Garry Straker- WorldatWork 2022 Series: Pay Equity Strategies

Connect with Garry Straker:

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
• HR Data Labs Season 4 Episode 3
• HR Data Labs Season 4 Episode 1

• “I think organizations are going to have to develop processes and systems to be able to compile a more complete data. It's not going to happen overnight . . . If they're going to continue to do pay equity analyses credibly, they're going to have to make a commitment to obtaining that data all the time.”
• “I think US companies, and I think employers generally, are probably going to have to start helping employees understand how they're using their data.”

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 interesting people and interesting places to talk to you about the world of HR data, analytics and technology. Today, we're talking to our friend, Garry Straker from salary.com Hey, Garry.

Garry Straker: 1:02

Good morning, David. Glad to be here!

David Turetsky: 1:04

Right. And we're here at the World at Work conference, the 22 World at Work conference. And so Garry and I were just having an interesting conversation about pay equity and the data that underlies pay equity. So Garry, one of the pitfalls that we're talking about is actually starting with a good set of data, and some of the pitfalls in where we're going to source that data. So from your perspective, what are some of the biggest issues that you find in being able to get good data to underlies Pay Equity Analysis?

Garry Straker: 1:41

Yeah, I think that that's really a fundamental challenge for all organizations. And so one of the things that we see is that the data is typically incomplete. And so there are there are key data components that you would love to have in a perfect world. So for instance, educational qualifications of your employees, prior experience of your employees, and most organizations, virtually all don't have that information when they're in their human capital systems. And so I think as we look at pay equity, you know, there's so much time and energy being focused on doing that analysis, the analysis that we're doing today is based on the data we have today. But we need to get better, we need to, we need to improve, I think the quality and completeness of the data we use for pay equity analyses. And I think that's going to take time.

David Turetsky: 2:27

So we were talking one of the sources, we could possibly get that data from as the recruiting systems. But as we know, recruiting systems get their data from either applications, or they get them from resumes, they may even get them from LinkedIn! To you, what are the pitfalls of being able to leverage those kinds of sources to be able to get at that the kind of data you're talking about.

Garry Straker: 2:46

You know, accuracy is always going to be a challenge, particularly if you're, you know, sourcing it from places like LinkedIn, and even even resumes, right? I mean, resumes don't always reflect, you know, I'm not gonna say that the truth, but, you know, sometimes there's those data elements missing in the resume.

David Turetsky: 3:02

Let's call it, unaudited.

Garry Straker: 3:03

Yeah, that's right. I mean, you only put in the resume what you think is going to be important for a particular opportunity. And so it may, it may miss critical pieces of information.

David Turetsky: 3:13

So is there a good practice? Or is there a best practice when it comes to being able to get access to that data?

Garry Straker: 3:20

You know, I think it's evolving. And I think organizations are going to have to develop processes, and you know, systems to be able to compile a more complete data set, it's not going to happen overnight, it's going to take time, but I think there needs to be a concerted effort and a commitment on behalf of organizations, if they're going to continue to do pay equity analyses credibly, they're going to have to make a commitment to obtaining that data over time.

David Turetsky: 3:46

So if someone's thinking about doing a pay equity analysis, is there something they should be preparing for in order to be able to get that data ready?

Garry Straker: 3:54

Well, I mean, the obvious starting point is looking at the data you have and the completeness of the data you have. And often we see gaps in the data that organizations have in their systems. And it may be because, you know, they haven't let fully leverage technology over time. And maybe they're starting to, you know, utilize it more optimize it more. And so when what they're seeing is that there are holes, and they, you know, to the extent that you can go back and fill those holes, I think that's a good starting point.

David Turetsky: 4:21

But that shouldn't prevent that shouldn't prevent them from moving forward and doing the Pay Equity Analysis, right?

Garry Straker: 4:26

No, I mean, you have to start somewhere. And so, you know, not doing it. On the basis of, you know, we don't have all the data that we'd like to have, you know, it's probably not a good approach. You have to start somewhere. And I think those incremental steps that organizations will have to take in order to get to that point where they have a more complete dataset and feel confident about the analyses they're doing.

David Turetsky: 4:48

And I love the term incremental because you can't, you know, we always hear the term Rome wasn't built in a day and being able to make progress on the Pay Equity Analysis and being able to move forward is a good start, right. So, if you if you worry about all the things that might go wrong from the data perspective and not having enough, you're never gonna get started, because there is no such thing as perfect dataset.

Garry Straker: 5:13

That's right there isn't. And even the data you have can be looked at interpreted in different ways, there was a very recent announcement about an organization we're all familiar with who, you know, were investigated by the OFCCP. The OFCCP, found that there were statistically significant pay disparities in the data that they were using. The organization, on the other hand, said the complete opposite, they said that they had done the analysis, and they found no statistically significant pay disparities. And so, you know, these models are run different ways, perhaps the, you know, the data is looked at differently. And, and I think for organizations, they need to be prepared to sort of run multiple datasets, multiple models in terms of their multiple multivariate regression analysis to, you know, really look at it from all angles.

David Turetsky: 5:59

We've called this and I've said this a lot the Rubik's Cube, right, you have to keep twisting the analysis, you have to keep looking at it from a different perspective, right?

Garry Straker: 6:08

Yep. You need to wallow in the data, that's the term I use. And you have to, you know, you got to leverage technology to be able to do that efficiently.

David Turetsky: 6:16

And sometimes that technology is Excel.

Garry Straker: 6:20

Good luck with that.

David Turetsky: 6:21

And Excel is not perfect, but it's a good start, right?

Garry Straker: 6:24

It is! It is a good start. And but at some point, it just becomes untenable, unmanageable, because of, you know, we all know that, you know, the analysis is only good as the data that's in there. I think leveraging other technology solutions that can capture that data, maybe drawing it in from HCM systems, real time, preferably or as close to real time you can would be very helpful.

Announcer: 6:47

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David Turetsky: 6:59

So Garry, one other thing that I wanted to talk to you about, which is that we heard, I think it was last week, that California is potentially going to put out a bill that would it would mandate that companies tell employees what they are doing with their data, meaning how they're storing it, where they're storing it, in what systems, what they're using it for, what analyses they're using it for, to me, this is kind of scary from a compensation perspective, but it also scares me from the people analytics perspective. What's your perspective on laws on these types of privacy laws?

Garry Straker: 7:32

Yeah, you know, I think it could be a real challenge for employers to if these new laws are introduced to adhere to them, and still be able to conduct business in the way that they need to, in order to make sure that people are, for instance, being you know, paid appropriately, to make sure their benefit programs are being administered effectively and efficiently in order to control costs. Of course, that's a, you know, a big part of any plan sponsor as a health insurance plans in terms of their need. And so, you know, I think it is a concern, we'll, we'll see where it goes. But certainly, organizations are just under a lot more scrutiny. There's a lot more focus and attention, certainly around human capital, risk and exposures. And we're, this is probably not the end of it, I think we're going to continue to see these types of, you know, legislative changes being put into place to protect information.

David Turetsky: 8:21

The thing that scares me about it is the potential for opt out where a current employee could potentially opt out of HR, basically, HR processes, I'm not really sure how we would deal with that, because then how would you be able to even pay them? I mean, you know, thinking about things like, you know, we talk a lot about pay equity analysis, we talk about just doing market pricing for jobs, how do we, how do we run our business without having that employees record?

Garry Straker: 8:50

Yeah, I find it hard to imagine how an employer could allow an employee to opt out of certain aspects of their, you know, information, whether or not it's demographic, pay, any other information that they keep on file without without being disruptive to your business operations.

David Turetsky: 9:09

I think you were mentioning one example yesterday when we were talking about this offline, which was self insured company. It's their business! How can they manage the benefits of that happens?

Garry Straker: 9:19

Yeah, I mean, the vast majority of employees in this country get their health insurance through employer sponsored self funded plans, and self funded plan sponsors. They spend an awful lot of time scrutinizing utilization data, you know, and claims history and information. Obviously, they need to do that to manage the risk and budget appropriately but in a controlling costs and a healthcare sector is a challenge that, you know, organizations are faced with in order to do that they have to have data, claims data of their employees and participants in the plan.

David Turetsky: 9:49

Now, while I can understand and appreciate the government, or the state government wanting to protect an employee's rights, especially right to privacy. Got it, understand, especially in an era where we hear a lot of data is being stolen and you know, they are, they're obviously legitimate reasons why you'd need to inform employees of those kinds of issues. But I'm just trying to think if, you know, we, a lot of times a lot of companies, because they operate overseas, they are already trying to operate under GDPR. Yeah. So, you know, is there an alternative to this for US companies?

Garry Straker: 10:31

You know, I think US companies, I think employers generally, are probably going to have to start helping employees understand how they're using their data. And so if employees have an expectation that their employer is going to, for instance, control health care costs, in order to make it more affordable to their employees, they're gonna have to understand that we're gonna have to look at data! Same with the pay practices and policies, I mean, if an organization is committed to pay equity, and employees are demanding it well, guess what we're gonna have to look at pay, we're gonna have to start scrutinizing demographic data and understand it. And so but I think, you know, employers are probably going to have to be maybe a little bit more proactive in terms of, hey, listen, we're going to use data about you in order to manage our business and manage our human capital risk. And and I think employees who are not comfortable with that, well, they have a choice to make.

David Turetsky: 11:20

Absolutely. We talked a little bit before about pay transparency. So in this case, I think what we're talking about is process transparency, as well.

Garry Straker: 11:27

Yeah. And I think most employees, you know, many of them probably don't really care, it doesn't mind. I mean, I happen to be one of those employees who doesn't think twice a worry about, you know, in my information out on the internet, whatever I do, my wife, on the other hand, is just completely paranoid about it. And she never puts any personal information on anything, any purchases, or anything. So. But I think, you know, employees I think are gonna have to, you know, get get sort of used to it, some are gonna have to decide whether or not that's a trade off they're willing to live with in order to work for a particular organization.

David Turetsky: 12:00

Well, we've seen Apple get much tougher on how data is shared through their apps, we've seen Apple, even advertisements that have just aired recently, which talk about them clamping down on how organizations can collect data about their users, especially when it comes to email and other things. So in the consumer world, people are getting access to more secure, or more secure ways of being able to deal with their data through like, for example, their phone and their provider. And so I think what they're like they always do, they're going to come to expect from their employer, kind of what they're expecting in the consumer side too.

Garry Straker: 12:39

Yeah, and I'm not quite sure that it's gonna ever reach that level. On the employer side, as I said, it's just it could be disruptive to, you know, business operations and practices, especially in the whole human capital area.

David Turetsky: 12:51

Absolutely. So Garry, thank you very much. That was insightful, and I'm sure we're gonna be talking more about this.

Garry Straker: 12:56

Great. Thank you, David. Enjoyed that.

David Turetsky: 12:58

All right. Cool. Take care. Thank you and everybody, stay safe.

Announcer: 13:01

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