Craig Wallace, Ph.D., organizational psychologist and Department Head of Management in the College of Business at Clemson University, dives into data and analytics in the HR realm. In this interview with Deborah Stadtler, managing editor of the SHRM Executive Network, Wallace discusses how to solve for the right problem and the future of analytics.(length 38:29)
Craig Wallace, Ph.D., organizational psychologist and Department Head of Management in the College of Business at Clemson University, dives into data and analytics in the HR realm.
In this interview with Deborah Stadtler, managing editor of the SHRM Executive Network, Wallace discusses how to solve for the right problem and the future of analytics.(length 38:29)
Debbie Stadtler (00:02):
Welcome. I'm Debbie Stadtler, Managing Editor of the SHRM Executive Network and the People + Strategy Professional Journal. The SHRM Executive Network is the premier network of executives and thought leaders in the field of human resources. We advance the HR profession by engaging thought leaders and executive practitioners to create solutions and drive success for people and organizations. I'm excited to talk with Dr. Craig Wallace, Professor and Head of the Department of Management at the Powers College of Business at Clemson University. He is also the founder and managing partner at involved talent. Today, we will be diving into analytics in the HR space. Thank you for being here, Craig.
Dr. Craig Wallace (00:45):
Hey, my pleasure been looking forward to this. Excited. So, let's jump in.
Debbie Stadtler (00:51):
Well, let's start at the beginning. You started your career in industrial and organizational psychology. How did you get interested in people analytics?
Dr. Craig Wallace (01:00):
Well, there's this saying, "A blind hog every now and then finds an acorn." That was kind of me going through school, and I found psychology. I found IO psychology, and I think that's actually when I got interested in it. That was when I started grad school and IO psych. I wasn't really aware that I was studying people analytics. Don't want to date myself, but it was a while back. That term didn't really exist. But as you may or may not know, IO psych, it's really about field of people in the workplace. And we use data, lots and lots of data in just about everything we do. And we're using that data to help us make better decisions about people, helping them at the workplace, as well as helping the organization and then, that intersection of those.
And so, I guess I've been reflecting on data and people and analytics for a while now as our field is evolving and growing. And I still have this deep-rooted passion at this intersection of people and data. And as an academic, I research these things. And so, I'm interested in lots of employee phenomenons, like selection, performance management, motivation, leadership, climate, culture, emotions, how all these things interact to boost performance and wellbeing, which SHRM, I'm pretty sure, is behind a hundred percent.
But backing up to your question, when did I get interested in it? In the '90s. But, like I said, that term wasn't really around. People analytics really started coming of age with that name 10 ish years ago, around 2010, 2011. So, I guess I've been doing this for a while, but it's a new label. And it seems to really resonate with people as opposed to the dreaded S word, statistics, which they're not synonymous, but stats is a part of that.
Debbie Stadtler (03:05):
Well, it sounds like it was a natural progression for you. And yes, I think people analytics does go over a little easier than statistics. So, to start us on the same wavelength, analytics is a huge field. What is your basic definition of analytics, and why are they so important to the HR profession?
Dr. Craig Wallace (03:28):
Yeah, that's a big question, and it's an ever evolving kind of answer. So, I got interested in that definition about three, four years ago. Everybody tends to think analytics is new, and it's actually not new at all. Right? You boil it down, it's using data to make decisions, and there's some old stuff we can poke fun at, like in the 1400s, 1500s, phenology, where they're trying to equate bumps on somebody's head with intelligence. We laugh at that now, but that was an early method of trying to make better decisions by looking at comparisons and data. Yeah. Like I said, that is silly. And let's fast forward, and my definition right now would probably be, off top of my hat, analytics are just data-focused tools, techniques, methods, that allow for enhanced insight or understanding of some phenomenon that ultimately should enable better decision making.
So, when I'm teaching this class to whether it be undergrad students or executives, or I'm walking clients through this, it could be something very, very simple, such as a Bivariate correlation, right? That's a statistical method or tool with some output. And that correlation, the one I like to use, is the relationship between ice cream sales and weather, right? Pretty simple explanation, hotter days, more ice cream sales, so we better have more ice cream in stock. That's a very basic analytical tool or method. It could be as advanced as text mining decades of movie scripts in the hopes to identify patterns of box office success, or revenue that can then be used to inform movie producers which ones they should actually make and which ones should never see the light of day. So, two examples there for my definition about tool or data tools for techniques or methods, but the data and the analytics are key across those things.
So, taking those kind of extreme examples, same thing for HR, data analytics can help us make decisions who to hire, what's driving engagement, finding root drivers of effectiveness of leaders. And the thing it does that I try and really get folks to understand, this isn't some perfect tool or set of tools. There's still going to be error, but what analytics should be doing, if you know what you're doing and reading that output, is really reducing error in our decision-making process.
And that's a lot better than what I run into a lot of the time when I'm talking with HR leaders or CFOs or CEOs, whoever it might be, "Well, why do I want to do that? What we've been doing seems to work just fine, and that's not going to work in the near or far future." And I get that a lot from executives.
Back to this, though. I always have a cautionary side with a couple of these. I don't want to give the impression that analytics will replace the human brain or expertise, at least not in the near to moderate future. These are just tools, techniques, and methods that humans can use to enhance that decision making process. So, that was probably a long answer there, but trying to get analytics into one sentence is pretty difficult.
Debbie Stadtler (07:01):
That framework makes perfect sense that it's something that has been around for quite a while, but we've finally put a positive label on it. And with the rise of technology, people are able to become so much more useful with their analytics. Well, as the amount of data and the use of analytics grows, what is the role of human expertise in all of this? You kind of mentioned it there in your previous answer, that there's always a space for the human side of this. And I especially think of things like talent recruitment and selection that tends to have sort of vague, "I'll know it when I see it," kind of overtone. So, where do the analytics and the human part intersect?
Dr. Craig Wallace (07:49):
Yeah, that's another tough one. And I've talked to a lot of analytics experts in different fields, whether they be engineers, mathematical folks, or in the business analytics side or people analytics side. And the one thing I do keep hearing, and it actually might surprise some folks that are all on the analytics train, is there is still no substitute for human expertise. So, these are tools, and we have to become expert users of them. And again, I like to use examples that most of us can resonate with.
So, let's think of basic tools we've got sitting around, hammers in the house that we use to fix things. I can use it, but I might hit my thumb every now and then. We're actually getting our porch redone here next week. So, I've been thinking about this, and I know when these framers come out, well, they'll probably use nail guns, but if they do use a hammer, my guess is they're not hitting their thumb. They have become that expert user in that tool. And this is the same thing we have to do with analytics.
Now, not everybody has to be a complete expert on creating some algorithm or statistical method or text mining, whatever it is, but you need to at least understand that language, so you can make that decision with your human brain, which there is still no substitute for that.
Debbie Stadtler (09:19):
Absolutely. Expert users of analytics. That's what we need to grow into. So, you mentioned a little bit in your movie script example earlier that a lot of data can examine what's already happened. It looks back at the past, but then there's also the component of predicting for the future. What's the balance between those two, looking backwards and predicting forwards?
Dr. Craig Wallace (09:47):
I think we're at a pivot point or right at the edge. And I consult and do some work in the HR space, but i also researched this. I'll say what I observe out there right now. And most HR analytics are... They might call them the new analytics, but when I look behind the curtain, it's the stuff we've been using for a while. And don't get me wrong. That's that's still pretty darn good. There's some great modeling techniques out there, but what we're seeing with those is we're predicting what's already happened. And I don't want to get into the weeds too much of analytics or stats, but we're trying to replicate known data structures to optimize our decision making. Well, if we're trying to predict what's already happened, and we know what's happened, how good is that going to be in the future?
That's the pivot where we're at, where we need to change over and start looking at some real predictive algorithms, machine learning, not necessarily AI yet, but how these models take known data and try to lay out what's in the future. And that's very different from, well, frankly, how I was trained in graduate school. I was trying to replicate known data structures, and now what I'm learning and teaching is how do we predict what's coming next? And the one I like to use is I was at Tulane when I started my career and went through Hurricane Katrina. So, even though I'm in upstate of South Carolina, I'm not worried about hurricanes, but I like to look at those forecasting models and weather forecasting. And in a very reductionist example, would you rather have a model that's pretty accurate at where something's going to happen, or, "Well, this is what happened last time."
And I think that's what HR needs to embrace, and firms are doing it. And when I'm talking about the effectiveness of these different models on my research side, we got a paper right now that looks at our older methods of predicting job performance through selection models. And it says, well, those methods are about 40 to 60% effective. When we compare it to something like a predictive model, whether it's neural network or decision tree modeling, those models are predicting effectiveness like 90%. And so, when I get out there and I'm talking to HR executives that [inaudible 00:12:22] "Oh, I want that. That's what I need."
And yeah, I completely agree, but they're new, and given our human reluctance to change, we just default back to we know what works. And so, when I talk to folks, right, I encourage everybody listening to give these predictive models a really good look as you're going to make this pivot, because we are. We have to make this pivot, but I strongly advise you at the same time, look at multiple models to understand that data. And I think that goes back to the balance that you're talking about. So, we really need to understand kind of where we've been to help us move forward and be more predictive as opposed to just understanding, "Well, that happened, and that's how it went down."
Debbie Stadtler (13:08):
That makes total sense. I feel like the pandemic sort of blindsided people in the business world and that led to sort of an increased interest in figuring out what's going to happen in the future, which is so tricky with everything changing so quickly, all the time unexpectedly. I could see why predictive modeling would be of great interest to business leaders now, as we try to figure out what's going to happen.
Dr. Craig Wallace (13:36):
Yeah. And I completely agree with that. The pandemic really exposed some of these issues. And we don't have known data structures to say, "Well, this is how hybrid work should work. And this is who could be effective at home versus coming back to the office." That's where these newer predictive-modeling machine learning techniques are really going to bail us out. Because we were behind that curve. Now this pandemic is forcing us to adjust and learn these new techniques.
Debbie Stadtler (14:07):
Absolutely. Well, obviously, we don't want to have analytics just to say we have analytics, so how can companies sort of connect the dots between the data and analytics and the business results that they're looking for? How can the data and the analytics actually drive the results that they're wanting to achieve?
Dr. Craig Wallace (14:28):
Yeah. That's an awesome question. I'm super passionate about that. Why have all these fancy analytics, if you can't drive positive change. It's like having the best car out there, and it just parked in your garage because you're too scared to use it or you don't know how to drive. Change for us with people, I look at it not only to boost bottom line revenue, job, performance, whatever, but also simultaneously enhance employee wellbeing, get these folks to thrive while they're helping the company. And the first thing I do when I'm talking to companies or order my students about this is, "Well, what's the outcome? What is it that you want to improve?" And a lot of times, it might be 3, 4, 5 things. Say, "Okay, let's just focus on one right now. If you had to pick one, what is it?"
And in HR, you talk to CHROs or just general practitioners and they say, "Oh my God. Turnovers killing me." Right? And it's this unseen driver of cost. It eats up a lot of money. Performance is in the tank. Morale's hurting. And I have a paper out on this and a lot of folks use turnover calculators in this. Take an employee and say, I don't know, 60, 65 grand a year salary. That can easily cost 38,000 bucks to replace. Doesn't sound too bad. But if you've got a turnover problem that can actually exponentially explode to six or heaven forbid, seven figures, but it doesn't fit nicely in the CFOs spreadsheets and often goes unseen. So, when studying turnover, I've found most organizations don't understand real drivers of that, right? So, when we're talking results, turnover is a really important business outcome. It costs a lot of money.
We typically shoot from the hip or guess at what is driving this because it's something, and I love this phenomenon, it's called false consensus. I know about it. And I still fall for it all the time. But it's this notion of believing everyone thinks like you do. So, what I think's going to fix turnover's going to work. Well, actually only 20% of people think like you. So, let's flip that around. You've missed 80% of the real turnover problem. Analytics ain't going to miss that.
So, to really understand turnover, for this example, you've got to examine a lot of data and put that into the analytics machine to look and find out, "Well, what's really driving it?" So, what I mean by that data is like, "What survey data do you have sitting around? My guess is you got a lot of engagement data. You probably have exit interviews from people that have departed. You've got selection data. You've got data everywhere. You can do interviews and take qualitative text data and put that in there. And when you run those analytics, let's just say, it's a predictive model trying to understand this outcome. It's going to be informative. And you're going to look at this output. It's like, "Oh, I had no idea it was embeddedness in the community driving turnover. It had nothing to do with the firm. That could be one example. In other words, "No I just don't like living in this town. There's not a lot to do. Love the firm," but that's why they left. And you wouldn't have sensed that because you love living in this town.
And so, that false consensus can come back and bite you, but having these data and putting it into action. Yeah, you can reduce turnover in a big way. You're going to save a lot of money. Once you're reducing that, it should go hand in hand with benefits and morale, wellbeing, performance. And that's what you're after as an HR leader. You want your folks to be engaged, satisfied, committed, and that's going to spill over to performance in reduced turnover.
Debbie Stadtler (18:22):
It's a great example, though. You really show that the human desire to simplify and reduce complex ideas to something that's more comfortable, data and analytics just take that to the next level. Like you mentioned, that can take so many more types of data, much more input, and really give you the answers that will be much more helpful instead of just trying to fit it in your spreadsheet or take a guess. I think that's brilliant.
Well, let's move from sort of the general view of analytics and business results and talk a little bit more specifically. Let's look at learning and development. So, how can L&D be improved by data and analytics, and more specifically, how do you know what the right goals are that you can link your data and analytics to? How do you know that your L&D programs are achieving the goals through using analytics to evaluate everything?
Dr. Craig Wallace (19:26):
The first thing you probably need to do, if you're the CHRO or EVP, HR, whatever that might be, before you start tackling this and moving down to L&D, I'd say, go talk with your partner in all this, your CFO, maybe get your CEO involved, and figure out, "What is our goal? What are the 3, 4, 5 main things that we need to be looking at and boosting from a talent perspective, right? It could be coupled with revenue. It could be something else, but find that goal or goals or outcomes. Once you've got that decided on and this the upper echelon or C-suite, whatever it might be agrees, now you can dig in.
So with L&D, let's take leadership, I do a lot of leadership development and what are the key outcomes they need to be driving? How can firms develop such skills? One of the things I often see, we have an individual contributor in some role, that's just killing it. And because they're killing it so much, they're promoted up to a management position. And there's some things in industry, or sayings in industry, that go, "Well, you just lost a great employee and got a crappy leader, and we need to work on that." So, L&D can target that, this first time emergent leader. Case in point, I was doing some work for a chain of pastry shops a few years back, and revenue was the outcome. Most store managers had started as just an associate within the store, selling pastry goods and coffees and things along those lines. And they were really good at that. And this was a pretty big pastry chain or moderate-size. Corporate was charging these store managers with, "Increased revenue. Increase revenue. We're going to train you to deliver exceptional customer service." That's it.
Fast forwarding, they weren't really having the impact that they thought they were going to have with this L&D initiative. Revenue was pretty flat. Wasn't going down. It wasn't growing as they had hoped. So, I got a call, said, "Hey, come in here. Fix this. What are we not training them? How can we deliver better service?"
"Well, let's back up. What data do you have that says, this should be focused on?" "I don't know. It's what we've always done." Well, I'm kind of a smart aleck, and I like analytics. And I said, "Well, let's look at the data." So, I asked for it. I got it. I set my outcome in these models. Revenue, top three drivers is store revenue, communication, servitude, and authenticity. Customer service wasn't in the top six. So, I showed that to them. At first, "This can't be right."
I was like, "Well, here it is." And they didn't understand the analytics that I was doing. So, my first step was to convince them I wasn't a complete moron. And then, once we got over that hurdle, we took these results to the store and said, "Well, let's just go talk to team members and see what they say. And they all kind of validated it. And they helped me drill this down. And we realized that my data drive hunt was actually right. They needed to be focused on internal customer service, boosting communication with your team, being authentic, not faking it. The servitude towards them was what they were screaming for. Fast forward. We put in some change actions, revenue went up, and then that's going to spill over. And this isn't a new phenomenon. I think Richard Branson is the one that made the saying most famous is, "Take care of your people. They'll take care of your customers."
And that's exactly what happened. So, the pastry firm was putting a lot of money into training managers for external service. They were missing the mark. They needed to be putting those dollars towards L&D for training their leaders to take care of their team. Now, let's fast forward a few months. We were able to track this. They tweaked that development. Revenue began to increase. And guess what? Turnover did. Morale did? The employees were happier and thriving, and that's really what we're after. So, that's one example of how, at least in my experience, analytics, when they're targeted correctly for L&D, you can boost those outcomes. And it was very powerful for them.
Debbie Stadtler (23:52):
And I love that. It's breaking the mindset of the way we've always done it. It's such a challenge in almost every area of business these days, but it makes sense that you have to ask the right question to really use the data and the analytics to find the right answers. That company assumed that customer service was the right area to focus on based on past experience. But truly, once you came along and forced them to step back and take a look at the picture overall, they got much better results. What a great example.
Dr. Craig Wallace (24:30):
Yeah.
Debbie Stadtler (24:30):
Now speaking about the field of analytics, which is huge and encompasses so many different aspects and also features a lot of cutting edge technology, you mentioned predictive modeling and machine learning and all sorts of things, how do C-suite leaders get their arms around this? Where's the place to start for that top leadership to kind of understand a little bit and help guide their HR department and their company into more useful analytics.
Dr. Craig Wallace (25:05):
Yeah. I think you said it best. How can they get a little bit of this? Because most C-suite executives, CHRs are right there with them. They think, "Well, I got to master all this." And that's not the case. I mean, it would be great if C-suite leaders could go in and crank out new algorithms and machine learning, all that. But no, you don't need to do that. They need to understand the methods that feed into these models and understand the output so they can speak the language, if you will. But C-suites really like a, and this is the example I use to explain it, analytics kind of top to bottom and where people and jobs might fall.
But the C-suites like the top layer of a three-tiered analytical cake. Before we get to the top, I like to start at the bottom, the bottom where you've got mathematical wizards creating new algorithms and codes and stuff that just blows my mind.
The middle layer is more of the data and business analysts who are using those models to provide the top layer, the upper echelon with outputs or results. So, that top layer is really the CHROs or managers, decision makers who are going to use these results. that hopefully, if they're doing things right, and they should if they've got this cake in place to move that business forward. Now, it's kind of an easy analogy. You've probably got a nice, beautiful three-tiered cake in your mind, and you've labeled it already, but this cake is probably going to look like a cake I made. It's going to be like a big lump. And if you cut through it, you might see three layers and icings up and down. And if you went for three colors, let's just say red, white, and blue, it's going to all mold together. That's really what it looks like. But you've got to have people that understand the true nuts and bolts of creating this, those awesome users. And then, the C-suite leaders who can interpret it and direct it forward.
So, that's how I like to talk about this. And when I do this in person, I can see the relief on a CHRs face like, "Oh, thank goodness. I don't have to do all this." Because it's intimidating. And then, when they want to go out, and I think this to your second part of your question, like, "Well, how can they learn about it?" But there's some short courses that are out there that you can just sign up for. I actually took one to see what it was about on edX on predicted modeling to see what I'm missing. Because I got intimidated. When, when I was in grad school, I was learning all Covariance modeling and multi-level structural equation modeling.
I thought, "Okay, these are amazing tools." Those are commonplace now. So, I took a course on edX, and I had a friend in industrial engineering help me understand true predictive modeling and across those two things, I got it. Now, I'm in that middle layer where I can really use these tools. I'm not the mathematical person creating the new algorithms. That's a whole different skillset, but see what's out there. Maybe where you got your degree from has something to do a tuneup with you at a university or conferences. There's a lot out there on this. So, just start looking.
Debbie Stadtler (28:23):
That makes sense. I mean the C-suite leaders really need to use their knowledge of the business to know how and why and where to deploy the analytics. But you're right. You're going to need those specialists that know how to write the algorithms and apply the methods of analytics to really get where you want to go. So, looking at this field, you've mentioned a couple of things that are sort of coming into play a lot more now, machine learning, predictive modeling, artificial intelligence. They're becoming a little bit more mainstream. Well, let's look farther out. What are some of the other areas that are emerging in analytics? Maybe what are some technologies that are on the horizon that are going to come into play more? Where do you see the future of analytics?
Dr. Craig Wallace (29:16):
Obviously, the future is going to be some form of AI. Now that term is kind of mainstream, but our methods for true AI, they don't exist. I know that's kind of lik, "What?" And I say that based upon the definition, but fully-autonomous, decision-making machines, they're not here yet. And it's kind of funny because when we started this talk, we were discussing people analytics, and I was doing that before the term existed, but now this term's been around for a while, but I don't think we have true AI, but at some point, and I don't know when, it will be here. I think our more immediate future actually though, rests in what I've been talking about with machine learning and predictive modeling. And I say that because with this emergent trend, and by the way, this encompasses a lot of stuff like text mining, web scraping, all data that you possibly have at your organization, but what machine learning does it refines the algorithms based upon your data with more data that's put into that.
So, what might start out as, we'll just say, 50% effective model, the more data you put in, the machine learns to optimize those data and predicting, and we get better and better and better, where our error just keeps reducing and reducing. For example, I can tell you I've got weather stuff. So, I went through Hurricane Katrina and Tulane, and then I went to Oklahoma State University, and we didn't not have hurricanes. We had tornadoes. And it was amazing with the predictive stuff they could do with those data. They could tell you effectively what block a tornado was on.
And when we first moved there, we're kind of storm shy, right? We just went through Katrina. I don't want a tornado. So, we've got all our alerts set up, but I mean, I watch the TV or on my smartphone and say, "Oh, this one's actually two blocks away. We should probably get in the shelter now."
We're not there with HR, but that's what machine learning and predictive modeling should allow for the next 5, 6, 7 years, to have that pinpoint of accuracy. I've been thinking a lot about where the future's going, and this is where my human brain comes into play, another emerging, not necessarily technology, but trend we're going to have to pay attention to, the ethical and legal implications of using these advanced technologies.
As HR practitioners, we've got to be exceedingly careful that our tools do not outrun our ethics, morals, laws, in how we're helping the people that we're really here to serve. Not just the firms, but the people that constitute those firms, which that is the number one most valuable resource in organizations. Now in the ''70s, it was physical assets like at 80 something percent. Now, it's people, over 80% of those unique resources that we have in firms. So, I don't want to scare folks with that, but we need to make sure we're balancing the right thing and legal thing with these advanced technologies, and when and if we ever get to this true AI, I don't know how they would balance those. So, there's a lot to be determined as we move forward.
Debbie Stadtler (32:38):
That's so true. I think we've been swept up with technology over the last 10 to 20 years of just being enamored with the newness and the capabilities and the acceleration. And now, especially in the media and other things that you've been seeing,, that whole ethical dilemma of technology has really been showing up a lot more. So I can definitely see that's going to be a huge crux of figuring out where things go in the future. So, along that same lines, younger workers, millennials, Gen Z, folks that are just coming into the workforce now, they are savvy with technology. They have grown up with it. They have known nothing but technology. They also are concerned with social issues and things like you mentioned, ethics, legalities, having a moral compass for your company. So, how will these generations with their increased technology, increased interest in ethics impact the field of analytics?
Dr. Craig Wallace (33:48):
That one I'd have to bring out a bunch of crystal balls and see about. I can speak to it a little bit. The one thing I am confident, I'm a hundred percent confident, they're going to bring this digital mindset to the working world. And it is going to impact people analytics. How? Where? When? I'm not sure. Actually, I mean, they already are, if we think about it right now. So, one of the trends that I do see is given our ability to quickly collect a whole bunch of data, coupled with Ys and Zs wanting feedback yesterday, they move into this workforce and they want to know, "Well, how'd I do? How can I improve?" And that's awesome because a lot of my age, we get feedback, it's like, "Ugh, tell me what's wrong. What did I do? What did I screw up?"
It's like younger folks, they don't necessarily have that mindset. They look at as like, "Okay. Well, I did that. How can I get better?" And I see this in my kids' soccer teams. They might mess up, but they come to the field or excuse me, the side of the field, talk to coach, "What did I do? Give me feedback."
But this notion of feedback and desire is bubbling up, and we're seeing upper-level folks realize like, "Well, let's develop them." And as a consultant, I see this, and I'm giving 360s now to individual contributors and first time managers, and this tool is usually reserved for higher-level leaders or managers, whatever. And now, heck, [inaudible 00:35:25] been working on using 360 for skills and selection, and then repurposing that data to have a development plan ready to go and be enacted day one. And then, we use those data with analytics to track progress over time. And that's one way that these analytics and different generational values are coming in to shape how we use data to help these employees.
Now, the other thing is firms better realize that this is one thing that younger folks want. They better adjust because I talked about a little bit a while ago, this talent is critical. And right now, as we're doing this podcast, hopefully really coming out of COVID. I'm optimistic. This whole notion of post-COVID talent war is about to explode. People are realizing they see true colors of organizations. And if the companies haven't pivoted, adjusted, one way or the other, talent's going to go somewhere else. So, that's one way to talk about this, the ethics and social issues. Firms, they can't hide that stuff anymore. It's really a time to practice what you're preaching, whatever that might be.
You need to follow through on that. And that's that authenticity of the organization. Don't say this and do something else because younger folks are going to see through that and not put up with that. I engage. I have kids. They're six, eight, and 16, and we're talking about this. I want their views on it because they're educating me on some of this.
Now, on the flip side with all this, the ethics and the data, a cautionary side too, kind of like a book end, most things I talk about here, we really need to be mindful of any stress or anxiety that can pop up with so much analytical or data feedback because you can get fatigued with it, and that can hurt performance and outcomes and morale, all of those things, because still even with all this great data and awesome tools, we're still humans, and the bots haven't really taken over yet that I'm aware of.
Debbie Stadtler (37:49):
That's a good point, though. You don't want to drink from the fire hose to the point where you become overwhelmed by the data and what you can figure out with it.
Craig, thank you for sharing your expertise with us today. You've given us some real thought provoking examples, but also some great direction on how we can think better about analytics and how it works in the HR space.
For more information on the topics we've discussed today, or for further details on the SHRM Executive Network, please visit SHRM.org.