People analytics is not just for psychologists anymore!
“I think that not only will people analytics be the decision maker themselves. In the future, I actually think people analytics will be what HR is.”
–Cole Napper on People Analytics
My guest this week is Cole Napper, IO psychologist extraordinaire, People analytics thought leader, and inspirational podcast host.
In this fun filled episode I take a page from Cole’s playbook and loosen my collar, clearing the air for super engaging personal stories interwoven with thought leadership about creating a new era for people analytics.
We discuss a range of topics, including Cole’s background from Louisiana, his experience at Louisiana Tech’s IO Psychology PhD program, and his role as VP of People Analytics at Orgnostic.. Cole is also a co-host of the Direction Correct podcast about People Analytics and is dedicated to creating a future where people analytics and technology combine to have a positive impact on business outcomes and happiness at work..
The episode covers various aspects of people analytics, its role in business, and its intersection with technology and AI. Additionally, they touch upon current issues like COVID-19, its impact on the workplace, and the future of people analytics in HR.
Topics Discussed:
Introduction to Cole Napper: Cole’s Louisiana background, education at Louisiana Tech’s IO Psychology PhD program, and his current role.
People Analytics: Insights into what people analytics entails, its significance in the business world, and how it’s shaping the future of work.
Impact of COVID-19: How the pandemic has affected workplace dynamics and the role of people analytics in navigating these changes.
The Role of Technology: Discussion on the intersection of people analytics with AI and machine learning.
Challenges and Future Trends: Exploring current challenges in the field of people analytics and predictions for its future trajectory within HR.
Key Takeaways:
- Podcasts can be fun and still pack quite a bit of learning into the mix.
- People analytics is a vital component in understanding and improving organizational dynamics.
- The COVID-19 pandemic has accelerated the need for robust people analytics.
- Technology and AI are becoming integral in advancing the field of people analytics.
- There are ongoing challenges in data privacy and quality in the use of AI and analytics tools.
Unedited Transcript
Transcription for:
“S4HCole.mp3” (Uploaded File) (New Transcription)
Speaker 0: Welcome to Science for Hyre. With your host, doctor Charles Handler. Science for Hire provides thirty minutes of enlightenment on best practices and news from the front lines of the improvement testing universe.
Speaker 1: Hello, and welcome to the latest edition of Science. It’s for hire. I am your host doctor, Charles Sandler, and the parade of all star guest just keeps coming. Today, my guest is Cole Napper, I will say he is a fellow Louisiana person here, which there’s not that many of us. We all have to stick together.
And so he’s a veteran of the Louisiana Tech program, which is really a great program. It didn’t really exist when I was looking for grad school. So glad that we have options in the state. And as I always do, I’ll let Cole introduce himself. But one more one more kind of thing to mention is that I was on calls directionally correct podcast, which was the most fun podcast I’ve ever been on, and he’s really inspired me to make mine more fun, so we’ll see what we can do. I don’t have any silly sound effects or anything like that other than the ones I can, you know, make on my own. But but we’ll be able to, I think, have a really fun journey today through a lot of different topics. So without further ado, my guests, cool, never.
Speaker 2: Yeah. Well, we can we can make some sound effects if you want. I don’t know if the audience will like it. Though.
Speaker 1: There’s a dog in the office and I’d be able to get it to bark on q.
Speaker 2: But, yeah, like, maybe I’ll do kind of a nontraditional introduction just because of the the context you said, but I am originally from under Louisiana, which is probably a big part of my identity, and I appreciate you bringing it up because I’m very fond to the state of Louisiana. I actually live in Texas now, but I live a stones throwaway, you might say, and I come back quite often. I’ll actually be there later this week. And Louisiana is the only place that that I know of that has, like, drive through deckeries. And So I’m a I’m a big fan of that when I go back. It’s a it’s just a cool part of the culture and everything. But so like you mentioned, I I graduated from Louisiana Tech’s Io Psychology PHD program. I was actually in the first cohort there, and so that definitely dates me in terms of the the time frame of how long that program has existed, but I’m really a huge fan of that that program and and some of the faculty there like Tilman Sheets was my adviser. He’s just a phenomenal guy. And I think they they’ve really done things the right way in terms of bridging the scientist practitioner gap.
My day job, I I’m the VP of People Analytics for a company called Organic. And as you mentioned, also I’m the co host of the direction correct podcast about People Analytics with Scott
Hines as well who’s another graduate of Louisiana Tech’s program. And we do try to have some fun. And so I hope I hope we can have some fun on here today, Charles.
Speaker 1: I know we will. Absolutely. So I’ll tell you you have to drive through Jacklyn.
Speaker 2: I did have a question actually. Yeah. Is You said it was the most fun you’d ever had in a podcast. Was that the only podcast you’d ever been on? Because I was like, I mean, we’re we’re a low bar.
Speaker 1: Well, you know, I mean, I’ve been on a few. I’ve been on a few, but not a whole lot. I expect to probably be on more as time goes on and people find me, etcetera. I don’t know. I mean, you know, I’m pretty I’m not a buttoned up guy to begin with, but on these things for whatever reason.
I’ve I’ve kept it a little bit more business like and you guys had a lot of little fun non sequencers. I will say, hey, and this is I don’t know when this episode exactly is gonna air, but I am working on taking what you inspired me to do and turning it into almost like a little quiz show live video stream called take it or leave it. And in in that,
Speaker 2: I’m intrigued.
Speaker 1: Yeah. In that, it’s gonna be, you know, I’ve actually played a game show host before in a lot of different, you know, roles in terms of dramatic productions and things. And so I I’m good in that role. I can really I can really get into the character. It’s just gonna be current, you know, news articles.
I’m a have a guest and we’re gonna, you know, thumbs up. Take it or leave it. There will be sound effects most likely, and I’m learning to do, you know, video production. I don’t know if you’ve ever seen something like this. This is a called a stream deck.
I had never heard of one of these before. But I
Speaker 2: don’t know what that is.
Speaker 1: Well, it’s got all these buttons. Right? And so you as you’re doing whatever your thing is, you have macros loaded on there. So cue the intro, bank, press the button, you know, cue the
Speaker 2: Oh, it’s like a sound board.
Speaker 1: Yeah. Kinda, but it’s all macroed out so you can just, you know, do what exactly you need pre programmed for someone like me who’s not a sound engineer. So I’m gonna be playing with that, but that’s gonna be a lot more a lot more whimsical and a lot more centered around, okay, here’s some latest news articles, you know, about stuff we do. Right? Technology, especially, you know?
Is it bogus or not? And we’ll just we’ll just kinda have a vote. So stay tuned. You might get invited to that. And it’s a safe bet.
And so you mentioned the drive through deckery shops and so we do have those here and I I belong to this in a car club with where we we all have vintage cars. And so we have a thing every year called the decker y rally. So If you know about, like, the Paris to the car rally, it’s a take on
that. And it’s like a true rally start. You you get a an envelope.
You open the envelope. You stamp your time, and there’s heaps of people. And then in there, it’s a it’s a map to four different daiquiri shops around the area that spread out. So you’re you’re like a thirty or forty mile radius. Yeah.
And you have a coat, you know, a coke pilot navigator. And you go in, you get the daiquiri, you bring them back at the end, everybody brings them back and then, you know, you got doctors to drink. No drinking while you’re driving. But and then you stamp the you stamp the time stamp when you get back. So it’s like a true rally, but you’re actually collecting deckeries from different decker shops, and we just did that.
It’s every October. So it was super fun.
Speaker 2: I don’t have a classic car, but that would be so fun to participate.
Speaker 1: Yeah. You don’t have to. Some people there were people in, like, just an SUV, you know, just you don’t actually have to have a classic car. I will say that I’ve done it maybe four times in my success rate of actually Finishing has been about half. Seems like my stuff breaks down a lot, but, you know, that’s that that just happens.
It’s I’m used to it by now, man. The AAA is a really good investment. So let’s hear a little bit about people analytics. I mean, it’s a term you see everywhere. If you’re just seeing people’s titles on LinkedIn and stuff, it’s always, you know, some not always.
There are a lot of people analytics, people analytics. And I’m sure it has a pretty pretty umbrellaed meaning. But I bet it means a lot of different things too. And so as someone who is in people analytics and as a podcast on people analytics, it’s probably damn good at people analytics. Tell our listeners what it means to you.
What should they think when they see somebody’s job title, you know, that says people analytics. And why would you be out to somebody like that or something. You know?
Speaker 2: Yeah. Yeah. I think I think it’s a good question. And there’s definitely not consensus on what it means. And so I think it’s it’s it’s worth asking.
And I’ll I’ll kinda pick on Scott here for a second just because he’s my cohost and we’ve talked about it before on the podcast. But I think in his mind, I o psychology and people analytics are somewhat synonymous. Uh-huh. I I am actually not in that camp. I I would say that Think of it like a Venn diagram.
There’s definitely overlap between the two, but there are I o psychologists that are not doing people analytics and there are people analytics folks that are not doing I o psychology. And so I I take a big tent definition of what you might call traditional people and analytics. Now, like, the biggest tent would just be any analytics related to people, but I I don’t think there
Speaker 1: That’s an easy out right there.
Speaker 2: I I I don’t think it’s accurate either. Because there’s lots of analytics that go on in the world about people that aren’t called people analytics. Right? And, like, just an example would be, like, analytics related to sports players. Well, they don’t call that people analytics.
Speaker 1: Right.
Speaker 2: They they just don’t. But that would be that would fit the definition. Yeah. Generally speaking, it would be any kind of analytics or data about employees of an organization or maybe even the labor market that’s outside of the organization and trying to use that information to inform business decisions. It’s probably the easiest way of putting
Speaker 1: it Gotcha.
Speaker 2: In the aspects that are related to IO, is a lot of what we learn in terms of, like, the research methods, the multi variant stats, and just understanding the cognitions of employees in the workplace Right? The things that are people analytics but not really related to I o psychology is things like data engineering, data management, some of the machine learning techniques, like some of the more esoteric or sophisticated things that are going on out there you There are other psychologists out there that know those things, but it’s not a part of our core discipline that we really learn about in graduate school.
Speaker 1: Sure.
Speaker 2: And so I I’d really say that’s kinda where the Venn diagram loses its overlap.
Speaker 1: I like thinking in Venn diagrams and, you know, I just wrote down on my notepad here, data scientists. So, you know, now I’m starting to to visualize a VIN diagram that has all the different things that, you know, IO’s do in terms of titles. Right? We we certainly work in different areas, you know, executive coach or curriculum developer, whatever it is, but but more just in the idea of, you know, how we’re we’re labeling ourselves around the the things that we do with data, I guess, really. Right?
That’s that’s pretty interesting. And, you know, data science is one that for the past probably five years, I’d say, it’s really started to enter into the vocabulary you’re starting to see. And I think even in my program, you know, there’s such an overlap between IOs and, you know, use of statistical analysis even in more sophisticated ways. Right? We’re we’re trained To do that, luckily for me, it was pretty simplistic back then.
You know, I’m not really a good programmer developer type person. And it really crosses over those kind of skills when you’re looking at, you know, writing code to do all kinds of interesting things. It’s it’s a dimension I wish I had in my back of tricks, but, you know, I know people. So that that’s how you get that done. You know people.
But that’s that’s cool. So and I’m channeling you here. What what’s your wackiest people analytics moment? What’s the craziest thing as a people analytics person that you’ve ever dealt with that you’d like to, you know, tell that story?
Speaker 2: Well, Ken, before I get into the funny because story. I think it’s an important clarification on along the lines you’re saying about data science. And and I find a really practical way to translate this for I o psychologist to understand kind of some of the similarities and some of the differences and what it would take if you wanted to bridge that gap. Is thinking about traditional research versus traditional product development. Alright?
And so Traditional research, let’s say let’s say just for the sake of argument, you’re gonna do a research project. You go through the entire scientific method. It takes six months to do, you collect your data, you do all the the things that we know how to do really effectively. It just takes time.
Speaker 1: Yeah.
Speaker 2: Well, and and part of that process is usually modeling.
Speaker 1: Yeah.
Speaker 2: Right? Now a data scientist comes along and maybe they have a different lexicon in terms of how they talk about things, but they they’re doing modeling as well. But chances are the modeling that they’re doing isn’t going through the scientific method and process. It’s probably putting what they call a model into production.
Speaker 1: Yep.
Speaker 2: Which means it’s just running natively in the background using software. And so the way I put it is what data scientists are doing is just an extension of what we do. It’s just doing it in real time. And so science shouldn’t take as long as it takes. And so how could you shorten that time frame?
It’s by putting things into production and building products around it to do it automatically. In terms of, like, wacky people analytics stuff, let me think for a second because that that’s really a good question.
Speaker 1: So think about it while I come in on what you’re saying if your brain can work that way. Hopefully, it’s not a cross cognitive processing challenge. I don’t think it is for you though. So the first thing I thought of is sometimes this is not a knock against data science. This is the way I’m using this hyperbole is is maybe sometimes it’s soulless, like, you just look at the data, you follow the data, it’s almost purely empirical.
Right? And and I’ve worked with a lot of data scientists. And the hole is greater than the sum of its parts when you do that. I mean, there’s a lot of common procedures, but there’s just a fundamental philosophy. If you’re not trained in any kind of psychology or, you know, more mental measurement, whatever, that you’re just looking for features and, you you know, what they call validation is, oh, the the model predicts, you know, it works, but but it doesn’t have that external validity of, oh, this is actually predicting something that’s really important for us in a specific way and accounting for variance in this.
There’s subtle differences and I might even be butchering those, but but I feel like the best combination is again this VIN diagram of of an individual or team where you have both of those mindsets. Right? So you’re good at exploring and building the models, but you also understand the ingredients of the models features of the model, you know, how important those are and what they relate to. It’s it’s just goes back to the, you know, basic I o grad school idea of Dust Bowl empiricism, which we had a lot of in our field a long time ago. And when you follow those things, you know, what I think you lose a lot of time is the generalizability of the model too. Right? You have this model, it’s very trained on a very specific set of data. And then you try to say, okay, we’re gonna select all call center representatives across the world with this model we built in, you know, Akron, Ohio, and it’s it it may not generalize, especially the more faceted it is and everything. So that’s that’s kind of my my take on that. You know, it’s about the external validity And I would say, you know, we’re trained to do the research methods if you think about doing a validation study.
Like an IO validation study. I’ve probably done a zillion of those. I love them every time I can
because you can prove the value hopefully of of what you’re doing or show the company that what they’re using, never something built by me, of course, isn’t working. So from that standpoint, it’s great. And that process takes it does take time.
It’s it’s not a laboratory research project, but it does take a lot of time you know, if you have the archival data available, that’s great. The the projects where I’ve been lucky enough to work with a corporation that has an internal HR data you know, warehouse and team that only does that where you send them an empty spreadsheet and they send you back a pretty clean data set or set up data sets for you to analyze? That’s amazing. Like, that that
Speaker 2: that that has to be the best.
Speaker 1: Yeah. It’s a unicorn, but it does happen. So hopefully that gave you some time to think about your wackiest story. If not, let me keep talking and when it comes up, just just just let me ask.
Speaker 2: No. Actually, I thought it’s pretty it’s pretty wacky. I’m not gonna get into the specifics just because of agreements I have with prior employers, but I remember when COVID happened, a company I worked for actually had some essential workers, and our team got tasked with trying to model, like, build, like, epidemiological models to see how much COVID would spread throughout the teams that had to work in these facilities. And that was wild. First of all, it was wild in the sense that our models were completely wrong.
But it was also a while in a sense that, like, I remember there was, like, these famous p like, researchers, I think, in Britain somewhere who had built these models of, like, how COVID was gonna spread around the whole globe and that was, like, kinda what went into the the reason why lockdowns were put into price. And we had kind of used those exact same techniques internally, and it was wild just to be feel like you are part of history.
Speaker 1: Yeah.
Speaker 2: Yeah. Like, I don’t know.
Speaker 1: It’s cool.
Speaker 2: It it was it was crazy because, like, I’m not epidemiologists or epidemiologists. And so we probably did everything wrong, but Just to make a plug here, it was probably directionally correct, let’s say.
Speaker 1: Yeah. There you go. Plug for your podcast. But that’s super interesting. I mean, everybody’s I think, got a COVID work story.
I mean, modeling that. So I’m just thinking about, okay, what variables are in the equation? Like, do they have PPE? What PPE are they using? Like, there’s a lot of things like that. Right? And I would we would all hope that that, you know, wearing a hazmat suit would would keep you away from from the COVID thing. It’s really interesting. Man, when COVID started, I I watched so I’ve never done this before, but I watched on this like it’s called like masterpieces series or something. It’s like it’s like academics, there’s There was twenty four episodes of this thing with this woman of several different people, but one one professor of medieval history It
was the entire, you know, entire story of the plague in Europe.
I mean, from beginning at twenty hours, I watch this. I can’t believe it.
Speaker 2: You clearly have more free time than I do.
Speaker 1: Well, I like to watch a little TV in the evenings. I mean, it took me three weeks. It’s not like a binge watch Yeah.
Speaker 2: Okay. You didn’t just ninja all the way.
Speaker 1: And she was knowledgeable. And, boy, we’re lucky first of all that, you know, what COVID was controllable enough for us because that thing wiped out about half the population in Europe, and it lasted on and off for about a hundred years. And so, you know, you never got rid of it. You have these waves. So, I don’t know.
I always like a little perspective. I’m like, okay, I feel pretty lucky that we just got the COVID or you know, I think if we’d had obviously modern medical technology back then, we’d be fine because it’s just it’s boot it’s it’s it’s a known thing with an antidote now, but, you know, at that time. And then there’s a Spanish flu epidemic, which everybody kind of glossed over. You know, that was also, I think, early early nineteen hundreds that killed a lot of people. So we’re all lucky to be on the other side of COVID.
Did COVID change anything about doing people analytics for you at all, like, anything?
Speaker 2: Well, in a kind of a micro sense and a macro sense, yes. In a micro sense, COVID changed the whole game of the focus of people and analytics teams where they went through, like, the whole like, leaving the office and return to office debate. Right? And they had to do supporting and confirmatory and disc confirmatory research throughout to say, where are our employees? How productive are our employees?
If we go back to the office, what is the sentiment gonna be around our employees, how many people are gonna quit, you know, all of that kind of modeling that goes into these type of decision. And a lot of that modeling was actually neglected by organizational leaders, by the way.
Speaker 1: Of course. Of course. That’s part of what we do, isn’t it? I can’t ignore that.
Speaker 2: Yeah. In the in the macro sense, it it thrust people analytics into the limelight. And we were, again, just I I I don’t mean this in a negative way, but we were just a a base sick HR function before that. It gained a lot of momentum prior to the pandemic. But during the pandemic, you know, I know of teams that were meeting with the CEOs every week. You know, and that that is not a typical occurrence for a people analytics team. And so it it really got thrust into the limelight. And I’ll actually say just because the pandemic sort of waned. I think our field of people analytics has sort of had an identity crisis of sorts since then because We were so important for a period of time and our importance is somewhat being diminished now because things are going back to normal. And and I think some teams are struggling with that that they, you know, maybe it’s like a retired athlete.
They used to be at the top of their game. And now Now they’re kinda having to go back and sit on the couch some, and I think I think we’re struggling with that a little bit.
Speaker 1: So you’re saying people analytics is a one hit wonder or something, but but they ignored you. It sounds like some of
Speaker 2: them They say you have your whole life to write your first album, and then you’ve got, like, a year to write your second album. And so we gotta write our second album.
Speaker 1: Yeah. Well, we know it’s valuable. You know, one of the interesting things you just brought up, and I’ve been working a lot with this as I kinda get in more to the to the AI ethics and AI governance, which is fascinating to me and and I’m enjoying continual education on that, but you know, one of the things you look at is psychological safety around the use of AI in the workplace. And you mentioned, you know, monitoring productivity and I know that there’s, you know, if there’s a poster child for what makes you feel insecure in a workplace, you know, it’s being monitored, although Tara, Barron, and her lab have some really great research about, you know, that that I would like to just kinda call out is that Yeah. If the company informs people and gives them a a reason why and they they it’s not some a sneaky thing where, you know, people feel like they’re they’re being big brother a whole lot.
It it really tends to be more accepted, but just in general, I don’t think anybody necessarily likes that. So how are you if you could even say? I mean, how are you guys measuring productivity? Did you have, like, some kind of software or data collection device that was looking at what people were doing.
Speaker 2: So a lot of there’s a you could anything you could think of, somebody probably tried it out. Right? And so it’s hard to say a general theme. I will say, for some of the people I were affected out there that did this effectively is they would find functions where productivity is easy to quantify and then they would do research beside that to show self reported productivity correlated to actual productivity.
Speaker 1: Right. Right.
Speaker 2: And then they would use those measures of self report throughout the rest of the business to say, if we can see this correlation in one part of the business where it’s easy to quantify and we show that there’s that relationship. We could probably logically expand that to the rest of the business. And so I think that’s how some of the companies did it. Actually have really strong feelings on the the whole electronic monitoring employee productivity debate. And I’ll I’ll tell somewhat of I don’t know if this is a pocketful.
It’s just a real story. We actually had a company reach out to our podcast who does this electronic performance monitoring stuff, like the mouse clicking, the eye tracking, all of the like, and they wanted to sponsor us. And I was like, Well, I mean, we’ll take your money, but we’re not gonna be nice to you.
Speaker 1: Right. Right.
Speaker 2: That’s right. I and so they said, oh, no. No. No. We wanna come on because we wanna explain how we’re the good and ethical kind of doing this, and we’re not the evil, like, dystopian, you know, or wellian type of company who’s doing this.
And I said, that’s fine. Bring your data. I promise you. I I have a scientific orientation I will definitely give you a fair shake. But just know, I’m predisposed to think that you’re probably
doing some screwed up stuff here.
And so I I I come down on this stuff very difficult. And the one the reason why I mentioned them because you said, like, is people analytics doing this? The primary customer of this organization was actually IT functions, not people analytics. And that’s why they were trying to get a name in
the people analytics face by coming to our podcast because no people analytics teams were buying their products.
Speaker 1: Interesting. Did you change that forum?
Speaker 2: No. We we ended up not doing it. But yeah.
Speaker 1: Gotcha. Interesting. So one thing that, boy, this is another, like, I need a your There’s things in future episodes I might do again inspired by you. But I need some kind of like a sign or a sound effect that’s like it’s it’s LMM chat
Speaker 2: It’s LMM time.
Speaker 1: It needs to have, like, a a there’s a game show. You bet your life with you know, gotcha marks. I don’t know if you know that or anything about that. It’s it’s from the fifties, I think. There was a he would say say the magic word and the duck comes down, and duck would would come down from the ceiling, and that would be like the, you know, a pee wee’s play house, the word of the day, everybody’s excited.
So I
Speaker 2: I I gotta say this, man. You seem like you would be fantastic to have a beer with like the podcast because you got a lot of interesting stuff I’d love to learn about.
Speaker 1: Oh, yeah. Well, I’m I’m sure you do as well and, you know, we can always make that happen for sure. You can’t do that over the over the ether, though. I I went to some beginning of the pandemic. I went to some virtual cocktail parties with, like, my friends from college. It was great to see them because I haven’t seen a lot of them, but, you know, it’s it’s it’s nice to be able to hug people. Yeah. Anyway, so One of the things, you know, in looking at kind of the what LLMs are doing to help people be more efficient. Right? There’s a whole thing of people out in the workplace using them to get their jobs done faster, not telling other people because they either feel guilty about it or, you know, they don’t they feel like their employer would be upset with them because they’re getting paid.
So I read an article where, you know, it’s just different stories from people and one guy’s like, yeah, you know, I I work remotely I can get my all my assigned work tasks done in in three hours. And the rest of the day, I just goof off because I’m getting everything done that I need to. And so, you know, there’s kind of a little bit of a of a ethical dilemma there. Right? And a dilemma for the company I think if they knew that, they would probably not be super happy with it.
But at the same time, the guy’s getting his work done. Right? So I feel like a lot of people would say, hey, I can do more. So I interviewed a chat GPT for a podcast episode. I asked it a little bit about that.
You know, hey, is that what would you do? How how should companies handle that? And is it a problem? And, of course, nothing that GPT does is a problem in its opinion. But It just said, well,
that guy needs more work responsibilities.
You know, the company needs to give him more stuff to do, but they may not know. So I guess the point is, you know, you could take it either way. You could you could be more productive with it or or less. But, you know, I wonder if you know, maybe that’s another reason why employers might wanna monitor, you know, what’s going on with their employees at at work, especially remote. Employees because maybe there is more time they could give, but but I I don’t know.
That maybe that was a little bit of a noncommcommuter. But these are the modern problems that were starting to have around productivity. And if you track that guy’s productivity, I guess, depending on how you did it. Right? Either you would see that he was logged on to his work computer for eight hours and only three hours of stuff got done, or you wouldn’t be tracking that. You’d ask him how productive he was, or you’d see that his objectives are met. So the data behind that’s very different depending on what the source is. That guy could get away with not getting exposed for a very very long time. But with some monitoring, he might be busted, you know, right away. I don’t know.
Speaker 2: So I I’m not even sure that some of the employee performance monitoring software would actually catch it. What they, again, are usually doing again, if the if the person just, you know, left their desk for hours at a time. They’ll catch that, but they wouldn’t actually be able to diagnose what the person was doing or why they were away from their desk. That the the the software from from what I gather just not that intelligent to be able to say, oh, this person just wasn’t working in one way or another. I will say it does bring up a lot of interesting points on in terms of what does it mean Like, is there like a winner’s curse for being really good at your job? Alright? So presumably, if you’re able to automate seventy, ninety percent of your job. That’s because you were really good at your job. And it’s very a curse that now you have to work even harder because you were so good to automate it whereas maybe your peer group wasn’t as good as you and was not able to automate it. Alright?
But then there’s the other side of it, which is the organizational perspective, which is If ninety percent of your job can be automated, is that even a job anymore? Right? And should automate all of it for all the people who do this type of work. And and I mean, I feel like there’s a variety of different perspectives beyond what I the two I just mentioned in terms of this debate, like, what what should an AI be doing? What should a human be doing?
Yeah. Do we lose our humanity? Do we lose all of our jobs? Like, what what happens here with the outcomes of some of this?
Speaker 1: Yeah. Well, I mean, I think it’s what PT said, maybe that person needs a promotion. They obviously showed some good problem solving skills and some, you know, some applied some good characteristics to figure out how to make their job more efficient. I mean, if everybody did that and companies took advantage of it, I think there’s value that’s that’s created there. And people that have those skills.
Those are transferrable skills, I would imagine. Right? I think there’s classes of jobs that will be eliminated just because they’re so repetitive and and, you know, don’t require people very much, but there’s also kind of you know, the the creepiness factor, the caveman effect, whatever, where there’s certain things you just don’t have the warm fuzziness of a machine. We’re gonna have
synthetic fuzziness Yeah. Soon enough with agents and stuff.
But I did
Speaker 2: I got a question for you, Charles. I I heard this the other day. I thought it was fascinating. Do you know what the number one employee job prior to, like, the invention of the automobile was?
Speaker 1: Elevator operator?
Speaker 2: No. It wasn’t. It was Whaler. Whaler. Yeah.
And the so I guess or maybe it wasn’t the invention of the automobile. Maybe it was invention of, like, extracting oil from the ground or something like that. Yeah. Yeah.
Speaker 1: Got it.
Speaker 2: And so they were using oil for from whales for lamps.
Speaker 1: Yeah.
Speaker 2: And then immediately once oil started being extracted from the ground that was overnight those jobs went away. And I think you’re gonna see something along those lines with some of the repetitive jobs you see now. It’s like, there’s gonna be a time remember, like, people used to be typists. Like, that was like a thing. And like now, everybody knows how to type.
Speaker 1: Well, you know, I I always enjoy going through onet. Right? And one of the jobs in onet, there’s a bunch of jobs. I don’t know if they’ve take an amount that just don’t exist anymore. But I said elevator operator because elevator operator was a job and an important job because the logic of what floors to go to and stuff was in your head?
Well, you know, now there’s there’s circuit boards and all kinds of things that do that. So you might find an elevator operator in a in a place that’s trying to be super luxurious and old school, but, you know, as a host for the elevator ride, probably heard a lot of elevator pitches. Right? Didn’t No. What?
Speaker 2: Nice one.
Speaker 1: Yeah. You know about the the Peter principle. Right? People rise to the level of their incompetence. So what does AI do for that?
You know, we talked about people like making their jobs easier and then, you know, could they get promoted? Is there an AI Peter principal where, you know, incompetent AIs get get lauded and people are excited about it. I I don’t know if there’s an example of that or not, but,
Speaker 2: oh, you see, you’re in a totally different direction than what I was thinking. I was thinking that AIs in conjunction with human beings that actually raised the ceiling of Peters to get higher and higher because they would be more you know, have more power at their disposal using these AIs.
Speaker 1: Ah, very interesting. I like that or, you know, but in an organization where there’s you know, AI agents doing stuff. I wonder if I don’t know. There’s so many offshoots of all this stuff, but let let’s pull it back into, you know, people analytics and our audience. So I think a good
amount of our audience are non IOs.
I’m always strive for that, you know, and
Speaker 2: seven man. That’s what we tried to
Speaker 1: Yeah. Yeah. Yeah. And, you know, sometimes, I worry, oh, we get so esoteric with the IO stuff. But but I That’s just who I am.
You know, I live a dual life of what not really a dual life. I’m a scientist practitioner for god’s sake.
Speaker 2: Yeah. So we’re we’re double agents by nature.
Speaker 1: Yeah. Exactly. And I’ve always been very diplomatic and able to work the the middle ground of stuff, see both sides of it, etcetera. So if you’re if you’re talking to people, you know, that are in talent acquisition, who I would think would be our primary audience, you know, because we’re science for hire, of course. You know, what would you say to those people about a people analytics function, how they could you know, think about utilizing that function because I’m sure a lot of people analytics serve as internal consultants internal center of excellence that that move around to different business units and problems.
So how should an internal, you know, person in hiring think about their people analytics function or the need for one, etcetera. Right?
Speaker 2: Yeah. I’ll say recruiters and servicers don’t just automatically come to thinking about people analytics. But I will say that people who lead talent acquisition are Yeah. Almost always some of the first adopters of people analytics internally because operationally to run their functions, they need to have those analytics, whether it be as something as simple is, like, time to fill or how long a candidate has been in stage, like, you know, all all of those kind of basic type of measures, but even when it comes to things more sophisticated, like, how many people should we hire next year? What is our hire what are our hiring plans and projections? What are the projections of turnover and the gaps that’s gonna create in the organization? And just more workforce planning, more generally, People analytics can make those talent acquisition leaders really shine. And and so I think they are usually some of the first adopters of this type of work.
Speaker 1: So here’s a thought I had. If you walk the floor at HR tech and you said that all those things that you needed, there’d be a lot of people from companies with large booths who would say, oh, you don’t need a people analytics platform for that person for that. You need our platform. We have all that data together, and we have all these dashboards and, you know, why even bother hiring somebody for that? And so, you know, what would your response to that b, you know, I could tell you what mind is, but you have more perspective problems. Can you automate a lot of that stuff you just talked about to wear to our earlier conversation, a a people analytics specialist wouldn’t even be needed because the dashboard would show you all that crap.
Speaker 2: Well, I’ll say first of all, ideally, those platforms could do what they say. But generally speaking, they are not so plug and play that you can just turn them on and it fixes everything. Those those tools just don’t exist. And so you need somebody who can actually run
these tools internally. That’s problem number one.
Problem number two And this is a big and multifaceted problem. It’s just data, data quality, data privacy Yeah. Getting data to merge from one place to another. Mhmm. None of these tools do that.
You have to have human beings who can do that and improve it and make these things work. Kinda to the story you mentioned earlier about every Blue Moon. You work with a team that has all your data in a perfect place and they just give it to you when you need it. That took a lot of work to get to that point. And none of the technologies just do that natively, and so you’ve got to have a a very talented team to get to that point.
Of maturity. And I I don’t see that going away anytime soon.
Speaker 1: Gotcha. Here, I like to think about I I love analogies. I’ve always been good at it. So I’d say that receiving a data set a lot of times is analogous to looking at the street after a mardi Gras parade. It’s cluttered with all kinds of chaos.
And to be able to use the street again. You gotta clear all a crap out of there. It’s not a perfect analogy, but that’s what popped into my head. You know, and there’s a lot of that cleaning out. I do I’ve been hearing and I’ve never tried this, but I’ve been hearing, you know, you can feed a dataset to chat GTP and say clean this dataset for me.
I’m you have to give it more parameters than that. I don’t know if I trust that, but I mean, you know, I think where we’re we’re headed. So that’s a segue into this. And we’re, you know, we’ll we’ll we’ll we’ll start wrapping up soon because we could go on forever with this. But there’s kind of the next evolution of all this stuff that I’ve there’s, like, the CEO.
I can’t remember his name of oh, man. I can’t remember the name of CEO or the company. But he talked about so I can’t give a reference people could look at. But he talked about the future of all this stuff moving into what he calls interactive AI, which is essentially that you have an agent and you say, hey, I need to find an answer to this problem and the agent is running a lot of little models underneath it. It understands how to stitch them together and it gives you an output. Right? So my point is, like, we’re we’re headed toward that, but there’s still that fundamental question of do you trust it? I mean, you gotta have a leap of faith. So, you know, would you trust GPT to clean a dataset for you? Or have you ever ever tried anything like that?
Speaker 2: Yes and no in the sense that I’m trying to I have a lot of these, like, standard, like, go to things I say around these things because I speak on it often, and I just wrote a book that or a white paper that was, like, fifty pages long going people analytics operating models with Jynerv AI. Yeah. What’s the
Speaker 1: need one of these. Every time you have a standard answer, if you have this set up, you just hit the button.
Speaker 2: Exactly. Just automate me. You don’t need me on the podcast. Just automate it. But the thing I’d say is, I’m waiting on these autonomous agents.
Right? Because that’s what they’re called and I like like, here’s the example I use. Oh, well, I’ll talk about the data set in a second, but I want an autonomous agent to manage my calendar for
Speaker 1: me. And
Speaker 2: when since AI has gone live, people have told me over and over again, it’s like, there’s gonna be this AI that’s gonna manage your life for me. And I haven’t seen it yet. And so I am waiting for the day, I can turn over my life to an autonomous agent that can do all the things to schedule my appointments, cancel things, move it around, find things on my calendar, or give me breaks in my day that knows me better than I know myself. I want that so badly. And and so I think there is a little bit pipe in this space.
So, like, oh, that’s right around the corner. That’s right around the corner. It’s like, was the corner a year from now? Is it five years from now? Like, when is it?
Because I wanna see these things and I wanna get past the hype. In terms of the data management example, I would never encourage you if you’re using proprietary data to just put it into one of these open AIs that are out there.
Speaker 1: Right.
Speaker 2: Like, that is
Speaker 1: I think it’s
Speaker 2: a bad bad idea. The thing is and this is this change is occurring quite rapidly. Is a lot of companies are investing in closed AI systems that are accessible just to their employees. I would say if you have that type of system, absolutely try it out, but I would measure twice cut once because it can probably do some things really fantastically and it can probably screw up things royally, and you’ve gotta learn the difference.
Speaker 1: Yeah. So one, again, thoughts here. So if I had an an an AI agent that handled my calendar, it would need to know how to make up believable excuses. For things that I didn’t really wanna do. Right?
So is AI good at that? That’s where you start to really get into the
Speaker 2: Yeah. But then the excuses are gonna go to the other person’s AI agent that is gonna to to show this, like, actually, this is BS. Charles just didn’t wanna speak with you.
Speaker 1: Right. And do you trust it? And then, you know, the I mean, I just think of so I don’t use an Alexa or any kind of personal assistant, you know, I I keep thinking when even when you see the commercials, like, Alexa ordered me more diapers. You know, do you really need that? It’s like a WiFi or refrigerator.
I mean, do you really need a WiFi refrigerator? Come on. So a lot of that stuff to me is just kinda neat stuff, you know, where it’s home automation. Turn on the lights or whatever. I mean, that’s kinda it’s kinda okay.
So so we have that nailed down and, you know, we don’t really need it, I don’t think quite honestly. But I’m sure we will have those type of AIs that do that. It’s just a sophistication and the capabilities they have to manage kinda less scripted, more subtle conversation. So it’s the same exact conversation you have about using AIs to do a lot of stuff. But you’re right about the you know, and that’s another big thing about using these things internally.
And I would imagine something that that people analytics folks would face a lot if if they wanted to use an LLM or their company wanted them to use one or whatever is, is that data security issue. So, you know, I’m sure there are applications. I was just in San Fran visiting with a friend
of mine. I’m gonna have him on the show. He doesn’t do anything like what we do. But he’s got a startup where he’s built a platform that you can run different LLMs on that keeps them contained. But you can use external ones. I’m learning how it all works, but there’s a need in the market, apparently, for this. And it made sense because I immediately said, well, there’s probably some HR technology companies that could build something like that into their product. Right?
So It’s it’s a lot of applications, but it’s it’s very clear that data security and privacy is a guiding force in all AI ethics or any ethics really?
Speaker 2: Well, if if this technology revolution is anything like the prior ones that have happened in the last twenty years, HR will be the last one to adopt it.
Speaker 1: Yeah.
Speaker 2: So
Speaker 1: Yeah.
Speaker 2: You know, I I look forward in other places first, and then HR will get it as the last
Speaker 1: Yeah. I I would agree with that. You know, we don’t need to even start going into that whole thing. But, you know, people analytics transcend HR? Do people still? Do you sit within HR? Obviously, that’s people. But, like, is there a hope that people analytics could rise above the stigma or whatever Yeah. Above HR.
Speaker 2: Oh, I I am very much in this camp. I’ve talked about this a lot before, is people analytics needs to ascend in the sense that right now, when they talk about people analytics not being a part of HR, they just mean, well, let’s put it in IT or something like that. And that’s usually a horrendous answer. That’s that’s not usually very good. But what I mean is perhaps in the future HR is people analytics.
Right? And that’s all HR is. And I actually am very I think that’s an exciting future. I think that that’s a probable future. And and the reason why I say that is because If you’re using AI, you’re using data and you’re training models and to to automate what HR does, what function it’s HR is most likely to be the most progressive and the most use like, that’s used to doing this type of thing on a daily basis.
The only one that’s even close is people analytics. Therefore, who’s gonna be driving the ship on this transition is going to be people analytics and therefore who are gonna be the leaders of the future. I think it’s gonna be people analytics. And so I think that not only will people analytics be a like, usually right now, people analytics just gives recommendations to other people and they make those decisions. In the future, I actually think people analytics will be the decision maker themselves.
And I think that’s an exciting future.
Speaker 1: And that is an exciting future. And I’m doing something right now. I’m multitasking. I’m asking chat GTP to tell me a joke about people analytics.
Speaker 2: It probably
Speaker 1: does fine. It’s really good. No. It’s corny, usually. But and it was also over taxed. The server was over taxed. This morning and I wasn’t able to get through. So let’s see what happens. Tell me a joke about people analytics. Okay.
Are you ready for this? Or Oh my gosh. That’s really a horrible joke. I’ll read it. I’m a ask for another one.
Why did the people analytics team get excited about their latest report?
Speaker 2: I don’t know. You tell me.
Speaker 1: Because they finally found out what everyone in the office was thinking, they all wanted a coffee machine in the break room.
Speaker 2: Wow. I’m hilarious.
Speaker 1: Yeah. This one is that’s, like, ridiculously not even funny at all. This is the last one I promise. Why don’t people analytics experts play hide and seek?
Speaker 2: I don’t know. You tell me.
Speaker 1: This one’s making me chuckle. Because because good luck hiding when they’ve already predicted your next five moves.
Speaker 2: I think that’s what people worry about for sure without you. I don’t think we’re that good.
Speaker 1: That one’s pretty good though. It does come up with some good ones. So cool. Well, look, leave us with some parting stuff. I always open the door for for promo, and I’m gonna I’m gonna pump your podcast again too as a template for fun and exciting educational podcasts are actually correct.
But beyond that, what do you wanna leave our audience with?
Speaker 2: Yeah.
Speaker 1: So they walk away with a smile and thinking, man, cold napper, what about it?
Speaker 2: Well, I mean, yeah, you know, if if you’re interested in anything I said, feel free to add me on LinkedIn. Definitely check out direction correct. But I would say, listen to Charles. Science for hire. Yeah.
You got he’s he’s literally and metaphorically gonna be on a rocket ship. I’m excited to see where you’re going with this Charles and you’re obviously a really interesting and eclectic dude, so I’m a fan.
Speaker 1: Cool. Well, thanks. So likewise, good stuff. Well, Until next time, bid you would do. Speaker 2: Absolutely. Thanks. Thanks for having me.
Speaker 1: As we wind down today’s episode to your listeners, I want to remind you to check out our website rockethire dot com. And learn more about our latest line of business, which is
auditing and advising on AI based hiring tools and talent assessment tools. Take a look at the site. There’s a really awesome FAQs dot commit around New York City local law one forty four that should answer all your questions about that complex and untested piece of legislation. And guess what there’s gonna be more to come.
So check us out. We’re here to help.