Danielle Bushen - Demystifying HR Data Governance
37:51 Share

The world of business is more complex than ever. The world of human resources and compensation is also getting more complex. Welcome to the HR Data Labs podcast, your direct source for the latest trends from experts inside and outside the world of human resources.

Listen as we explore the impact that compensation strategy, data, and people analytics can have on your organization. This podcast is sponsored by Salary.com, your source for data, technology, and consulting for compensation and beyond. Now, here are your hosts, David Teretsky and Dwight Brown.

Hello and welcome to the HR Data Labs podcast. I'm your host, David Juretsky, alongside my best friend, partner in crime, and co-host from salary.com, Dwight Brown. Dwight, how are you?

David Teresky, I am wonderful today. How are you doing? You're wonderful, and I'm freezing my butt off here in Massachusetts. It is a cold spell from hell, or, you know, if hell were cold. I won't rub it in what the temperature is here. Please don't. But today we have with us an even colder but brilliant person.

Danielle Bushin. Danielle, how are you? I'm good. I am chilly. I hope I'm not cold. No, no, your personality is certainly not cold. But you are potentially colder because you are coming from the great white north of Toronto. North of the border, and it is very chilly here today. It is well into the...

Double digits below zero Celsius. For those who can't convert that to Fahrenheit, we'll actually put the equation in the show notes. Equals cold as hell. I think it's minus 32 degrees.

times five divided by nine, something like that. Oh, you're right, honey. I just know that some things, I'm a Canadian, right? So I'm almost off. Some things get measured in Celsius, but my pool temperature gets measured in Fahrenheit. Go figure. Wow. Oh, interesting. Wow.

Well, if it's like exchange rates, it's like 0.6 to the Fahrenheit or something. Exactly. So, Danielle, tell us a little bit about you. I know you're a repeat offender on the HR Data Labs podcast. You were with us for the HR Data Labs podcast from the HR Technology Show. I was. Fun in Vegas together. At the time, I was working as a data governance leader.

For Sanofi, and I'm still at Sanofi, I'm now focusing on our HR technology strategies. I'm the global head of people and culture technology strategy at Sanofi Pharmaceutical Company.

Globally scaled footprint in about 70 plus countries. Wow. And close to more than 120,000 people in our total workforce, including both our employees and contingent workers. So big footprint. Yes. Wow. Big job, too.

So, Danielle, we actually ask every one of our guests, what's one fun thing that no one knows about you? So you have to come up with something different from the last time. Oh, that's unfair. I'd forgotten this question. That's okay. I'll tell you if it's something that I remembered from the last time. So last time, I think I told you that I make jam and practice archery in my spare time. Yes. Today, I will share that I spent the New Year's in L.A.,

And I was so fortunate to get a chance to see that part of the world and spend time in Pasadena and Altadena and Malibu right before everything went sideways. Literally, I got on a plane to come home. I took off and everything was fine. And I landed and the news was just exploding.

So enormously grateful that I got to spend time in a greater LA area and feeling really badly for everything that's going on in that part of the world right now. Yeah. And our hearts go out to all the people who are our regular listeners and even the people who aren't our listeners.

um for how this is affecting you it's really horrible and i can't make any jokes about it because it's just so terrible no but i will tell you the uh bucket list item i got to go to the rose bowl parade while i was out there really fantastic highly recommend this as something to go and spend your time on if you have the opportunity that's really cool nice rose bowl who played in that this year

Oh, now you're asking me a football question. I don't know. I went to a parade. Well, you went to a parade, so the Rose Bowl was there. No, I'm pretty sure it was Ohio and Oregon.

Okay. But you are at the far limit of my American football knowledge now. That's okay. That's okay. If you tested us ever about the Canadian Football League, we'd probably have less knowledge. Yeah, I would also have less knowledge. It's not a league that anybody follows. But maybe we can talk hockey a little bit. I have to admit that I couldn't even have answered that question. I don't. I like watching football. I don't pay attention to who's doing what. So I have no idea who was in the Rose Bowl.

But if I asked you if the Toronto Maple Leafs are doing okay this season, would you know? No. I'd say, is that a basketball team? If you really want to upset me, you can tell me they're an ice hockey team as opposed to a hockey team. There's only one kind of hockey. Big differentiator in there. There is one kind of hockey.

Although my friends who actually play field hockey would be upset with me if I said that. So today we have a phenomenal topic to talk to our friend Danielle about because it is one of those things that are near and dear to the hearts of Dwight and myself, HR data governance. So Danielle, from your current role as well as your past role,

What is HR data governance? What does that actually mean? It's a great question. And I think people often think about data governance as a bunch of data lineage tracing and technology stuff and deploy a bunch of tools and talk to the IT department and get the data office involved and worry about privacy. Yes, that is a piece of the story.

It is not the piece of the story that I find most compelling or that adds the most value. For me, HR data governance is about making sure that your data reflects the business value propositions of the company, the people that work there, the processes that you want to transact. And it is all about getting that story right. It's about digitizing the HR function in a way that you can automate it. It's about making the experience of work as seamless as possible.

And we talk about HR data probably two ways most of the time. One is transactional HR data, and the other is analytical HR data. And when you're talking analytics, directionally correct is great, probably good enough. You can help people understand lots of good things about what's going on in the function. And it doesn't have to be exactly right. But when you talk about transactional HR data,

guess what? It makes a huge difference if one of your person's gender is wrong or their salary was off by a zero or that your termination date was in 25, 25 instead of 2025, which actually happened in a colleague system just recently. They found the data error. But this is the stuff of exactly right. This is what data governance is all about. And when I talk about data governance, I,

I always say to people that it is owned by people. It is owned by the HR function itself. It's not an IT thing.

It is supported by process and it is enabled by technology. And it's all those three pieces coming together that makes data governance so interesting and so impactful. I think one of the complications is it's actually also owned by the employee and by the manager and by everybody else in the value chain that, or I mean, even candidates have a hand in your HR data governance, right? Because

They're entering information into a system, might be your candidate application system. Contracting system, yep. Yeah, but then everything they enter could be right or wrong or could be complete baloney. And that's getting into your analytics and your transactions, which make them wrong. So users have an obligation duty of care to try and give you as complete a data set as you can.

They also have rights to choose to give you their data or not. Yes, right. And that's really important to understand as you're thinking about what data am I collecting, for what purpose, how do I want to use it? Have I informed the owner of that data about how I'm going to use their information? But it's also sort of a duty of care when we think about process design to encourage people to give you the data in the easiest and most complete way possible. If you're collecting postal code,

As a consumer, I can put my postal code in all in lowercase with no spaces. And it's a Canadian postal code. So it's got letters and numbers in it. And guess what? Amazon figures out right away. Absolutely. Did I mean capital L6, capital L, et cetera? Right. It tells you that. And I think when HR thinks about data and data capture and data governance, we need to bring that sort of consumer grade experience to bear. Yeah.

and helps the user, to your point, the user who's providing that data at the top of the funnel to give you the best quality data they can. That's about a guided experience and it's about thinking through what things are likely to be problems down the road.

I think back historically, and it's amazing how quickly the landscape has moved with this, to your point about Amazon being able to sense those things. It wasn't long ago that there just wasn't the technology available to be able to do that. And so being able to have those stops in place that confirm the information or whatnot is

There's a lot of that stuff in the data governance arena that wasn't there even two years ago. I mean, you look at where we are relative to that, but it also underscores the need for both solid data governance as well as changing in the ways that we administer it. You make a really good point, Dwight. When we think about technology deployment in HR, very often our budgets are sort of with a once-and-done flavor, implement it, carry on.

But the reality is that all of the major HR systems are making releases every six months, maybe more often. They're continually upgrading. They're bringing in new components, adding AI that may be helpful. It may just be a data validation role that's being checked in the background.

If you don't stay on top of those things, then you fall behind that consumer experience very quickly. And that makes the HR process owners need to be much more attuned to technology than perhaps they were in the past. It's not something you can just throw over the fence to IT and ignore. You really have to be great partners as a function in terms of continually developing how is the technology supporting the execution of the organization.

Yeah. So many people still like to try to toss it over the fence to IT because it's just easier. Like, yeah, you guys can program it. You can make something happen. Just press a button. Right. Yeah, exactly. Exactly. Your jobs are easy. Well, there's another issue with HR data governance and

I mean, there's another explanation about HR data governance that touches on so many things across an organization. HR data governance isn't just HR data governance. It's also, it's ties to all the other pieces of the value proposition of a company. So whether that's security, whether it's finance, whether it's

Everything, workforce planning, all those things aren't just HR, they're everything else. And so whenever I deal with my colleagues from the finance group and they say to me, hey, I have an accrual that I have to create for the bonus program.

well, can you give us some numbers? Well, that kind of takes it out of the realm of HR and now puts it into the realm of finance. So now I have to interface with them and the things that I do and the decisions I make, I now have to lay out all my assumptions for them because now my data is being used inside the context of finance and they have rules for this stuff. It's beyond just us. Now they have rules. So, you know, HR data governance is also how do we interface with other teams?

teams, isn't it? Or am I just trying to make my arms a little bit bigger and try and grab more? I don't think it's about grabbing anything. I think it's about understanding that the accountability of HR is to structure the people data of the company. That may sit in HR tools. It may sit elsewhere. Headcount generally doesn't sit entirely in an HR tool. There's a whole bunch of finance components to that.

You're in the middle of hiring somebody for a global role in a multinational. Guess what? There's going to be transfer pricing behind the scenes. You want to structure the real estate footprint for a hybrid workforce. You need to understand how often are those people on site. And that's partly badging data and partly security data.

And partly contracts, you know, who's on a fully remote contract, who's supported for their tax treatment of that. You're bringing in so many departments that all need to talk to each other and learn to speak a common language. And I think the opportunity for HR is to be that translator, particularly around the people data. I mean, we need to look to others to be the experts in certain things, right?

But we also need to bring that conversation together. Last week, I facilitated a conversation between our single sign-on team, our ServiceNow team, our Workday team, our Microsoft 365 team, and our Coupa procurement team. Sure. Because we were trying to solve exactly one of these kinds of problems. And so there's five functions represented around the table here.

And we're talking about what portion of our workforce are non-employees? Do they still need network IDs? What's their single sign-on identifier? Do they have a workday identifier? What's our regulatory obligation to those people?

HR is at the heart of that conversation, but we can't do it without all those other people at the table. Oh my God, there's such a compliance issue there too with all the rules that California had created or that had been started in California about what is an employee and what is the, if you start putting them in the HR IT, what does that mean?

Co-employment risk. Super interesting data governance topic. Exactly. But critically important. Yeah. Yeah.

And these are the things that create a lot of anxiety within an organization. You know, this tribal myth that, oh my gosh, we can't touch that. It's a regulatory problem. Right. And one of the things that I focus on a lot in those kinds of conversations is, tell me more. What regulation? What exactly are we trying to comply with? In what part of the world? Because if you can articulate that, then you can figure out where's the room for creative flexibility, right?

How do you create a seamless model? At Sanofi, we're in the process of looking at bringing our total workforce into a single set of systems. And for years, we had a belief within the company that we could not put contingent workers into workday.

Anybody who's a Workday user knows that's possible. It's just about how you think about the configuration of that profile. Make sure that you've got the right kind of data for the type of worker that you're looking at. I think one of the other complications is that when you're trying to balance all those different regulations you're talking about, for example, California has regulations on pay transparency. Illinois just created pay transparency legislation that says you need to keep five years of history.

how you paid and the decision points along the way and the posting information. So you might have an employee comes along and says, I want it to be forgotten. So who are you going to follow? Are you going to follow California's rules? Are you going to follow the EU's GDPR? Are you going to allow them to be forgotten and then now kind of break the law in Illinois if they're an Illinois employee? So, you know, there's so many things that you have to comply, have to worry about from a compliance perspective. It's

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. That's kind of lends me into our next question of what's the problem with HR data governance? I mean, I guess we're talking about it right now. It's complex, right?

It is, and people want a clear answer, and there isn't always one. So your example is a perfect one. If you look at the deployment of AI in HR, New York City has its own rules for this stuff. So the range of jurisdictions that you might be looking at

The ways that you look at how your technology and your data retention plans and all these other things fit into the conversation are going to be varied. So there's not going to be one clear answer. And I think it's incumbent upon HR leaders to think about thoughtful risk management. Yes, you need to give the employee the ability to ask to have their data removed after they leave the company. So they want to be forgotten.

But you have to balance that with we may have obligations to you as a future retiree. We may have components of tax treatment that need to be managed. And so those different regulations are a little bit in conflict with each other. Right. Which one takes precedence. And as long as you can demonstrate that you've taken a thoughtful approach to removing data that's not essential...

retaining things that might have a specific obligation for the company or for the individual that they're just not thinking about, then you've got a leg to stand on. Then you've got a way of justifying your approach. And most regulators are quite receptive to that. They will look at it through a lens of, have you done your due diligence? Have you thought about reasons for retaining that data? And have you been clear with the individual? Yes, I've asked to have my data removed. No, we haven't removed all of it. Here's why.

That's fair and reasonable. I think there's also the expectation of privacy that may not actually exist anymore, given things that they posted on LinkedIn or that they actually applied using LinkedIn, but they put their entire employment profile, even from your company on it. And so, you know, how, I mean, how do you balance that too? Because the data is in the public domain. And a lot of people aren't very, um,

astute or aware about their digital footprint the amount of information that could be sourced about me from the public environment is probably akin to what we store in your hrs a lot of the time until you get to things like um you know we just had a conversation about health data being stored in the hrs and why would you need health data in your hrs and and there's two really interesting use cases one is um

understanding disability or ability, which is actually a health status. There's a diagnosis attached to that. And it is really relevant and helpful for the right people within the HR organization to be able to provide accommodations. So there's a real good reason to store that data in a constructive way.

But of course, the risk is that data is going to be misused. It's going to be misinterpreted. It's going to create bias. And so now you're into a conversation about who has rights to that data for what purpose? How are we using it? How is it being aggregated?

And having good rules in place for those kinds of analytics, which are all about the story of the whole, not about the individual, is really critical to a successful data governance program. And good security protocols, too, on top of it to make sure that it doesn't get out. Yep.

And in the land of Excel, that is a running rearguard battle a lot of the time. Wait a minute. You're talking about the most popular database program in the history of business.

If we could all just get rid of Excel some days, we'd be a lot happier. I understand why it's used. I get it. It's easy. It does bring some baggage. It's not even just that it's easy. It's proliferatable. I mean, you don't need, I mean, you need a license for it, but everybody's got a license. You know, thank you, Microsoft. Everybody's got a license and it's not hard to actually be a database programmer to

If you can put a value into a cell and then put a field header at the top of that column. It's ubiquitous. It's everywhere. It is. It is. And people's skills in it suck, but that's another issue completely. That is not a data governance problem. I heard an interesting one, a client I was working with up until a short time ago,

Half of their HR functions were in an Excel spreadsheet as opposed to they didn't have an HRIS yet or they were just beginning and it was a little micro piece that was built out and

So I think, I think things like that and the data governance side, I mean, it just grows. Oh my God. But that may be completely realistic, you know, for a small startup that they don't need to invest in a fancy HR system. And it probably isn't a good use of their limited dollars. It's just, if you can apply the same principles, it's,

of governance to, you know, a spreadsheet that has really clear locked down restricted rights that you've understood who's able to edit which fields, all the same things that you would do in a sophisticated tool with role-based security, you can do in a spreadsheet. You just have to be thoughtful about it. That's a huge assumption though, Danielle, that you're going to have all of those things locked down in a, in a canvas. That's kind of what Excel is.

Yeah, it's true. It takes forethought. It takes the ability to recognize, hey, this data is not generally available. Just putting it in somebody's

personal drive is not safe enough. How do we secure this? What passwords do we put on it? And there are good ways to apply data governance principles, even in a lower tech solution like Excel. I think the bigger challenge is when we start to email files around to each other and, oh, here's a copy of this. And David, you should see this information about, you know, retirement rates. And then you realize that you've sent a file that has all the source data of everybody's ages and dates of birth on it.

Right. Not ideal. Well, I can't tell you how many times I've seen analyses that have been pasted into PowerPoint, but they paste in the Excel object. Source file. Which has exactly, it has the entire source file with all the employee names in it, with all their pay in it. And that is an absolute data governance nightmare. So, yeah, I mean, it happens.

And I guess the other thing I want to mention, Danielle, is you were talking about, you know, sending an email. Remember the days when we used to send those manila envelopes with the extreme security measure of that? I love this of that little rope, a little little string, the red string. And then you'd you could even put a piece of tape very secure. You have a piece of tape across the front.

So it would put a squiggly line on it to see if anybody had opened the tape. Or you'd stamp confidential. So you'd line up and you'd make sure that the C O N was absolutely perfectly aligned when it came back to you.

The beginnings of cybersecurity right there. Yeah, right. Yeah, let's see how you can hack that one, Dwight. But I mean, that's where we started with sending inter-office mail that had approvals on forms for people's pay. So, you know, we're not too much better than that right now. I mean, I think we are. I think the... I'm being overly... The reality is that the problems of data governance are...

There's the obvious ones that we've just been talking about, the issues of continuity of care through a value chain. Those things are important. I think the harder ones are when we use third parties, which most companies do.

And now you have what's called a data controller is changing hands. Somebody else is responsible for the information. And what happens at their end? Do they use a subcontractor? Are they providing good governance on it? Did they have a hack and somebody got access to all the credit card data for your entire company, which happened to include social security numbers? Yeah.

No longer in your control, but as an employer, you're still accountable. You think of the Facebook debacle a few years ago with the analytics company. I can't remember the name of it. Cambridge Analytica. Cambridge Analytica. Exactly. Yes. And everything that happened there. And you see that play out company after company after company. It wasn't isolated to Facebook, but a lot of that gets back to that company.

that data governance piece of things. And what is the data? How are we using the data? What are the rules we're putting in place? And unfortunately, a lot of companies have to learn it the hard way to really get there. And I think the opportunity for HR is first to really have clear ownership of your definitions. So what kind of data are we collecting?

For what purpose? What's our pick list? How do we change it? And that's really setting the rules of engagement for the HR function itself, which if you're thoughtful about it, you can have enormous impact and really enable great process for your employees. Yeah.

Then the challenge is to teach those data owners who own those different processes and all the different functional areas how to think holistically about contracts and engage their procurement specialists and their legal specialists at the right time for those pieces of the process. And they don't always know to do that. So there's an education campaign around saying you don't need to know everything about all of this.

but you do need to know when to ask for help. Yeah. Hey, are you listening to this and thinking to yourself, man, I wish I could talk to David about this. Well, you're in luck. We have a special offer for listeners of the HR data labs podcast, a free half hour call with me about any of the topics we cover on the podcast or whatever is on your mind. Go to salary.com forward slash H R D L consulting to schedule your free 30 minute call today.

One thing that stabs me in the heart right now, though, Danielle, is there's a lot here. So where do we start? Because this is what you've mentioned cuts across and you're in a 120,000 employee company, whether you're a 50 employee company, whether you're a five employee company.

Whether you're a 10,000, this is a big deal. And the range of obligations doesn't change. Even if you're only doing it for 40 people, those obligations are still there. So where do you start at? You start with ownership.

And you start with the actual concept of governance, who gets to make the decisions on data fields. And if you can get that part right, then you're having the right business conversation. What technology you put behind it is truly secondary. And when I say it's owned by people, enabled by process, supported by technology, the technology is the tail on the dog here. So if you can start with having clear ownership and make sure that people understand that

What is their role to play in defining the values, the attributes, the reasons that you might want to update it? The whole classic move, add, change, delete. Who gets to do those things? If you can get that part right, the rest will follow. And I think sometimes in HR, a COE or a functional lead will say, oh, yeah, that's...

That's the system team's problem. That's the workday team's problem or the success factor team's problem or whatever. Pick your technology, which isn't true. The people who need to really feel ownership are the people who are collecting and using that data and understand its purpose better than anybody else. Organizations need to also understand and have in place tools

data governance processes because, and it depends on the size of the organization, what that looks like, but it's,

But where I was previously, we were a 60,000 person organization. We literally had hundreds of different systems out there. And in that case, you know, our starting point to your question, David, was, all right, we need to centralize this data governance function in some way or another. And then let's take stock of the systems that we have out there, get the right stakeholders around the table.

Now, for a large organization, that makes sense. For smaller organizations, it may be a little bit different than that. But that's the, and it kind of gets back to what we were talking about at the beginning of the conversation with what you said, Danielle, that not only is the HR system doing HR data, but you may have finance data and other data systems in place.

And so to the extent possible, being able to bring together everybody so that you've got cohesive data governance processes, whatever that looks like in the organization. It's just, it is hard to find that starting place and who owns it. So...

I think that's the one of the big issues there is who's the data owner. And to Danielle's point, you know, who is the one who gets to make all those decisions? And how do you make that as a team? How do you have those? I mean, do you have committees, Danielle, where you sit down with your colleagues from across the company and say, okay,

you know, this is yours, this is mine, this is yours, this is mine. Right. I mean, we kind of do. Yeah, we do have an enterprise data office. And so there are standards of care for how data gets stored and manipulated and moved into warehouses and everything else. Those are great foundations and tools that we can leverage in each function and each business line. But each function and business line has its own accountability and framework for data governance. In people and culture, we have a data council.

And you get into those difficult conversations around, well, who gets to define what a promotion is? Is it comp who decides grading and grading structures? Is it talent management who says, well, lateral moves with this kind of change in scope would be really helpful to call a promotion? Or is it...

Because the person moved from one division to another, that that's considered a cross move. These are all the conversations that you have to really go through. What does promotion actually mean? What does demotion actually mean in our organization? And it might be different than somebody else's organization. But if you can quantify that, you get to a great place. Reasons for departure. Is it voluntary? Is it involuntary?

well, what do we mean by constructive dismissal? There's legal components to that. There's labor law components to that. There's

actual conversations with the person and what you want to say to them and what that means in terms of whatever their termination paperwork looks like in the country they're in. Right. Getting all of that put together is critical. Also, if they're part of a labor council or a labor union and, you know, who do you have to go to once those kick off, once those processes kick off, especially if you're terminating someone. Yep. Yeah. Yeah.

And thus, start small. Real interesting real work. Yeah, exactly. Start somewhere and then go through all of these processes. I think that's one of the overwhelming things about this though, Danielle. And that's the reason why I kind of asked the where do you start is because this is really complex and it has so many...

There's foundational elements, yeah. But then there are also these elements that exist in the fringe all over the place, all over the world, like learning systems and reward systems. They're not only tangential, but sometimes they're built in. You know, commission systems, where it's owned by sales ops. It's not owned by HR. It's not owned by compensation. It's sales ops.

All three of those examples have come to my desk just in the last, like, two days. Sorry. It's okay.

But those are the right conversations to be having, right? Like you want to have a sales compensation calculation engine that understands profile-based information based on the job you're in, the function you're assigned, the territory you're working in, the market opportunities. You're bringing together sales data and product data and HR data all in one place. And it actually doesn't matter who sponsored that work. Yes, it might be owned by sales ops, right?

What role does it fill? What data is it being populated with? Who has governance of that data? That may not all be sales ops. And figuring out how to have those conversations in a constructive way together to say here at Guardrails is what matters most. I said it's all about ownership. It is all about ownership, but it's also all about partnership and making sure you understand those guardrails and who owns which piece. And it's rarely an all or nothing conversation.

I love this conversation. We can have it all day. I think one of the problems with a conversation like HR data governance is that for many of the people who are listening, it's something that they have to deal with. And it's in a mountain of other priorities. And one of the problems is, is that if you don't deal with it and deal with it quickly, it can become a

its own big problem later on, right? I mean, there are, you mentioned analytics at the beginning and I've gone into analytics projects and said, hey, what's the shape of your data? And they go, oh, it's great.

Okay, well, why aren't you using analytics today? Well, our executives don't believe the data. That doesn't mean that it's great. It means that, you know, we need to do some work to earn back their trust. And so one of the things I always think about when we go into projects like this is what are the major issues that we have to solve? And let's try and start there first, solve those problems, and then work on the hygiene issues afterwards.

I fully agree. I am actually working on an HCM core remediation project right now that is focused on the super sexy topic of worker types and subtypes and contract types. Great. But it's foundational work.

Yeah. It's about making sure that those definitions support the business requirements. Right. Which will then allow us to do much more interesting work around automation of onboarding and pre-boarding and improving the employment experience, which is what we want to do. Yeah. But those data elements are the prerequisites for that. And it's about understanding why it's worth doing that work and what it's going to enable and unlock in terms of value for the organization. Yeah.

And also what it's going to prevent. Like, how much time do you want to spend investigating data breaches? Like, that is like the last thing on my list that I want to spend time on. If I can stop that from happening, great. Yeah. Well, I think I know what our next topic is going to be, Danielle. Which is? It's going to be those worker types. Oh, okay.

Great topic. I can imagine the number of downloads we'll get on that one. It'll be in the millions, Dwight. It'll be in the millions. Exactly. Well, no, I'm serious when I say this, that it is such a pleasure talking to you because, you know, we could geek out with you, you know, pretty much all day on this.

But it's just so wonderful to speak to someone who speaks our language and thinks the way we do. So thank you very much for being on, Danielle. Thank you for having me. Great having you here. And thank you, Dwight. Thank you.

Thanks for being with us. Like David said, we could, we could geek out on this stuff for ages. I'm not sure what that says about us, but that's all right. I'm okay with that. I think that's the reason why people listen to us now, Dwight, is that we get to have conversations with brilliant people like Danielle. Exactly. Yeah. I'm into my third decade of doing some version of this kind of work and it's still interesting and I'm still learning new things all the time. Yeah. We're with you. Yeah.

Well, and thank you all for listening. If you're still listening to this after this, we love you. You are our people. Take care and stay safe. 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.