“The Future of People Analytics” with Cole Napper, Vice President, People Analytics & Chief Evangelist, Orgnostic

[MUSIC] This is the Future of HR podcast, episode 63. [MUSIC] People in analytics, the future of it is going to be who's going to be the most in tune with what's going on in the business to ask the best, most scientifically informed questions, who's going to use AI to its greatest extent because it's still probably going to require some technical knowledge to get it to do the most sophisticated analysis. And then taking action based on those results, again in partnership with the business. What you see is that's going to put a premium, more so on consulting rather than technical acumen than what has previously been the case in people analytics. How can HR use people analytics to drive business results? How AI impact not only people analytics, but HR. Hi, I'm your host, JPL8, and this is the Future of HR podcast. The only podcast use mission is to inspire the next generation of HR leaders. On each episode, I interview successful HR executives and thought leaders who are reimagining, rethinking, and leading our field into the future. During our candid conversations, you will learn about their career journeys, their lessons learned along the way, and their insights on how to take our field, and most importantly, their career to the next level. My guess this week is Cole Napper. Cole is the VP of People Analytics and Chief Evangelist at Orgastic, which is one of the best people analytics, Gerda AI, and employee listening tools on the market. In his role at Orgastic, Cole has spearheaded the establishment and leadership of the People Analytics function backed by Gerda AI. And over the course of his career, Cole has also held People Analytics roles at Texas Instruments, Ranger, and Toyota. He's also the co-host of Directionally Correct, which is the number one podcast on People Analytics, and one that I listen to regularly and think you should check out if you want to know more about this space. Cole's also known for his thought-provoking articles on sub-stack newsletters, being a frequent speaker at industry conferences, and in his spare time, he organizes and leads People Analytics meetups across the country. His knowledge of People Analytics is only matched by his desire to give back and contribute to the greater HR community, and I think it's this common purpose and passion that really brought us together and built our friendship. I learned that's hunting this conversation, and I know you will too, as we discussed, why People Analytics is much more than just dashboards. Why he believes People Analytics is a team sport. What organizational network analysis is, and how it can diagnose breakdowns in collaboration and communication within teams, the potential value and use cases of generative AI tools HR, and how AI will impact the future People Analytics and much more. Cole, welcome to the Future of HR podcast. How are you? I'm doing good. How are you, JP? I'm doing great. Let's talk a little bit about your career. I really want to know why you decided to focus on People Analytics, and not another report of HR or organizational psychology, etc. The irony of this is I never really tried to be an HR or in People Analytics. HR existed when I was in graduate school, but People Analytics really wasn't a term back then. I was pursuing my PhD in I/O psychology. I thought I wanted to go into the executive assessment, executive coaching realm, but the thing is nobody really wants, no executives want to learn from a 25 year old. You get to figure out what are you going to do between now and when a cohort of people will actually listen to you. I got into the selection space and then that evolved into the workforce planning space. There was this point around the Moneyball Revolution and Google's Project Oxygen and Project Aristotle where all of a sudden everything wanted to become at the time they called it Workforce Analytics and HR Analytics and then People Analytics. My workforce planning job morphed into a Workforce Analytics job and I realized this was a really hot space and I always just assumed that everybody was decent numerically and statistically if you had some kind of undergraduate or graduate training. I figured out really quickly that that's a really rare skill set. I happen to have a really rare skill set in an area that was increasing in demand and so I just rode that wave and found out also that I really, really liked it. When I started I would really just call it like a applied research. I was doing a lot of research through my PhD but it's like how could you do applied research in the workplace? Well, join a People Analytics team, one of the few areas in HR that might hire a PhD. That's interesting because I think the evolution of workforce analytics, HR Analytics, the People Analytics, then such a hot topic and it's really progressed a lot especially now with AI. We're going to get into that later and that's one reason I wanted to have you on the show. But besides being an expert in People Analytics, you also co-host a very successful podcast. Why did you decide to launch Directly Correct? It was actually kind of an interesting story that really wasn't the plan for right away. I had a New Year's resolution one year that I wanted to start giving back to the field. I feel like I had learned a lot, I had learned a lot frankly from people that are your guests on your podcast. I was like, "Hey, I've got some really great experiences. I want to find a way of giving back." But I was frankly just kind of scared to do so. What I did is I'm going to write one article about how to build a People Analytics function and that's going to kind of meet my New Year's resolution. Before I even published that, one article turned into eight articles. Those articles just blew up when I published them one week for eight weeks. I got a few invites to be on other people's podcasts. I was actually on Al Adamson's People Analytics and Future of Work podcast. And Scottie, my co-host, he listened to that and he reached out because he's a friend of mine as well. And he said, "Dude, we got to start a podcast." I was like, "Ah, this is not going to happen." He was like, "Oh, man. I don't know if I want to like do any of the marketing or like trying to get guest or anything like that. I just want to focus on the backend editing." And I was like, "Oh, thank goodness because I don't want to do any of the backend editing. I just want to focus on talking to guests and getting the word out there about what we're doing." I said that Scott is the end to my yang. That's what makes the podcast a lot of fun and pretty magical. I think ours is like, "We don't get as big a name guest as you get, but I think we get really authentic conversations." And what I've heard from some folks is you'll see some of the people that are on multiple podcasts or multiple venues, but they say they'll listen to the conversation on ours because it'll be like the one where they really learn about that guest as a human being, not just as a leader in the field. Now, I take a lot of pride in that. Yeah, it's true. I think not only do you guys get into the science where people analytics and tackle contemporary issues, frankly, but you have a lot of fun on that podcast. And I think that's one reason why it is engaging in fun to listen to. I think people need to check it out. And actually, you kind of sell like Scott at one point there, so I wasn't sure if you're doing it in a personation, but a lot to just shout out for. Was I? Did I sound like it? Oh, yeah, dude. I don't know if that actually sounds like it, but yeah, directly correct. It's a lot of fun. And we cut up a little bit. And I think that's what people aren't used to is I would say probably 75% of our guests have had a PhD or something like that. But they're not used to hearing a bunch of PhDs cut up about current events, but also get into deep technical science debates. Yeah, I think it's a great, great podcast, not only for folks who want to learn more about people analytics, but just for HR leaders in general. How do you recommend it? You are a great writer. I think people should definitely follow you and LinkedIn and check out your writing because they're always really innovative. You're always pushing preconceived notions around typically HR issues, but trying to bring data, which I think is great. And I heard you say one time that people analytics is not a dashboard. Tell us more about this perception and why it frustrates you. I think what I said is I said people analytics is more than just dashboards because what I find is I think this happens to any area in business that gets hot. It gets really popular is everybody wants to be a part of it, even if they don't know necessarily how to do it. Right. One of the easier parts of people analytics is dashboards. People are like, well, I can do dashboards. Therefore, I can do people analytics. Therefore, that's all that people analytics is. I'll be the first person to tell you in terms of trying to communicate large swathes of data at scale to business leaders. Dashboards can be a great tool. And actually, the first article ever wrote was how to build an effective dashboard or something along those lines because it's a really great place to start. But if you think that's the end of people analytics, that's where I take issue is because I'll go back to the point earlier I made about applied research in the workplace. There's no part of a dashboard that is what I would call applied research. It's not really answering a business question. There's a popular framework of how to do people analytics. It's called descriptive, diagnostic, predictive and prescriptive. All a dashboard is going to do is going to answer descriptive statistics. So what happened? It's not going to tell you why something happened, which would be diagnostic. It's not going to tell you what will happen, which is predictive. It's not going to tell you what you should do about it, which is prescriptive. And so I'm very interested in all four of those, not just the descriptive of what happened, although that's the best way to do it. starting place and probably some of the most important questions that can be answered. Well, I love that model and a categorization I think that's helpful for people. Maybe when I come back to that a little bit as we talk about the technology and where it's going. And where we should be thinking about for each of those areas. The other question I've got for you, and I think you've kind of expanded on this, but I think about HR and business leaders, like, what are they getting right and getting wrong about people analytics? Because there's a lot of preconceived notions beyond just dashboards, right? Sure. My general criticism is what I call magical thinking. And usually this stems from the predictive part that I just mentioned a second ago. Because a lot of people equate prediction with magic. We will be able to tell the future, right? And like, that's not what is selling. A lot of people don't really think about things in terms of prediction as probabilistic thinking. So if something has an 80% chance of happening, it means it has a 20% chance of not happening. There is no certainty. And so what you're really looking for when you're doing prediction is uncertainty reduction, but it can never be reduced to zero. I wrote an article not too long ago about things like black swan events, the term that was popularized by Nassim Taleb about events that you would not have ever predicted that happened happening. Well, many of those type of black swan events think of COVID being one of them or maybe Russia's war with Ukraine being another things that no one predicted were going to happen happening. Those had vast impacts on businesses. Well, guess what? All of those predictive models that people analytics functions were building before those events occurred all got invalidated. Those predictions were all wrong because those black swan events came along and invalidated those predictions. This is where really trying to understand probabilistic thinking makes a big impact on how effective your people analytics function can be. I'd like to kind of put one caveat on this too because there's really two different kinds of prediction. And in layman's terms, I think they get confused often. And this goes back to the point about prediction versus diagnostics. One of the things that you learn when you're going through that applied research bent on how to look at people analytics is diagnostic analytics. Why something is happening is so much more helpful most of the time than trying to predict a certain event happening. And so let's dig into the predicting turnover case. And really what you run into many HR endeavors, not just people analytics is, okay, well, we can do something. But how is that going to sit with our employees and is it going to seem to be seen as okay, ethically, from a regulatory standpoint from a legal sense, let's use the example of predicting someone leaving. Well, I remember attending a conference one time where I heard a guy bragging who was one of the speakers like, oh, one of our features in our model that predict with great accuracy if someone was going to leave is whether or not someone was downloading the paystubs. They pulled their paystubs out of our HCM system. And I thought about that. I was like, first of all, did those employees know that you were using that that as a feature in your model that you were going behind their backs and seeing whether or not they pulled paystubs? Second of all, if you actually intended to act upon that and take some kind of remediation effort, let's say you want that employee to stay, are you going to have a manager or an HR business partner go and talk to them and say, hey, we saw that you started pulling your paystubs lately. Are you thinking about quitting? We'd love for you to stay. That seems incredibly ethically fraught and creepy for that matter. And I don't think the person, I think would have the opposite intended consequence that they would be hoping for in that situation. But if you go and you lend yourself to the diagnostic framework, why might somebody be leaving? Well, think of like some of the root causes, why might somebody be pulling their paystubs? Well, it's probably because they already found a job somewhere and they're looking to get a mortgage or movers to move cross country or something like that. That might be why that's a leading indicator. That doesn't really tell you anything about why they're leaving. Right? Just tells you they are leaving. Well, I want to know what are some of the leading indicators that are causing someone to make that go, no go decision to say like, you know, today was my last draw. I'm no longer going to stay with this company. Right? I want to get ahead of that, figure out why those things are occurring and then put countermeasures in place at an organizational departmental team level, not at the individual level. So that it fixes that problem not only for that individual, but maybe for that whole department for someone who wasn't necessarily thinking of quitting, but maybe would have six months from now. Why don't we put those kind of changes in place? That's a great example of how predictive analytics can go wrong, but diagnostic analytics can be incredibly effective at trying to help remediate things that you don't want to see in the future. Yeah, thanks for that, Cole. I think that diagnostic overview and why that is a smart way to go after it versus predictive is super helpful. You know, one thing we've talked about before is the impact the people analytics can have. And the CHRO came to you and said, Cole, my people analytics team to have a bigger impact on the business, how would you respond? Well, after I got done slapping them, I would, I kid about that, but I remember one time I got some 360 degree feedback from one of my internal customers and it said, Cole has a maniacal focus on providing business impact to the company. And so the reason why I reacted that way is because I can't imagine a CHRO coming to me and saying, how could you make it a bigger impact? Because that would have been the question I was asking them every time we met for the last five years. But the reality is, and this is some advice that I give people who are newer leaders of people analytics functions, the generic or typical advice you get is go get some quick wins. Find a small problem that's out there that you have readily available data, find a solution that problem and go fix that problem. And I think that that's generally speaking pretty good advice. It's just not what I've done in practice. When I've gone into organizations, I go meet with the whole executive team or individually usually, but I have met with whole executive teams at once. I just find the biggest business problems the company is facing. Generally speaking, the biggest problem the company is facing has a component that has to do with people or talent or engagement or inclusion or turnover or something. And then we're going to address that problem directly. I have this quote that I find to be quite motivating. It says, what's going to call out the best in you directly challenging the thing that's challenging you the most. So what's the thing that's challenging the business the most? I want to go directly after that. Right. And so the C H O comes to me and says, Hey, where can we have a bigger impact on the business? I'm going to ask them, where's our biggest problem and how can I help? Because that's all I want to do. And then would you say to C H O, Hey, turn us loose on that problem, right? We can get the data figured out. I also believe in and this is a concept that was popularized by Alec Levinson. I know you've had some guests on from the universe or the Center for Effective Organizations at USC before an institute I really admire. But he says, people analytics is a team sport. And what he means is, again, I mentioned like most big business problems have a people component to them, but that doesn't mean it's the only component. So that means that we need to partner with other teams internally, work cross functionally breakdown silos, breakdown barriers, and to try to attack this problem systemically rather than just the thing that I care about. And I think about this through the Lean Six Sigma framework of if you have an assembly line and you just fix one part of the assembly line, well, that's going to create backed up work at future areas down the assembly line unless you have that kind of one piece flow throughout the entire unit. Well, that means that we need to be working every area of a systemic problem all at once to do that effectively. And that's the strategy I've tried to take in the past. I like that. People analytics is a team sport. It makes a lot of sense. What's like with the future of people analytics in general? What is the most promised that value of the HR functions? And I'm going to just name off a few. These are just a few at the top of mine. Predictive analytics, AI for selection, train, career mobility, probably everything else. Organization network analysis. What else is on your list? I'd probably just flip the order of those JP just for the sake of discussion today. One of the topics we talk about often on our podcast is organizational network analysis. And for those of you who aren't familiar with that term, it's it's looking at the linkages between individuals or sometimes even concepts and showing how the relationships between perhaps the communication flow in organizations or how collaboration occurs, how that impacts the operational excellence and effectiveness of a business. Right? The reason why this has become quite popular lately is due to the remote versus hybrid versus in-person debate about how work happens effectively in 2023 and onward is I don't have the exact statistics in front of me, but many businesses or remote, many businesses are now hybrid. There's not that many companies that are fully in-person anymore. And when you know you have those mixed methods of how people are interacting with one another, O&A can be an incredibly powerful tool in trying again to diagnose and understand and try to action how collaboration breakdowns are occurring, communication flows are breaking down, lack of things like focus time and things that contribute to the impact of how teams are operating. The second one you mentioned was AI for selection training and career mobility. I would just say AI in general for HR. I'm most familiar with what's going on for AI and people analytics just frankly because I don't do AI for benefits or something along those lines. It's not really my cup of tea. And even my organization I work for a company called Orgastic we've leaned heavily into this space but I think it would be hard pressed even for the non-initiated for folks to not come up with some pretty interesting use cases for how LLMs are what we're called large language models and the application because really what's going on with generative AI is the advent of these LLMs and their introduction with other machine learning components that have been around historically to how you could tackle new problems. And so when the people in the space let's go back to people analytics is more than just a dashboard for a second. Imagine right now some data analyst at an organization has to pull data they have to visualize the data they have to find a software to publish that data and then they probably have to write up like emails or PowerPoints or something to explain what's going on in that data when they create a dashboard. Well right now there are products on the market where you can just ask them a question and they will come back and create a dashboard for you with multiple views you get to choose the best view that you like the most for what's happening and it'll explain it in plain English as well so one of the other hindrances to the effectiveness of people analytics is data literacy. Not everybody is good at interpreting what a chart or a visualization or even a text explaining is saying because of the technical language and jargon that is used. Well imagine you have an LLM that's coming in in plain English explaining exactly what a chart is saying with no room for interpretation. And then the last area is because these LLMs can basically query anything you want them to. We have put our LLMs at work to query all the scientific literature to say okay you've asked a question we identified a problem in the answer to that question. Here's some research or here's the top five articles with based on best in class research that are going to tell you how to address that problem. Maybe it's even going to send a link to JP's future of HR podcasts on minute 47 he talks about this issue here and that exists today that type of technology is available today and I expect you to see that proliferate more and more throughout people functions and there's been a lot of pushback on AI because frankly how open AI and how Google and a few other big players in this space have just queried the entire web. I'm very much of the mind there's a wide AI versus narrow AI debate. But what does that mean? Wide AI is querying all the data that has ever existed on the internet and trying to tackle every problem under existence. So we're going to have one AI to do every problem ever. I'm very much in the narrow camp of let's just use data from our own organization or from or just from very targeted research that we've done and just build our models based on that. So then you don't get into the legal regulatory ethical concerns that you will from potentially stealing other people's work or having the type of Samsung issues where employees were feeding back their own data which was training the model to give them the wrong responses like let's not do any of that stuff. And then we're just going to do this based on organization will have very specific AIs. So not every AI is going to be able to tackle every problem. Let's make it tackle one problem and do it really really really well. What we've all seen most of us have interacted with these type of applications now is that it'll do some things really great and other things really poorly and I hear a lot about the phantoms in the machine or the type of responses that we bring back something and it's actually completely untrue. Well, if you train really narrow models you can train those things out of the model. I'm very bullish on narrow AI and very bearish on wide AI. That was a masterclass in AI. Thank you for that call. But I think it makes a lot of sense for the narrow AI for lots of reasons because you're starting to see I think smaller startups right going after certain things like law and AI for law. It's a right legal breeze right makes sense because the bigger it gets the wider when you use chat GPT or others it does get generic that's because the issue's so it's really fascinating is this going to change the role of people analytics leaders and team members are the day to become more AI experts or is it really that we'll keep progressing. I think everyone's always worried we're going to lose jobs over this but actually look at technology it's actually typically generate more jobs and new jobs but what's your take on that? Yeah, I have a actually really really comprehensive article that I'm working on with my company called changing the people analytics operating model in the age of AI that's probably coming out in like a mother show but it's going to go into this in great depth but I kind of gave a teaser to this a few months ago in an article I wrote called the inquisitor in the change agent if you really boil down people analytics to its simplest form it's really three steps right it's asking a really good research question it's analyzing the data to answer that question and then it's taking action based on those results. One of the things that we found is the hardest step historically was the second one which was analyzing the data that was usually a very very difficult step it took up probably 80% of the time when it came to trying to answering these problems. The crazy thing with these AIs is as they get better that step is almost going to be fully automated so then the what that leaves is just the other two steps which is asking a good research question which I called the inquisitor and taking action based on the results which is I call the change agent. What I said in that article essentially was people analytics the future of it is going to be who's going to be the most in tune with what's going on in the business to ask the best most scientifically informed questions who's going to use AI to its greatest extent because it's still probably going to require some technical knowledge to get it to do the most sophisticated analysis and then taking action based on those results again in partnership with the business. What you see is that's going to put a premium more so on consulting rather than technical acumen than what it has previously been the case in people analytics. For the last 10 years they've really hired a bunch of PhDs because technical acumen was at a premium. Now I think you're going to see more folks with those MBA McKinsey type backgrounds that enter this space because the consulting frameworks are going to become that much more important to making an impact with these tools. It's a really interesting take on the inquisitor and change leader right I think it's really smart. Let's talk a little bit on the same token I wanted to get your help on resources books courses obviously your articles call or a must read for anyone wants to know about people analytics but what else would you recommend for HR leaders who want to get better versed in the field. I mean in all seriousness I went ahead and made a list before I joined I just went through some of your previous guests Mark Efraud and Miriam or those are great Alan Churres Dave Ulrich John Budra Peter Ramsted Thomas tomorrow per music you know Rob Cross and Karen Dylan those are just some of the let's call them greatest hits these are people who I've admired and looked up to and read their stuff for a really long time and you can start there in terms of really specific books and things like that there's a book called the HR scorecard by Mark Hueslid yeah Mark Hueslid and a few others I think wrote that together I'll give Alec Levinson a shout out again with his book Strategic Analytics and another really prolific writer who's been kind of a mentor to me as Max Blumberg he writes a lot on LinkedIn and is very often cited and obviously folks like David Green has his monthly newsletter that you can find a lot of really great resources I'll actually say this if any of your listeners are wanting to get into the technical aspects of people analytics a book that I've been waiting for for like 10 years came around which is Craig Starbucks books the fundamentals of people analytics using R this is the best thing that has ever been published by a mile in terms of how to learn how to do the technical components of people analytics just like 300 pages I read it in two days when he gave it to me I was like this is freaking amazing I can't say enough good about what Craig is doing I believe he's over at Roku when he's got his own kind of non-profit people analytics consulting firm as well I can't remember the name I think the world of what they're doing yeah I heard about that book and it's made a lot of ways or it's a terrific book great resources thanks cool we really convinced the podcast that asking you about what career advice you have for early career people analytics professionals especially now thinking about AI yeah one of the things this was not my idea I've copied some other folks who did this but one of the ways that I focus on giving back is every few months or so I'll just get on LinkedIn and I'll post a link where any early career person can book time with me to do mentoring and so I've probably in the last two years done maybe a hundred mentoring sites sessions. A pretty absurd number. One of the things that I found from those mentoring sessions is it's sort of like a factor analysis of sorts just to use some technical jargon where just a few concepts will bubble up to the top of being the same things that I mentioned over and over again despite the kinds of questions I was getting asked. And one of the answers was, and I always just says, "Be unstoppable." And what I mean by that is like, let's say you were trying to learn a really technical discipline in 1990. You really had to try and work at it and you probably needed to go to the top research university and have access to library resources and had to work really hard at it and go to the right conferences and read the right books and yada yada yada to learn that skill. The beauty of existing in 2023 is there are so many really, really great resources out there that are either free or incredibly cheap where you can learn something like more than I will ever know about people analytics without ever stepping foot into a graduate program somewhere. And so I say first, if you're really interested in doing this, if you really want to do this, you know, dedicate two to three hours a week, be unstoppable and just go research these resources, go find real data sets, go do real analysis and try this out, go build a real career in people analytics because people are always worried about not having enough on their resumes. Like, how can I show the impact that I've had if I've never had a job in this space? And I said, if you do one research project a week for three months, you'll have more work, more like of a corpus of knowledge and resources and examples and recommendations that you've made, then most people have in their entire career, right? You'll have that before you've even started. You will be incredibly former. I said, you know, I've been a hiring manager plenty of times in the past. I would hire that person at the drop of a hat if they really came to me and showed me that no one's ever done it, but it is completely within anybody's ability to go do that right now. It could be either free or very, very inexpensive to do that. You're really right. There's really no snow excuse to not learn a skill if you want to learn it because of the internet and all the resources we have now. So great advice, Cole. Last question for you is, what is one word of phrase that you believe will define the future of HR over the next five to 10 years? This is kind of a variant of what we were talking about earlier with AI. I used the word automation. And the reason why is because the point I made earlier about magical thinking when people talk about predictive analytics or AI, usually they're talking about magic. How can this do something that I didn't think it was possible? What I found is the most applicable things and the least creepy things that are going to be happening in the next five years that I'm excited about is using things like AI to automate things that we never wanted to have to do in the first place or never wanted to do again. Why don't we get excited about that? Because I'm excited. There's so many times that I was having to manipulate data sets annually, clean up things, transposing, just getting rid of crappy data. Imagine if you didn't have to do that ever again. Automation and AI are here that are going to allow that type of thing. It may not completely fix the problem, but it's going to be a pain reduction mechanism. That's great. I'm super excited about that. And I think it's going to have a huge impact on our field going forward. Automation, I think you'll have a huge impact as well. Cole, thank you so much for being on the future of HR. You're an awesome guest. We learned a ton. Really appreciate you being on. Hey, I'm just glad you invited me, Jay-P. And I hope we are able to get lunch together again sometime. We definitely will. Thanks, Cole. Thank you. Thank you so much for listening to this episode of Future of HR podcast. Thanks again to Cole for sharing his insights on the future of people analytics and the impact of AI on HR. As always, you can go to futureofhr.com to view all of our past episodes and learn more about our mission to inspire the next generation of HR leaders. And if you enjoyed this episode of Future of HR, be sure to subscribe, share our podcast with at least one other person or even better, please leave a review on Apple or Spotify podcast. This really helps us grow the podcast and helps our mission to inspire the next generation of HR leaders. We'll be back next week with Antoine, Tired, and Claire Harbor, who co-authored the book Disrupture Career, had an navigate uncharted career transitions and thrive, and co-host their popular podcast also called Disrupture Career. A talent management advisor's Leadership Consultants, Executive Coaches and Authors, Antoine and Claire have much to share on what they call the new career paradigm, the factors that can lead people to misjudge a career move, affect the transitions, and much much more. Thanks again to listening to Future of HR and being part of our community.