Belinda Roberts – WorldatWork 2022 Series: The Future of Compensation Data

Connect with Belinda:

Connect with Dwight:

• 
• dwight.brown@salary.com

Connect with David:

• 
• 
• david.turetsky@salary.com

 

Podcast Manager, Karissa Harris:

• HRDataLabs@salary.com

 

Production by Affogato Media

Resources:

• WorldatWork Conferences
• Compensation Consulting Services
• HR Data Labs Season 4 Episode 5
• HR Data Labs Season 4 Episode 3

• “We need to question what competitiveness is. I think base salary is kind of table stakes, right? It's all [about] the other stuff that factors into how competitive is your job that you are trying to hire or retain people in.”
• “You mentioned pay transparency before—it's that transparency of the philosophy around how organizations pay. I think a lot of employees don't believe that their comp teams really know what they're doing, and I think that's because they just haven't been transparent.”

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. Today, we have some very exciting guests. First of all, we have Belinda Roberts from Mercer. Hello, Belinda.

 

Belinda Roberts: 0:55

Hello. It's nice to be here.

 

David Turetsky: 0:57

Great to have you. And we have my colleague and friend Kevin Plunkett from Salary.com. Kevin.

 

Kevin Plunkett: 1:03

Hey, David!

 

David Turetsky: 1:04

By the way, Kevin hosts his own competitive podcast called the Get It Right podcast. Listen to it on your favorite podcast platform. So today, we want to talk to Belinda about what?

 

Belinda Roberts: 1:20

The future of data, right? The future of compensation data.

 

David Turetsky: 1:22

The future of compensation data.

 

Kevin Plunkett: 1:24

Absolutely. I mean, what's interesting is, you know, as we come out of the pandemic, there is obviously a great need for data, great need to look at pricing jobs, salary surveys have been around for a long time. And you know, there's new data sources popping up, whether it be real time data, or posting data, or whatever the source is, and it gets touted as AI data or new data. But is it any better than the survey data? And, you know, what is the future of of compensation data? And I guess, specifically, you know, survey data, as we know and love it?

 

David Turetsky: 2:00

Well, they are definitely different sources. So maybe what we should do is, let's talk a little bit about the survey data, where it was and where it is right now.

 

Kevin Plunkett: 2:09

Sure.

 

David Turetsky: 2:17

So Belinda, what's your opinion about survey data itself and where it's been where it's going?

 

Belinda Roberts: 2:22

Look, I think that most traditional survey vendors have been collecting data the same way for a really long time. I think the technologies have changed, you know, we're moving away, I'm think we had paper back in the day. And now, you know, we moved to Excel based collection kits. And now most vendors have some kind of technology that organizations can provide their data to, to the different vendors like Mercer. And that technology has helped, it's helped improve the quality of the data, it's helped the experience when a company gives us their data. But it's still a very high friction process, right. So it's still something mostly annually that an organization has to commit quite a significant amount of time to whether it be matching the jobs to that vendors taxonomy, to putting together the spreadsheet to load up to some tool to ingest it.

 

David Turetsky: 3:12

But one of the things that it really helps with which used to kill us practitioners who used to fill out those things, was the friction of getting real time feedback, the QA loop used to take, if not months, is take weeks to be able to get that response from the survey vendor when you submit it finally, and then you get back the data. And then you had all of those red marks you had to correct or all those things in that Excel spreadsheet that you had to correct all those rows. So that real time feedback, though, that's a really big innovation, right?

 

Belinda Roberts: 3:43

It is. And it's really shorten the amount of time that it takes organizations to do it, because they don't have this piecemeal, step by step. It can all be sort of done in an afternoon, if you want to, you know, it's it's still a commitment, you still have to go through the audits, although our tech has made it smarter, to say this is really a problem. And this isn't necessarily a problem. So I do think overall, it's greatly improved, it's greatly improved for us the quality of the data and the volume of the data that clients are giving us. Because our tech has things like matching algorithms can help you kind of pair that out. So it's, I mean, it's lightyears to where it used to be when it was just the Excel based. And we're sort of trying to migrate to that point where everything is collected through our online platform, which Mercer data connector. And so I think when we get there, that's just going to be a huge improvement. But I do still think there's quite a lot of friction in the process, because clients still have to acquire the file from their HRIS system, and then they have to format it, and then they have to go through the process of loading it to us. So we're still trying to think through how that how we can reduce that friction and an effort involved in clients giving us data for our traditional kind of annual surveys.

 

David Turetsky: 4:53

So we're not talking at all about integrating with HRMSes to make that process easier. Is that still too much friction?

 

Belinda Roberts: 5:03

clients. And that might be multiple systems. So, you know, historically, we know that the data required to input to a survey doesn't just come from HRIS sometimes there's other information that's held in other, you know, systems, tables, spreadsheets, that needs to be integrated, not to mention matching and things like that which need to come together into one input. So we're looking at ways to try to create those connections directly into the main HRIS systems, the main payroll systems, cost management, that type of thing.

 

Kevin Plunkett: 5:42

And so what what we've seen, right, is that that friction in the market has sort of created an opportunity for some of these, you know, real time datasets, right, or AI built datasets, you know, the promise is that they're faster, they're real time they track what's going on. But the real in reality, though, a lot of times, we're unsure and unclear about what the source data is behind. Oftentimes, the matching the job matching, because it's not based on a taxonomy typically can be fraught with challenges. You know, what, what is there in your experience about what you've seen or heard about the sort of these real time databases? Are they AI based? You know?

 

Belinda Roberts: 6:25

Yeah, I think a lot of what you've said, I do think there is some value to them, I don't necessarily think we should throw the baby out with the bathwater, I do think that they offer an interesting lens on the market. Depends on how they're collected, from who, what's the demographic making it up? Or is it truly an aggregated data set that may have question about how real time is it really? So I think that those are really interesting. They don't answer that the traditional questions always they may answer the base salary questions on certain jobs and how quickly they're moving. But they're very weak in the areas of equity, long term incentives, policy. So what our clients get from a traditional survey is just so much more than that, like I said, I do think there's a place for them. And I think that they are an excellent, third, fourth source, I just don't think that we can base all the decisions, a client can base all of their decisions off them yet.

 

David Turetsky: 7:20

But I think, to your point, I think there's a lot of value to having a rich data set of lots of different things being brought together to answer multiple questions, because, as you say, it's not like we're asking one question. And the question is what's going on with base salary? It's not, it's not only what's going on with total reward. It's what is going on in the marketplace. And the marketplaces are very complex, the dynamics of not just the job, but also the pay and the benefits, and even the makeup of who is actually in that role? And how is the work getting done? Those are still very interesting questions for a lot of companies, they can solve things with a gig worker, they can solve, you know, getting skills, acquiring skills, different ways these days, they may not even meet the people ever who get things done. In fact, now we're talking about AI doing work and robots doing work. And so there's a lot to the equation. And so these other databases or data sources can help with that, right?

 

Belinda Roberts: 8:23

I totally agree. Yeah. And I think you raised a good point, in that we need to question what competitiveness is. I think, you know, base salary is kind of table stakes, right? It's all the other stuff that factors into how competitive is your job that you're trying to hire or retain people in. And there's just so much more data that you need to look at that's maybe, in addition to the historical information that we've had in surveys, however they're collected, or data products, however they're collected.

 

Announcer: 8:52

Like what you hear so far? Make sure you never miss a show, by clicking subscribe. This podcast is made possible by Salary.com. Now back to the show.

 

David Turetsky: 9:03

Until the question becomes different, the question is, what are we measuring right now? And so Kevin, to your point, if we're measuring pay, and total compensation, you know, are the traditional sources still the place to go? Versus what are the other things we can learn from these other sources?

 

Kevin Plunkett: 9:22

Or right, or to your, you know, to what Belinda said, you know, what are the benefits of utilizing some of these other third sources to supplement? You know, if, if, if your choice is, hey, I'm going to stick with the traditional sources because they're tried and true, right? Then what is the what is the value of the role of some of these newer datasets? And how do you use them? And how do you you know, how do you map the data because the data elements are different, right? And, you know, some of them are based on job title. Others are based on job description. How do you map those successfully together, or do you not really worry about that? I mean, how do you tie the datasets equally enough together to be able to be useful?

 

Belinda Roberts: 10:05

Right.

 

David Turetsky: 10:05

I think a lot of these things bring up that this is going to need some evolving skills, of being able to create the right story, based on the datasets not only that you have, but even the ones you don't have. And then maybe even some of the ones you find online, to be able to weave what's the right story for my organization and the business problem we're trying to solve right now?

 

Belinda Roberts: 10:27

Yeah, and I would say that, you know, to that point, it's an organizational thing. But it's also within the organization understanding the segments of jobs or employees that you need to focus on slightly different data sets, because I think we know quite a lot about traditional data sets and non traditional data sets, and how they tend to skew towards different directions. Like, you know, traditional data tends to skew towards the bigger cities, the bigger organizations, the, the crowdsource data tends to skew towards more junior roles, younger people, often more rural representation. So but that can be really useful if you're looking at certain jobs in your organization. Right? So I think that categorizing the use case of the different data sets is important within within clients' organizations, as well.

 

David Turetsky: 11:12

And I think it's really tough to discount those things when employees bring them to you and say, Hey, I found this online. This is what it says my pay should be. Why aren't you paying me that? We as HR practitioners have to work as HR executives have to come back and have something to hand to say, well, this is how we're going to deal with that. This is how we're going to answer that. And it needs to make sure is what we're you need to make sure that you have good responses for your managers as well, because they're the front lines of this. I was talking to a client today. And they said, we deal with a lot of that where people bring stuff to the managers. And I said, when I was an HR practitioner, I had that too. And I did comp. And one of the things I would say to them is, we do a really good scientific job of measuring you and your skills against the pay of our competitors. And we do extensive analysis to say what is competitive pay. And here's what it looks like. And we talk to your managers about how this works. And we give them the tools to make the right decisions around pay. And this person said, Wow, that's great. Can you write that down for me?

 

Kevin Plunkett: 12:20

Well, it's funny. So you know, I'm gonna toot Salary.com's horn for a second here. When we came out with the Salary Wizard 20 plus years ago, that's exactly what we found ourselves in, right? Our customers and clients were like, hey, these people are walking in the door with this printout from your website, telling them they should make X, Y and Z. We don't like this. And we're like, well, it's a great opportunity for you to talk about being pay transparent, and talk about why you're paying the way you're paying, instead of going the opposite direction, which is run and hide, take the opportunity to be be transparent. Now this was way before pay transparency with a was was really in the in the nature of the conversation. And it's taken 20 plus years to kind of get to that point where this is now a more normal and natural conversation for both employees and employers. Employees were having a hard time having the conversation as well.

 

David Turetsky: 13:14

20 years ago, we were telling employees that if they talked about their pay, they're gonna get fired.

 

Kevin Plunkett: 13:19

That's right.

 

Belinda Roberts: 13:19

Sure.

 

David Turetsky: 13:21

And so the conversation has evolved a

 

Kevin Plunkett: 13:21

Now you have to talk about it! That's right. little bit. Now, because of the disclosure laws in lots of

 

David Turetsky: 13:24

And now you have to actually advertise it. And so states and municipalities. You can't tell people, they can't talk about their pay anymore. once you start advertising it, then you need to have a strategy for how you talk to people about it internally. So pay transparency becomes the new methodology for how we have to democratize this data and give it out to everybody in ways in which they can digest it, and make mature decisions about their career and their work. And literally their work life balance and be able to make mature decisions around it.

 

Kevin Plunkett: 14:03

So Belinda, what are you guys doing at Mercer to kind of look at the landscape on and look at how those, how do we take what has been our bread and butter traditional data sources and evolve it to stay current and to stay, you know, valid, right?

 

Belinda Roberts: 14:20

Right, right. So I think that the difficulty we have, to a certain extent is not just a technical difficulty of collecting more data more regularly. Yeah, we've got certain rules, we have to follow safe harbor rules where data has to be 90 Day aged and all of those kinds of things to ensure that we're not helping our clients collude with each other and set pay pay decisions in the market. So I think we are a little bit hamstrung by that. But that doesn't mean that we have to throw out the idea of having more regular, more frequently given to us data. There are a number of products that we have and I think the ideation to move some more of our products to more of an evergreen collection model, where we're doing it on the client's time, when if they want to give us data after their phone call, which happens to be in July, we'll be happy to collect it, then. And maybe we move some of our products to more quarterly based reporting, which certainly helps that that's definitely more real time than we have. And we have some products like that now, which most contracts is is managed that way. Yep. I think for us, that's probably going to be like a key step that reduce the amount of friction it takes for someone to give us data and then be able to look at different models for collecting it more regularly. I would say we, we still think that the rigor while it's a lot of work, the rigor that goes into giving data to Mercer, so like, we engage in a partnership with our clients, where meet with us pre survey, we go through matching, you know, they're in the room with the peers, and they're talking about, well, I match this job to this specific role. To your point earlier, you talked about, you know, it's the taxonomy isn't necessarily always there with some of the organizations. So the job taxonomy, we have a very robust taxonomy, and we have clients help align each other to it. They're not sharing data or anything like that. But they're saying, I match this person in my organization to this role, how do you do it, and then we all come to consensus that every bit is going to match that specific job to this. And then the rigor around the collection of the actual data, which is extremely extensive. I don't think that's going away. What we're trying to figure out is a way to make the all of that easier. Get it to them more regularly. And then maybe think of other sources to answer some questions that traditional data doesn't answer. So to you know, crowdsource data or data that source through Salary.com, or a Pay Scale, or even Glass Door, for example, there are, you know, ways to integrate that into a compensation philosophy. And we're open to that. So, you know, we're certainly talking about that with our consultants.

 

David Turetsky: 17:02

It certainly means an evolution in thinking. Because the traditional sources have always

 

Belinda Roberts: 17:05

It does. been arm's length from non traditional sources, because the moment you open yourself up to the non traditional sources, the purists say, but that's not the survey. That's not what I've invested all my time and energy in. So it's not as valid a source than the ones that I've traditionally used. Which now we're in a different world. And the world of data has evolved significantly since the 1980s, or the 1970s, when these things were first being worked on. So hopefully, people are getting smarter and better at being able to be better consumers of the data and know how to actually put it all together. Right? Yeah. And I think you mentioned pay transparency before, it's that transparency of the philosophy around how organizations pay. I think a lot of employees don't believe that their comp teams really know what they're doing. And I think that's because they just haven't been transparent. And we know that there is a lot of thought and effort and work that goes into that. But I think they just need to share that a little bit further. I think, to your point, David, the, you know, the employee that came and said, Wow, can you write that down? And that's important.

 

David Turetsky: 18:20

Having good scripts and giving managers and employees the tools to make better decisions. Not a bad idea, but it's a different idea than we've been used to in the past as comp professionals, because we always had to keep things in a box. Right?

 

Kevin Plunkett: 18:35

I mean, it's, you know, the, the managers, right, are they're the ones at the frontline that are having these conversations. And I think a lot of cases, we've done some polling, and the managers are just as much of a loss about how to handle these conversations as the employee, right. They don't know any more than the employee does. And that's a real problem. I mean, you're not you're setting these poor, poor people up for failure, but asking them to move mountains. And, you know, that's just not fair. Right. And I think you got to give your man if you want your managers to be effective, and you want to keep your, your your employees in an engaged, you got to give the managers the tools to be able to have that conversation, because I think employees, they just want a little information, they just want to know that somebody's watching out and paying attention. And when you come to your manager, your manager can't even answer a simple question. That puts a lot of doubt inside the mind of an employee.

 

David Turetsky: 19:37

Well, unfortunately, what do the managers say? They say, Well, I don't know HR didn't tell me so you know, go talk to HR. And then HR gets overwhelmed by especially when it's a market that's the way it is right now. They don't have the answers either or they have to go back to compensation and compensation is now deluged with all these

 

Kevin Plunkett: 19:57

Well, and then you know, the first question is, requests. well, let's just pay more people, let's, let's just pay them more to get them in the door. And we'll solve for the problem later. It's not gonna, it's gonna create more problems down the road.

 

Belinda Roberts: 20:09

And you can't afford it. If you were to, like pay the market rates for hiring, and then bring everybody up to the spec, you'd go broke. Right? Right, there's no way that most organizations can afford that. I mean, there is, you know, some stories in the media about tech companies in Silicon Valley, and some professional services organizations, you know, paying West Coast and East Coast salaries for anyone who lives anywhere. But the reality of applying that is, it's just not feasible. You can't wait, nobody can afford that. Most organizations can't afford that.

 

David Turetsky: 20:41

That's why they need to do the analyses. And they need to make distinct, targeted investments where necessary, but they first have to do their homework. And understanding what the market is doing right now gives them that ability to be smart about that money. So where's this all going? Where is data going to be? And if we had our crystal ball, we're looking into the crystal ball, what do we say to competition and HR professionals today about where all of this is heading?

 

Belinda Roberts: 21:21

Great question. I would say probably not that far in the future, maybe with even within five years, I think we're going to have a lot more acceptance of the different non traditional data sets. Like I said, I don't think that the traditional data sets are going away. I think that vendors are going to refine how they do it and how they collect it. But I do think that other less traditional data sources are going to enter that market. But I feel like there's room enough for all of them. What I think we need to do as as organizations that are, you know, working with our clients to create these products, is to help them understand, you know, how to utilize that, how to talk to their managers, or employees about it, or the scenarios in which you might apply one and more than one. But I do think it's going to move quite quickly. And I mean, technology is going to demand it anyway. And there's younger people coming up through HR now who aren't wedded to the old model. And so they're going to demand it as well. I mean, everything's on your phone right? Now, I'm not saying do a comp on your phone, but it's that mentality of moving to the next generation of data. And that's where I think we're going to see ourselves in three to five years

 

Kevin Plunkett: 22:31

So if you had to kind of guess, right, you know, what would be in five years, we're sitting down having the same conversation? What's going to be the the latest trend we have? Is it going to be a technology trend? Is it going to be more of a data and sort of, you know, innovation in around data? Or how one consumes data? What what do you think that trend is going to be?

 

Belinda Roberts: 22:54

thing, because I think we already know that I think it's the type of data that we're looking at is going to change, I think we're going to pivot towards more skills based pay moving away from the traditional job. You know, I was talking about that, you see experts in the media talking about things like not asking for qualifications for certain jobs, but just having skills to do a job and paying for skills, to me, not just skills, that's certainly going to be critical for us in the future as compensation professionals, but other sources of saying, you know, am I paying competitively and you know, things even to the extent of going into the the employee value proposition as it relates to total rewards. So that's where I think in five years we will be and the data's kind of just how do we get there?

 

Kevin Plunkett: 23:49

So do you think so you think the stress around pay moves away from sort of what was considered typically either, you know, base pay and or total cash comp, and more towards total rewards, incorporating other aspects of comp or even culture and some other things, right?

 

Belinda Roberts: 24:09

Yeah, I do, I think and flexibility around it, giving different categories of employees or personas of employees choice around how they're compensated and a total rewards realm. So you know, you've got someone who's fairly new out of college and has huge overwhelming student debt, they may choose instead to take instead of a bonus, some way to pay down their their student debt burden. So I think that's just one example. But you know, people who are at the other end of the spectrum more around retirement type of assistance, and, and lots of places in between. And in that we're doing a lot of work thinking about personas and what personas want.

 

Kevin Plunkett: 24:45

So pay evolving less about the pure number, and more about the needs of the individual where they are in their career.

 

David Turetsky: 24:52

Employee personalization or personalization of rewards.

 

Belinda Roberts: 24:55

Exactly, yeah.

 

Kevin Plunkett: 24:58

Excellent.

 

Belinda Roberts: 24:58

So I think we'll be in five years. But you know, I've got my magic eight ball under the table.

 

Kevin Plunkett: 25:02

There you go. Give it a good shake.

 

David Turetsky: 25:06

Wow, that's a very specific magic eight ball by the way.

 

Belinda Roberts: 25:09

You're not wrong.

 

David Turetsky: 25:10

Very specific! Belinda, thank you so much. We appreciate you being on the podcast.

 

Belinda Roberts: 25:22

Thank you, David. Thank you, Kevin.

 

Kevin Plunkett: 25:23

Absolutely. Thank you! It's been great.

David Turetsky: 25:25

Kevin. Thank you very much.

Kevin Plunkett: 25:26

Absolutely, David, anytime.

David Turetsky: 25:28

And thank you for listening. Take care and stay safe.

Announcer: 25:31

That was the HR Data Labs podcast. If you liked the episode, please subscribe. And if you know anyone that might like to hear it, please send it their way. Thank you for joining us this week, and stay tuned for our next episode. Stay safe.