Marc McBrearty – WorldatWork 2022 Series: Compensation Surveys: Past, Present and Future

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• dwight.brown@salary.com

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• david.turetsky@salary.com

 

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Production by Affogato Media

Resources:

• WorldatWork Conferences
• Compensation Consulting Services
• Compensation Data
• HR Data Labs Season 4 Episode 6
• HR Data Labs Season 4 Episode 4

• “That's where I see big improvements coming in the next three to five years: artificial intelligence; data science.”
• “I wonder: when somebody says, “I want realtime data,” do they really want realtime data for their entire population, or do they want realtime data for data scientists?”

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 at WorldatWork 2022. We have with us Marc McBrearty from Willis Towers Watson is that did I say it okay?

 

Marc McBrearty: 0:56

You did, David, thank you very much. Thanks for having me!

 

David Turetsky: 0:58

Well, I care. And we have with us our special guest Kevin Plunkett, host of the Get It Right podcast.

 

Kevin Plunkett: 1:06

Yeah, co-piloting again, David!

 

David Turetsky: 1:08

We love that. That's awesome. And so Marc, one of the things we wanted to talk to you about today is the past, present and future of surveys because you work for Willis Towers Watson?

 

Marc McBrearty: 1:18

That's correct. I oversee North America surveys and software.

 

David Turetsky: 1:23

Outstanding. You know, there's one thing we do on the HR Data Labs podcast, which I think we've forgotten in the past. But we asked one fun thing that no one knows about you.

 

Marc McBrearty: 1:32

I am in the midst of becoming a TV producer of my own show called Guys Like Us. I've pitched it recently to the Food Network and the Food Channel got better traction with one than the other. So we're going back to what we call rewrites. But basically it's a food cooking show where on game day Sundays in the NFL season, my friends and I will assemble around a sort of a kitchen bar island kitchen where I'll prepare some food for the day. So I'm trying to give too much information because somebody else scooped me on it.

 

David Turetsky: 2:04

But one thing that you must tell me, Jets or Giants.

 

Marc McBrearty: 2:08

Actually New England Patriots, believe it or not. I'm originally from Boston.

 

David Turetsky: 2:11

Oh my god, heartbroken. I thought you were in New York now!

 

Marc McBrearty: 2:15

I live in New Jersey. I work in New York, but my heart will always be in Boston when it comes to sports.

 

Kevin Plunkett: 2:19

He's one of ours, David. He's one of ours!

 

David Turetsky: 2:21

Let's go Giants. That's all I'm saying!

 

Marc McBrearty: 2:23

Season ticket holder since 1978.

 

David Turetsky: 2:26

Well I was a season ticket holder for the Rangers for 13 seasons.

 

Kevin Plunkett: 2:31

As long as as long as we can be guests on your show.

 

Marc McBrearty: 2:34

Absolutely.

 

David Turetsky: 2:35

You're gonna have to have us!

 

Marc McBrearty: 2:37

When the Giants play the Cowboys it will be a nice brisket that we'll probably be preparing.

 

David Turetsky: 2:41

Can it be kosher brisket?

 

Marc McBrearty: 2:42

Absolutely. Absolutely.

 

David Turetsky: 2:43

I have to ask. Okay good.

 

Marc McBrearty: 2:44

Low and slow is the only thing that's important.

 

David Turetsky: 2:47

That sounds good. So Marc, our topic for today is surveys past, present and future when we say surveys, we're talking about surveys that the HR professionals use for doing market pricing.

 

Marc McBrearty: 3:08

Traditional salary surveys.

 

David Turetsky: 3:09

Traditional salary surveys.

 

Marc McBrearty: 3:11

HR provided as opposed to crowdsourced.

 

Kevin Plunkett: 3:13

Correct, right.

 

David Turetsky: 3:14

So let's talk about the past. Let's talk about the origin story of surveys. They've been around for a long time.

 

Marc McBrearty: 3:22

All 38 years of my career at Willis Towers Watson, beginning as an intern right out of Suffolk University.

 

David Turetsky: 3:28

Remember Eric Zitaner?

 

Marc McBrearty: 3:29

I certainly do!

 

David Turetsky: 3:31

One of my best friends

 

Marc McBrearty: 3:31

He and I worked together for quite a while.

 

David Turetsky: 3:33

One of my better friends

 

Marc McBrearty: 3:33

At two firms! I worked with him at Watson Wyatt and also worked with him at Willis Towers Watson.

 

David Turetsky: 3:38

Right, right. So let's talk about the past, though. I have been part of the survey organizations, I was part of the survey organization, Towers Perrin, I helped the survey organization at AON consulting when I worked there. Talk to us about the past. It's had a really wonderful history.

 

Marc McBrearty: 3:54

Well the past was tedious, right? Everything was submitted manually. At best, you had a turnaround document that would

 

David Turetsky: 3:57

Yep. report on what you reported previous years. And then you were asked to simply update the information, review the employee record, etc. At best technology back then was if you remember the old magnetic tape. Banks used to use, for example, we used to get a report out on magnetic tape loaded on the machine, get a data dump in the old weighing PCs. And we would go record by record get on the phone with clients, we view the entire report. You know, in some cases, a bank like Bank of Boston at that time, you know, could be as many as 15,000 records. And you simply review all the anomalies that were pointed out by your at that time, we had some pretty sophisticated algorithms for analyzing individual data and then we would do the same on a macro basis. And then that would lead to the production of the report and the whole process start to finish would take about six to eight months. Wow.

 

Marc McBrearty: 4:50

Nowadays, you can do a survey in four weeks.

 

David Turetsky: 4:53

Wow. From creation all the way through to?

 

Marc McBrearty: 4:55

Not so much creation, that may be a little aggressive but certainly from launching to production. With API feeds and data feeds and all the HRIS systems that exist today, you know, the automate the process of submission is much more automated than it's ever been. And on top of that incorporates a lot of the algorithms that we use to use internally proprietary algorithms are now much more common place in all software programs, so transcription errors, and things like that are much more easily identified and solved, actually, before the submission process so that it reduces the time we spend.

 

David Turetsky: 5:29

Well, the submission process itself is, I don't want to say it's a breeze, but it's much, much more simple and straightforward for clients, isn't it?

 

Marc McBrearty: 5:39

Yes, I believe so. Certainly, the elements have become much more standard, what I define as base salary, you define his base salary, you know, all of that has become much more common now. We typically are all using an April one effective day. Now, there are some differences here and there. But the definition of LTI incentives we're almost all using accounting value, we give you the flexibility to use other valuation methodologies, if you wish, but for the most part, we're all trying to incorporate the same standards in our collection vehicles, so that the client can really use one submission for all vendors.

 

Kevin Plunkett: 6:09

But there's still some friction now in the process, right. And the participation process as a whole. Some of that, and some of that is some of that is, is, it doesn't matter how great the software is going to be, there's a resource issue on the front end with the customer.

 

Marc McBrearty: 6:24

Exactly. If you heard my session this afternoon, or this morning, excuse me, it was exactly that, that there is resource constraints that really affect the process more than anything else, the compensation professional can only do so much right, they can get the data, they can retrieve the actual compensation. But what they can't do is the matching, and they certainly don't know every job in the organization, they got to work with line managers, they've got to work with people. And it's so it really needs to be a process in which the entire organization buys into. And if they don't, you basically end up with garbage in and then of course, garbage out.

 

David Turetsky: 6:56

But that's been the same for me at least my career, 30 years of doing matching on the practitioner side, as well as on the survey side.

 

Marc McBrearty: 7:04

I agree. But I think organizations are running much more lean and mean now. And I think so that the allowance of an individual to have the time necessary to do that effectively, is become challenged, it really has. And what we're seeing is that the process is being leveraged to the lowest point in the organization. So it's typically an entry level, professional or an intern in some cases even and of course, they just don't know enough about the organization to do it well.

 

Kevin Plunkett: 7:29

So if there are going to be so obviously, you know, the thought process was jeeze, if we could only just have a direct feed out of the HRIS system, or what have you, things will get easier, that hasn't necessarily then proven to be the case, because we're still stuck with the labor intensive work around matching.

 

David Turetsky: 7:49

Absolutely.

 

Marc McBrearty: 7:50

Correct.

 

Kevin Plunkett: 7:50

And looking at anomalies, and just basically combing and looking through the data.

 

Marc McBrearty: 7:55

That's where I see big improvements coming in the next three to five years artificial intelligence, data science, I think, you know, we even in our own methodologies now, and our own tools are finding more and more success in actually pointing you in the right direction. As far as matching goes, just based on a couple of key words, you know, whether it's an attribute of the level of the individual, for example, whether it's a manager or an entry level professional or an executive, whether it's in the you know, it might be in the financial discipline, but you're really looking at an accounting. So just in the way that reporting and accounting and the terminology that's used to describe the responsibilities of essential functions can really point you in the direction of a good match relatively quickly.

 

David Turetsky: 8:34

But Mark, I think one of the problems that I think Kevin was highlighting as well, is that the data that's there needs to be good data, and

 

Marc McBrearty: 8:42

Oh, absolutely.

 

David Turetsky: 8:44

And there's no job description!

 

Marc McBrearty: 8:45

Right, I hate to say it but you get what you pay for. Nobody's ever gotten a free lunch in my world.

 

Announcer: 8:53

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: 9:02

So one of the things that we've just been espousing to a lot of listeners on this podcast, from the very beginning is, HR needs to take a long, hard look at the data it has, especially in the job table, and be able to fix that fix function, fix family, fix job description, because people need to know what the hell they're being paid for, you know, we don't know the job description, the employee doesn't know the job description, and therefore, it's kind of unbounded, what am I doing? It's not based on what I was hired for, because I was hired like 10 years ago. What is my job description?

 

Kevin Plunkett: 9:35

So to that point, right, you do a lot in data analytics, data management, data governance in you know, you've effectively been doing that for quite a while and around the data side. I would I'm gonna throw this out there since I don't have 30 years of experience in this area. But it seems like the importance in data right, is becoming more and more apparent to those in HR than it ever has been in the past. And so that with all of this technology that's been created all of this software, it's coming back to time and time again is, hey, if you don't have good data, none of this stuff is going to work. And it seems like that seems to be a message that seems to be resonating more strongly now with HR than it has in the past. Is that, does that ring true?

 

Marc McBrearty: 10:23

Yeah, I think so. And I'll tell you, when you look back, and you see that the last crisis that really affected compensation was the financial crisis back in 2008. Since that time, we've been experiencing what 3% increases. You could you didn't even need to do a survey, right? Yeah, right, exactly. You just did with no inflation, relatively no unemployment. You could basically it was a 3% budget, and you didn't even have to conduct a survey to figure that out. Now, all of a sudden, we're at double digit inflation, unemployment also at an unusual low in such inflationary times, is really got people up in arms, because the talent is scarce to begin with, and very much, it's always a seller's market. Anybody, and I think my entire team is an example. I'm experienced it just on my own in my own organization. It's difficult. I also think with social media platforms like LinkedIn, and indeed, and the job boards, etc that there's been much more transparency in the availability of labor. So if you are looking, you don't need to go through advertising and recruiting and all the steps that were necessary in the past, you can relatively find the replacement on your own in a relatively short period of time, or at least a qualified

 

Kevin Plunkett: 11:39

Quality replacement. That's a different story.

 

Marc McBrearty: 11:41

That's true. Exactly. And that doesn't replace the effort needed.

 

Kevin Plunkett: 11:45

Right.

 

David Turetsky: 11:46

And, Marc, I'll agree with you, but I'll qualify my answer to Kevin's question a little bit differently. And I'll say, in data driven organizations, data is more important. In organizations where other things are causing pain, data is not what's necessary, and therefore, those fields don't get filled in, those fields get ignored, those datasets get ignored. And only when something bad happens, like we need to do pay equity on it, or you're losing too many people in this specific area, or we need to build pay transparency, let's start doing market pricing. Well, we don't have any job descriptions, Oh, crap, let's go back to the beginning of the job description.

 

Kevin Plunkett: 12:25

But my point is

 

Marc McBrearty: 12:26

That's a very good point.

 

Kevin Plunkett: 12:26

And the point I was trying to get to is, though, but at that same organization, they don't have that same problem in accounting, they don't have that same problem in sales, or in within the manufacturing and operations. It's only HR!

 

David Turetsky: 12:40

That they're lacking the data

 

Kevin Plunkett: 12:42

That they're lacking the data, or they're lacking the governance around data. And that has been traditionally acceptable.

 

David Turetsky: 12:48

Those areas are driving profit, and they're driving the business.

 

Kevin Plunkett: 12:52

And my point, and that's my point. Now, it seems like people are now take because there is more visibility, and there's more capability to have visibility and transparency into what's really going on in the organization, that there's more push to get data. But I still don't think the HR groups and the HR function as a whole is being resourced enough to really tackle that data on a meaningful basis.

 

Marc McBrearty: 13:17

Part of that, too, is over the last 10 years, there has been little risk. And now, there's a lot of risk and so risk mitigation really is causing and driving some of what we're seeing today.

 

Kevin Plunkett: 13:26

And that's always a problem, because that's a knee jerk thing. It's not a it's not a, you know, a maintain and sustain kind of thing.

 

David Turetsky: 13:32

And here's what happens. Without that data, HR gets tossed to the side, HR gets minimized yet again, because they're not bringing the data and the analytics, and the needed skills to the party, to be able to be sitting alongside the supply chain analytics, the sales analytics, the marketing and targeting analytics, and the well beyond the supply chain, but the production analytics that are driving the business, and they're driving profitability. And without that, and without HR without HR having good survey data, and as well, the comfort and the confidence behind it, to set context for the business. They're lost in that conversation. Right. And triangulation is so important, and being able to

 

Marc McBrearty: 14:17

Right. And that's a phenomenon we've experienced in North America, US and Canada, specifically for a long time, right, nobody relied on one data source. Internationally, that's always that has not been the case internationally, we find many a client that is heavily wedded to give clients confidence that the data makes sense. And the lack one particular organization, whether the data is good or not that good, you know, in some countries or in some industries, of availability of those sources, as you mentioned, all vendors are are not necessarily 100% you know in terms of the quality. And I do think that that mindset is starting to change too particularly with rapid that's hurting us, isn't it? inflation. If you look at South America as an example, or you know, technology rolls in semiconductor rolls, for example, in Asia Pac the competition for data and for good data in Asia Pac around semiconductor manufacturing is It is. unbelievable right now very, very hot. And you know, luckily Willis Towers Watson has a very good space in that particular industry, but not so much in other industries. And so that's always the challenge for us. But it's finding the right data source,

 

David Turetsky: 15:25

It's hurting the HR space.

 

Marc McBrearty: 15:28

Absolutely. I mean, there are a few players who have the breadth and depth in the industry as a Willis Towers Watson or Mercer or even AON. And I think, you know, we're fortunate that we have so much at our disposal, when it comes to identifying trends and insights and where we're headed, you know, we have a lot to be thankful for. And not so many organizations have that. So what do they do? They wing it, quite frankly, you know, you find organizations doing crowdsource surveys, you know, trying to build their presence there, not worried about necessarily the quality of the methodology or whatever, whether it's self serving or not.

 

Kevin Plunkett: 16:05

So, to that point, right, we are starting to see more and more data sources appear, right, and that are, that are AI that are, you know, scraped job data that's aggregated, you know, to some degree, right, the more of that that appears, the more acceptable it becomes. But to your point, we start to run into quality issues. Again, this is back to my my television sort of comment, right? Where, you know, because we now have more streaming services, and we have more channels, we have more content, that means we can get great shows and be entertained all the time. No, yes. And no, you have to still find the good shows to figure out what you

 

David Turetsky: 16:40

But also the world has changed. So the want to be entertained by. conversation around good, is a very different conversation.

 

Marc McBrearty: 16:47

It's a relative term.

 

David Turetsky: 16:48

Exactly. And when you bring up Marc, you know, a good source that Kevin's bringing up, you know, even the crowd sourced data, it has context. So if it's providing context around something that's useful, fine, but you still need multiple other sources to be able to confirm, as well as be able to have some comfort, that that's telling the appropriate As any comp practitioner would say, something's better than story. Because without it, it could be crap. It could be good. You don't know! nothing. I hate to say that, but that's true sometimes. Well, no I think that's the employee looking online.

 

Kevin Plunkett: 17:30

Where do I begin?

 

David Turetsky: 17:33

But the comp person, when their feet are held to the fire, they're not getting fired for using Willis Towers Watson data, right? And so if they were using, you know, CompAnalyst's market data, or they're using Willis Towers Watson data, they're not gonna get fired for itbecause it's got validity.

 

Marc McBrearty: 17:51

The process itself is defensible,

 

David Turetsky: 17:53

Right, it's defensible! You can go to your boss and show them how it works, and it's fine. But if you go to these other sources, it's crowd sourced data, you have to prove it, that it's good. So you have to bring three or four other sources or two or three other something to confirm it.

 

Marc McBrearty: 18:07

Well that's true. You find ABC magazine doing a survey suddenly. First of all, they're not compensation experts, so they shouldn't be doing surveys to begin with. Secondly, you know, they'll use maybe a university or grad student or something to compile, which is fine, statistically, maybe it's valid. But those surveys can tend to be self serving, right? Yeah, the job descriptions are very cryptic, you know, accountant one, and I'll have maybe one or two sentences at best. I hate to say this, but the Bureau of Labor Statistics promotes this kind of activity, because they too, don't really keep detailed occupational titles and descriptions, as least to the degree I think they should.

 

David Turetsky: 18:43

I'm sorry, are you saying OEWS is not useful? It's been updated, hasn't it, in the last decade?

 

Marc McBrearty: 18:51

That was the parrot. That was the devil on my shoulder. The angel is telling me it's spectacular.

 

Kevin Plunkett: 18:55

They're on a five year cycle, I think. Yeah.

 

Marc McBrearty: 19:00

But But nonetheless, that's the problem is that you just do question the validity of the intent. When an individual let's face it, if it's a magazine survey sponsored survey, and the individual is going to get the results of that, what do they want to do? They want to get headlines, it's self service.

 

Kevin Plunkett: 19:16

But to David's point, though, and you know, I know we're knocking the BLS, but the BLS does have, I mean, they're they do they do provide a good service. And their data actually does help with certain functions

 

Marc McBrearty: 19:26

It's a good foundational research,

 

Kevin Plunkett: 19:27

It's good foundational research,

 

David Turetsky: 19:29

It has validity in some things.

 

Kevin Plunkett: 19:30

Yes, I don't mean to throw it totally under the bus here. But it's, it's different than what we all

 

Marc McBrearty: 19:35

And for a lot of smaller organizations, provide. particularly on tight budgets, it's a great resource.

 

David Turetsky: 19:40

And being able to triangulate an answer is where you should go, it's where you should be. Having one source, is what we're trying to say, is problematic and can get you in trouble. Therefore, if you can find those sources, and one of the things I want to ask you, Marc is, If those sources don't exist, wouldn't it behoove the survey organizations to incentivize other players in the market to be able to help solidify the hold of that market? Like encourage other people to build a survey?

 

Marc McBrearty: 20:14

And listen, we can't be everything to everybody. Right? So you look at non exempt roles, in particular, some of the best sources are state run surveys or local build sequence surveys, because they are presenting the market that the National vendor can produce. Right, right. So I mean, we take it down to msa or CMSA level, particularly for non exempt jobs. But even that's a little, you know, you look at the tri state area in New York City, the pay rate in New York city of Manhattan is considerably different than Northwest New Jersey, Westchester County, etc. So, you know, I tend to you know,

 

David Turetsky: 20:46

Well Staten Island versus Manhattan, vs Bronx, vs Queens, it's all over the place, isn't it?

 

Marc McBrearty: 20:52

I do think that there is validity. And having certainly a lot of validity in having multiple sources. I think that there is in this particular market of the US the need for all types of players, vendors, whether it's a global, those that work on a global scale, like a Mercer or Willis Towers Watson or AON Radford or, you know, some much smaller organizations that work, you know, at a different level, there's a place for everybody, so long as you will articulate your methodology and the steps you're taking to ensure high quality,

 

Kevin Plunkett: 21:27

Transparency is absolutely key. And I think that's a challenge with some keep knocking these sort of the new AI or the new crowdsource the new datasets. But I, I think the challenge is they're trying to grow, they're trying to be valid, they're trying to add as much content and data as they possibly can, but lack the transparency, I think, because they're generally afraid that people are going to poke holes

 

David Turetsky: 21:48

And you can, but the problem is that if you in it. really want statistical validity, you can poke holes in lots of different vendors, right? And lots of different survey sources, ends aren't as high as they need to be in order to be able to drive statistical significance and be able to say, with statistical certainty, that this is the sample of the data set of the population, and be able to say for sure, that's how its measured. But we're doing the best we can. We can't force companies to participate in these things. We can't try and find new sources, unless we incentivize participation.

 

Kevin Plunkett: 22:30

Well, and and quite frankly, I think not just incentivizing participation, but also incentivizing pricing, because that's another big barrier, right. And so, we I think there's probably a couple of things we could do one, make the stuff readily available. So people know, hey, you liked this industry? Well, these are the 15, five or six players that that play in this industry, various different price points, package it together, and boom, you got three sources for a good price.

 

David Turetsky: 23:01

I'm gonna say a word. I know your reaction to it. Interoperability. Matching once, being able to match to a common taxonomy. that then point to the matches at Willis Towers Watson surveys, Mercer surveys, Radford surveys, McLagan surveys, whatever it is. I've matched once, and I'm not talking about O*NET, I'm not trying to be irascible and saying, you know...

 

Marc McBrearty: 23:27

You have to get the Rosetta stone.

 

David Turetsky: 23:29

Yeah. Exactly! But that's possible.

 

Kevin Plunkett: 23:32

Okay, Indiana Jones.

 

Marc McBrearty: 23:33

It's not out of the question, with technology it is very possible.

 

David Turetsky: 23:35

Through technology, it is possible.

 

Kevin Plunkett: 23:37

Yes, no, it is, it is. This is not just an Indiana Jones fantasy, this is a real deal.

 

David Turetsky: 23:41

Well, this is one of my fantasies is being able to tell a client, being able to tell client look, here are matches to this survey job. But here are the three survey jobs you could find in a Willis Towers Watson survey, that will give you more statistically significant sample for a population you're looking to measure. Based on your industry, your locations, it has a higher end. Or go to Salary.com CompAnalyst market data has a significant statistically significant sample here. But what you're doing is you're then incentivizing the behavior of using the group. And the group all succeeds with that.

 

Kevin Plunkett: 24:21

Right. I mean, and this is what I was, I mentioned earlier about the technology. And then, you know, our technology does this today. So does, we're not the only ones by the way. But I mean, you guys do something similar within your system. And I think that's where we've seen some significant increases or significant value in the technology, but the market still lags, I think, to some degree in in adoption.

 

David Turetsky: 24:42

Oh, so what you're talking about though, is the hero suggested matches based on your match, right?

 

Kevin Plunkett: 24:46

No, no, it's just talking about like match once. And then then

 

Marc McBrearty: 24:50

If we had the Rosetta Stone...

 

Kevin Plunkett: 24:51

and then you can match it once. And then the data can get populated Right. So there's, so there's a business model like job distribution and recruiting, right? Where you push all your postings into this one platform, and they sit and post it out to all the various job boards you go to? We're at that stage today. Technology wise.

 

David Turetsky: 25:11

Wholesaling postings?

 

Kevin Plunkett: 25:13

Different because you, you're that that one distributor is only pushing stuff out to where you have existing relationships.

 

David Turetsky: 25:24

Oh, okay.

 

Kevin Plunkett: 25:25

It's not it's not it's not a wholesale jobs. You have to go out and have relationships with all the various different job boards.

 

Marc McBrearty: 25:33

So here's what keeps me up at night: in Denver, when you post a roll, you also have to post the pay.

 

David Turetsky: 25:39

That's right. Lots of places.

 

Marc McBrearty: 25:39

New York City's considering adopting a similar statute coming in the next few months. Eric Adams has already

 

David Turetsky: 25:43

Should make you excited, I think. endorsed it. I think it makes me nervous that it's going to happen sooner than later. When you think about it.

 

Marc McBrearty: 25:53

Well, I'll tell you why. The more pay transparency, the less need for the independent third party like myself.

 

Kevin Plunkett: 26:01

I disagree.

 

David Turetsky: 26:01

I completely disagree. I think it forces companies to adopt that more!

 

Kevin Plunkett: 26:06

Because they need to know!

 

David Turetsky: 26:06

You have the validity.

 

Marc McBrearty: 26:07

Well, somebody.. Exactly.

 

David Turetsky: 26:09

You can't just go and sorry, to other companies who are here.

 

Marc McBrearty: 26:11

Why can't we all be that validity? I mean, we're all experts. And we all just develop our own way.

 

David Turetsky: 26:17

No you can't. What are you playing the operator, they say its $10 an hour. So I said it's $10.

 

Kevin Plunkett: 26:22

So so let's say you post a job and in Colorado, you have to do that today, right? So you're in HR and you go, so where did you get these market prices? Um, well, I looked at indeed you know, on the postings for Colorado, and this, well, they have to be transparent. So they must be right, right? Tell me, I'm gonna have a job tomorrow.

 

David Turetsky: 26:45

No. But that's the point, Marc, is that we will need to have defensible data when we're brought in a court of law. We'll have to be able to point to, I mean, look, if you do H1 visas, or H1Vs, you've got to have not just you gotta have a page. I mean, you have to have the page number, you have to have the survey source, you have to have the date of publication, you have to have to copy page two and, and then and yeah,

 

Marc McBrearty: 27:13

And the funny thing is that department labor uses our data as the primary comparative source.

 

Kevin Plunkett: 27:17

That's so funny. We still have arguments with them over our data.

 

David Turetsky: 27:23

So when I was at Morgan Stanley, we used McLagan, we used the green binders. Everybody had the green binders on the shelf? But but the point is, you will have to have that validit going forward. No one's going to abdicate their role to be able to say, I found it on...

 

Marc McBrearty: 27:38

Well thanks, David. You made my job security sounds secure.

 

Kevin Plunkett: 27:41

You gotta know you got another five years. Five years Marc, we just got you another five years.

 

David Turetsky: 27:47

This will go out next week. Marc, that check is in the mail, right? But seriously, right, you we have to stand on real data.

 

Marc McBrearty: 27:58

Yeah, I guess where I was going was the fact that anybody could provide any, you know, knowledgeable practitioner, with all of that data. If you think about every job listing and every, you know, transparent act, being available to you, you could easily develop a compendium of resources that would do that by industry, or by job function, or whatever it might be, you could develop a report of some kind or a data compendium that would give you the answers you need without having to participate. And that was where I was going is that I see a day where I don't want to, because what is the biggest pain point in using data is supplying data, right?

 

Kevin Plunkett: 28:36

Well, but then also, let's think about it, though, yes, while you may. Alright, so we'll use our hypothetical looking up and using Indeed, as a source. Your problem, though, is you're still lacking a taxonomy when you get down to it. And so what the job listing on Indeed says versus yours is probably different. And you know, the next level up may look different than that. And you're going to need that, that that structure, you know, in order to be able to put that data together, and you're not going to find that from the job

 

David Turetsky: 29:07

And here's another thing, you've gone out board. and been transparent to everybody looking for a job. But your current employees are also looking at that too. And they're saying, what does it mean for me, and they are not going to be satiated by go look at Indeed, you're going to have to publicly in your intranet, publish the data, the ranges, and then explain to them how it works and why it's there and what your sources were, and how valid they are and your process. You can't have things happen in a box anymore.

 

Marc McBrearty: 29:42

And I guess, coming around now, as I think about it, what you want to pay an individual in a role isn't necessarily what you end up paying that individual.

 

Kevin Plunkett: 29:51

That's also true.

 

David Turetsky: 29:51

Absolutely.

 

Kevin Plunkett: 29:53

There's and there's, there's a lot of pressures on that one.

 

Marc McBrearty: 29:56

As I'm also learning this past couple of weeks

 

Kevin Plunkett: 29:58

As your employees are coming to you with counteroffers

 

Marc McBrearty: 30:07

Not as easy as I once thought.

 

David Turetsky: 30:08

No, no, not easy being a manager is it?

 

Kevin Plunkett: 30:20

All right, so Marc, put out your crystal ball. What what does this business look like in five years?

 

Marc McBrearty: 30:27

I think what it looks like if you could get clients, as I mentioned this in the session, as well, it's a partnership between vendor and client. Until really work as a partnership, we both need to be accountable, right, we need to be accountable, to take the information, clean it as best we can, and have it represent apples to apples when you go out no lockup pricing, whether it be just to maintain your program, or recruiting or whatever it might be, the client needs to do the same. And the client, if they do that, if they actually maintain their job matches throughout the year, not just four weeks before it's due to the vendor. But if they do that throughout the year, and they're regularly feeding the compensation, payroll, and payroll data, but at least feeding in salary incentive, all the elements that we collect into the HRIS system, I can see a world where nobody has to do a thing, we just pull when we want we push when we're finished. And so you know, technology really can serve as the conduit for real time data in a much more cost effective manner than any of us are finding a way to do so right now. It's far too expensive for us to work with clients and get real time feeds and provide real time output at a price point that they're willing to pay. And so that's why I don't think many of the vendors have really pushed it, because no one's really willing to pay for it. And I think when you get below the enterprise type client, the global multinationals, fortune 1000, let's say it really is an expensive endeavor. And so what do we do with the mid market organizations and some of the smaller type organizations? You know, yesterday's Microsoft that was founded in a garage is eventually going to be 150,000 employee organization, perhaps. And so we have to be cognizant and work with those organizations, as well. And that, you know, to me, that's my challenge. It's the, the Enterprise Client is rather, you know, willing and has the tools, at least to be able to, to meet the partnership that as we expected, I'm not so sure small organizations have that ability,

 

David Turetsky: 32:27

But to them sometimes good enough, is a year, sometimes good enough is, as long as I'm doing this on a yearly basis. And it fits into my compensation management process, which I'm staffed for, which I have appropriate time for, which fits into the way in which my process is designed, it's all good, to the extent in which they need sooner, and other things, they may have to supplement with other things.

 

Marc McBrearty: 32:52

And I wonder when somebody says, I want real time data, do they really want real time data for their entire population? Or do they want real time data for data scientists? Because that job is doubling in salary every six to eight weeks? Because it's not really defined? Right? Is it really the job? Or is it skills that people are concerned about getting real time data on?

 

Kevin Plunkett: 33:15

I think, but I also think it's the environment they're in? Yeah. So. So like, so like, you know, real time scientists, real time data scientists in? I don't know, Whitefish, Montana, probably isn't seeing the same price increases as somebody in Redwood City, right.

 

David Turetsky: 33:35

Actually, I, I think it's the other way now, because they're moving.

 

Kevin Plunkett: 33:44

In fact, Redwood City is seeing the opposite, it

 

David Turetsky: 33:46

But that's the point, though. They want to know is going down. Yeah. what the trend is, right? They want to know what the inflection points are, and what what direction things are moving. If they can get that information, then it helps them supplement and complement the insights that they already have to be able to tell the manager look, you might lose Kevin, but we might be able to find somebody because there are more people moving into our locations that are favorable to us, or we can find people around the US or even around the world that can do what Kevin did. No offense, Kevin, I love

 

Kevin Plunkett: 34:19

Oh, no, I know I'm 100% replaceable.

 

David Turetsky: 34:21

No, no. I mean, it's using a good example.

 

Marc McBrearty: 34:22

We all are.

 

David Turetsky: 34:24

Exactly. We definitely are.

 

Marc McBrearty: 34:25

And minute you think it's safe. It's like Survivor, the tribe has spoken.

 

David Turetsky: 34:33

The tribe has spoken, Marc.

 

Marc McBrearty: 34:34

The tribe has spoken exactly.

 

David Turetsky: 34:35

But but to that extent, as long as you tell them what's happening, and can give them a more real time view of the trends. Does it need to be everything? No. But what's happening? Don't let me be surprised by 1000 people leaving my organization like that. What's going on?

 

Marc McBrearty: 34:52

The cost of replacement are just staggering. Yeah, yeah, that's a good point. I do think that you know, it's surprises me that a company like ADP, for example, with all the payroll data they have hasn't put a front end taxonomy in front of that. And really, when you think about it, they got more data points than anybody in the world or a workday or any of these, you know,

 

David Turetsky: 35:12

You're talking to the guy who built it.

 

Marc McBrearty: 35:14

I'm sorry?

 

David Turetsky: 35:14

I built it at ADP. But here's the thing. And you're right. And I'm not going to talk about the failings of ADP or the I'll talk to you about some of the positives. ADP has tremendous trend data, they pay one in six people in the United States. And on an overall basis, I can see how 38 million people are paid, and the trends from a location perspective and on an overall basis. But But if if I say to you with a straight face, that I would suggest to ADP clients that they just use ADP as a compensation source, I would never! I always tell them to triangulate not because there's anything wrong with ADP data, necessarily. But because the same thing is true of most sources. You want to triangulate to make sure that you're making the right decisions, right.

 

Marc McBrearty: 35:58

It's good point. Eric and I were working with They have a fascinating data too, fascinating. Equifax years ago on a similar, you know, similar model to what you guys developed over at ADP. More data than I mean, the power of the data is what it boils down to, the organization has got a ton of it and just don't know what they're sitting on.

 

David Turetsky: 36:20

Yeah.

 

Kevin Plunkett: 36:20

Well, I I would argue they do know what they're setting on. They just don't know how to organize it, but they don't they don't have a way to they don't have a way to they don't have a way to characterize it, put it put it in in a usable, digestible fashion.

 

Marc McBrearty: 36:35

Exactly.

 

David Turetsky: 36:36

The other thing is, who are their audiences? Who's buying from them, one of the things you have to find is and with Equifax, they actually have human capital management people they've hired, so they're getting there. So I'd say that might be a source to look out for if I'm a competitor, but the question is, what are you known for? ADP is known for payroll. No compensation person will go to ADP and say, Oh, ADP, you've got all this data. Let me see what you can do. Usually, it's the HR people and the payroll, people who go to the comp people and say, This is what's there. And the comp people go, it's a data point. And that's what I was saying before you triangulate from there, just like Equifax. If Equifax actually comes out with a product that can be used by compensation. First of all, we would have seen them here. Right? But second of all, they would say something about why am I not

 

Kevin Plunkett: 37:24

Well they are here but for a different purpose.

 

David Turetsky: 37:26

Okay, but yeah, yeah. But and so when they do come here and start touting compensation data market data, then we'll talk to them about tell us what context it sets and what value it adds. And that's the thing right? And then we can talk about how all this stuff comes together in something like CompAnalyst and how do you triangulate and make the right composite. So Marc, any other predictions or anything else you want to end up on?

 

Marc McBrearty: 38:05

Oh my gosh, all right. The Red Sox ever gonna win another world series?

 

David Turetsky: 38:08

Not before the Mets!

 

Kevin Plunkett: 38:08

I was just about to ask you is it gonna be this

 

Marc McBrearty: 38:10

Mets? Come on! year, Marc? Is it giong to be this year?

 

Kevin Plunkett: 38:13

So you know how Jordan's furniture does this you know where you buy all your furniture, but yeah if they hit the World Series.

 

David Turetsky: 38:21

Not gonna happen.

 

Kevin Plunkett: 38:22

So I just bought a new mattress, Marc, am I gonna have to pay for it in October or not?

 

Marc McBrearty: 38:25

You had better keep making those installments. Sorry to say.

 

David Turetsky: 38:32

You heard that on the HR Data Labs podcast. Wow. Marc, thank you very much.

 

Marc McBrearty: 38:41

It was great. Thank you very much. Thanks for having me.

 

Kevin Plunkett: 38:43

See that wasn't so painful.

 

Marc McBrearty: 38:44

No, it was very good.

 

David Turetsky: 38:45

Kevin, thank you for being a part of it.

 

Kevin Plunkett: 38:47

Thank you. Always a pleasure.

 

David Turetsky: 38:49

And thank you all for listening. Take care. Stay safe.

 

Announcer: 38:52

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