Peter Louch – Combining AI With the 80/20 Rule for Better Workforce Planning

Connect with Peter Louch:

• 

Connect with David:

• 
• 
• david.turetsky@salary.com

Podcast Manager, Karissa Harris:

• HRDataLabs@salary.com

 

Production by Affogato Media

Resources:

• Vemo
• Salary.com's Consulting Practice
• HR Data Labs Season 6 Episode 17
• HR Data Labs Season 6 Episode 15

• “The idea of the 80/20 rule is that only a certain part of your organization is shifting at any given time.”
• “There’s no standard for what a job description is . . . everyone’s interested in skills, but one company will say they have 40 while another says they have 200,000.”

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. As always, we try and find people inside and outside the world of HR to bring you the latest on what's happening with HR data, analytics and HR process and policy. Today, I'm speaking to Peter Louch, who is the CEO and founder of Vemo. Right, Peter? it's not Venmo!

 

Peter Louch: 1:05

Not Venmo. No Venmo complaints here today.

 

David Turetsky: 1:09

Yeah, well, you know, as I told you before, I'm having problems with my Venmo account. So we're gonna get to that later, right?

 

Peter Louch: 1:14

Absolutely. I've already written it down.

 

David Turetsky: 1:18

Well, Peter, why don't you give us a little bit of background as to who you are and how you got to this moment?

 

Peter Louch: 1:22

Okay. Yep. So I've worked in the space of workforce planning and people analytics for a long time for about 20 years, I would say. And I think it was called different things at different ventures. So that's because it allows me to feel like I've had a more varied career. Yeah, since the terms have changed. After doing some very different things. I originally got an HR through TMP and Monster back in the day. And then that sort of that definitely imploded that space overall, in the early 2000s. And so then I kind of worked, you know, for some consulting firms for a period. I was confused about what consultants did, you know, do they just, you know.

 

David Turetsky: 1:59

Consultants are confused about what consultants do, Peter.

 

Peter Louch: 2:02

Exactly. So, so I thought, well, technology is the solution. And so around 2005, I started Vemo, with the idea of

 

David Turetsky: 2:05

One of the nicer questions I've ever gotten. focusing on workforce planning. And we were sort of interesting, because it was one of the, I think we were very early to be a cloud based, we didn't call it cloud based, but cloud based tool. So there were certain objections to that as well, because a lot of data involved with it. But it turned out to be one of those good accidental, a discount choice, that ended up

 

Peter Louch: 2:28

It was really, really nice. You know, I'm not making a lot of sense later on. And also very early to the sure. I'm not sure if she was drinking at this conference or space. Early on, we partnered with BrassRing, I came to their, one of their last annual conferences, and I presented a presentation and then someone raised their hand hesitantly. And I was like, yes? And they said, did someone drop you on your head? As in like, why would you choose to work in this field? not, you know, so. But I think that was sort of the attitudes that people thought like, Oh, this is something you really need to do. But people don't really want to do but they do a lot. But they don't really want to be told how to do it and that type of thing. And so it's been a, you know, it's been an interesting ride over this period of time, getting involved in the space, being a boutique, with other small companies sort of inventing the category. Seeing things come back, we were like, Hey, I kind of came up with that terminology or things like that. And just advancing along. And then, you know, fast forward to today it's now actually considered a core pillar of what you do. In the space, although sometimes frequently misunderstood still, all core pillars are right?

 

David Turetsky: 3:36

Yeah, absolutely.

 

Peter Louch: 3:37

So So yeah, that's that's a brief synopsis. I think of going, you know, of that piece of me. So

 

David Turetsky: 3:44

Peter, we ask everybody this. What's one fun thing that no one knows about Peter?

 

Peter Louch: 3:49

Right. Well, I had to dig deep because I get asked that a lot. So it used to be I'd always say, Hey, I'm the I have a bachelor's in astrophysics from Berkeley originally. So you say, Hey, I'm the only person working in this space who can tell you it's not rocket science. But I've said that a lot. So then I started telling people that I'm a descendant of someone who sank on the Titanic. Now I've used that a lot.

 

David Turetsky: 4:12

Wow!

 

Peter Louch: 4:12

That and when you ask the question, I thought there's, there's this really interesting, one of my first jobs is I read books on tape for a person named Paul Egly, who was a very famous judge who ordered integration in Los Angeles. And he had lost his vision and needed me to read books on tape for him. So it was one of my first jobs. But for some reason, I think this first or second book I had to read was The Satanic Verses. So I was like, this kid reading this book. I mean, there were more curse words than non curse words in this book.

 

David Turetsky: 4:48

Who would have thought Salman Rushdie

 

Peter Louch: 4:50

Yeah, exactly.

 

David Turetsky: 4:50

would have that kind of book?

 

Peter Louch: 4:52

So controversial.

 

David Turetsky: 4:53

Yeah.

 

Peter Louch: 4:54

I'm reading it. My little brothers were running to my parents, Mom, Dad. Peter's swearing in his room! It's okay. It's his job.

 

David Turetsky: 5:02

He's earning money. It's okay.

 

Peter Louch: 5:04

He's working for a very important person. It's his job.

 

David Turetsky: 5:07

Right.

 

Peter Louch: 5:08

So, I mean, you meet some pretty interesting people along the way. If you, you know, if you commit yourself to doing interesting things, I think.

 

David Turetsky: 5:14

That is an awesome one. That's one of the more unique ones I think we've gotten. But that's a good one. So today, our topic is one that we love to

 

Peter Louch: 5:19

Thanks. talk about in the world of HR Data Labs. And that's talking about how AI, ML, predictive analytics actually work in the world of HR. And what we're going to talk about today is the 80/20 rule, and how organizations can use AI, ML and predictive analytics to solve 80% of the planning effort, and allowing HR practitioners to focus on the 20%, which is the thing that really involves or really requires their time and investment. So the first question, Peter, is, why do you think that 80% of the workforce planning effort can be automated through predictive analytics and AI? Well, I think the backup just a slight bit on it first and answer something interesting is I think there's I mean, there's a lot of hand wringing consternation about AI right now.

 

David Turetsky: 6:21

Of course.

 

Peter Louch: 6:22

There's, I mean, there's people who are spending two times as much time at work than they used to spend just extolling the virtue of chat GPT. You know, it's just like, sorry, chat GPT it's doubled your workload, sorry.

 

David Turetsky: 6:36

Or vilifying it!

 

Peter Louch: 6:37

Right, or vilifying it right? Yeah. But it's something that's kind of been around for a while and developing. It didn't just like, if you read that you would thought that it was like something that landed on Earth, and just like an alien invasion that took took, you know, root and suddenly everything changed. And I think it's been happening for a while. And so I think what happens is in planning, and this is something that I realized along the way, is that people at one point, I think, you know, maybe 2012 13, 14, around that range, you know, who knows, around that timeframe, I decided I'm going to look across every customer we have, and I want to

 

David Turetsky: 7:18

No one.

 

Peter Louch: 7:19

Not one single thought that any change in see is anyone ever planned a reduction plus two years in the future? Right? No one. investment downward, I mean, a lot in the near term. Tons in the current quarter, turn of the year, all that kind of stuff, but complete dissipates. But there was always like these best case scenarios, the future of this is the talent we kind of need. And I think what happens is that people, they look at each sort of line item, which is kind of too granular sometimes, and they just sort of apply these sort of sunny day scenarios, across the board. They put in their constraints they're forced to by sort of other forces in the company like finance, you know, economics, etc, or whatever it is. But then they sort of overestimate always. And I think people this is one of the problems people have in general, right? They overestimate in terms of planning for retirement. Other they don't, you know, factor in all those different things.

 

David Turetsky: 8:16

Well in some ways, Peter, they don't want to because it's difficult. And because it's bad!

 

Peter Louch: 8:21

Right. Exactly.

 

David Turetsky: 8:22

Really bad.

 

Peter Louch: 8:23

Yeah. And it's hard. And it's like really hard if you're the person who's staff ends up going down. No one wants to be a cost center, right? Everybody wants to be a profit center, every company, they dress up their cost centers, like oh, yeah, this is how we're creating external value. It's like, sorry, you're actually managing cost in this area. So I think what it is, is that I developed this sort of frustration where I thought he was the guy who let, allowed people to easily plan four percent too high. You know, there's like that you're like, what's the benefit? I mean, it's like, Hey, you were really struggling to plan 4% to high earlier in your more like finance, led budget process? Now we have this really cool HR process, does all sorts of this, that the other, you know, predictive things. And now it's more fun and easier to do the same thing over here. Right? So the idea of the 80/20 rule is that you know, only a certain part of your organization is really shifting at any given time. You know, if you're changing your whole organization, you're in chaos, you're falling apart. Or you're growing so fast, it kind of doesn't matter, right, you're just going to kind of see where it all ends. But for most companies, there's a lot of a lot of very steady behaviors. And I like to start thinking about like this sort of sort of this anthropological concept that organizations are like cultures. In that you get to be a big enough company, typically, you have all these processes you put in place like you're only allowed to open up a rec this time. You need seven levels of approval for this, but three here, you know, you sort of think about like, What business are you in? You think about like what the financial constraints are. And you end up being very predictable in a lot of your behaviors. And you have a risk, though also that you're going to sweep in the really important stuff in the sort of bureaucratic, predictable behaviors as well. So if you could actually create a picture for an organization that says, This is your most, you know, organizationally, behaviorally, trend wise, with the market forces on your company, these are the most likely future data that will describe what your organization will look like, you know, next month, quarter away, year away, a couple years away, etc, you think get a picture of the most likely scenario. It's not the crack, it's like, the, this is what we will do. But then you don't actually get the picture of like, what should we do? So then you can kind of think, and people are much better at editing, right? It's like, easier if you and I work together. And I'm, like, maybe trying to write a book and and if I call you and say, Hey, let's talk about our book and build it together, you know, pretty hard. If I say, Hey, here's chapter one, read it, here are edits, tell me, tell me how this sucks, you know, or whatever how it's really good, or something like that it's easier to do. So I think the concept is, is if we, if you show this is your most likely scenario, now evaluate it using sort of the strategic criteria, you'll quickly decide, yeah, this is pretty good. I don't need to spend a lot of time thinking about this, this is the most likely I'm not going to waste my time on this 80%. This 20%, this is the talent we're trying to create the most value with. So let's, let's not do what we would do. Let's not do business as usual. Let's move it from there.

 

David Turetsky: 11:33

But Peter, we're living in a more complex world right now, with things like remote work, as well as the economy and regulation, and don't you think that that 20 kind of is a little vacillating? Doesn't it grow and shrink depending upon the time? I know what you're talking about, 80/20, meaning that you're trying to solve for a lot of things by using data, and trends to be able to figure out a lot of the hard stuff. But but right now, we're dealing with complexity, shouldn't the models take into consideration some of those really big, deep, heavy stuff that we're dealing with?

 

Peter Louch: 12:07

I think so. I think that I think that humans do have to be involved with thinking through those problems, right? Around it so I think the solution is if if you kind of can move away from like, this whole workforce planning topic being like, we're going to do it sort of rarely. And we're going to kind of carrot stick it, and this is our cycle. And if you can actually get to the point where it's like a continuous process, that then allows you to always look at new data and new conditions. I think we started like the joke, you know, sort of like, in 2020. Like, yeah, we picked the wrong time to be in the predictive analytics business or the workforce planning business.

 

David Turetsky: 12:48

He's referring to Airplanes right now, one of the best movies ever made.

 

Peter Louch: 12:52

It was absolutely an Airplane riff. So you know, which you cringy to your kids and stuff.

 

David Turetsky: 12:59

Yeah, for sure. But but but even in 2020, you know, one of, I think it was General Patton, who said, you know, we're surrounded that simplifies things, right? In 2020 and I think this may be your point. And I'm trying to interpret for myself as well as the audience. So many things were figured out for us because it became so complex, because the world became so complex, and we were all working at home. And we were dealing with economic cycles that this economy has never dealt with before. And we were getting unprecedented support from the government, that it really did, A harm us, but it also basically supported us as well, at the same time. So we were getting one angle was horrific. And the other angle was, oh, wow, we may actually survive this and come through, but we won't be the same again.

 

Peter Louch: 13:48

Right. Well, yeah, I guess, you know, taken conclusion. Like, you know, things get pretty simple during a zombie apocalypse epic.

 

David Turetsky: 13:58

What's the number one rule of the zombie apocalypse? Right, right from Zombieland?

 

Peter Louch: 14:05

Well, I think, I think I definitely hear what you're saying, because I think there is this concept in the marketplace in 2023 of vast uncertainty, right? And it's pretty hyped about the vast uncertainty. There is a you know, one of the beauties of being the workforce planning business is that, you know, does give you a little bit of right to be wrong, right, because it's not 2024 2025 yet. Right. But I think though, I think that what I'm seeing is there's this gap between sort of this immediate perspective, uncertainty in rates and things like that, and how organizations are behaving. And I think one of the issues is, is that you can continually if you get data every week, or something like that, you can continually reflect on how an organization is behaving and changing and you can capture those trends more quickly. And so and what it does, is it. And I think also, it's like you don't want to plan all the time either, right? You know, it's like, you don't want to be organization never does, it only plans, right? So but you can ratchet up and down that level based upon the level of change, and how much you think those assumptions are changing. And you can monitor. So with data today, you don't have to plan to monitor, you kind of say, hey, it's really time to really look at our assumptions again. So I hear you, David, that there's a lot of sort of uncertainties and that that, and that you can't apply by, I still believe that there's a large chunk that you can say, we don't need to think about this part as much. And I think that lensing in a time of uncertainty, the ability to lens and make a 20%. Or even if it's the wrong decision, right? It's not like you're all rowing the boat in the wrong direction, you might be a little a couple degrees apart, and you might not be doing sufficiently, but you are sort of rowing sort of in the right direction.

 

Announcer: 15:57

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: 16:07

So I guess that gets us to the second point, which is what should HR practitioners and business leaders do differently once they actually get the ability to automate the 80%?

 

Peter Louch: 16:21

What I think should be done differently in this practice, and I think a lot of people some people would agree or disagree with this, is I think that a lot of times, we'll invent things in the human resource field that we kind of want to hold on to. And we want it to be like an HR only activity. And in an area that involves like people's budgets and effectiveness at work, if you try to like really hold it tightly in HR, then it's not the business plan. Right. But it is not involved. So I know one organization that says we've approached to fail forward, which sounds kind of goofy, but I think it's I think it's an excellent strategy, which is, our people don't fail forward in workforce planning. They want to perfect it in the HR shop. Invariably, there is someone who suggests, hey, let's pilot this in HR, which is like, that's no one's gonna actually care. Right? It's very valuable for you. Yeah, you should do it. And you don't want to be accused of being like cobbler shoes type of scenario. But I think really, one thing they can do is embrace the data. And, and they can also not have to feel like it has to be right. Right, I think it's, I think is one of those things where people determine that between people analytics, like someone owns it, like there's this whole concept like, oh, our people analytics data is wrong. Therefore, HR, this is like your problem. It's like, No, someone entered something wrong someplace, it could have been bad, it could have been missing information, whatever. It's like an effort is something that we work on together in a collaborative way. So I think they can really collaborate together. And I think what they can do is they can sort of look at the sort of forecasts and say, you know, critical questions like, has our competition changed? Has the workforce environment change? Are our strategies changing? And in really like our strategies? Not like, are we just like, you know, dressing up extra stuff? Do we have a sole purpose? And like, do we have 16 strategies? You know, have you ever been to a company where you see all 16 archetypal business strategies on the wall? Like, that's like, not how you drive value, like, so how do we drive value? And then how do you get those discussions of where we need to invest? And let's not be afraid of identifying way in advance where we need less people, because we can actually then do something like if we, if we do what companies in the pandemic did, which is large tech companies wich staffed up when they knew they knew they were going to lay off, right, they knew there was not sustainable, but like the market was requiring it. But they can identify this is the jobs we don't need a year, two years in advance, you can actually repurpose people or you can actually be sort of more human in that whole process.

 

David Turetsky: 19:00

Or use contractors instead of trying to make them all W2. Peter, I want to go back on a couple of things you were just saying. And that's the premise of the HR Data Labs podcast was always built, in fact, the first episode was, HR Data Sucks. But actually, we didn't go that far we did HR data is just full of holes. It's just terrible. And when you look at the business, the business, especially finance and marketing and sales, they all work with much cleaner data, even though that a lot of it is going to be wrong. They're working on the premise that the data isn't perfect, but it's as close to perfect as they can get it. Whereas in HR, I don't think anybody who deals with HRIT or even people analytics would say, oh, yeah, everything's perfect. No, it isn't. By the very nature of the fact that it changes on a moment to moment basis, whether we're talking about time data, we're talking about people's jobs or their skills. So let me ask the question, I want to go back on one of the things you just said and say that if we're building workforce planning models based on data that we know isn't going to be perfect. And the business understands that then automatically, in your your comment about failing forward, I love I love using the concept of failing fast. People have this hard time with failure that hate the word. They say a no, no, I don't want to go anywhere near that. To me if I don't fail, at least on a daily basis, I haven't tried. Right. So I guess my question goes back to workforce planning makes a lot of assumptions, but it's one of those things. And especially HR, shouldn't I love your comment, HR shouldn't be the HR should not be the leader, especially for pilots on this stuff. But managers get, especially for workforce planning that this is a hypothesis, right? I mean, that this is gonna probably fail. It's, it's not, it's not gonna be perfect, because we can't predict the future. Just like you said, in 2020 when we're looking at staffing models, especially for large tech companies. They don't they didn't know what's gonna happen next week, much less next year, we didn't know if the pandemic was going to be a five month thing, or it was going to be a 10 year thing. We were praying.

 

Peter Louch: 21:13

Exactly.

 

David Turetsky: 21:15

Anyway, since that's my comment, as well as the question, which is that that doesn't the business understand, especially in the workforce planning world, that these are models with, you know, built on hypotheses?

 

Peter Louch: 21:26

Well, I'm going to take issue with the comment that finance data is good. First of all, it's using the same data. It's using the same exact, a lot of the exact same thing, with so many adjustments, and general ledger, this that the other, it's just that the relationship with the data between finance and when when you're in finance, you're like, the state is not good. We do our best. There's like, No, we don't own this.

 

David Turetsky: 21:53

Just the acknowledgement.

 

Peter Louch: 21:54

Right. We have these limitations. And there's just, and people sort of accept that. And for whatever reason, HR has gotten this sort of awkward relationship, because they're also responsible for installing a lot of the systems, right? So so many of these systems are in the purview of HR. So it's like you own these systems. And then a lot of systems, even the best designed systems, don't, you know, how do you take into account some of this human behavior that goes into, you know, doing this, that and the other? But I feel that originally, when I first was in this space, people would say, our data is really bad. And I would lie and say, No, it's not. It's not that bad. It can be done, it's okay. And then they would say our data is bad. And I'd say it's spotty, but there's certainly good parts about it. And it was, it was Swiss cheese, like you sort of refer to. And now I feel like the data has really improved a lot for many organizations. And I don't think it's that big of a concern. I think there are so there's so many well established sort of like these ETL processes, that sort of scrub and clean data when it comes in and make it good. And so I think it's, I think in some of the core areas necessary for planning, it's pretty strong. I think when you go to the next level of skills, and things like that, then you have some real questions there.

 

David Turetsky: 23:21

Job titles, job descriptions, they're all crap! Because no one audits them, no one regularly looks at them. The one thing I will say back to the comment about the finance data is there are GAAP rules around being able to correct things. in HR, we don't have those rules, right? The only time HR data sees the light of day that the SEC gives a crap about is when there are things that we need to submit as a public company that go alongside our financials or our disclosures. So public disclosure of our data opens up this window, whereas finance lives in that world. Now, onto the latter that you're thinking you were just talking about. The acceptance of HR data as being imperfect allows you that license to do what you just talked about. To give that freedom to say, it's good enough. And I totally agree with you there. But except for the last point, which is on skills and job descriptions.

 

Peter Louch: 24:25

Well, I want to mention one thing real quick just to jump in. You mean what you said made me think of something, which is I've observed this pattern where the more important a job is, the more likely you are to call it one thing. And the less important the job is, the more likely you are to call it many things. So you'll sometimes look at this problem of perspective, but you might overlump important jobs because you want that freedom with certain kinds of developers to kind of hire different things that have criteria. But the more likely you are to really describe it well, when it's very important to your organization, and when you don't really care if it's a kind of anything else, right? Like, you're having people for dinner, you're really gonna, like, you know, set the table, you're gonna cook, you're not gonna have like, whatever piece falling off the edge of the plate, you know. So the other thing is that people have multiple structures that describe the same thing. A lot of things are fantastic org structures that really do a nice job of describing the work and just the jobs that for whatever reason, because they're used by comp or for other things for, you know, thing. So there's a lot there, you can kind of get at it. I think for a while, I'm a little bit less dubious, I think, then you have some of it. But I do know that there is sloppiness.

 

David Turetsky: 25:40

And that's okay. And I think your perspective is valid, because one of the things I do every day is I live in the world of the HR data. I'm always auditing, job descriptions, and job titles and job structure. So I live that every day. In the world of workforce planning, it's not even as much about the job title, about the job content, as it is, is about as much about how the organization is planning around it. Right? So, you know, one of the things I would love to talk to you about it probably at a different time in a different podcast, is the kind of the context for how workforce planning deals with some of these changes to the world, especially with regards to re skilling people. And how do we up and re, but that's, we're not gonna be able to do that today, because we only have a few minutes left. And I want to get to your third question. Let's get to your third question. 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. So what is on the horizon for workforce planning and people analytics?

 

Peter Louch: 27:08

Well, I kind of tricked you here, David, because I was going to actually mention something about skills, And job descriptions, but I don't think

 

David Turetsky: 27:15

Awesome! I can fully do it justice. But I think right now, this is definitely the area where there's no standard for what a job description is, right? There's no standard between a technical skill, a soft skill, a common skill, an activity in all this different type of area. And everybody's really interested in skills in one company will say we have 40. And one will say we have 200,000. Right.

 

Peter Louch: 27:45

And one company will say we have people with 1000 skills. And I say give me their number, I need a handyman, right? And then someone else is like these people, they you know, they have one skill, that skill is their role, et cetera. So I think there's been a loss struck, I think the workforce planning realm, a lot of it focuses on job disciplines, or job roles, maybe some sort of sub domain or sub role in some sort of standardized level, which there's been a lot of strides made. And I think that's often the information required to decide, wow, we have a real imbalance in the future of having this really, you know, highly tenured, too high a level of job roles, and not enough people bringing in and we need to do something. Workforce planning deals with those kind of concepts. But I think that there's a there in the coming years, if there can be some concepts around standardization, and using the same type of tools, the start to define descriptions and skills in a way that are portable, you can actually see, I think that I think that's like the current Wild West that has some emerging promise in it.

 

David Turetsky: 28:52

And I think that you're getting helped tremendously by the world of pay transparency, because transparency generates the need for people to understand what they do today, and where could they go? So career frameworks, which include function and level are not at their nascency, they've been around for a very long time, but they're now being utilized by companies to be able to show people, what do you do today? What's the pay opportunity for what you do today? And then where can you take that even in a more non traditional way where you can give them possibilities that are beyond where they see today! Beyond the current function and family that they're in.

 

Peter Louch: 29:32

Right. I think pay transparency is a huge thing. I mean, it's so. Naturally, anyone should be a fan of it. In a way, you know, they're sort of like thinking about, like equity among people. Right? Well, I think it is really helpful for this whole data collection.

 

David Turetsky: 29:49

And I love what you were talking about, about the concept of the job description and skills, needing to have much more normalcy, needing to have more standardization. I think that'll come when people start worrying about what do I need to disclose? How do I disclose it, we're going to start to develop some best practices around that, which kind of are all over the place. Whether you're in recruiting, and you need a recruiting job description, How do I enumerate skills in a job description? Versus what is compensation and HR need? Whether it's compensation management, whether it's job evaluation, or whether it's, you know, just being able to communicate what the heck does this job really do?

 

Peter Louch: 30:32

Right. When you read these jobs, right, can anyone do them? These jobs that are out there, like like, wow, wow, all that for an intern?

 

David Turetsky: 30:42

I mean, I think they're racing to how many tasks can I put on three pages of paper to print this outwhen someone's interviewing? I don't know. I don't know why they do it. Yeah, I think it's because they don't understand the difference between required duties and tasks. But But again, that could actually be an entire podcast. Peter, anything before anything else before we close for today?

 

Peter Louch: 31:08

No, I greatly appreciate being on this. And I, and also some of the tougher questions, I think you asked about this, because I think that I mean, obviously, with your framework, and what you work on, you really, I mean, you know, you know, this across a broad number of organizations. And so I think it's easy, you know, it's easy to be the Pied Piper in all this. I mean, there's a lot of that in workforce planning, right? We're just like, right? Like, sometimes some of these conferences, you're like, No, you know, it's not like, it's not that fancy. It's hard data work, right? But it's like, but you can make progress on it. If you commit to the hard data work, you really will have like a lot of success. And I think, and I appreciate that perspective a lot.

 

David Turetsky: 31:50

One of the things I love about workforce planning, which I don't think people appreciate is that if you, if you think about it, it's not really an HR thing. It's really something that management and leadership and executives rely on. Right? So it's beyond HR, it really does have that connective tissue to the operations, finance, and HR that enable us to really kind of show and shine. But it's also one of those things that you kind of are never sure who really owns it, right?

 

Peter Louch: 32:25

Or when it's done!

 

David Turetsky: 32:27

Is it a quarterly planning exercise? Is it a yearly? Do we do it for budget season?

 

Peter Louch: 32:31

Right? Well, I think it's kind of like similar to like, when it's going well, it could actually get boring and that's a good thing. It's kind of like, if an application is working really well and someone QA's it for like, several weeks, and finds nothing. Then they think, Oh, I wasted my time. Like no actually, you know, is actually running really well. It's like, it's fantastic, right? It's so good. Such a good job working through all the stuff. I think when it starts running smoothly, it's a cycle. I think the virtue of successful things end up being less exciting, maybe they can be reinvigorated with like new concepts and things like that and forward thinking concepts.

 

David Turetsky: 33:05

Well, we're living in a world where tomorrow something else is going to happen and throw everything into a tailspin. Well, Peter, thank you so much. It's been a wonderful discussion. Thank you so much for being on the HR Data Labs podcast. We really do appreciate it. You're awesome. And we're gonna have to have you back because it was so much fun.

 

Peter Louch: 33:32

Look forward to it, David.

 

David Turetsky: 33:33

Take care, Peter. Thank you and everybody, take care and stay safe.

 

Announcer: 33:38

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