Jason Averbook – Why Data Fitness is More Important than an AI

Connect with Jason Averbook:

• 

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
• HR Data Labs Season 7 Episode 2
• HR Data Labs Season 6 Episode 25

• “AI has exposed the fact that organizations that don’t have good data can’t use [tools like large language models].”
• “The unlearning effort of 2024 to prepare for 2025 is more than the technology effort—it’s more than the data effort.”

Announcer  00:02

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, pour their discussions into a beaker, mix thoroughly. And Wallah, 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, but 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  00:46

Hello, and welcome to the HR data labs podcast. I'm your host David Turetsky alongside my friend and trusted co host Dwight Brown from Salary.com. Hey, Dwight, how are you?

 

Dwight Brown  00:55

I'm good, David, how you doing?

 

David Turetsky  00:57

I'm doing awesome. And you know why I'm doing awesome?

 

Dwight Brown  01:00

Why is that?

 

David Turetsky  01:01

Because today we get to spend some time with my friend and brilliant human being. Jason Averbook. Jason just joined Mercer not too long ago, Jason, you're gonna tell us a story. But everybody loves Jason, tell us a story about how you got to Mercer.

 

Jason Averbook  01:18

Wow! So first of all, thanks for the kind words, and excited to be here. As always, I think I'd make the appearance even though I'd like to become a more frequent appearance, but a yearly I guess we'll have to do. Yeah, I've had a long journey in the HR and HR technology space in the digital space. And, you know, one of the things that I did about 20 years ago was decided that I wanted to be out of the product side of the business and into the services side of the business and really working with organizations to help them see outcomes, I guess I'm more of an instant gratification person than what I was able to sell the product side. And because of that, I've really enjoyed the services side. And as part of enjoying services, have had the opportunity to grow a couple, three different consultant organizations now and basically really wanted to work outside the United States. So we worked with so many multinationals in the organizations that I started that basically said, you know, you guys are great, but you really can't help us outside the United States. And that's the point from a satisfaction standpoint, was like, we need to grow, and we need to scale. So, you know, at this part of legions history, what we did last year was started to say, Hey, who's a good partner, that has a great platform for us to scale on. And that's really what brought us to Mercer. So it was really, really excited to join forces with Mercer. We did that in March of last year. So we're nine months in long enough to have a baby. And we've had that baby. And now in my role of leading HR transformation globally here at Mercer. You know, we work with over 5000 clients in driving the change that we're going to talk about throughout the rest of the podcast here.

 

David Turetsky  03:10

That's awesome. And for those of you who have heard Jason before, you know, you know, he's a brilliant speaker, you know, he's a great thought leader. But what you may not know about Jason, is one fun thing that Jason is going to tell us right now. What's one fun thing that no one knows about? Jason Averbook,

 

Jason Averbook  03:26

You know, I am, and this will tie back to a lot of what we're talking about today. Maybe people know this about me if they follow me on LinkedIn, but I'm a lifelong learner. Like, I have to keep learning the minute I stop learning, I, I will die, I will never retire. So for me that might, some people may not call that fun. But the fun isn't just it's not just learning about HR. It's not just learning but digital. It's not just learning about technology. It's learning about life. And I'd say now with two boys who are 19 and 60. And I learned from them constantly, I learned people ask why I do all these podcasts every time I do a podcast I've learned. So I think that that's the one fun thing I'm going to throw out. And I hope that most people think that learning is fun.

 

David Turetsky  04:12

And it gives them an opportunity to develop their careers as well and develop themselves. And so I agree with you. I don't think I'll ever stop learning. And I hope I don't. But it's it's wonderful thing. Great, great feedback. So now let's talk about our topic for today. And if you followed our podcasts, you know that when we meet with people at the HR technology conferences, typically we're trying to get their opinion about where things have gone in the past year, and where things are going in the future. And in the context of the last HR Technology Conference, we didn't get a chance to catch up with Jason. So we wanted to try and give you all his perspective on what transpired and 23 where it's going and 24 which we're living in today. And where are we going for 25. So let's start out. So Jason, the first question is, there was a lot of stuff that happened in 23. Can you summarize and tell us what were the most surprising things that you found from 23 that really impacted human resources.

 

Jason Averbook  05:24

Shared bucket that into three categories. The first thing I think that happened in 2023, that none of us should really turn our head away from, is the world continued to get smaller. We dealt with and some of us forget this because time just fleets by and all of a sudden you like on to the next thing? In a boat, we bet we dealt with two massive geopolitical wars, both in Russia and Ukraine, as well as in Israel. Those are massive things that basically why does that affect HR, because every HR organization, for the most part has employees that were affected by those events. And when I say affected by those events, just like COVID left us in a period of uncertainty 2023 left, it's in a period of uncertainty as we enter 2024 based on some of those geopolitical events. So that's the first thing. It's a small world. And in a global economy, which we all live in today. And an HR function, which deals with people. It's all we're all together, we're all tied as one. Right, right. The second thing that I think that's fascinating about 2023, that we need to learn from, from a pattern standpoint, is that we saw the outside world of technology do and advance more than we saw inside enterprises. So that's not just AI. That's everything from new technology, when it comes to how do we connect to new high speed connectivity to thinking about the fact that it's now acceptable COVID accelerated this to everyone turns on zoom all the time or turns on our camera all the time. And the other component I'm going to add that is the the hybrid work model, as well, that people got 2023 confused about do we go back to work? Do we stay home? Do we go back to work? Do we stay home? So that whole concept of the human outside of work, yet I'm a human inside of work? And how does that play really played itself out in 2023. And then the third thing that happened at the end of 2023, was the start of what I'm going to call the 2024 printing press moment, for HR, which is the generational generative changing AI component that came into play, that by the time we got to HR tech, magically, everyone had it all over their booths, that were a Gen AI company. Yet none of the people at the conference knew what that meant.

 

David Turetsky  08:08

And we're confused by all the different messages that every single vendor we're throwing at them constantly. Right?

 

Dwight Brown  08:13

Right.

 

Jason Averbook  08:14

And I mean, and making AI seem to be this new age thing, which you know, as someone who built and one of the industries DOS applications, DOS, D. O. S, that doesn't stand for do's and don'ts. It's DOS. In it, we had AI back then. So this is not new stuff. There's a new format that's in front of us of AI, but it's not new stuff. So if I take the geopolitical issues combined with the concept of where am I going to work? How am I going to work at home yet connected, combined with this new technology that people got confused, scared, excited by all at the same time? That was 2023.

 

David Turetsky  08:55

Wow! That's a very concise few minutes and nailed it. The one thing I'd go back to with the geopolitical crisis is that if you weren't personally affected by your in laws, or by your, you know, a cousin, or whomever was in either Israel or the West Bank, or, or Gaza, or in Ukraine or Russia, if you weren't affected personally by, you know, your your family being there, you know, somebody in your team who is either from there, or who has family there who is personally affected by it. And it caused that, whether it's friction, or it caused the momentary sadness or continual sadness. So there's a mental health aspect of this that still playing out today in the workplace of people in that mindset. Oh, holy crap, my family is in danger. Or my friends are in danger.

 

Jason Averbook  09:52

Yeah. And even if it's not, I mean, yeah, or it's a customer or exactly right. It's a customer with a family member there. It And it's not just that I mean, there are lines of business, for example, within our parent company, Marsh McClellan that pulled out of Russia, like we broke ties with Russia. So I mean, all of that happened in 2023, which was a huge drain, and a huge burden, I'd say to a lot of HR functions.

 

Dwight Brown  10:22

Yeah. And I think, I think the same goes for each of the three pieces that you listed, too. I mean, people, part of the reason that AI is sort of risen up again, like you said, it's an it's old, it's been around for a long time. But now people are starting to be affected by AI or with the world conflict it, people if, if you weren't personally affected, there's been all kinds of fallout from that kind of thing. And so in one way or another, everybody's been personally affected. And that's definitely going to drive what happens going into 24 or  25.

 

Jason Averbook  10:59

So one of the things that's really interesting about the third one, we'll get back to AI. Sure, David Turetsky, and myself, like we've been sing the sond, Data's too sexy for your love forever. Yeah. You know, data's too sexy for your love, too sexy for your doo doo, doo doo, doo doo.

 

David Turetsky  11:20

We have a clip of that, I see it right now, Jason,

 

Jason Averbook  11:23

Exactly. And people have thought we were frickin crazy. And one of the things that AI has finally exposed, exposed, is the fact that David and I weren't crazy. But what we said about getting your data right, having foundational data solid, having both structured and unstructured data in a way that actually could be consumed, not by a little tiny straw, which was an interface or an integration, but by a huge land grab, called a large language model in today's world, that we expose the fact that HR organizations that don't have good data, can't use these tools. And to me, that's a headline, that is a headline of December 31 2023, that takes us into 2024.

 

Dwight Brown  12:15

So do you think organizations realize that point that you just made?

 

Jason Averbook  12:16

Do they? Or did they?

 

Dwight Brown  12:17

Well, it both, I mean, did they and are we making progress? Where do they?

 

Jason Averbook  12:28

So they didn't understand for the longest time? Why having a data repository, why having data in one place why having good data, why not have a 19 formats of the same day, that wasn't a good idea, etc, etc. And they did not understand that. And now what they're finding is that, wow, and I'd say they're just like, literally, light bulbs are on dim mode, they're just starting to realize that, wow, if we're gonna use some of these tools, A, we better know where our data is, B, we better make sure our data is right, and C, we better have a way to make sure that people understand that the fact that this data is now being presented to them in a way that is very consumable, that they have some idea around what they do with that data, as it's presented to them. And literally, I mean, I hate to say this, when David and I are we've been talking about this for two decades, right? And not I mean, I'm gonna be, I'm gonna be very blunt. I didn't know I was talking about it. David probably did. I just knew that some day, you know, all of this data arbitrage that was going on, was going to come crashing down on us if we didn't actually get it right and now we're seeing that.

 

David Turetsky  13:49

And Jason I, I love your analogies, but I like to think of data as cockroaches. And that a lot of the bad data are the worst cockroaches, the the biggest ones with the gigantic wings that come flying at you. People don't uncover those cockroaches, until they start to use them in ways that they were never intended. So when we built things like the analytics platform, we uncovered really horrible datasets, and really horrible hierarchies and structures that were only meant to fat satisfy the finance need, not to satisfy the need of being able to report on or analyze the data. So when these new technologies and techniques come up, and they start exposing these cockroaches that come out of nowhere, and they go holy crap, how do I put them back? Or how do I get rid of them? The answer is, you need to go either way back in time, or ignore what was done, start building better processes around your, your data capture today, and fix what's there. So that the expectation that you have of the new process, or of the new data, for whatever purpose you're driving it towards, will actually It makes sense given what you're trying to accomplish. And that's what I think is, is the biggest problem right now with what they want to accomplish, which is what you're talking about with corporate AI, in the context of HR, the data that's there sucks so bad, that trying to drive models, or at least an interpretation of the understanding of the data that's there, is going to uncover so much crap, that people are going to be like, I can't use this stuff, this doesn't work for me, and then give it up. When it could be an advantage.

 

Jason Averbook  15:31

You know, David, you use the word sucks. And I think what's really interesting that not about the word sucks, but about your statement is that we've been selfish, you know, there's this concept within sports about playing volleyball. And the, you know, the definition of volleyball is basically I'm not going to pass the ball, I'm not going to really worry who else is on the court? This is in basketball specifically, that's what I'm gonna do, I'm just going to drive to the hoop and try to score. And I think that if I take that same concept and apply it to silos within HR, we've been playing volleyball, right? So I would say that the comp, people would say, our data doesn't suck. I'd say the benefits, people would say, our data doesn't suck. It's self serving, it serves me. And through manual heroics, it allows me to get stuff done. But now all of a sudden, when I'm playing a game, which is a team sport, you know, and I have a bunch of spectators who are not watching one player there watching the whole team win or lose, all of a sudden, I'm realizing that volleyball doesn't work. And, you know, so I think we have to have a new definition of sucks, you know, which is a it might not suck for me as a function. But the way that I've been doing it sucks as an enterprise. And that makes us responsible for rethinking, how can we not just consume data, but how we produce data, how we gather data, and what the end result is of how that data is going to be used.

 

David Turetsky  17:02

But even in the context of that data, not sucking for others in let's just say, the job table. The job table is one tool that's being used by let's say, compensation to do promotion activities, career frameworks, job matching for salary surveys, and whatnot. So it does serve to your point, it serves the purpose of comp. But when workforce planning is using it, and they have a different definition, because they're looking at today, tomorrow and the longer term, and they have needs about the job table that aren't being satisfied there. The current systems don't technically help them with that. And so they have to go to your point before they have to create other data lakes, other Excel spreadsheets, other access databases that capture this information. And so it takes it even out of the realm of AI, it takes it out of the realm of data sources that are available to be used. And so we're creating those even greater silos. And there is no cause Dwight as an expert in data governance, there is no data governor, there's no there's no one person who works in an organization who sees the forest for the trees and says, Hmm, I think instead of trying to drive three different databases, let's use this one definition. And let's use it in different ways or extend the model in other ways.

 

Dwight Brown  18:19

And there may be data governors, but it's only at the individual silo level, there is no central data, governor, to your point.

 

Jason Averbook  18:27

Can I just say one thing before I respond in without my snarky humor? I have never been on a podcast where I talked about job tables, they may talk about, we're going deep and geeky. Like we're talking job tables, baby.

 

Dwight Brown  18:44

Or we love talking job tables.

 

Jason Averbook  18:46

Oh yea, I mean, we can talk job tables all day long. It's kind of funny. By the time people listen to this podcast, I'll given a presentation I'm doing next week in Davos. And the last thing I'm going to bring up to the group of people in Davos is job tables, but I love it.

 

David Turetsky  19:01

You're a shout out to the HR Data Labs podcast.

 

Jason Averbook  19:03

Let's go. But you know, the thing that I think is just so interesting about that, is that, that what you just brought up the fact that you went to that level, the fact that you stoop that low to the job table, you know, that just reinforces where we have to start. Because that's the foundational element of what we're doing. That's the that's the foundation to everything that we're talking about here. And, you know, one of my biggest fears in 2024, is that people are buying this gen AI stuff as the shiny object syndrome, and basically saying, guess what, this is gonna bandaid, all of that sucky data, and all of that bad job table stuff, because we're just gonna put this layer on top of it. Right, and it's not going to do them. Right. And we're seeing more than ever. Hey, guess what, I've got a copilot. So the copilot can actually help you understand this data. But once again, it's helping you understand bad data. And guess who we're going to blame? Gen AI. So in what I said earlier about a printing press moment, I truly think generative AI has the opportunity and potential and I've said this before, to be a printing press moment for HR, for knowledge, work and for business. And I only say potential, because it's going to require data governance, data, fitness, data ethics, that we are not good at today.

 

David Turetsky  20:38

And does the AI help us get there?

 

Announcer  20:42

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  20:52

Why don't we take that into 2024 is that where 2024 begins, is that where we really need to rely on the artificial intelligence to help us fill gaps that we as humans kind of suck at, which is being able to do all those things you just mentioned.

 

Jason Averbook  21:10

So generative AI and AI is going to help us understand where our problems are. So it actually gives us the opportunity to see that I've got bad data, right? It actually gives us the opportunity to converse with intent, very intentional, the way I said that converse with intent with my data, to help me understand how I could fix the data. Right, but it's not going to automatically fix the data. You know, if I have a David Turetsky that job code 001, and a Dwight Brown a job code 001 A, and all of a sudden, I'm like, Hey, which one? Should it be 001 or 001 A, you know, Gen AI is not going to fix that for me, what, and tell me all the downstream implications of salaries, etc, etc. And that goes back to whether I'm using the A at the end, I'm getting a little geeky there, sorry. But it's going to help identify that I've got a problem. But as far as solving it, and it couldn't give me ideas, but it's still going to require, which is a huge concept, this concept of human in the loop to say, based on what Gen AI has uncovered. Now, what do I do with it?

 

Dwight Brown  22:28

We think it's going to help us overcome data laziness. That's what that is, is data laziness that we, you know, we, we've got the bad data, we don't want to spend the effort to fix it. And so yeah, I mean, we're we're looking for, we're looking for that magic, magic wand to help make it go away. And I agree with you that going into 2024, everybody's going to expect that it will do what we have been hoping for for years and years and years. And it's just not going to happen. And like you said, they're going to blame it on generative AI, and, or a generative AI.

 

David Turetsky  23:07

But But I think Dwight to your point, what it's going to do, it's going to force new cottage industries, where people are going to be SWAT teams that get brought in, whether it's consultants like Jason or us to say, hey, let's look at your data set. And let's diagnose where we see the problems in your job table, in your architecture, in your hierarchies, things like that, that will prevent you from being able to utilize some of the really cool functions that are coming out in the HCM world over the next year.

 

Jason Averbook  23:40

One of the reasons I think people are data lazy, is because they don't find the value in not being data lazy.

 

Dwight Brown  23:48

Yeah, good point.

 

Jason Averbook  23:49

Does that make sense? I just don't I it doesn't, it doesn't mean enough to me, the fact that I, it's easier to set up a 001 a, than it is to try to figure out the answer. But now when all of a sudden, I realized that by having visibility to bad data, what it's doing, and the conversations that it's causing, and the turmoil it's causing. I'm gonna it's gonna force me not to be data lazy, okay, is that data fitness is going to become much more important.

 

David Turetsky  24:18

But Jason, those are skills that people especially in HR IT do not have understanding what they have on their plate right now, the maintenance of data tables, which should be their forte, which should be what they focus on. They've been keeping the lights on, especially through COVID. And now that it's over, do you think that there's a an impetus to go back and make sure that all the data that's there is still accurate, and is still representative of the organization as it stands?

 

Jason Averbook  24:46

Oh, my gosh. I mean, do I think I know. I mean, if I said I mean, the advice I give clients every day is don't jump into chatbots you know, because 99 percent of your Chatbot is going to give you bad data. And guess what, the minute you do that, what are people gonna say I hate the Chatbot. Because it's either creepy, it's crappy, or it's clunky. You know, so I would focus on as that technology continues to evolve, I would spend the first part, if nothing else, if 2024 on data cleanup exercises, data optimization exercises, data, fitness and data governance exercises. And by the way, you could use generative AI along the way, in small little pilots to see if, if it's working, and how the data cleanup is going on the optimizations going and looking for areas for optimization. But as far as full scale deployment of Gen AI, like, don't do it until you fix that data Foundation.

 

David Turetsky  25:45

And that's why I'm saying I think there are cottage industries there to be able to help companies get there, where you can hire outside resources to be able to put your data into better fitness, in order to be able to utilize some of these much cooler things that are coming out.

 

Jason Averbook  26:01

And I you know, and why, unfortunately, Dave, I think we're gonna go from cottage industry to the industry. You know, data is the spine of what makes all this stuff work. And it's going to become table stakes, and your comment about HR IT people not doing this or not knowing how to do it or not knowing why to do it. Like, I mean, those days, that that those days are over. That's not acceptable anymore.

 

David Turetsky  26:29

And so to all our HR IT folks out there, I love you. And Jason and I are hoping that we can help you gather skills in order to be able to get your data back on track.

 

Jason Averbook  26:40

Yeah, and one of the things so I mean, David, I'm just gonna play with you for a second data back on track.

 

David Turetsky  26:49

Yeah,

 

Jason Averbook  26:50

Let's start let's what does that what does that mean data back on track.

 

David Turetsky  26:55

So for me, the way data is structured today doesn't represent the organization, nor does it represent the needs of the organization. It's meant to satisfy whether payroll, benefits, compensation, whatever, to your point, before, it's solving individual problems, and not looking at the whole health of the organization, nor the purpose of the use of the data. Getting data back on track means leveraging the data to solve business problems, and being able to use the HR IT to help you do that. And it's not just payroll, it's not just benefits, not just comp, and it's not just workforce planning, it's giving us the the things that we need from this system from the set of systems to be able to satisfy a greater need.

 

Dwight Brown  27:38

I would also add to that, that getting data back on track also means having the processes to keep it on track. Because I think that's part of the issue that we have too, we, you know, there have been efforts to do some of the cleanup along the way, and whether it be the structure or whether it just be the data itself. But we we don't have processes to keep it that way. So we keep going back. So I, you know, I think David's point is spot on. And I in addition to that, we got to figure out how to keep it that way going forward.

 

Jason Averbook  28:12

You know, and Dwight and David, I mean, I think the thing that generative AI is going to do is basically I've been able to have all of this data within my walls, but you know, what, how it was organized, and what the drawers looked like, and if the floors were a mess and things like that, you know, guess what? I just put up walls in front of it. So no one saw it. What generative AI does is it changes those walls into Windows. Right? So now everyone sees it. Everyone sees where there's issues. Yeah. And you know, if I if I'm not focused on it's going to just continue to be an ongoing cockroach, Mr. Turetsky.

 

David Turetsky  28:50

So the walls breakdown and all the cockroaches come out. That's what you're saying.

 

Jason Averbook  28:53

Exactly. Exactly.

 

David Turetsky  28:55

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 HRDL consulting to schedule your FREE 30 minute call today.  Jason, let's pivot to 2025. Where does this lead us to? in next year's conversation? What will next year look like? If this is what we're going to face this year?

 

Jason Averbook  29:33

I know that we do three types of work. We do hands work, we do heads work and we do hearts work in HR. Hands work, heads work and hearts work. Now some people call that transactional relationship and expertise. I call it hands heads and hearts. What's going to happen throughout 2024 and as we move into 2025 is we are going to do a lot less hands work, and a lot more heads work and a lot more hearts work. I guarantee that like, to me, this is not even up for debate. Now, the pace in which I get there is going to depend on data, the cleanliness of data, my foundation, I don't actually think it depends on whether I'm in the cloud, whether I'm using the latest or greatest HCM system, or things like that, it's going to depend on my data architecture, my ability to take my data, put it into these models that allow me to be conversational, and intent based. And to think about how do I actually present that in an experience that's usable to a group of humans who are trying to get stuff done? And I really think that this concept that we've been talking about forever about how do we change HR from being so transactional to being so strategic? Now, for the first time ever, and I apologize if I'm offending anyone, for the first time ever, we actually have technology that allows me to do that. I don't believe self service ever did. Okay, I actually think we have the first technology ever that speaks the language of a human, instead of speaking the language of HR, right, that allows me to do that. But, but it's going to take a massive amount of unlearning the unlearning effort of 2024 to prepare for 2025 is more than the technology effort. It's more than the data effort. It means I have to reimagine how I work, how I think, why do things, et cetera, et cetera, et cetera, so that I can take advantage of this. So David, and Dwight, you know, I talk a lot about we put way too much weight on the technology. I'm watching this every second of late 23 and into 24. Where, oh, do you see that new copilot? Hey, did you see that new GPT store? There's a GPT store to do coaching and career advice. Right? Right. Awesome. But if I don't have good data, if I don't have good policy, if I don't have good stuff in there, for that tool to use, it's worthless. So I just think that we're at this seminal moment, which is either going to turn the function forever into a function that truly is data driven and strategic. Or we might be at the moment where if I don't do it, the functions get replaced. And I don't want to be that, like, grandiose, but you HR is not going to be the last one saying Guess what? You still need to call me if you have a problem. Or guess what, if you need that report, you still need to call me? Yeah, like you'll be replaced.

 

David Turetsky  33:02

Do you need me to fax you something?

 

Jason Averbook  33:09

It's not going to? I mean, it won't happen. Right? You'll get replaced.

 

Dwight Brown  33:13

Yeah. Yeah,

 

David Turetsky  33:14

I know. I agree. Well, I think what we're going to do is leave it there. Because what I want to do, Jason is I want to come back to this in 2026. And we're gonna say, Holy crap, look at what Jason said it was so spot on. He nailed it.

 

Jason Averbook  33:33

And if I'm not here in 26, that means that we all get a big fat ass.

 

David Turetsky  33:38

I think if you're not here, and 26 We're all gonna be sad. And we're all going to say, hey, let's get on. Let's get Jason on our other podcasts. We're just talking about sports.

 

Jason Averbook  33:51

It's going to be talking about mental health. And what happens when we don't do a good job of unlearning?

 

Dwight Brown  33:55

That's not a bad idea. That's true.

 

David Turetsky  33:58

We've actually had people talking about mental health before as well. So

 

Jason Averbook  34:01

You guys, mental health. Just I mean, David. Yeah, you know, I don't shut up. And that's why that moment of silence was the fun fact. Mental health is one of the things that's really really important about this particular topic. And the reason it's so particular is I'm gonna tie it back to closing off with what the leap and leap Jen stood for. It actually stood for something. Not everyone knows the story. It stood for love, energy, audacity and proof. How do you love what you do, which generates energy to do be audacious and then to prove that value? And I truly believe that for those organizations that do make this transformation to 24 into 25, and are successful is going to require a lot of love, to generate energy to do the audacious because to change one of the oldest professions in the world is not easy. And that's what we are in and now some moment that we have in front of us. So find the energy podcasts like this do a tremendous job of hopefully giving you that energy. Thank you, David and Dwight, for putting it on. Thank you. And thank you for having me on.

 

David Turetsky  35:21

Jason, it's always a pleasure. Thank you for being here. We love having you on because we love learning from every moment that you spend with us, Dwight, thank you.

 

Dwight Brown  35:30

Thank you. Thank you for being with us, Jason. This has been an awesome conversation.

 

Jason Averbook  35:35

Thanks Dwight. And David Someday I hope to have the same type of a radio voice that's so soothing. And call that you do.

 

David Turetsky  35:46

Thank you, Jason. We'll work on that together. How about that?

 

Jason Averbook  35:49

It's a deal.

 

David Turetsky  35:50

Take care everybody, stay safe. And we'll talk to you at our next episode.

 

Announcer  35:56

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.