereas with HR, there's multiple dimensions to most questions that get asked where, if you leave it at the high level, you're still kind of lost. You have to drill and drill and drill, like you mentioned before about turnover, if I don't go behind the what jobs are being turned over right now? You know, okay, well, is it, is it a manager or location? That's the second click right, that I need to have that that thought process can vary from one conditional logic statement to the next, in order to be able to get down to that. Do you see AI as getting to that place or especially more modern ones?

Schon Parris: 24:24

Well, I mean, look. When I when I talk to you about the different types of faces that you encounter, when you bring up the word AI, we we have somewhat the furrowed brow or maybe that that reflects a little bit of fear.

David Turetsky: 24:40

Right.

Schon Parris: 24:40

You know, I think I think there's there's no need for someone within an HR capacity to fear with the evolution of AI stealing jobs in that capacity.

David Turetsky: 24:52

Absolutely.

Schon Parris: 24:53

Because to the point there is a great degree of expertise that, you know, AI is gonna make it easier for you to perhaps arrive at conclusions but those conclusions are going to be yours. AI is going to enable the serving of data to support your hypothesis, you know, more efficiently, but the actions are still going to be yours to take. The way I think of it, like even in my own job, I've now got a partner with an incredibly high IQ riding shotgun with me, I'm still going to make all the decisions, you know, it's just giving me advice along the way. And who wouldn't benefit from having like a high IQ companion going with them around helping them to make better decisions? My companion is not going to steal my job, just going to make me better at mine.

David Turetsky: 25:48

Right. Well, but I think your, the way you're putting it, you're giving an embodiment to something that has been thought of as a technology enabler. Which I've been espousing, and especially in the conversations I've had, especially at HR Tech, was giving a personality, giving a job, giving a voice and a even a name to the AI to say, we're going to employ this technology, and I mean, the word employ. And you know, that that person or that thing becomes a partner of yours in getting your job done better, not, to your point, replacing you. And so the more we see it as that relative to, oh, well, you know, the car is now there, so I don't need the horse anymore. I don't see AI doing that for a very, very long time.

Schon Parris: 26:41

Yeah, I totally agree with you, you know, that the common analogy, and for those that hadn't heard it, I'll just bring it up quickly. As you know, when the ATM machine came out, you know, everybody was like, well, all the bankers are going to lose their jobs now, because you know, people don't need to go into the bank to get money anymore. And really all that happened was that the skill set for desired bankers changed from more financial acumen to more customer service acumen. Still need all the bankers, but they have a different focus and different way that support. I just, I think, I think embracing the change, right is obviously, you know, if want to stick around, is what you want to focus on. But just understand that this technology is here not to replace, but to help and to assist. And it's going to, we're going to change, technology is going to change, and to the degree that we can continue to change and complement one another us and our technology, I think that's just going to take us to better and better places.

David Turetsky: 27:38

So don't fear the robot is what you're saying.

Schon Parris: 27:41

It's not Terminator. There's no Arnold Schwarzenegger out there. No, I'll be back. None of that stuff.

David Turetsky: 27:48

I don't know. Maybe, maybe it'll be back, maybe. Schon, is there anything else that you'd like to talk about with respect to what you've seen from your clients' perspective in the world of analytics, even from a practical perspective that might help them get started?

Schon Parris: 28:16

You know, I hate to provide advice that's stale and old, but it's been around because it works, which is keep it simple when you're getting started. And I mentioned before, if you focus on one problem that you're trying to solve, that's what I'm talking about. If you focus on a simple problem, like like, you know, reducing turnover, for example, you're going to learn a lot going through that process about what works, what doesn't work, what's the most efficient way to put this data together to provide insights around the problem I'm trying to solve? Once you once you start to get more comfortable working on one problem, then you can begin to expand that into other areas. Now, what is it going to take to, you know, reduce overtime, right, you might expand into that area. So you start simple, you're going to learn so much by going through that process, it's going to allow you eventually to expand over time. And that's what we've seen our most successful clients do, start on one small business problem, then when the comfort level is there, and I don't mean just the comfort level of the person that's, you know, that's going through the paces, I'm talking about the comfort level of the entire organization and the leadership that they're comfortable as well, then you're going to start to get buy in and it's going to make things easier to open that up a little bit.

David Turetsky: 29:33

Sound advice from a brilliant person solving client problems on a daily basis. Schon Parris, thank you so much for being a part of the podcast.

Schon Parris: 29:41

Sir, the pleasure is mine.

David Turetsky: 29:44

As you can see, this is the reason why I love my friend, Schon. Sean, thank you very much. Take care, and please stay safe.

Announcer: 29:51

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