What Your HR Dashboards Miss: The Untapped Workforce Intelligence Driving Change

All right. Welcome, everyone. Thanks for joining us today. I'm Tom Labretto, uh, the president of Workhuman. Um, I'm happy to be joined by my panelists today, Mesha, uh, Mesha-Ann Martin, Workhuman's head of people research, Jesse Herriot, Workhuman's head of AI, and Kathy Andaris, uh, from The Josh Bersin Group. Welcome, panelists. Um, we're gonna have an exciting conversation over the next, uh, half an hour or so on, on people data, um, and, and, and really what that has meant in the past, what it means now, and what it may mean in the future. Uh, so big broad category and topic to dig into, so let's, uh, let's, let's hit it. Um, I think we can all kind of. You know, we've all lived in this people data world for quite some time, uh, and the archaic processes and ways in which, um, HR leaders have gone about collecting and synthesizing and trying to make sense of people data, uh, that, you know, really has been through ad hoc, um, you know, employee surveys or, uh, performance management processes, or in some cases, incenting employees to declare, um, things about themselves and their performance and potent- and potential. Um, so in a lot of ways, it's backwards-looking, um, and, and in, in our experiences, rife with all kinds of inaccuracy and bias. Um, so we're gonna start right there, and I'm gonna direct the first question to Mesha-Ann. Um, so Mesha-Ann, from your research, where do tools like service, uh, surveys and performance reviews fall short? What are they missing about the totality of the employee experience? Yeah, so you know, I have spent about 20 years or so in people analytics. I love an employee survey. I love a dashboard. I've used both. But I think we're in a new era of employee experience, where I wish we would think about organizations as societies and come into this work from an anthropological perspective, because there are so many artifacts around the actual employee experience, and not just how people feel about the employee experience. And so while I love employee surveys, and I think it's absolutely important how people feel, what they're missing is why do people feel that way? The same thing about dashboards. It's always going to be important to measure things, but it misses the context around why you're seeing what you're seeing. So, I would say to sum up that surveys and dashboards are important, they're necessary, but they're no longer sufficient for where we're going in this space. Yeah, I think very, very well said. Um, Jesse, let's move to you for a second, just to talk to ... talk about the, the, the technology underpins. There was a reason why, uh, you know, dashboarding has been over the past even couple of decades really the only way in which data could be visualized and synthesized into something that could tell a semblance of a story. Um, what are, what are you seeing now as that, you know, the, the, the underlying technology infrastructure moves from data visualization into, um, more conversational insight generation? Yeah, I mean, we know with the advent of AI techniques that are all the rage right now, we're able to really use many different types of data sources to find signals that would otherwise have gone unnoticed. I mean, these signals could be things such as unconscious bias that we might use in our language, where you might not have noticed that in the past, but you can use generative AI to kind of pick some of those things out, or signals that help predict, you know, what's gonna happen in the future, because dashboards and surveys are great for telling you what happened last quarter, but not great necessarily at predicting what will happen in the future. So, the ability for AI to pick up on subtle signals that we ... that might go overlooked, as well as the ability for AI to, to help predict or forecast, those are two of the advancements I'm most excited about. Yeah. Yeah, yeah, me as well. Um, uh, Kathy, you speak with loads of the biggest organizations on the planet. Um, you know, what are they telling you about w- how they, uh, w- where they are in their journey or their transition from reliance on old, um, people intelligence techniques to perhaps new techniques? Yeah, it's such a, it's such an important consideration, such an important discussion. Um, and what we're seeing actually from talking with organizations, but then we also studied this just recently. We did a big study on people analytic actually, people analytics, and we had, um, about 1,000 organizations responding to this big survey, and what we saw is that still 90% of companies are actually struggling to move beyond these basic dashboards where they are ... Uh, we've all seen the, the frustration there, where we're saying, "Well, this dashboard, uh, I see something concerning there, and then I want to know why. And then of course, I can't get this from this dashboard." So the 10% who do this differently, they actually approach the entire process completely differently, because they are integrating multiple data sources to tell these database stories, not just show kind of disconnected data points. So, what the- what that means- looks like is they are integrating data, of course, from their surveys and like the more traditional things, but then they also integrate sentiment analysis, for example, from maybe email or collaboration tools, thinking about when the tone changes, for example, uh, before somebody gets disengaged. They're also looking at passive datasets, so they are looking at leave patterns, uh, sick times, uh, like eh- uh, like -... tool usage, for example, to predict when something is happening on a team. Um, they're of course using active listening on call service, town halls, crowdsourcing, all of those. And then they're also using cultural insights in some cases, like the things that they can get from a, for example, from, from your, um, human in- what you call human intelligence, where they're thinking about, "How can we get insights from cult- on, on the cultural health of teams or organizations?" And then they tie all of this together with business metrics, like sales data, customer satisfaction data, financial data. So, they are thinking about the bigger context, not just point insights. Yeah. That's, uh, I, I think that's a great point, Kathy. And it kind of, it brings us to that other theme that we're hearing more and more about, is the changing role of the CHRO. Uh, and, and, you know, and that in and of itself, that role expansion is forcing them to take a more holistic view of what they're measuring and, you know, what that measurement is telling them. Um, and, and it's not just about the people asset in the organization, but it's more about the business outcomes that people are, are contributing to. Um, so I think there's, you know, kind of a perfect storm (laughs) of transformation. Both at the data, you know, at the data level, um, at, you know, the human level, the leadership level, um, you know, all at once. Fascinating. Absolutely. Yeah. Um, and, and, so the, uh, I wanna, I wanna pivot off of one thing you said around cultural data, um, and I'll throw this at, at Mishaann. Um, how important do you think, in the research that you've done, um, that, uh, identifying cultural signals, um, is to moving the hearts and minds of an organization? You know, it's so important, and yet traditionally really hard to, to measure, right? So, when you think about what culture is, it's how things get done around here. And the how really is, um, indicative of how successful the business is, right? Like, how are you accomplishing your outcomes? Is it a good way? Are you innovating? Are you doing things efficiently? And yet traditionally, culture has been really difficult to measure. So, I was talking to a client about this, I would say a week or two weeks ago, and they were saying that they were doing a culture audit, which was, you know, has been a fairly traditional thing to do. It involves surveys, focus groups. Very, very labor-intensive. Well, the new way that we're doing it is with recognition data. So, you've got a robust recognition practice where people are interacting with each other, um, in a way that is natural, right? So, it, it's very different from a survey where you sit down to answer the question. This is something that happens in the flow of work, right? People are being really, um, explicit about what they're thanking people for, and so they're describing work. And so what we're doing is we're taking the information from those messages to give our clients an idea of how their culture is actually playing out in real life. We are able to do this extra well, because the way we set up these recognition programs, each person has to pick a value that aligns to the recognition message they're sending. So, for each value, we're able to see, to what extent is this actually playing out in real life the way that you intend? Which allows you to adjust and refine into the culture you want. You can strategically reinforce those behaviors that are missing that you want to see more of. A really common example of this is, you know, for organizations, for example, who want to drive a culture of thoughtful risk-taking, but you only ever recognize successes, right? That's something that's hard to see through a survey or any other data, but when you analyze recognition data, when you do recognition well, it gives you that intel right away. Yeah, spot on. Uh, it, it, uh, reminds me that, that whole kind of definition of what culture is, one of, one of our largest and most successful clients, Cisco, uh, Kelly Jones, their Chief People Officer, describes or defines culture as, "How you feel on Sunday night as you're looking (laughs) at the workday starting on Monday morning." I love that, I love that expression. But then, um, how, how do you measure that the, you know, what you want to achieve, um, from a cultural energizing point of view is actually taking root and happening? Um, which is what you just, uh, what you, you just explained vis-a-vis recognition data. So, Jesse, um, uh, uh, you know, uh, moving over to you, um, what more can recognition data start to tell us? You've been working on some really, really cool things in the lab here, so to speak. What, um, what, what are you excited about? Yeah. I mean, you know, the, the amount of information that's in a recognition message goes beyond what most people would think. I mean, there are a lot of signals around how employees are feeling, what they're working on, who they're working with, the value they place on particular contributions for employees. But, you know, Mishaann talked about the surveys and how a lot of that as an approach is outdated. I mean, we, we know organizations like MIT have done studies on survey results and seen that most employee engagement surveys, people will tend to give the same...... response for all the questions in the survey. There's, like, a halo effect there, so you don't get a lot of depth. You don't get a lot of diversity of information. But in those recognition messages, we're able to do things like, uh, measure employee burnout. So, we've created, uh, uh, a burnout indicator to look for the language or the subtle signals in those recognition messages that someone or a team might be hitting stress levels that would lead them to be absent in the future. So, language like, you know, "Thank you for working over the weekend," or, "I know your team is stressed, but thank you for doing X, Y, Z." So, things that, you know, maybe the person close to them would know, but organization-wide, it would be very hard to put your finger on the pulse of where burnout is happening. But with recognition data was one example of something that we can do that would be hard to do in the past with, with older methods. I love- Yeah. ... that example, Jessie, because, you know, when I was doing employee surveys way back when, and you see responses around burnout, then you have to do this whole thing around, "Okay, why are people experiencing burnout?" And it doesn't necessarily give you intelligence that you can act on to prevent burnout. And so, what I love about this specific example and use case, is once you see and know what the indicators are, then you can train managers to look out for that language when they're approving messages, and it stops burnout before it happens. I mean, you're changing people's lives here. You know, this is a big, big, exciting deal. Awesome. Yeah, love it. Now Cathy, in your, um, uh, again, we'll go back to the conversations you have with companies as you're, you're, you're kind of helping not just understand what they're doing, but then guide them on better ways of doing what they're doing. Um, what are they saying about the, that old chestnut of the performance management process these days? Which, in the past, has been one of the, if not the only way to collect some semblance of, you know, the, uh, what an employee is, you know, in, you know, in air quotes, "worth" to the organization? I mean, every company that we talk with has a- actually always been, but, um, now again, wants to redesign their performance management process. So, it's that kind of process that never works quite well. Everybody hates it. We actually did a study a few years ago, where we saw the net promoter score of, uh, performance management in organizations is minus 60 on average. Minus 60, so it's like everybody hates it, you can never get it right. But still people, of course, feel we need to do it, because we need to make sure that we, uh, appropriately classify where our employees stand in terms of performance. Also, to do compensation and promotions, but it's, it's something that organizations never quite get right, and you always see the pendulum swinging. Sometimes it's more around development and opportunities, and then the, uh, especially when maybe the economy turns a little bit less, um, favorable, then you have a much more constra- constraining performance management process, where you're saying, "We have to know who to weed out and how to get rid of people." Uh, literally it's, it's that kind of thing. Um, so it's, it's that process that nobody ever gets right. Um, and yet, uh, of course we are always, uh, trying to also incorporate not just manager's perspective, but employee perspectives, other p- Like, 360 reviews and all of those kind of things. But what, uh, what happens then is, is one of two things. Either, um, the 360 process doesn't really work, because people are trying to game the system and try to make others look less good so they could look better, right? If you're having, like, a forced ranking in, in organizations that have that, you ask for feedback and they're like, "Oh, maybe if I rate one per- my peer less favorable, I'm gonna have a, a, maybe better rating, ah." Or vice versa, sometimes you have a very nice culture where everybody says, "Everybody else is great," and you still don't get something that's useful and, um, kind of supportive of both the employees, but then also the, uh, the organization's perspective on how to support people's growth and development, and put them into the right spots. I, uh, just a little sideline because we all were in Europe, um, last week. I've, uh, what we've seen in, in, in our studies is that the European companies tend to be better at performance management than the American companies, because they have to be. Because they can't just let somebody go very easily, right? They have to find somebody the right spot to make them successful, and have to work much harder to help them find the right place. And we believe that every person can be what we call a super worker, given the right opportunities, so that kind of supportive, uh, environment, uh, finding the right place. Well, insights that you can get from peer recognition, for example, will help you find the right spot for people, because you will immediately see, "This person is great at, at customer suc- uh, support," for example. Because everybody says, "Oh, thank you for helping our customer make, uh, make our customers more successful, or more, um, satisfied," or whatever it is. Or, "This person is really good at strategic thinking, because thank you for helping us, uh, create our strategy," or whatever it is, right? You have much deeper level insights and much less biased insights on what are the strengths of every person. And that way, you can calibrate and bring, um, bring them to a, uh, a job or to work where they can use their strengths. So, that kind of strength-based and peer-driven, uh, supportive performance management is, I think, where organizations eventually should go and, and have to go. Yeah. Uh, uh, it's a, it's a super important topic. And, and I think this concept of, of now relying on a, a process which is rife with, with issues, be it recency bias, um, the variability of managers and their, you know, how good or not they are, um, in giving constructive feedback, both positive or negative. Now, um, leadening that process with, oh, and also, um, you know, please make sure you're, you're, you're collecting, um, the, the skills of this employee, um, either, you know, self-declared or manager declared, so that we can become a (laughs) skills-based organization, right? That's the, the kind of buzzword of the day. Um, Jessie, I'm gonna throw this one at you. In the world of skills, um, you know, how incomplete, inconsistent, or inaccurate are the legacy ways of, of, of really understanding the skills profiles, strengths, weaknesses, gaps of an individual, the team, the department, or an overall organization? And, you know, and, and what's a, what's a new, perhaps, and better way of, of, of analyzing skills data? Yeah, that's a great question. A- and both you and Cathy touched on, on some of the weaknesses there. But when you think about the skills marketplace, and, and so many organizations wanna be skills-based, but the majority of those skills inventories of an employee are either the employee raising their hand and saying, "I'm skilled in X, Y, or Z," or we take a job title, and we kind of infer the fact that because you're in a certain job, you must be skilled. Uh, but we don't have a lot of skills validation measures. And so, in recognition data, that is a crowdsourced moment of an employee telling another employee, "I saw a skill that you demonstrated." Now, certainly, they're not saying it in that literal sense. Yeah. But that's all in the recognition data. And so we're able to, at Workhuman, we pull out about 30,000 different skills that people will talk about in recognition data, and we're able to roll those up to teams, to, to help with career path and internal mobility conversations, and provide a different lens, you know, a very complementary lens, to the traditional skills data sources that they have. And we've even created, um, custom leadership skill taxonomy. So what does leadership look like in th- your particular organization compared to maybe another? Because that culturally can be different as well. So that, that recognition data as skills in action and performance-validated, skill-based, uh, data, data points is really unique compared to what else is out there in the market. Yeah, 100% agree with that. Um, and I'm thinking back to what you, you initially talked about with that, that, you know, the moving from reactive to predictive, um, you know, pattern recognition-based, um, data analysis. Skills is the perfect one, because the, you know, in, in, in the work that you're doing, uh, what we're observing in, you know, in, in our client base, um, understanding what skills look like early on in someone's career, that, um, that look like what a, a future leader, um, looked like 10 years ago. And being able to kind of identify who those future leaders are in an organization based on what people are saying about them now is unbelievably profound, um, and, you know, and, and has, I think, a lot of potential for helping organizations that truly want to be skills-based, um, really take advantage of that validated source, uh, of, of skills data that they don't even know exists- Right. ... but certainly does within the context of these, these robust recognition programs. Um- Oh, I'm dying to jump in here. Yeah, I was gonna throw it over to you, Mishea. Have at it. (laughs) I'm s- I'm so passionate about this. You know, from a, from a people science perspective, I am excited about, you know, this thing we're doing where we are triangulating from different sources to get a better read of skills. That is definitely better than single source, whether the source is manager or the person, or inferring from job descriptions. But what I'm also excited about is for people to gain an understanding of their magic as other people see it. And I think that is so powerful, and that's something that we, we definitely, um, need to talk about more that is related to what Cathy brought up, which was strengths-based leadership. When I think about my own career and then I th- you know, I think, you know, back in the day, and I think about now, if I understood back then what my special magic is the way that I understand it now, I would have approached each role a lot more intentionally. I would have been a lot more effective. And I, I think that that is true for the average human being. Sometimes we do things and we don't realize, oh, this is a special strength of mine. I think when people start to understand that through the len- lens of others, everything changes for the better. Well, and it also, from an organizational perspective, knowing that about Mishea Ann years ago starts to help them identify hidden gems within the organization, um, and take action on that information way earlier on, which is- Yeah. ... which is exciting. Yeah, yeah. I, I want you to- That recognition data is really just a profile of what excellence looks like, whether it's for the person, whether it's for the team, whether it's for the company. And so, a lot of our clients will have... You know, when somebody's a new hire, they join the company and, and they're asked to look at the, the feet of recognition for that team, so they can see what excellence looks like. They can see how people are showing up for one another. And whether it's in a re- a self-reflective moment or it's to see what excellence looks like in your organization and how do you kind of plug into that, that data's very powerful and it's unlike anything that's available. Yeah. I, I love this conversation. Go ahead, Cathy, and then I'll go. (laughs) I love this conversation because we work with m- so many companies on becoming a skill-based organization. I know everybody is trying to go there. Uh, but I think what you just said, Tom, is so important. Taking action on the skills is actually the most important thing, so having an inventory of all the skills of the organization. We always discourage organizations to actually do that because then, it becomes an infrastructure project, but not with a purpose, right? So, I mean, we worked, for example, with a big telecommunications company and they have 25,000 employees, and they said, "We've been on two-year journey of inventorizing and doing our skills taxonomy for all of our 25,000 employees. We now have 30,000 skills all mapped to every employee." And we said, "Oh, this is fantastic. What are you doing with it?" (laughs) And they said, "Well, hmm, not- nothing yet. Uh, it took us so long." And then, of course, they look at it and it's... Where they started two years ago, the skills have changed so much. So in the meantime, this is already obsolete. So rather than thinking about this as an infrastructure thing to just say, "Okay, we need an inventory," why do you need it, right? So rather than thinking about that, think about the problem that you're trying to solve as an organization, and then back into, "What skills do we need from what group in order to solve that problem?" Is it a, I don't know, "We want to go into new market, we n- want to have different products. We have a new customer offering that we wanna get to." Whatever it is, think about it from more falling in love with the problem rather than falling in love with the skills-based organization kind of framework. I think that's a, that's a great point, and I think that's where I get excited about AI on top of those skills inventories, where you can start to ask those questions- Right. ... about career pathing. You can start tying it to initiatives that are happening inside of the company, uh, rather than just treating it as an academic exercise 'cause you're absolutely right. Yeah. The, you know... If it's just a profile of an employee, how useful is that, right? You wanna be able to- Exactly. ... apply it to outcomes for the business, and that's where adding AI and LLMs and bringing that together with that skills data is the most powerful. Yeah, absolutely. And such a brilliant way to think about another level of skilled insight from the recognition data, because I think most companies don't think about it. They have recognition pro- programs, and I actually talked with, with one of your clients, we won't mention the name, um, a few weeks ago, and they said, "Oh, yeah, we're using it and it's great and everybody loves it, and we get some- so much good culture change and all that." And I said, "Well..." And they also told... Talked with us about their skills effort. And so I said, "Well, have you thought about using that skills data?" Um, th- this woman, she was a senior leader there in their HR team, and she said, "Never thought about it." So, I think it's something that you don't even- Yeah. ... realize that you can tap into that. And she immediately sent an email to her team and said, "Hey, why don't we mine that data?" So I think that's, like, a- just a great bottom-up kind of insight that you get from your employees and listening to your employees. And as we all know, listening to your employees is one of the superpowers that we have at our disposal at... In, in an organization. I'm also excited about this from the perspective of new leader onboarding. You know, I think about, um, years ago when we would spend all these efforts, and some companies still do, onboarding a new leader to their team. But if you have access to the historical recognition data and you can layer AI on top of that to see skills, you can do a skills analysis of your team like that and know exactly what you're stepping into, like which skills you have, which skills you don't have for the team you're trying to build, and the direction that team is trying to go. Yeah. I think the, the use cases are almost endless, um, and- Mm-hmm. ... you know, and, and unbelievably exciting as they really tap into almost every aspect of what people organization is there to both provide from an employee experience point of view, but more from a- an employee development growth, um, you know, perspective that then benefits the, you know, the, the ultimate outcomes, uh, for, for the organization. Um, so we're gonna wrap with a little bit of a, a rapid fire round here. Uh, I'm gonna ask you all to put, um... To, to, to break out your crystal balls- (laughs) ... um, and, and talk a little bit about the future. We know where we are now. It's changing day by day with the... Just the rapid, um, you know, cycles of AI advancement that we're, we're experiencing literally every day. Um, the application of, of AI to people data has unbelievable promise. We've been working on that here at Workhuman for, uh, for many, many months now. Um, when, when you think forward, I'll start with you, Misha, and when you think about the future and the application of, of AI to, not just recognition data, but any, any type of people data, what, what does the people intelligence function of the future look like? What are they w- what, what are they delivering for the organization? Yeah, I love that question. I think, you know, part of people intelligence is going to be, um, some kind of AI-driven skillset. And what I mean by that is organizations are trying to-... figure out how to be e- people, um, AI powered, and I think people intelligence has a role in that, because part of tha- that, doing that well is the dataset, right? And so we're gonna have to become more focused on the quality of datasets that we're layer- layering AI on top of. I love that we're using AI to improve the dataset, you know, through our Rekognition advisor, encouraging people to write better messages. I know so many people are focused on, you know, the va-va-voom (laughs) of AI right now, but I think part of what will make this successful is layering that methodology on top of a dataset that is solid, dense, right? Information, information-rich, and bias-free. All right. Jesse, over to you. What is your crystal ball telling you? Uh-huh. You know, one of the things I, I'm excited about is, is team performance. So much of work is done as a team, and when you have unique people together in a group trying to accomplish something, that dynamic gets really complicated. So that lends itself really well to human data combined with AI to help understand, well, what would the highest performing team be that we could possibly put together to drive an initiative for our company? Those kinds of questions are really historically rife with bias and basically done in closed-door sessions inside of an organization. But now, with all of this data that's available and some of the AI techniques, our ability to answer some of those questions bias-free and come up with answers that are more transparent, I get, I get really excited about the possibilities for that in the future. I do too, and it breaks, it kind of breaks that whole, it breaks the organizational hierarchy approach to team formation- Mm-hmm. ... um, down completely, uh, and, you know, eh, the, the assembly of teams and the ability to do, um, scenario generation around what that team and output might look like if we put this, you know, this collection of people together on a task is, is unbelievably exciting. Um, so Kathy, uh, your crystal ball, you, you, you know, you and, and the, the wider Josh Bersin team last week talked about the concept of the superworker, um, a- and, and I know you have, you have some unbelievable thoughts here. So what, what does the future look like for you? Well, I mean, I'm very, very excited about what AI can do, both for every employee and then for the HR function of the future. So for every employee, um, as we're seeing that, um, it can help every employee become a superworker, which means, for us means any employee, frontline workers, knowledge workers, supervisors, managers, executives, wherever they sit, can actually help use these AI tools to have much higher performance and productivity, but then also much more meaningful work. So in essence, the AI tools can take all the things that we as humans are not great at, that we don't like to do, that, like the repetitive using an enormous amounts of data, for example, seeing patterns on, on, a, like billions of data points, we can't do this as humans. But AI can, and elevate every single person's job. Incredibly powerful opportunities for employees to have just much better work environments and much more meaningful work. But then for us as HR organizations as well, it can help us to actually elevate from being these kind of sitting back, reactive service delivery functions where somebody knocks on our door and says, "Hey, I have this, uh, question," or, "I have this issue that I need you to solve," much more problem-oriented, consultative, that actually sit at the ta- not just sit at the table, but actually make business decisions and support the business to be much more, much higher performing, much higher growing, much more customer oriented, all of those kind of things that we maybe never had the opportunity to do, because we always had to just take care of all the nuts and bolts of the HR kind of service delivery stuff. So I think incredible opportunities. Uh, Nicol Lamoureux, who is the CHRO of IBM called it "HR's moment in the sun," and I love that. Mm-hmm. Right? It's our moment in the sun, and I think it's ours to take. This is the op- our opportunity to, to, to take and, and run with it and add incredible value to employees and to make work better, which is, I think all that I'm very passionate about. Yeah. Uh, we like to say, "Make work more human." Uh- I love that. (laughs) Oh, it's in your name, right? (laughs) Yeah, it is in the name. Uh, all right. Well, thank you, uh, esteemed panelists. I think we've, you know, we've determined that, and, you know, I myself love a good dashboard, uh, that, and, you know, that, that way of, of collecting and synthesizing data will likely never go away. But the, you know, exciting future ahead is when we can marry different signals of which Rekognition data gives you immense, um, an immense portfolio of signals that can be far more powerful, um, a- and that trusted window into how work really gets done at around an organization, and how leaders can then use that to advance not just their people agenda, um, a- but more importantly, their, their organization's agenda. Uh, so we covered a lot of territory. I thank you for your time, everyone who's, uh, who's connected into this webinar, and, uh, we'll talk to you soon. Thanks very much. Thanks so much. Thank you. (instrumental music)