David Turetsky and Dwight Brown – ADP Pro Series 2022: Compensation Benchmarking

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:

• Compensation Consulting Services
• Compensation Data
• HR Data Labs Season 4 Episode 8
• HR Data Labs Season 4 Episode 6

• “I can take two different surveys looking at the same industry and I can get vastly different results. . . .I see people come in using one survey and I think they don't necessarily know what the positives and pitfalls of that particular survey are.”
• “Anybody who tries to make matching and survey processes into science experiments—they fail. . . . You can know who the participants are, and the person could have done a really good job matching, but they could have excluded a bunch of people because [of whatever reason]. . . . [I’m] not trying to say they did anything nefarious, but it just happens.”

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 the most fascinating people to talk to about the world of HR data technology, and analytics. Like always, we have with us today, our guest and co host, Dwight Brown. Hey, Dwight.

 

Dwight Brown: 1:04

Hey, David, good to be here with you in this different venue than what we're used to working in.

 

David Turetsky: 1:10

Why don't you tell the audience where we are?

 

Dwight Brown: 1:12

We are at the ADP Pro Conference in the Gaylord resort, I believe down in Dallas, Texas.

 

David Turetsky: 1:19

Yeah. And it is a wonderful place if you haven't been here. It's like a BioDome the entire hotel. And all the facilities in the hotel are actually in one place.

 

Dwight Brown: 1:28

It is crazy. And it's crazy, huge.

 

David Turetsky: 1:31

Yes.

 

Dwight Brown: 1:31

I've gotten lost like four times just on the way over to the conference here.

 

David Turetsky: 1:35

I think that's the reason why they designed it this way to make people walk and walk and walk. It's, it's the size of Texas.

 

Dwight Brown: 1:42

The only thing worse was Vegas. Vegas is like you're walking miles and miles anywhere you go

 

David Turetsky: 1:49

Exactly. Well, you know, everything in Texas is big. So that's, that's why this hotel is wonderful. So today, we're going to do something special. First of all, we're not going to do a fun thing, because all of our fun things are out. Yeah, exactly. And then we're going to do a, we're gonna have a conversation around compensation benchmarking. And we want to talk about compensation benchmarking, the good and the opportunities, not good and bad. But we like to call bad things opportunities, opportunities for improvement. And so when Dwight and I were talking, we're, we were kind of going back and forth on, there's a lot of things around compensation benchmarking that I think can be demystified. Especially for people who want to learn more about how compensation benchmarking works. If you kind of think about it Dwight, compensation benchmarking is like one of the original HR analytics, because you're taking lots of forms of data, you're summarizing it, you're looking at the statistics, and you're trying to measure the population by taking a sample.

 

Dwight Brown: 2:47

Right. Yeah.

 

David Turetsky: 2:48

And we do that by utilizing salary surveys, right? Published by major vendors like Mercer, Willis Towers Watson, AON Hewitt and companies like Salary.com that actually aggregate a lot of that data and create really good data out of it. So what we're going to do today is we're going to talk about things that are good. And then we'll talk about opportunities.

 

Dwight Brown: 3:10

That's perfect.

 

David Turetsky: 3:11

So let's get started.

 

Dwight Brown: 3:12

Let's get into it! Yeah, so, you know, from my perspective, the

 

David Turetsky: 3:13

So, Dwight, from your perspective, let's talk one of the good things that we have is that we have all kinds about the good of compensation benchmarking, and how companies can utilize that to understand more about their workforce and more about the market in which they compete for scarce talent. of data that we can tap into, you know, like you just talked about, there are several large companies that do this, there are a number of smaller companies that do this, and the spaces that they work in oftentimes, they're specialized or, or they may be a large company, and they have a specialized survey. So if you want a healthcare survey, or a manufacturing survey, or whatever that might be, you have better data to be able to match to and the other the other piece that's an advantage is just where we are technologically these days. You know, if we were doing this 10 years ago, we probably would have been working off with PDFs and paper the entire time trying to benchmark things where we have ready access to huge datasets. And we have enough interoperability among all the tools out there that it's easy to ingest, analyze and output the data. So those are those are a couple of the big positives that I that I see with things right now. And let's let's dive into a little more because if you're in an industry that has specialized talent, you definitely want to find either a server that you feel comfortable with that has competitors of yours so that you know that you're actually benchmarking to what the market it is for those people. And you want to make sure that the descriptions of the jobs are accurate for what your people are doing. So if you're in like, for example, biomedical engineering, you're not going to use the same survey necessarily, for your engineers that somebody like in the automobile industry is going to be utilizing, right? Because even if the job is engineer, it's totally two totally different skill sets, right, with vast educational differences, scarcity issues. And so what you're going to do is you're going to look for those surveys that aligned to your industry that align to your competitive team, they're your competitive market, as well as the locations that you need to hire. Right. And let me let me touch on the technology thing, you were talking about Dwight. When I was doing compensation 30 years ago, at an investment bank, the concepts of spreadsheets were brand new, right? We had just been using, I think it was 1 2 3. For those of you who don't remember, there was a spreadsheet called 1 2 3 by a company called Lotus.

 

Dwight Brown: 6:06

Lotus 1 2 3. Yeah!

 

David Turetsky: 6:08

Yes. Which I don't think people remember. But now I think Lotus was it was acquired by IBM?

 

Dwight Brown: 6:14

I think so. Yeah.

 

David Turetsky: 6:15

But that was a long time ago.

 

Dwight Brown: 6:18

You're dating yourself, man.

 

David Turetsky: 6:19

Yeah, well, you know, you know, I'm safe.

 

Dwight Brown: 6:22

But you're dating me too, because I remember it very well. And I remember working with it, so.

 

David Turetsky: 6:27

And for those of you don't remember 1 2 3, you're definitely not gonna remember VisiCalc, which was one of the original Apple spreadsheet programs that accountants went, Oh, my goodness, I don't have to do an on paper anymore! But but but that's a different story. But But to the point of the evolution of tools, right? It is 2022. And the evolution of HR technology, and technology and in in and of itself, has gotten to a place where not only do we have computers, and everybody's computer on their desk, we have computers on our arms, our wrists and our in our pockets. One other thing about that, to go back and make everybody cringe, I interviewed for a place for a car manufacturer, where I was going to work in compensation. And this was in the early 90s. And I was gonna have to share a computer with the entire department.

 

Dwight Brown: 7:16

Share a computer?

 

David Turetsky: 7:17

Share a computer. Not like sharing a laptop, share,

 

Dwight Brown: 7:21

Not sharing a cubicle.

 

David Turetsky: 7:22

No, no sharing, the department had one computer. And I said, But how am I going to do all of my analytics and my analyses on the market with everybody sharing the computer? They say, Oh, well, you don't really need it that much. You'll take you'll take turns. And I said I would have loved to work for you. But no, thank you. But But that's but the evolution there of technology has now gotten to a place where as we're doing compensation analytics. And we're doing and we're using surveys that are very advanced and doing a lot of QA activities actually inside of surveys inside of survey technologies, and not having to do back and forth using paper to your point before to participate, or spreadsheets to participate and getting feedback through through lots of red marks on spreadsheets. Now, it all happens within technology. So a lot of those, either QA or data quality issues can be dealt with, quickly, easily. I think one of the other benefits of that technology is now we can involve managers much more easily in the matching process. And therefore the data gets better! So there's a lot of really cool things about compensation benchmarking in the modern era, that enable us to have more confidence in the data, as well as have more confidence in our analyses.

 

Announcer: 8:38

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: 8:49

So let's now talk about the bad and the things we see that we like to call opportunities. So what's your opinion on from what you've seen on the kind of the where, where things can improve in the world of benchmarking?

 

Dwight Brown: 9:07

You know, of course, it's all these things all fall on a continuum. I think one of the one of the challenges that's out there, and I don't see this a bad or anything like that, but people's knowledge of the process itself of what goes into benchmarking, what the output is and how they're able to use that, you know, oftentimes, we'll get people in from HR, they've dabbled a little bit in compensation, but beyond that, they haven't really gotten into the process itself. And so there's there's a good amount of teaching that has to go around there. There's also a lot of back and forth throughout the process, helping to educate people and so this is actually something that I love is being able to work with clients who they know some but they need they need that coaching along the way and and the more people that we can get educated on the process, the better. Another. Another component that I see with things is the the challenge of of the surveys and what their strengths and weaknesses are. And kind of what the trends are because I can take two different surveys, looking at the same industry. And I can get vastly different sort of results, when you look at that median to the composite median or composite 50th with them, where one is going to be high, one's going to be low. And oftentimes what I'm doing in there when I'm doing the job matching with those I'm matching to both so that we get sort of this hybrid thing. But I think it I think it speaks to the fact that, you know, I see people come in using one survey, and I think they don't necessarily know what the positives and pitfalls with that particular survey are, if that particular survey happens to happens to aim high, then they're paying more than what the market is, if it is low, then they can't get the people, you know. So I think that's the thing that sticks out the most for me, in terms of the opportunities out there is, is just understanding the surveys that you're using, understanding the data that you're using, and understanding what the outcome is going to be.

 

David Turetsky: 11:22

But it also goes back to your point, which I really love, which is education. Whether you're a company that's been doing surveys for a really long time, or whether it's so an industry vet, or whether it's a person who's doing market pricing for the first time. To me, there's always more education, there's always something new that they can be taught. And then I can learn actually even though I've been doing it for over 30 years. And still, I learn something new every day when I go do it. And that's because it evolves all the time. The point that you made around multiple sources, we do have to educate our clients to not rely on just one source. Because the opportunity there is that to your point, one source could skew a result because either they have a participant or they don't have a participant that is either high or low, and the other survey doesn't and therefore is a little bit more moderated. Having both of them together enables you to have a more even sample. And I like to use the word triangulation, it helps you triangulate what the market is. Because, you know, for those of you who've heard me speak, I love talking about this in the context of this is an art form. It is not a science. Anybody who tries to make matching and survey processes into science experiments, they fail, because they're saying, Oh, well, you know, I want to know who the participants are. And I want to know this. And I want to know that you can know who the participants are. And the person could have done a really good job matching. But they could have excluded a bunch of people because.

 

Dwight Brown: 12:58

Exactly, inadvertently.

 

David Turetsky: 13:00

Inadvertently or on purpose, not trying to say they're doing anything nefarious, but it just happens. People make mistakes. We're all human. And therefore, when we

 

Dwight Brown: 13:08

Right, exactly. talk about surveys, when we talk about benchmarking, remember, we're trying to measure a population. And there are statistical methods to do that. And they are methods, they are estimates. And when you're setting your compensation

 

David Turetsky: 13:22

And if you communicate it as these are ranges, and you're setting how you're paying your most precious resource, make sure that you understand the assumptions that go into it. And that you are careful about how you communicate it. Because if you communicate it as gospel, people are gonna believe it as gospel. estimates, people are going to know, okay, well, these are good benchmarks, exactly what the word implies, we have to use a little bit of thought, and affordability, and a lot of other reasons why we may or may not, you know, set our rates by that.

 

Dwight Brown: 14:00

Exactly.

 

David Turetsky: 14:01

So, to me, I love what you're talking about. They're all opportunities.

 

Dwight Brown: 14:04

Yeah. The other major opportunity that I see, and I see this over and over and over again, companies come, they want a benchmarking project to take place. And when we ask them about job descriptions, they'll say, well, we really don't have job descriptions. And that makes it a major challenge and it impacts the downstream integrity. Because if we're doing if we are going out and finding benchmark jobs in the market based solely on a job title, we're probably not going to have the same integrity of our data. Now, granted, there are some there are some a handful of jobs out there that are fairly standard, an accountant is an accountant, a nurse is a nurse, but that can that can vary too by by company, there's duties.

 

David Turetsky: 14:55

Yes. A derivative products accountant is not the same as cost accountant. So yeah, right. And I like to call that a good opportunity around data integrity. Because your point is that descriptions are part of the fundamental need while we're benchmarking.

 

Dwight Brown: 15:14

Yes.

 

David Turetsky: 15:15

A lot of times, though, and we've seen this many times with clients is that whether or not they have descriptions is one thing, but the data that underlies a lot of the employee and jobs, they're very ripe for, for cleaning up.

 

Dwight Brown: 15:29

Yep, exactly.

 

David Turetsky: 15:30

And the thing that I would tell clients, and I tell clients all the time, is, let's spend some time fixing your data before we start doing analysis on it.

 

Dwight Brown: 15:40

Yeah, yeah, that's a really good point, it's you get, you get some of the data that comes through to us. And you look, and there are blanks, and half the fields that they have. And we, we can't even get the underlying data that we need to do the good analytics, that goes with it.

 

David Turetsky: 15:59

And key demographic and job fields that we need for our benchmarking. Yeah.

 

Dwight Brown: 16:04

Yeah, it's amazing the number of clients that come in, and only about half of them have any sort of gender identified or zip code, you know, some of those things that can really help us build more robust analytics around the data. Good luck with that! Let us know if you figure

 

David Turetsky: 16:17

Especially around pay equity. And when you're trying to ensure that the, the analysis we look at are as complete as possible, and have all of the right well, all the right things checked all the boxes checked. And so I guess the the other thing to say on this is, you don't need to have every field perfect, it's never gonna be and trying to chase perfection is a fait accompli, right? And a lot of times we see paralysis, because, you know, companies worry that how am I going to get my data perfect? You're not! out how to do it! Because we certainly haven't. And the answer is don't chase perfection, right? Chase good enough. Good enough is great.

 

Dwight Brown: 17:01

Perfect is the enemy of good.

 

David Turetsky: 17:03

And we can help you do that we can try and get you there. But to be honest, it is not going to happen tomorrow, there's gonna be a transaction, you're not going to know about for a month. And so the manager is not going to report it, or the HR person got busy and kind of forgot about it, don't worry about it, it's not going to change anything, one data point doth not a company kill. So to kind of summarize, the benchmarking processes are evolving, we're in a very different place than we were 30 years ago, even though the survey processes are very much the same. And getting data from survey companies is still the standard in the market. But that being said, it's evolving, and changing. And if you're going to embark on benchmarking, and you're going to embark on these processes, the answer is do it. And if you need help, experts are out there to help you do it. But if you need help, ask for it. Get it. Because if you do it on your own, you could do it on your own. But there are people who've gone through before, who have expertise in it, and who can help you do it faster, cheaper, better.

 

Dwight Brown: 18:23

Yeah, exactly. Yeah. If you're trying to learn it on your own as you're going through and doing it on your own. It is very difficult

 

David Turetsky: 18:31

Dwight, do you know anybody who does that work?

 

Dwight Brown: 18:34

Jeeze you know, I don't I don't think I know anybody.

 

David Turetsky: 18:37

Okay, well, we'll figure it out.

 

Dwight Brown: 18:38

We'll put it in the show notes for people.

 

David Turetsky: 18:40

We'll ask somebody at Salary.com. Maybe they know. Oh, yeah, that's right.

 

Dwight Brown: 18:44

Well, yeah, we do do that.

 

David Turetsky: 18:45

We do. Yeah, that is part of what we do. That's the most salesy this podcast has ever become. So apologies, everyone. But thank you very much for listening. And Dwight, thank you for being here.

 

Dwight Brown: 18:55

Thank you!

 

David Turetsky: 18:56

And there'll be more coming from the ADP Pro Summit, and here in Dallas, Texas.

 

Announcer: 19:02

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