Is People Intelligence the Future? Unpacking a Manifesto - Cole Napper & Alexis Fink - #179

Speaker A: Hello, friends of the podcast, and welcome to Directionally Correct, a People analytics podcast with your host, Cole Napper. We will switch things up today, where I will be interviewed by today's guest, Alexis Fink, founder of Propeller Insights and co founder of the Data Driven HR Academy. In this episode, we will cover what People Intelligence actually means and if it represents a genuine evolution beyond People Analytics.

Speaker B: So what I'd love to do is have you talk a little bit about the distinction between People analytics and People

Speaker A: Intelligence, the People Intelligence manifesto that I wrote, and why I feel the field needed it right now. It's just the beginning of what we're doing. And really, all of the impact that we can have as a function really comes after the dashboard, why People analytics is decidedly not dead. And where Alexis sees the function heading from here.

Speaker B: And so really making sure that you understand the attributes of your data, the, uh, meaning of what you're trying to do, defining that meaningful question, that is a hard thing.

Speaker A: This is going to be a special episode. Now let's get down to business. Hey, Directionally Correct fans. This podcast is dedicated to you to help democratize People Intelligence for the world of work. If you're looking to support the podcast, please make sure to listen weekly. Subscribe to the Directionally Correct Substack newsletter. Sign up for the data driven HR Academy at datadrivenhracademy.com Purchase Cole's book People analytics, or check out everything else@colegnabber.com before we get into it, a quick word about HR bench the company powering this podcast. You know, when we all started in People analytics, we wanted to do strategic work building predictive models, workforce planning, advising the C suite, and most of all, quantifying the impact for the business. Instead. Instead, we spend months building dashboards and reports that should already exist. HRBench eliminates that entire phase. Your HRIs connects, metrics calculate, your benchmarks, populate. This is not novel. This is day one, not quarter two. That means skipping straight to prescriptive analysis, storytelling, and taking action for the business. Want to learn more? Book a demo@hrbench.com Directionallycorrect find out more about the company powering this podcast and building the future of people Intelligence. As always, all opinions are our own. And thanks for being a listener. Well, Alexis, thank you so much, uh, for joining me today and agreeing to be a guest host. I know you're doing kind of, uh, you've done in the past with Intel. I think you hosted a podcast there, and I think you and friend of the podcast, Steve Hunt, have kind of your own thing going on now. But I had kind of a special topic today, and I needed someone to be a guest host. And I could think of no one better than you, especially since your old team at Meta was about as close as what I could agree to being. What is People Intelligence and, uh, my definition. So I wanted to talk about that today, but I want to turn over the reins to you to be the guest host today. So thank you so much for joining

Speaker B: the podcast again, Cole, I am so deeply honored that you would invite me and trust me to lead this conversation. So the first thing I want to do is start out by asking you a little bit about your new job. You're doing some exciting things. I'd like to hear about them.

Speaker A: Sure. So I recently joined, uh, HR Bench as the Chief People Intelligence Officer. Obviously I do the podcast. I have a lot of other extracurriculars, but, um, HR Bench is kind of my primary focus. And, uh, it's been an exciting time. So, uh, I don't know if I've shared this before on the podcast, but, you know, HR Bench actually sponsored an episode last year, and after that episode, uh, they, uh, they showed me the product and, and I had a chance to kind of poke around and see what they were working on. And I had this, this kind of illuminating experience where they were showing me what they were doing and they're giving their reasoning behind it. And I was like, guys, you know, this sounds like exactly like the kind of stuff I say out in the world. And. And they mentioned to me, they were like, yeah, we know. We follow, like, everything that you do. And I was like, oh, okay, this is amazing. And so kind of fast forward to join the organization. I believe it's the only Chief People Intelligence Officer out there. And I, I put together kind of this thesis about what is People Intelligence, what why it comes, you know, is kind of the successor ideology to People Analytics. I. I don't hate People Analytics. I don't think that People analytics is dead. We'll get into that probably here in a second. Uh, but I really think it's an opportunity to kind of build the future. What the function is and how we bring together data from all these resources and just frankly, have some fun with it. But I'll pause there. Uh, what was your reaction to it? I guess, when I joined.

Speaker B: Okay, so what I'd love to do is have you talk a little bit about the distinction between People analytics and People Intelligence. What's really the difference? And why is that new framing of, uh, People intelligence going to matter?

Speaker A: Yeah, I, uh, kind of use a like myopic viewpoint about what people analytics is. Obviously there's functions out there that are doing amazing work, but many of the folks that I talk to, especially that are kind of in that mid market, you know, 5,000, 10,000 employee and less. People analytics was just a synonymous definition of dashboarding and reporting. Really. That's all it meant. That's usually what in the scientific fields we might call descriptive, uh, analysis is. Descriptive statistics is the core of what was being done. And usually that's frankly only backwards looking in nature. So you're getting to what you might call a shared set of facts about what happened in the past. And that's just kind of one pillar of what I coined this term, the tree of value. So there's these functions, there's people analytics, there's talent intelligence, there's workforce planning, and there's behavioral science, all of which are different branches of the same tree. They come from very different origin stories and so they don't necessarily play well together. But people analytics is kind of the internal viewpoint in the organization. Talent intelligence was the external viewpoint. Workforce planning was looking at the supply and demand dynamics of what's going on with the workforce internally and externally. And then behavioral science was taking kind of the wisdom that we knew from things like iopsychology, behavioral economics, organizational development, and applying those methodologies and using data at its core to change an organization. And in my opinion, people analytics was just one component of that greater whole. And when I wrote the tree of value, uh, I was really trying to say, hey, we should all come together and do things, pool our resources, pool our technology and really start to collaborate more. Because at the organizations I had worked at, there wasn't a whole, heck, a lot of a collaboration amongst those functions sometimes. And I found that to be personally frustrating. But in the moment that we're in, those functions are actually being collapsed and it's because of AI. And so I say, what, what is the logical end state of this collapse? It's a singular function. Instead of having four branches of a tree, there's a single branch or is there a single trunk of the tree? And that trunk to me is people intelligence, where you have a singular data layer that is pulling all of your data from all the internal and external resources and then a singular intelligence layer on top of that data. And that intelligence is going to look a little bit different than perhaps it has in the past when you were just focusing on analytics or just focusing on talent intelligence or workforce Planning or behavioral science.

Speaker B: You have a recent piece talking about this, really updating what you put in your very excellent book last summer. Just a few months ago, you kind of take a saucy post that we should never build another dashboard. I'd love to hear you talk about what makes you say that. And as you note, a lot of people analytics teams are really just dashboarding teams. So how should people answer when their clients ask them for a dashboard?

Speaker A: Yeah, I mean, I think, uh, first of all, to your point about the book being published less than a year ago, I think it's pretty rich to have a book come out and then say, hey, you know that thing I wrote less than a year ago? It's completely out of date. And, um, the reality is it's not out of date because there's sections in the book. Uh, it's really split into three areas. There's a strategy section, which I think is pretty evergreen. I think I reference a lot of your work in there, so I appreciate you. There's a section about generative AI. Frankly, a lot of that stuff gets dated quite quickly just because the technology is changing so quickly. And then there's this third section, which is how to win and how to strategically take the changes that are happening with technology and apply them to your workspaces. And I think that that is still very, very relevant at this moment. And frankly, some of the concepts related to people intelligence are very salient from that section of the book. But all that to say about what you said about the manifesto and never building dashboards again. I've felt this way for a long time. That, uh, I just feel like every team there was like a ceiling and a floor to people analytics. And I always thought that building dashboards was kind of the floor of people analytics. It's like everything we do is beyond this. But a lot of folks, and I talk to many, many people out there from many shapes and sizes of teams and organizations and industries. They all, uh, oftentimes saw dashboarding as the ceiling of people analytics. That was the best thing that we could ever accomplish. And to me, that was just so fundamentally disappointing. And so I had always been kind of, even before AI had been rooting for a day that we could get past this notion that dashboards was the ceiling of the function. And lo and behold, AI has gotten good enough now that really it can build all the dashboards for us. We don't ever need to build them again. And with products like HR Bench that are out there, not only can they spin up all the Dashboards that you would ever need. But again, you can ask AI agents to help you understand the questions and never even need a dashboard. And if you don't choose to need it. Right. And I think that that's, that's really an exciting, like, to a lot of people that's scary because they look at it as like, oh, dashboards were kind of my, my little like comfort blanket that keeps me warm at night. And I always saw it as like, no, it's just the beginning of what we're doing. And really all of the impact that we can have as a function really comes after the dashboard, not in the process of building it.

Speaker B: Excellent. Well, that dashboard point was the first one in your manifesto. And I think the manifesto is good enough. I actually want to do something a little unusual and read that out to you and to the listeners. So please bear with me while I read your own writing back to you.

Speaker A: I'll try not to.

Speaker B: Which is a universally painful experience. So just hang on. Here is your manifesto. This is what I believe. I believe we should never build another dashboard or pull another report ever again. I believe people analytics has been treated like a luxury item when in reality it's been a commodity for quite some time. I believe people analytics has always looked in the rear view mirror. People intelligence looks at the road ahead and drives business value through better decisions, sharper intelligence gathering and faster action. I believe all intelligence in HR is collapsing into a single function called people intelligence. And the overall HR operating model is consolidating. I believe people intelligence is flattening the tree of value into an intelligence layer and a data layer, and that's all there is. These layers, in conjunction with human effort, will drive value. I believe if all you know how to do is count things, then your work is already completely automatable. I believe there will be winners in this space. People intelligence work is exciting. There's a bright future for those who are early to change and adapt. I believe you'll never see another hundred person analytics team again. A, uh, people intelligence team will be nimble, technology forward, AI native and cost effective. We're almost done. We got two more. I believe the ability to pay for people intelligence team 10 times over is too low a threshold and it should easily become 100x if done correctly. And finally, I believe none of this is controversial. And these sentiments are already largely shared among most leaders in the field. Those leaders are only worried about saying them out loud. So I will say it for them. So there you go. I recognize it's weird to listen to your own words, back to you. But I'd love to hear you talk a little bit about this. There's some really spicy stuff in this. And also for those of us who have been forward looking for some time, it's exciting to have kind of a public conversation about this.

Speaker A: It really is. I actually want to start with that last point first. And yes, it is cringeworthy, uh, to hear your own words repeat it back to you. Um, but I talk to leaders in this field all the time, just like you do, Alexis. And we would be having these kind of like whispering conversations about. Are you seeing what I'm seeing about where the field is going? Um, oh, you know, and I think what everybody was really looking for was somebody to come out and validate this notion publicly so that they could kind of rally behind it. And I'll say, I mean, I think it's still early days, but I've already seen like probably seven different leaders globally already change their title from People analytics to People Intelligence just after this manifesto. So like, I think there's some, something's happening. Right. But uh, going back and you and I have talked about this a lot. We did the uh, the uh, session at S earlier this year about paying, you know, the ROI of People analytics and why it's important. ROI is a simple calculation. If you, you control the numerator, the denominator. Right. And what we care about is, especially at HR bench is how can you do all of these advanced capabilities and collapse again, that intelligence and that data into, into singular layers for HR is why can we do this like in a really, really commoditized and cost effective way so that when teams come along instead of again aspiring, which I talk about, uh, a great deal in my book about aspiring to pay for yourself 10 times over. What if you could pay for yourself a hundred times over, wouldn't that be great in this moment? And I think tools like HR bench make that capability not just a possibility, but a reality. Right. And then I think about, you know, the, we, we talked earlier about the tree of value flattening, but I, I, I, I, again I, I just came back from a conference in Costa Rica yesterday where I was talking to, you know, 300 professionals that are doing people analytics and HR work all over Latin America. And I started my talk out and I said like, how many in this room are people are worried about their future job prospects because of AI and how it's changing people analytics before eyes. And many people in the room raised their hand, they were worried. And then I Gave my talk and I said, I, uh, want you to hold this thought until after my talk is over. And then I want to kind of revisit this from a pretest post test perspective to say at the end of this, are you going to be more excited about the future? Are you going to be more worried? Right. And, and the reality is I, uh, what I tried to do is to give them the exact skill set, tool set and mindset to say, you're in the driver's seat. And if you become an early adopter here, not only is this not scary, not only do you not have to worry about your job security, but the prospects for the future are super bright. And every HR team, as far as I can tell it, every organization is saying, who's going to be the leaders of this transformation? And if People analytics and other teams like workforce Planning and Talent Intelligence step up to the plate, they have a really exciting future. And it all it's about changing the mindset to say we want to be the early adopters, we want to disrupt ourselves, we want to be the leaders of the future. And if you adopt that mindset, it is quite empowering and quite exciting.

Speaker B: You know, Cole, one of the themes in People analytics over the last, call it 15 years, since it got cool, was there are lots of entrants from other disciplines, in particular data science, hard sciences, et cetera. And one thing that strikes me about this moment is it really re emphasizes the importance of the IO psychology roots, the people who deeply understand how work happens, when what motivation looks like, how these things come together. So I wonder, are you seeing something kind of similar with IO, uh, psychology reemerging now that we're transforming work, not just building systems to count things?

Speaker A: Correct? Yeah. I didn't get to it too, in too much detail because I feel like I could have written a whole nother book about what is people intelligence and what role do all the branches of the tree play. But I can't tell you. I know you see this as well, how many times we're talking about how we reconstruct work. Right. Wasn't job analysis like the most boring topic for like the.

Speaker B: I loved job.

Speaker A: I know, I love job analysis too. But it was like one of these things where we came up with a methodology in like the 1980s and then we just kind of lived with it, uh, until, like for, for decades. And the reality is, for the first time in a long time, topics like job analysis, rearchitecting, work org design, job architecture, all of the data that goes into that is exciting again. And it takes that IO psychologist lens and it really pushes it to the forefront for organizations because again, you mentioned people are coming in from all these other disciplines into the function. Data scientists don't know anything about job architecture. Uh, I guess you could say compensation professionals probably do, but there's not that many folks in compensation that moved into analytics over the last few years. I think it's a really exciting time and for other disciplines to learn from iopsychology, to really adopt some of those methodologies. But as long as we're kind of staying with the times and really being again, leaders in this space, I think it's really exciting for IO psychologists. But all of the analytics functions combined.

Speaker B: You argue for getting ahead of disruption. That's the talk you just gave in Costa Rica. That's a theme in a lot of your conversations, and it's definitely a theme in this manifesto. What are your some of your favorite tools for you personally for building and for managing your own work? Well, not just what you're doing for organizations, but Free was a professional.

Speaker A: Well, I'll say this on a personal and professional level. Um, I've started kind of combining my lives. So I always say I had kind of like three lives. I had like my personal life, I had my, you know, personal business life. So directionally correct. The newsletter, Data Driven, HR Academy, all the different things I'm doing kind of in extracurriculars. And then I had my work life in terms of like HR bench and some of my prior employers. And I had always had to keep all three of those lives siloed away from one another. And that was quite stressful and just took a lot of time. And administratively, it just was very challenging to just run your life in that way. And recently I've adopted notion Claude Cowork and then all of the tools from the G suite and, um, used a lot of automation to combine those three into one. So that I have a kind of a singular life that's based on a lot of notion databases and all the automation that's created through Claude Cowork that has really just frankly made a lot of the administrative parts of my life that I used to hate and dread. Actually kind of fun and interesting because I get to build them. It's kind of like I'm an artist creating my own work future, if you could say. But one of the things, yeah, work tapestry, let's call it that, and it's been quite exciting. Um, I would say on the work front, you know, one of the things that I hear about all the time. And why I mentioned the thing about disruption and getting ahead of the disruption in, in the manifesto, I think I mentioned it a few times. Is build the thing that you're worried about getting disrupted by, Right?

Speaker B: Yes.

Speaker A: And so people would come to me and they say, cole, why would you join a software company when software is going to be dead? And that's a very provocative question. And people talk about the SaaS apocalypse and things like that. And, uh, I would just say to them, I was like, look, if you're worried about AI coming along and doing something that you do, why don't you just build it before AI does? And why don't you sell it at a competitive price point? Again, it's a commodity, not a luxury. So that organizations can go and buy this thing and then they don't have to do it themselves. Because, you know, I don't know about you, but most organizations already stretched for resources. They're being asked to do more with less. They don't have the time. And frankly, a lot of times they don't have the will to go out and just experiment. Because the stakes are too high, right? The stakes are too high with the privacy, the security, all the data infrastructure that you have to have in place. It's just one wrong move. And you showed up on the front page of the New York Times, and not in a good way. And so I just think that it's a really critical time for people to be proactively disrupting themselves. And that's the mentality we have at hrvances. Can we build the product that we're worried about disrupting us before anybody else builds it, and then we're in the free and clear. And I think that's just an exciting mentality to take that any people, analytics team, or any other of these functions that we talked about today could adopt for themselves.

Speaker B: You know, for about 15 years, I've said that I love this work, not because I want to predict the future, but because I want to be able to change the future. I want to see where something is going and I want the ability to bend that curve. And what you're describing is exactly that approach to the world. You can do that strategically, as you've just described, with, hey, I can see automation coming, let me disrupt myself. Uh, you can also do that in the flow of our work, playing in people or talent, intelligence. I can see this outcome is going to happen. I now have done the research to understand the drivers that are making this thing happen. And so I know which levers to pull to make a different and better thing happen. And it's such a nice coherence between the work that we do and the way you're describing how we should think about the work, uh, at scale. So that is just really delightful.

Speaker A: Well, can I give a quick aside here, just because this is such a delightful experience for me to have someone I admire so much join me and do this. Like, this is like, ah. In my. In my estimation, it's the ultimate form of gratitude, is to put somebody and let somebody else shine. And so I really appreciate you doing this, Alexis, and I've really appreciated doing the Data Driven HR Academy with you. You know, some of the things I mentioned about Notion and Claude co work, we're all kind of in that how do you develop the skills of the future to kind of transform yourself? And that's one of the things that we're, you know, actively. We've done together with me, you, Gina Tavis, and Sonali Kumar. The Data Driven HR Academy is an excellent opportunity to kind of build those skills. And so I have just enjoyed so much getting to spend time with you over the last year or so. And so I just. I'm incredibly grateful for you being here and, uh, willing to do this today,

Speaker B: Cole, it is genuinely a treat and a delight. I always love my conversations with you, and at the risk of boring all of the listeners, uh, I'll just share how much I appreciate your scrappiness, your deep intelligence, your lovely humility. Usually you are the host and you don't get to shine in your own thinking. And so just listening to you in this format has been really fun. So thank you for giving me this opportunity, uh, to give you space to really help the field move forward.

Speaker A: Absolutely. Well, thank you so much. And I just. I enjoy the heck out of you, Alexis. I hope you know that. Do you, um. Is it time for Cole's Corner, or, as you were calling it, Alexis's Alleyway?

Speaker B: Yeah, I didn't want to be on a corner that didn't sound like something my mother would approve of. Uh, but, yeah, let's switch gears.

Speaker A: Welcome to Cole's Corner. All right, let's. And I think I'd love to bring you into this conversation because I think your viewpoint is just invaluable. But since you've been a guest on the podcast recently, I don't think there's any need to do any kind of, like, rapid fire, all that kind of stuff, because we've done it. And so I'm going to skip straight into what Am I reading? So let me share my screen really quickly. The first one today, and I suspect that you have thoughts on this, and that's why I brought it up, is a recent article published in Human Resource Development Quarterly called what are we Learning about the Research Practice Gap for from HRD Scholars and HRD Scholar Practitioners? Uh, by uh, Kelly Moore and some other folks. And this was summarized by, uh, uh, I guess you could say friend of the podcast, Cheese. Cheeseman. Because I don't know if this is a real person or an AI bot, but they published a lot of really good stuff out there. And so, uh, basically what Cheese, uh, shows, uh, that they found is that the gap, uh, between scientists and practitioners isn't really about people being lazy. It's more like academia and the workplace are two completely different countries. They speak different languages, move at different speeds, and care about totally different things. Researchers are chasing tenure and getting published in fancy journals that nobody can access without paying. And practitioners are just trying to solve real world problems before their next meeting. And so the study found that there's three main reasons this gap keeps growing. Academic incentives are pushing researchers to write for other academics, not for people doing the work. There's a jargon problem that I think we've talked about on this podcast quite extensively and then scholar practitioners. So people who do both research and real work are kind of a secret weapon that nobody's fully using yet, and they advocate for this really strongly. And so they said the fix isn't just writing, more simply, it's about actively trying to co create research between practitioners and researchers from day one. So, uh, I'll stop there and I want to get your thoughts on this article and just your perspective more broadly on um, the scientist practitioner gap.

Speaker B: So first of all, the scientist practitioner model has been the one that has fueled my whole adulthood. Right. So I love that you were highlighting this article and this has been one of the key themes that has fueled my entire adulthood. I flatter myself to think that I am really in that scientist practitioner model. But the article, and the main article it refers to is exactly right. Academics are rewarded for really different things. And in fact, if, uh, you pay attention to this, you may have seen the recent announcement that MIT Sloan Management Review is being shut down. Oh, and part of what, oh yeah, their last issue is coming out in October. They're going to move to like a social media strategy. And one of the things that broke my heart about it is it's uh, a major vector that the practitioners get,

Speaker A: I mean, behind Harvard Business Review, that Was like the second most popular practitioner read scientific journal.

Speaker B: That's right. And you know what? Sloan articles don't count for tenure review. And so, right. People who published there did it out of a desire to help, but not because it was going to help them professionally. And that's just a really screwed up, a, uh, really screwed up, um, reward system. And then you're right, the jargon problem, that one is less, less painful to me. But the reality is that it takes an awful lot of work and intention to bridge those gaps. And when you've got universities, uh, and faculty getting really squeezed, there are fewer and fewer tenure track positions. Grant money is harder and harder to come by. You've got practitioners who can barely keep up with their own email inboxes, let alone read the literature. Uh, and then you end up with well meaning academics who are like, I want to do real research. And then they come to an organization and they kind of behave in an extractive way. This is my theory that I want to test on you. Or practitioners will go to an academic and say I need cheap labor. Uh, or in the case that I usually had to sit in, I desperately wanted to work with leading, um, thinkers in an area. And what I would get back is, well, we can't share our data. Well, we can share our data and have them do it, but they can't publish it. And either I can't pay them or I can't pay them enough. And publishing is the reward they're getting for it. So the reward structure and the constraints on our side as practitioners are limited as well. There are a couple of organizations that do this. Google for a long time has had sort of academics that would come and do basically adult summer internships. Yeah, they come and do like a fellowship, um, and then Stanford will have practitioners, for example, come and spend time there. But the Silicon Valley context is a really unusual one. Um, and uh, and so it is, it's one of the things that I find really, really vexing. I will do another little commercial for psyop. It is the best science practice forum that I'm aware of. But even so, you have to kind of work like you have to get out of the things your friends are presenting and go look at something being pushed out by the academics. If you're a practitioner and if you're an academic, you have to kind of get out of your comfort zone and go sit at a panel discussion that maybe you think they didn't prepare enough for because they aren't presenting papers. Uh, and look through it. There Was, uh, this summer a second employee listening event. UT Austin hosted it. And it was very intentionally science and practice. The, the gap was really obvious in the way people even thought about what listening meant. So that was fascinating, but you have to really work these days to bridge that gap.

Speaker A: Yeah.

Speaker B: Having said that, I think that the people who do bridge that gap. And speaking specifically to engagement in the practice context, whether you're coming as an academic or you're a practitioner, you also have to engage in some science practice. Translation. Right. You have to translate the jargon into something people understand. As much fun as it is to like drop big science words, um, they tend to be quite off putting. Uh, and so helping people, not just, not just bringing the concepts in, but applying them and making them understandable. Those are actually all three different skill sets. And, and you have to develop some competence. Maybe not full mastery, but competence at all of them. Sending. I've, uh, I've done it a lot of times and my success rate is in the low, like low teens. Like sending somebody a journal article doesn't work that well. No, you can. I've done it occasionally, like I hit the, the like nerd jackpot and somebody loves it. But usually it's like, look at this one figure and let me tell you what it means because it's really important. And uh, that's about as far as I can get with, with um, people in the rest of the organization trying to live in these two places.

Speaker A: Let me, let me build on this for a second because I kind of have two points I want to make. One is, and I know correlation is not causation, but, uh, just to the point here about one of the authors of this article, I looked them up on LinkedIn and they had the green open to work sign. So I don't even think that there's necessarily an incentive for writing the article that they wrote.

Speaker B: Um, no, there's not.

Speaker A: And so I want to say, first of all, I'm extremely appreciative for them doing it because again, to the point about incentives, I think it's actually against writing what they wrote and that might have ended up hurting them in their career. And I hope that's not the case though. But the second point, and you touched on this briefly, where you said, you know, uh, an academic reaches out to a practitioner about partnering, and then they come in, they, they want to test all their wacky theories rather than doing something that's actually relevant to the business. I've had that experience quite a few times because I used to even make open calls on this podcast. We're like, hey, if you're a, uh, researcher, reach out to me. I've got data. I want to partner with you, but it's got to be mutually beneficial. And the mutually beneficial part is the part that always got hung up on. And frankly I never got a good outcome out of it. It always broke down at one point or another. And so the question I have for you is, have you ever been able to navigate this effectively in your own career? Because frankly, I haven't.

Speaker B: So I've had similar frustrating experiences. There are a couple of um, outliers that were great. So in one outlier, uh, shout out to Kevin Murphy, who I completely adore. I was doing a bunch of competency work with a very science forward group of folks. Um, Mort McPhail is still mad at me for having Kevin Murphy basically be the umpire in our work. And so I had Kevin Murphy come in kind of as ah, um, to pretend to be opposing counsel. And we could stress test everything we were doing in this really large actually job analysis and competency modeling product. So that that's clearly faculty member was super pragmatic. It was great. The other one that stands out, that was really terrific. It was a group of researchers at Berkeley and they said, hey, what can we do? And I said, actually there's one thing you can do for me that I really need because I was doing a whole bunch of career mobility work at the time, said here is 10 years of anonymized, um, internal mobility data. Basically every move everybody took looked over a lot of information. And I said, and here, the, here's the hypothesis I have that I want you to test for me. Um, I think that staying in a career stack, um, is sort of immediately beneficial but longitudinally detrimental. Um, and so they showed us with our own data that if you stay in your own narrow stack, you get to director faster and you never get past it. Whereas if you move around and you take what we were calling esoteric career moves, erratic career moves, then it took you longer to get to director. You took some laterals, you took some weird side quests, but you made it to VP or above. So the, the demands of uh, a director little job versus the demands of a VP job were different and you were a lot more less risky to the organization when you were cheaper. And we use that to sort of inform our career philosophies. We used it to inform some of our other like programs to people, et cetera. So the, please be really mean to me that Kevin Murphy did for us. And the. Please validate this very longitudinal hypothesis. That's going to be very difficult from a data wrangling standpoint. Like the math wasn't really that hard, but getting the data set structured in a way that you could normalize it, really test out the hypotheses was. It was a lot of like data rodeo. And, and so having those researchers do that, that really helped. But, but that was very specific. I had a clear need. They were clearly curious about it and willing to help. And I've had probably 30 projects that were frustrating or went nowhere. So it's definitely it. It's possible, but unusual.

Speaker A: Yeah, I, first of all, I love that and I really appreciate you sharing it. Second, the, the thing about erratic career moves, I, I resemble that remark.

Speaker B: Yeah.

Speaker A: And I think uh, it at the time when you're doing the erratic thing, people look at you crazy and then, you know, five, ten years later they ask like, well, how did you get to where you're at? And I was like, I made a mini erratic career move. So, you know. Yeah, I wouldn't necessarily recommend it sometimes, but it is, ah, it does pay off sometimes as well. Well, um.

Speaker B: Yep.

Speaker A: Well, let's do the next one. And so before, while I'm pulling this up, I'll kind of tee it up because in the manifesto I talked about, uh, a few articles that were written lately. One of them by you, uh, called People analytics is Not Dead. There was another one that we'll cover here in a second, um, as context. And then there was a third series, a two part series by Yuan Son and Colby Nesbit about People analytics is having a midlife crisis and they're all kind of scratching at this same itch of we all feel like the ground is shifting beneath our feet. And some people are prescribing that this is the end times, and other people like myself are prescribing this is just the beginning of a new exciting journey and kind of everything in between. But you, you wrote an article here in your substack, which I think is amazing by the way, called People analytics is Not Dead. And there's three key points and I'll let you take the reins. But it says privacy and confidentiality requirements mean that most data isn't available broadly. So therefore organizations are going to still need People Analytics. Defining useful questions and identifying appropriate analyses is harder than it sounds. Therefore People analytics is still necessary. And the last one is the role of research and metrics in creating a shared understanding is very important. Therefore People analytics is still relevant and not dead. But I wanted to kind of, uh, first of all get kind of your perspective for writing this one and then bring some more color because I gave a very cursory overview of your article. I want to take a break real

Speaker B: quick just to let you know about a new show we've just added to the network. Up next, at work, hosted by Gene and Kate Akiel of the Devon Group. Fantastic show. If you're looking for something that pushes the norm, pushes the boundaries, has some really spirited conversations.

Speaker A: Google.

Speaker B: Up next at work, Gene and Kiel

Speaker A: from the Devon Group.

Speaker B: Yeah, thank you. And I appreciate, I'm glad that you enjoyed it. Um, the first one is sort of the, uh, feels almost protectionistic, but there's so much of the information that for legal reasons, for ethical reasons, can't just have the barn doors flung open. Right. We have to protect employee information in ways that we don't have to protect server uptime information. It is fundamentally different. I can't be sharing your Social Security number, I can't be sharing your birth date. I can't be sharing your latest performance review with just every person who might be interested. So that means that I need to take some. I need to have some small, contained, managed group of folks who have access to those kinds of pieces of information that might be relevant for whatever other research you want to do. The other one, you know, Keith McNulty, one of my favorite people, um, published a little table, uh, on LinkedIn recently, which you might link in the show. Notes were like, hey, if your data has these attributes, this is the flavor of correlation that you need. And it just made me smile because I remember in graduate school really getting distressed over all the different types of correlation. In my high school stats class, I figured correlation was correlation was correlation. And it turns out that's not true. And if you are asking, um, a machine, what should I do? It doesn't know if two represents a number. It doesn't know if it represents just a category. It doesn't know what the attributes of your data are necessarily. So it doesn't know what type of statistical analysis would be appropriate and give you an answer that has meaning. Um, I remember years ago somebody sent me, um, because I'm a nerd, sent me a picture of like a sign from some little mountain town. It was like, ah, established in 1732, elevation 12,000ft, population 71. And at the bottom it had a sum. And I'm like, those are all numbers. You could add them, but they don't have any meaning. And so really making sure. That you understand the attributes of your data, the meaning of what you're trying to do. Defining that meaningful question, that is a hard thing and it does require a little bit of expertise. I have a play with these tools and occasionally ask them maybe trick questions. And so far they're still flunking most of them. So that's the second one. Then the third one is the one where I'll push back just a tiny bit on that. We'll never create another dashboard because there is an important role for people analytics and for our functions in creating shared meaning.

Speaker A: Mhm.

Speaker B: And creating a shared version of the truth. Um, I used to say that when, uh, I ran the employee listing program at Microsoft a long time ago, my first deck of Results had over 10,000 data points in it. And I had about a day to figure out what were the six that we were going to tell the board of directors. We created shared meaning. We decided what was going to be knowable by a group of people who were going to make significant decisions that affected a hundred thousand people. Right. That shared meaning responsibility is actually quite important. And when people complain m about an off by one error, they're complaining about the fact that I might have a different version of this, of this shared meaning than someone else. I think this is the piece when I use an analogy of global cancer rates and defining the correct question and knowing who the audience is being able to tell what makes sense. If I'm recalling it correctly, there are different ways you could ask that question. The first would be how many cancer deaths are there in total? Is cancer going up or going down? If you look at 100 years or so, there are more cancer deaths. So cancer must be going up. And if you are a chemotherapy manufacturer, that's important information to have. You have to know what your total addressable market is. But if you care about, you know, public health, um, you might divide that by population and you would see that, oh, the rates are pretty flat. Um, and so, golly, I care about health. And we haven't been able to do anything to budge these rates. That's really frustrating because if you just look at deaths and normalize for population size, it's pretty flat. But if you normalize for population size and age, we know that average age has been going up pretty dramatically. Cancers are much more common and cancer death is much more common in, uh, an older population. And so if you normalize by age, you see it's really coming down. So from a public health standpoint, from an epidemiology standpoint, we're making real strides against cancer. So if you ask, are, uh, cancer rates flat going up or going down? The answer is yes. It just depends on what's the purpose of the question and what are you trying to solve for and how do you run that analysis? And it's such a nice little analogy for that question about shared meaning, because the exact same data set can give you three completely different answers. It's going up, it's flat, it's going down, depending on why you would care.

Speaker A: Yeah, that's almost like a Simpsons paradox or something like that. I don't know if that's the appropriate use of that term, but it is a confounder nonetheless. I love that example, though, and I also loved what you were saying about creating shared meaning. Uh, one of the things that I covered in the manifesto, as well as a model I put forward, I think, in 2022 or 2023, called the Inquisitor and the Change Agent. And so I said, you know, people, analytics at its simplest form is essentially three steps. It's asking the right question, it's analyzing the data, and then it's taking action based on the results. And I always kind of mentally, rather than lumping and creating a shared meaning in the second step, which is the one that's kind of being disrupted and commoditized before our eyes, or really it already has been, frankly, uh, of that model, and I called it the Inquisitor and the Change Agent. So it's right, you know, it's asking the right questions, the Inquisitor, and then being the Change Agent on the back end of taking action based on the results. I always put creating the shared meaning in that third step. And so I actually think that that's almost the most important thing that you can be doing with people intelligence is creating that shared meaning for an organization. Because essentially what you're trying to do is to influence decision making at scale. And to do that, first you have to create that shared meaning. So I love that perspective, and I do think it's complementary. And I also don't think necessarily that People analytics is dead, but I do think there's a successor ideology on the plane and, and we're going that direction. But I think that's actually a good, um, segue to the third article that we have for today. Because when I wrote the Inquisitor and the Change Agent, I was at another organization at the time, uh, called Orgnostic. And, um, Luka Babich, who wrote the third article today, was the CEO of that organization, which is now A part of, uh, culture amp. And so he wrote an article called Epistemology People analytics and the Post Truth Organization. What he goes through here is he talks about how when People analytics was started, it was supposed to be this fact finding, very highly scientific, Karl Popper inspired organization, creating falsifiable hypotheses to test for organizations. And in reality what came along is that People analytics kind of fell victim to what he calls the politics of the organization. And so they, instead of trying to find the truth, they were being more influenced by trying to please people to try to, uh, you know, be in the good graces of decision makers and the like. And so the, he says the trouble starts when an analyst becomes a diplomat, not an investigator. Right. And, and so he calls this the agreeable machine. So we're trying to create agreeable results. But he, he puts forward this thesis that we have a duty to disagree. We have a duty to be kind of the fact finders, not necessarily the diplomats. Um, um, and interesting, I'll give kind of my commentary after this. But the interesting part to me is when he wrote the original LinkedIn post, uh, to put this out there, is that you can see he's been quite disenchanted with People analytics in the direction that it's gone in kind of this post truth world that he describes here. And I thought this was quite interesting. But before I get my thoughts on it, I want to get your reactions to it. Alexis, what did you think about this article?

Speaker B: Uh, well, first of all, I recognize the trend and it's one that makes me really sad. I, um, have, I've said for years that my job is to be the Lorax. It's like I speak for the trees, except they're like random forest trees. Right. I speak for the data that can't speak for itself. And that is my job. Uh, and it is really tragic to me the number of folks who feel like their job is to get invited into the room, they feel like their job is to get promoted. They, they feel like their job is to be a service provider instead of to be the Lorax instead of to speak truth to power. And that's uncomfortable. Um, I will confess that I got, ah, bolder and more willing to do it as I got older and less economically anxious. When I was really afraid that pissing off the wrong person was going to leave me and my kids homeless. I had a hard time pushing back when they were executives who were flatly wrong. Right. I still did it, but I did it kind of tentatively. I said well, but you might consider, and as I got kind of older and a little spicier and a little. Had, uh, a little less anxiety in my life, I was much more willing to say, um, I can see how you got there. However, this is what the data says. I'll also say that Luca's article really puts the onus on the organization. It really puts the onus on those people doing the intelligence work. Um, and in my experience, it is a dance. I've supported a lot of chros and a Fair number of CEOs in my life, and there have been some who have invited me in when I've been a contrarian. There have been some who've been thrilled when I showed them data that let them be smarter than they were five minutes ago. And there are others who reacted really poorly. Um, and so some of that is your ability to create a coalition with them. Um, so there is a little bit of diplomacy in. I know you're wrong. Change management and influence skills. Tell me the only way you're going to accept that you're wrong is if you feel like you asked me to test something. And so we're going to do a little bit of dancing here, um, so that I can tell you that you're wrong in a way that you're able to hear. Um, but it really, There is some mutuality. And if you're. If you are in a position where there's a CEO or a chro who is flatly uninterested in. In contrary opinions, um, that's not going to be a productive place for you to stay long term.

Speaker A: Yeah, absolutely. Well, I'll say this really quickly. Um, so Luca, friend of the podcast, but also just a friend of mine. Right. And, uh, um, he really took a chance on me being hired at Orgnostic was the first vendor organization I'd ever been a part of, and I was in kind of a tight spot when it happened. And so I'm incredibly grateful for the time I spent with Luca and the work that we did together. Um, and I think we did some really cool stuff. And the thing I'll say, though, is, and you and I have talked about this before, I, I put forward this notion of the scorekeeper fallacy.

Speaker B: Right.

Speaker A: And, and so there. And really this, this article touches on it well, that there's a debate about kind of the ethos or the core, the heart of people, analytics. Are we the scorekeepers or are we players on the field? Right.

Speaker B: I know what I think. I'm very strong in the player.

Speaker A: I know you are. And that's, that's why I wanted to talk about this one today. But I think what Luca is mourning is he's mourning the fact that we he the version of People analytics is where we are the scorekeepers and scorekeepers alone. And that's all he, all we wanted and all we ever wanted to be. And I think that we're just beyond that. And so I had actually written a comment on his post. I said, are we the scorekeepers or are we on the field as players? The more time has passed, the more I've realized that we are players on the field whether we like it or not. I said, thank you for posting this Luca, and good luck on whatever your next adventure is because I believe he's leaving the organization and starting a new venture. And I, I truly mean that because I know it's a lot of fun but also scary when you're going off and doing something new. But I said as, as much as People analytics would like to solely be Karl Popper, oftentimes we must also be the change agent to make a difference and influence those decisions. And so um, I think this is an excellent kind of ah, article. I think it, it's really good that we have these kind of debates within the field about who, what is our core. But ultimately I think to be and, and really just to, to bring back the manifesto for a second. Who, who are the people that are best equipped to take the intelligence that is being created and make decisions on behalf of the HR function? Is it people who know nothing about data, who are just, you know, you know, the legacy kind of HR generalist professionals that are out there? Or is it people that are data experts who understand the context? Who are the ones who again are the players on the field who is best equipped to be making those decisions for HR of the future, especially when AI is transforming things before our eyes? And I actually think it, it is the legacy people, analytics people, it's us, it is the IO psychologist, it is the workforce planning people, it is the talent intelligence professional. We are the ones that are equipped to being the decision makers of the future. So if we just want to be scorekeepers, we're giving away that future. We're giving it away. So I'll pause there, I'll let you comment on it.

Speaker B: Yeah, one of my sort of frequent themes in coaching kind of earlier and mid career PA folks, particularly when we're dealing with something really thorny and maybe they're a little anxious about going up against sort of a C suite level leader right to Share some of these things. And I'd like to give them that exposure and bring them meeting. I'll be here to backstop. But you really need to do this. And they'll get anxious about, well, you know, such. And so really wants to do this or do that. Like, that's great. That leader has spent two or three hours thinking about this. You've spent 500 hours thinking about this. You are now the most knowledgeable person that has ever walked the planet on this specific problem. And if you do not advocate for it, if you don't show up as the Lorax, you are abdicating your responsibility and failing in your job. It is our responsibility to do this. And it's one of the reasons, um, I've held so tightly to this sort of dual identity between I.O. and O.D. organization development. Um, because sometimes the I.O. and certainly the people analytics world kind of has this mic drop strategy to things. Like, I had good data, I'm going to like drop the mic and walk off stage. I'm like, that's just the beginning of the interesting part, actually making the change and then validating that you're right in the first place. Actually making that change is where it matters. And if you don't see the project through, if you don't make the, the change, then you're just, you know, kind of a cool party trick as opposed to something that really is meaningful. So I am 100% in the camp that we have to be players on the field and that actually we should be. We must be leading the function and leading organizations as jobs change. We are the ones who understand work workers. We understand business process. We understand how to pull it together. We know what's unique and special about people. We have to be architecting this transition.

Speaker A: Absolutely. Um, and I know I've told you this before, but I'll say it again. Uh, the people analytics and OD being two sides to the same coin is the most cited thing I put in my book with you and Alec Levinson and Maura Stevenson. And so, uh, I just, I love that mentality. I've taken it to heart in my own career and I want to keep sharing the good work that you've done with the world as, as we move forward. But Alexis, I appreciate you doing this episode so much today. I know. I, I want to say thank you to HR Bench for sponsoring this episode. So if you want to learn more, check out HRBench.com Directionally correct. And also if you want to learn more about what Alexis and I and others are doing with the data driven HR Academy? Definitely check it out at datadrivenhr academy.com but if people want to reach out to you, Alexis, and they want to learn more, where can they find you?

Speaker B: The easiest place to track me down is LinkedIn. There are not that many Alexis finks on LinkedIn. I'm the blonde one who does the People analytics stuff.

Speaker A: She is the blonde one who does the People analytics stuff. Well, you've been listening to Directionally Correct, a, uh, People analytics podcast with your host, Cole Knapper, and today's guest host, Alexis Fink. Thanks so much for doing this and giving me such a joyous experience today.

Speaker B: Alexis thank you for inviting me, my friend.