Richard Rosenow – HR Tech 2023 – Predicting the Future of HR with Lessons Learned from 2023

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• david.turetsky@salary.com

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Production by Affogato Media

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• One Model
• Season 6 bonus 3
• Season 6 bonus 1

• “You’ve got to actually give HR the investment. [It needs to be data-driven], like every other function in the business.”
• “A lot of times [HR] has to overcome the fact that there are multiple data sources, and they’re not always wrong, but they have to add up to a story.”

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 fascinating and fun people inside and outside the world of HR to talk to you about what's happening today. We're at the HR Technology Conference for 2023. And I'm speaking to my good friend, Richard Rosenow, from One Model. Richard, how are you?

 

Richard Rosenow: 1:05

I'm doing great. This is my first HR Tech. I've been trying to come here for years, and my budget kept getting cut. So I'm beyond thrilled to be here.

 

David Turetsky: 1:12

Yay! Your budget didn't get cut! That's awesome!

 

Richard Rosenow: 1:12

Yeah, I'd be happy to. So over at One Model, I've had that happen, too. So I understand. So Richard, tell us a little bit about what you're doing at One Model. I'm our VP of people analytics strategy. One Model, we are a people analytics platform.

 

David Turetsky: 1:25

Awesome. And we really play in three spaces. We do data orchestration, we get all your data out, get it architected, get it organized. We do data storytelling, we've got our front end with our great tools to visualize, tell stories, all that good role based security. And then we have a full end to end data science platform, to tie it all together to take that great data, we get out, the stories we need to tell and try to tell those deeper insights about that. So if you do you have a data science team, they can actually use One Model as a platform that they can use to play around in, find really cool things and actually then be able to democratize the data.

 

Richard Rosenow: 1:58

Yeah, absolutely. And beyond that, too, we treat it like a like a data cleanroom essentially, because the data doesn't have to leave the system. It stays in there. It's tracked, it's auditable, you can lift the hood, you can see what happened. So it's a way to kind of go at this in a way that's going to be replicable in the future, you'll be able to look back and say, What did I do two years ago, or that person who quit, what were they working on? When that audit comes around, you'll have it ready.

 

David Turetsky: 2:18

Sounds really cool. All right, Richard, what's one fun thing that no one knows about Richard Rosenow?

 

Richard Rosenow: 2:24

I was talking about this earlier. But the fun thing about me is that

 

David Turetsky: 2:27

So somebody knows it, then you can't use that! No. I'm just kidding.

 

Richard Rosenow: 2:30

I think the fun thing is that I used to be a baker. So I spent a summer as a baker at an Amish smorgasbord. And I made all kinds of fun baked goods, and thoroughly enjoyed that job because I got to eat at the buffet every day.

 

David Turetsky: 2:41

And for those of you who know Richard, and we'll give you a picture on the post. He's not overweight. So Richard, how did you lose all that weight from from from being at a bakery?

 

Richard Rosenow: 2:50

I'll be on this podcast every day. Keep the compliments coming.

 

David Turetsky: 2:54

We'd love to have you on every day. That would actually be a really cool podcast. We could do a live with Richard Rosenow every day!

 

Richard Rosenow: 3:02

A day in the life!

 

David Turetsky: 3:02

Yeah, exactly. Hey Richard, what did you have for breakfast this morning?

 

Richard Rosenow: 3:06

Free bagels at the conference. They were excellent.

 

David Turetsky: 3:10

Las Vegas bagels. Yes. That's a concept.

 

Richard Rosenow: 3:12

Yes.

 

David Turetsky: 3:13

So today's topic is going to be kind of fun. We're going to take the lessons from 2023 and we're going to apply them to 24 and 25. So what did we learn in the world of people analytics in 23? And how can we apply that learning to what we might see happening? You can put on your prognostication hat, your level of predictive analytics and say, what might happen in 24 and 25? And by the way, we'll come back in 24 and 25 and see if you are right!

 

Richard Rosenow: 3:38

Yeah, you got to test it. You get the prior, you run the experiment, and you test it afterwards. That's the way to do it.

 

David Turetsky: 3:43

Exactly.

 

Richard Rosenow: 3:52

Yeah, I think 2023 was an odd year for people analytics. I think we kicked off the year with a tough wave of layoffs. I mean, that's everybody just within across the kind of tech space. But people analytics being a field that really came out of that tech space and that investment that those tech companies made, we saw a lot of teams get cut. And we saw a lot of people kind of on the market in different ways. And it was a surprising year, I think, because it was it was a year where like finance grabbed the wheel, hung left and said 7%.

 

David Turetsky: 4:17

Right.

 

Richard Rosenow: 4:18

And across the board, we saw teams to get doubled down. I think there's some really exciting learnings coming out of that, though. One is that people analytics is not a tech industry thing.

 

David Turetsky: 4:27

Yes.

 

Richard Rosenow: 4:27

We're seeing people analytics teams across the board, across pharma and defense and just all of the other industries have scooped up that talent. So I've been tracking to see kind of who went where, where they go, making sure people land in different spots. All of that talent has gotten scooped up. And that's been tremendous.

 

David Turetsky: 4:43

And for those of you who don't know, Richard actually created a website where people can go to either post an opening that exists or post a profile, right?

 

Richard Rosenow: 4:54

Just the openings for now. So it's, and even that like I'm scraping Indeed and LinkedIn. It's a labor love.

 

David Turetsky: 5:01

Yeah.

 

Richard Rosenow: 5:02

I go in every two weeks, I pull all the people analytics jobs, because they're they're tough to find because you got workforce analytics and talent analytics and colleague insights. And like, there's there's 400 different names for the space.

 

David Turetsky: 5:11

Right.

 

Richard Rosenow: 5:11

So that's something I do to kind of keep a keep a feel for the market. But really, it started out of that layoff that happened kind of across the industry. I said okay, there has to be good jobs out there still. This is still happening. And that lesson learned was, again, these people are getting picked up and a lot of more teams are starting.

 

David Turetsky: 5:26

Yeah.

 

Richard Rosenow: 5:26

I think the other lesson I'll add, though, is I think we're exiting the phase of the mega team. There was a phase of people analytics where we just needed more bodies.

 

David Turetsky: 5:34

Right?

 

Richard Rosenow: 5:35

We had more research scientists, more analysts, more data engineers, like, let's go. And we saw a lot of these teams really explode.

 

David Turetsky: 5:41

Were they basically building to search for a problem?

 

Richard Rosenow: 5:45

No, I think they had real problems. I think well, let me let me clarify that. They had real business problems they were working on. These teams are doing incredible work. And having been on one of the kind of like the mega teams out there. I have not seen better work business driven work. But it was before we had a lot of the technologies we have in the past 10 years. And so a lot of these teams that started had to build everything from scratch and get everything up and running.

 

David Turetsky: 6:05

Right. Which became very expensive.

 

Richard Rosenow: 6:07

Oh, absolutely. And we also just didn't have the cloud software we have today. I mean, Workday has come a long way in 10 years! These different ATSs that have popped up, these different systems that are more data minded, it's easier to get data out now than it was. And there's a lot of workforce analytics platforms that have popped up to start to support these teams. So coming out of that sort of layoff, I think we're seeing a bit of a retrenching where we say, Okay, is there a terminal size for a people analytics team? Is there a size where that team should hit? And then from there, we should automate as much as we can. And I'm seeing more and more teams come at it from that perspective of a platform orientation, and saying, we need to build for scale, we can't just build for kind of ad hoc or firefighting.

 

David Turetsky: 6:44

Right.

 

Richard Rosenow: 6:45

And, again, not to not to diminish those teams that were those kind of mega teams. And there still are some teams that do just tremendous work at scale. It's just a, we've got to find a way to do this and democratize the work. So every company can do it. And every company can't hire 80 PhDs.

 

David Turetsky: 7:01

Does that mean we might be able to use or should be using more automated technologies, like we're, we may have been talking about at some point today, like AI to ensure that those things once they're pointed in the right direction, to do things on a regular or routine basis?

 

Richard Rosenow: 7:18

Yeah. And I'll be really careful about answering this because I think I think people analytics should not be automated. I think there are things that people analytics people do that should be automated. But people analytics inherently being a people and analytics function, you're making decisions about people. And a lot of times when you have those decisions, I always go back to that IBM quote that a computer cannot be held accountable therefore a computer may not make management decisions. And coming back to that spot where it's like, at a certain point, we say, okay, you've got the best information, right? Now somebody has to make a decision a human does. So I am in the position still today where people analytics should not be automated, but we should help them with automations.

 

David Turetsky: 7:56

Right. But I'm not necessarily talking about making the decisions. I'm talking more about the delivery mechanism.

 

Richard Rosenow: 8:01

Yeah, gotcha.

 

David Turetsky: 8:02

Automate the ability to be able to send out and I'm not talking about just about notifications, I'm saying that on a regular basis. And maybe this is script, maybe it is a notification, but being able to put this in front of managers not so it becomes just another bit of noise that HR keeps sending me. But that there's useful information that gets sent to managers that, again, not being routine, but becomes something that the manager can rely on.

 

Richard Rosenow: 8:30

Yeah, absolutely. So I'll say to people listening, if you're still running your own SQL, if you're still doing things in Excel, if you're if you're building everything from scratch, every time, that it's time to look in the market to see what's out there, because the technologies are there to support you now, and 10 years ago, they just they just weren't there. Because people analytics was barely there. And we had to do everything kind of ad hoc and but now that that market is there, the vendors have stepped in and stepped up. And if you find yourself stuck in that, like, I've got to go back and tweak something every single time for someone that wants a new cut of something, right? Or I've got to add a new dataset, and it's just horrifically manual, right? Go go take a look, go talk to your vendors.

 

David Turetsky: 9:03

And you're not having to learn R to do this.

 

Richard Rosenow: 9:05

Yeah! There's a time and a place for it. And there's there's a time and a place for that like highly custom, highly curated like this is the thing that CEO cares the most about, let's go really hard after that, and get that data scientist on board to do it.

 

David Turetsky: 9:18

Right.

 

Richard Rosenow: 9:18

But a lot of our work in people analytics, a lot of the value is what is my headcount? There are so many companies today that still struggle with that. And there's there's some empathy that comes from it that says, Okay, if you're struggling with just like, how many people do we have employed, talk to your vendors, get some support.

 

David Turetsky: 9:33

And one of the things I find funny about that specific comment is that a lot of the times when we talk to CFOs about how many people do we have employed, the answer isn't necessarily a number. The answer is with a number with an asterisk where we're talking about, we're talking about butts in seats, are we talking about FTE, are we talking about contractors? Are we talking about, what is the definition that, or the definitions or the assumptions that we're making? Because you and I both had conversations with angry financial executives who say, your data is wrong, go back and go back and fix it.

 

Richard Rosenow: 10:09

It like, could you imagine, even today, like the IT team knows how many printers you have. The finance team knows the dollars down to the cents that's in your bank account.

 

David Turetsky: 10:17

Right.

 

Richard Rosenow: 10:17

These other teams have the tools. And a lot of times I think HR can get a lot of flack for like, Oh, you're behind, or you need to be more data driven. It's like well invest, you have got to actually give HR the investment to go after this stuff, like you have every other function of the business.

 

David Turetsky: 10:30

And we deal with people and people are different. And people change, things change. We're talking about skill acquisition. You know, one of the things I find funny is, is that that's a moving target, right? And the definitions of the skills and the levels of skills and behaviors. And actually just doing an assessment on skills one day doesn't mean that you don't need to do it another day. So yeah, you have to be able to get the investment in HR to be able to measure measure, measure.

 

Richard Rosenow: 10:55

And we treat HR like it has a subject matter that is somehow easy to measure. But it is human behavior. Like this, this is harder than rocket science, to figure out what a human wants to do, how they feel, what they're up to. And like, we're not doing that kind of creepy level individual stuff. But just even in the aggregate, it is incredibly difficult. And we've been seeking after it for 1000s of years as a civilization, and somehow we think HR can do it with Excel.

 

David Turetsky: 11:19

And sentiment changes on a moment's notice. I mean, you could have a conversation with the manager in the morning, and everything's great. And then all of a sudden, something happens during the day, and that person quits. So you know, it's not like this is something that can be measured once in a quarter, and everything's cool. And you know, there are some systems that check people's pulse every day. That's not going to work either.

 

Richard Rosenow: 11:40

Yeah. And we saw a great session this morning. Microsoft was here talking about their total listening tools that they use internally, and looking at both the active listening and the ambient listening, and how do we get the data, we need to actually make sense of things. There was a quote, I'm gonna see if I get it right. But it was, apparently way back when a product manager of Window said, I can look at my screen and see how people feel on a daily basis about Windows, the product, but I have no idea what's going on with my employees. How do we change that? And Dawn Klinghoffer, to her credit, has changed that at Microsoft. She's a spectacular people analytics leader, and they even talked about how they're starting to bring that people analytics insight into their external products too. Which that was a highlight of the conference so far today.

 

David Turetsky: 12:18

If you're listening, Microsoft, I love what you've done on the Mac. It's awesome stuff. Thank you very much. And I appreciate you continuing the investment in the Mac platform.

 

Richard Rosenow: 12:25

Oh no, there goes your sponsorship opportunity there, David.

 

David Turetsky: 12:29

Hey, listen, I love Microsoft. I'm a shareholder too. So there you go.

 

Announcer: 12:35

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: 12:44

So now let's take these lessons that we've talked about, and kind of put our Carnac hat on from the Johnny Carson or the tonight show days and say, what does the future look like in people analytics? Because we've seen so much change in the last just two years, three years, from the highs of the you know, coming out of the pandemic to the lows, as you mentioned in 2023.

 

Richard Rosenow: 13:10

Yeah, I think this may not be in 2024, but it's coming. I've said for a little while I think HR today will look less like HR in the future, and HR in the future will look more like HR did maybe 20 years ago, in the sense that I think HR is going to be moved more and more towards a people oriented function, which it is. But at the end of the day, like I think we've hit the zenith of how technical HR needs to be. And from here out we're going to have more systems, products, tools, support, that's going to let HR move back into that people space, where really the ethnography and the anthropology skills come into play. So I don't think HR is gonna have to learn SQL, like Chat GPT is really good at SQL. I don't think I'm gonna learn SQL again, I knew that at one point, I've lost that over time. But I think we're going to see a double down in this almost like anthropological sense of how do we understand culture? How do we understand humans? And how do we support the whole human at work? And that's an exciting vision for where HR can go. So I think in the short term, what we're going to see is more and more emphasis on how do we deliver in the natural space of HR, and HR works in language. So I like we're getting through a podcast without talking about generative AI until this moment, but the truth of it is, I think, for HR generative AI unlocks the way we actually work, right? Because we don't work in dollars and cents in ones and zeros. We work in human language. And finally, there's a tool coming to bear that can actually access the way we interact with our workforce and interpret that in very new ways. So I think there's a lot of excitement yet to come on that front and 2024.

 

David Turetsky: 14:37

So if I'm interpreting what you're saying a lot of the routine things that we're not good at an HR become more taken over by the robots, whereas we're allowed to be human. We're allowed to go back and deal with the human aspect and allow those more routine robotic things to be taken over for us from the things that we haven't been very good at.

 

Richard Rosenow: 15:01

Oh, spot on. Yeah. And I'm realizing now that I'm taking a very risky bet that generative AI will play some part in HR in 2024.

 

David Turetsky: 15:09

Well, I mean, all the signs are pointing. And in fact, we're sitting in the HR Technology Conference and if you look around,

 

Richard Rosenow: 15:15

Literally all the signs.

 

David Turetsky: 15:16

I haven't seen one of them yet that don't mention AI.

 

Richard Rosenow: 15:20

Yeah.

 

David Turetsky: 15:20

And, and I think one of the things that we're maybe pushing a little too hard is that it can solve the problem today.

 

Richard Rosenow: 15:26

Yeah, we've, we've taken a little bit of a cautious approach in this sense. So just with kind of who we are as One Model, we're very much so like, let's get it done ethically, let's get it done correctly. And let's do it the hard way and the right way. And then let's scale that over time. And I think we're seeing that too, across a lot of the major players like the Workday, Oracle, SAP's they've come out their announcements, but they are all taking a sort of like, let's see what's going to happen. And let's, let's not jump immediately, and just hook the LLM up to our data, but let's find the right way to do this. And there's a there's a tough balance there, because you want to capitalize on the marketing. But you also you have a real diligence to your customers to get this right and to do it right.

 

David Turetsky: 16:04

Well, ADP jumped in with both feet. And in the last A day, they really put a lot of emphasis on artificial intelligence as a chat. I don't want to say chatbot necessarily, because chat bots are, you know, they they have this visualization that many of them have right now. But but really to kind of ask questions and get answers. And I'm I'm simplifying or tremendously oversimplifying what they're saying, but they're leaning very heavily into that message. And I and I kind of thought Workday was as well with some of the latest pronouncements they were making about their investments in artificial intelligence.

 

Richard Rosenow: 16:36

Everybody's definitely talking about it. I think everyone's taken a very thoughtful approach, like more thoughtful, like, I am thankful that the HR tech vendors are taking a thoughtful approach for the most part in this space. Because at the end of the day, this is not, this is somebody's livelihood, this is somebody's work. And humans have such a relationship with work, that if we're going to see these tools come in and start to inform or make decisions, or support, like, we can't get this wrong. And the first person that gets it wrong is going to ruin it for the entire industry.

 

David Turetsky: 17:03

Yeah, but if you go back to when, and probably when I, when I graduated college in 1989, we started to see the PC, get more intimate in the world of business. And there really was that also that concern back then that PCs were going to basically take the humanity out of work. And we weren't going to use them as tools, but they were going to replace people. And to some extent, they may have like, you know, they've become our assistants, they've become our secretaries. We've become our own secretaries, we've become our own assistants. But in that same way, are we trying to be too careful then with generative AI, that it changes the world of work, it changes the business?

 

Richard Rosenow: 17:41

I think part of it where it's, this is happening so quickly, and the changes are happening so quickly. Just this year, like this was just this year, that we started talking about generative AI the whole kind of year through here. And the rapid pace of change, I don't think it's stabilized yet. So even as we think about kind of what's the applications are we're seeing here at HR Tech, a lot of them are what we used to do, we can do it better now. I think what's not yet apparent is we started from scratch with generative AI and we built up. And that thing, maybe that's the kind of prediction for 2024, I think we're gonna see some generative AI native applications that are just looking very different than something we've seen in the past that are approaching this space in a new way, and maybe capturing more of the human at work. Whereas we couldn't do that before in a very personnel based historical systems.

 

David Turetsky: 18:26

Well, if you remember generative AI in the past has been or AI in the past has been very shitty, pardon my expression of being able to take very complex thoughts or issues or conversations, and enabling someone to talk to them. You really have to learn how to interact with Chat GPT in order to get it to do what you really want it to do.

 

Richard Rosenow: 18:49

Yeah.

 

David Turetsky: 18:50

So you can't just ask it any question and it goes, here's what you need. It won't do that. So you know what I'm saying? It's, so it needs to then evolve. And I don't know whether it evolves itself, or you evolve as the user, being able to ask the right questions.

 

Richard Rosenow: 19:06

I think there's a little bit of both. It's just happening at such a condensed scale that those of us that have tracked technology change over time in the past are like, well, this will take 10 years to adopt. It's like, No, this, this is going to take a year. And it's going to just happen very differently and very quickly that I think will break some of our molds around that.

 

David Turetsky: 19:21

Yeah.

 

Richard Rosenow: 19:21

But again, maybe not a risky prediction.

 

David Turetsky: 19:24

No, but I think so in the context of looking at everything. One of the other problems, though, is the regulations that are now being potentially imposed on using artificial intelligence in World of Work and in the world of commerce and everything else. Where you're not, you're not necessarily being too cautious if you say there may be legislation on this.

 

Richard Rosenow: 19:49

May.

 

David Turetsky: 19:49

Which doesn't make sense because the people who are making the you know, the laws about it, don't really understand it!

 

Richard Rosenow: 19:58

Yeah. And I think what's funny too, is like we talk a lot about, there's generative AI and then there's like traditional AI, this like machine learning space, which which was hot and still is hot, but just isn't talked about as much. What might actually happen here is that the legislators will legislate against generative AI, and accidentally legislate traditional AI, which is for the best, it's for the, like, the the thoughtfulness of the worker, like, let's, let's make work better. But I think there's a lot of people sitting on traditional AI, that may also get caught up and surprised by the speed of legislation when it comes to this generative space. Because it's top of mind for the legislators, because we might actually see action on it.

 

David Turetsky: 20:32

Yeah.

 

Richard Rosenow: 20:32

So I think those regulations that have been long coming, we know they're coming, they might be coming. I think they're going to come very quickly in the next couple of years here, especially at the state level.

 

David Turetsky: 20:40

Yes.

 

Richard Rosenow: 20:40

And then potentially, at the federal

 

David Turetsky: 20:42

Again, you know, if you're a legislator, do your level someday. homework on this, really understand what you're talking about, because if not, it's not just that you're stifling technology, you could actually be setting us back from being able to really make some really cool inroads. And don't watch movies that talk about AI! That's horseshit. You know, it's not like, you could pull the frickin plug out of the computer, it can't re, it can't replug itself in! So, you know, be smart about this, you know, we don't live in a world that's dystopian, well, okay. I probably shouldn't have said that. We don't live in a dystopian world where the computers can just take over everything.

 

Richard Rosenow: 21:23

Let's pull that snippet out, come back to the 2024, and I hope you're right.

 

David Turetsky: 21:29

Yeah, that's one prediction that we're hoping: we don't live in a dystopian world. But you know, dumber things have happened. 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/HRDLconsulting, to schedule your FREE 30 minute call today. Let me transition for a second and talk about beyond 2024. And let's kind of think beyond where, because 2024 is only a few months away.

 

Richard Rosenow: 22:12

Yeah.

 

David Turetsky: 22:13

And we might revisit this and you say, yeah, that really wasn't such a long putt. What do we see like beyond the horizon for people analytics? You know, beyond AI? What do we see for the, for the world of people analytics.

 

Richard Rosenow: 22:28

Here's a fun one. And I kind of referenced this a little bit before, I think the past, really the past century has been IO psychology driven. We've seen IO psychology come in, and the assessment companies that are out there, the tools that are around surveys. We've taken what IO psychology knows and loves and we found in built technologies around it, I think we're gonna see the other social sciences come to bear. So I don't think there's a spectacular technology for anthropology yet or for sociology. These different ways of looking at the way we exist as humans and kind of like human nature, there is a ripe space for technology to come in and say, Hey, how do I scale that? How do I support that? It? Can I support that? Or can I scale? Or is it just still a very one to one very human thing. But I think that's my like long term vision is kind of where this is going. And more short term for people analytics, I hope and I've been saying this for a couple years, I hope 2024 is a year where we see more qualitative analysis come in. I think a lot of times HRBPs can be seen as the qualitative analysis. But HRBP is really like the artisan. They've done this for decades, they have a rich experience, they've learned kind of how to intuit and think about people and kind of live within the space of work. And that's sometimes gets derided as like going by your gut, there's a lot of value there. So HRBPs do great work in understanding the workforce. That qualitative space of like a rigorous qualitative science. I've seen just a few teams tap into it and when they do they get powerful results with mixed methods, researchers or people that can go in and do that, like user experience research for the world of work. So again, like I'm hoping that 2024 is that year, where we see a couple of teams start to break through. The tough thing about that investment request that was HR has to say, I know, I was kind of qualitative, and I asked for a lot of money to become data driven, I now need to become more qualitative, again. And to the finance leader, it's going to look like we're going backwards. But the reality is, it's a double down on this space of science and social science and bringing that to bear in the workplace.

 

David Turetsky: 24:14

But if the CFO sees the results of where we went, and then they were brought along the way, and they like what they're seeing, because obviously, sales and marketing went through all of this as well, in lots of different generations, I think. Wouldn't the CFO be the first person to basically sign up for this?

 

Richard Rosenow: 24:35

I hope you're correct about that. And I, I think when I when I think about that, it's like we try to be product driven as an HR team. And I've heard a lot of HR teams head down that direction, like a consumer, great experience for my employees, I want to be product orientation, good products have good qualitative research done. And if you're trying to get there by just kind of what we've done in the past, plus quantitative analytics and skipping the qualitative analytics, you can't do what these these companies all around us sitting here in the expo floor, they have great qualitative researchers that do really good market research and understand their customers. And HR's gonna need to bring that to bear in a way that's not just, hey, it's the HRBPs job still.

 

David Turetsky: 25:10

But who's the consumer of it? And are you educating them enough in order to be able to justify that investment?

 

Richard Rosenow: 25:16

Yeah, I think that I mean, who's the consumer of HR?

 

David Turetsky: 25:20

But who is the consumer of those metrics? Who's the consumer of those results? Because if you're not teaching the leaders and the managers how to interpret.

 

Richard Rosenow: 25:29

I think that's the storytelling component. So when we think about storytelling, it's, I've got the data, I pulled the data, I understand the data, but I now have to tell a story about that data. That storytelling is there's qualitative work there happening, it's just not usually rigorous. It's sometimes kind of a little bit on the fly, we're kind of bringing that kind of like, Hey, here's my best guess plus the data, which is gonna give me better than my best guess. So I think it's a fine tuning and a honing of that approach where you can tell a story. And it's not just okay, I trust them, because the data, but I trust them, because they've done this incredible qualitative approach that is justified, tracked and thoughtful, in it's its approach.

 

David Turetsky: 26:05

In compensation, we hope we've been there for many years.

 

Richard Rosenow: 26:08

That's true.

 

David Turetsky: 26:09

And we have a relationship with the manager, and the manager kind of trusts us. And what we find is, is that where managers have done their own research, and they ascribed certain, I guess you could say, data points they have, they basically come to the competition people and they go, Well, I have data. And here's my data. And here's what I believe. And so sometimes we have to sell them, sell them, sell them, and keep selling them over and over again, to say, there's nothing wrong with your data. But here's our data from our research. And so what we find is, is that a lot of times, we have to overcome the fact that there are multiple points of view, there are multiple data sources, and they're not always wrong, but they have to all add up to a story. And they have to be able to be contemplated in the context of the things that we come up with.

 

Richard Rosenow: 26:59

Absolutely. Yeah. And it's interesting to think about qualitative research in the space of compensation. Like what comes to mind with that as like, what what does it feel like to get paid? is a fascinating thing. And then to think about like, Well, are we are we leveraging that in a way that brings people joy that helps them be retained? Or what does it feel like they access these benefits in that way, and to kind of work through that total reward package? There's a lot of studies there that I think could benefit a company, if they had just a little bit more of that talent on their HR team, to be able to go in and say, Okay, what's the experience of being part of this organization and being paid or being compensated to come join it?

 

David Turetsky: 27:35

And I think, unfortunately, one of the things that we get back from employees about their pay, there's only one word about their pay. And that's more. They want more. And I don't know if you can ask someone get back honest feedback about pay, what do they think they get paid fairly?

 

Richard Rosenow: 27:54

You know, here's a funny one. So this is an old people analytics, I won't I won't mention the company, but they took a look at a conjoint analysis, looking at the different types in different parts of your package. So looking at your salary, your base, your your bonus, your equity, your benefits, and kind of it's a little bit of that, like, you go to the eye doctor, it's like, is it a or b, C or D, and you do that with every little piece of it. And the people analytics team was actually able to create a package that was less costly to the business, but granted more satisfaction to the employee. Because $1 a pay in my equity is not the same as $1 pay in my bonus is not the same as dollar pay in my salary. And they were able to fine tune that per role within the company to actually deliver a a more thoughtful experience of being rewarded.

 

David Turetsky: 28:36

Right.

 

Richard Rosenow: 28:36

And I think that's, that's, that's getting closer to the space of kind of the qualitative access and how do we think about how employees are engaging with this? Because there's, there's just new questions we could be asking.

 

David Turetsky: 28:46

And it's certainly true, that not every employee wants to get paid the same as another one.

 

Richard Rosenow: 28:51

Yeah.

 

David Turetsky: 28:51

It's certainly generational. It's certainly situational. I know people in their 50s, who say, I'm going to be retiring soon so you know, I'm okay deferring pay. Whereas people in their 20s are like, pay me everything I can get paid in base salary right now, I don't give a crap about benefits.

 

Richard Rosenow: 29:05

All cash.

 

David Turetsky: 29:06

Yeah, they want cash! And they're not thinking about their future, which is probably pretty dumb. But you're right, there is a personalization to pay. And a lot of companies can't, A, afford that or, B, even contemplate it, because the tracking would be a nightmare. And it would also be a nightmare from pay equity perspective to be honest. There are ways that companies are getting there. But I like where you're going with this, which is asking people what they want. And then tracking the satisfaction of that, also tracking the turnover of that, right? Because a lot of times people leave about pay, but not the reason, one of the, and certainly one of the driving factors of.

 

Richard Rosenow: 29:44

Absolutely.

 

David Turetsky: 29:46

I could talk about this all day. Richard, is there anything else that you think might happen in 2025?

 

Richard Rosenow: 30:00

2025?

 

David Turetsky: 30:01

Yeah, 2025! Because remember, we were talking beyond 2024. So anything beyond 2024 that you'd want to put out there into the world so people could hear that came from Richard Rosenow that we can come back to in 2026 and go, Hey, Richard, remember that prediction you made?

 

Richard Rosenow: 30:17

We're going to open this time capsule and dig in. No, I think I think my head, I'm probably about a year out at this point. I've got I've got two little ones at home that are four years old, eight months old.

 

David Turetsky: 30:28

Okay.

 

Richard Rosenow: 30:28

And so my life is very, very week to week, sometimes.

 

David Turetsky: 30:30

Very short term.

 

Richard Rosenow: 30:31

But yeah, looking looking forward to. 2023, all in, great year, a lot of big things happened. But just tremendously excited for 2024 and what's coming for this space, especially after being here at the HR Tech conference today.

 

David Turetsky: 30:43

So remember that there's a political cycle happening in 2024.

 

Richard Rosenow: 30:47

Oh no!

 

David Turetsky: 30:48

And there will be tremendous upheaval in the world of HR and the economy.

 

Richard Rosenow: 30:55

Always. Yes, always.

 

David Turetsky: 30:57

So it'll be a lot of fun.

 

Richard Rosenow: 30:58

I'm looking forward to it.

 

David Turetsky: 30:59

Richard, thank you very much. It's a pleasure to have you.

 

Richard Rosenow: 31:01

Thank you for having me on.

 

David Turetsky: 31:02

And we will have to do this again.

 

Richard Rosenow: 31:04

Anytime.

 

David Turetsky: 31:05

All right. Thank you very much. Take care, everybody stay safe.

 

Announcer: 31:09

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