HRD: How To Implement People Analytics

Thank you for listening to HRD Masterclass. It's a unique resource featuring over 130 HRD experts across 55 episodes. As a fan of the series you can help make season 6 happen. AHRD is crowdsourcing funding for the season and you can donate today at givebutter.com/HRD-Masterclass-FHRD. With your help the season can bring you 11 new episodes focused on major HRD research articles. Their practical implications, the need for further research and how researchers and practitioners can help each other to advance research and practice on the topics. It would be amazing to continue the series and I hope you'll consider donating today at givebutter.com/HRD-Masterclass-FHRD. Right, let's dive into the episode. There's data that has like the shape of information but it's not actually telling you. I think we really have to be cautious about that. There are absolutely people who say you're sitting on mountains of data to turn it into something and that's harder to do than it sounds like. Welcome to Human Resource Development Masterclass. The podcast series from the Academy of Human Resource Development, the organization that leads HRD through research. I'm your host, down in short and here in our fifth season we're exploring some of our listeners' top how-to questions with the help of leading authors, researchers and scholars. In this fifth episode of the season we're focusing on the question of how to implement people analytics and you'll hear a conversation recorded in April of 2025. To explore this important question I'm joined today by three experts. My first guest is Dr. Sung Woon Yoon, professor of Human Resource Development and People Analytics at Texas A&M University. Sung Woon's research focuses on enhancing employee and organizational performance by integrating leadership, learning and knowledge sharing and technology. He frequently applies frameworks from social capital theory, network science and data analytics in his work. He currently serves as the president of the Academy of Human Resource Development. My second guest is Dr. Alexis Fink, who is a leading figure in People Analytics. Having led people analytics teams in several major tech companies as well as extensive work in organizational transformation, organizational culture, leadership assessment and the application of advanced analytical methods to human capital problems. Alexis is a fellow of Syop and was recipient of Syop's Distinguished Service Award in 2019. She earned her PhD in Industrial Organizational Psychology at Old Dominion University. My third guest is Dr. Brad Schuck, an internationally recognised scholar, entrepreneur and thought leader and employee engagement, organizational culture and leadership development. He's the author of Employee Engagement, a research overview published by Routledge in 2020 and has published numerous peer-reviewed articles, books, chapters and invited presentations. Brad is a tenured full professor of Human Resource and Organizational Development at the University of Louisville. He's also the co-founder of Org Vitals, a purpose-built research-driven culture management platform used globally to improve strategic alignment, leadership and employee experience. Just visit allbypodcast.com/analytics to learn more about the bios of our three guests and also to connect with our episode sponsors, the Educational Human Resource Development Program at Texas A&M University and Concordia University Wisconsin. Earn your Doctorate in Business Administration Online. Talking of sponsorship, Human Resource Development Masterclass is only made possible thanks to the wonderful support of our sponsors, who cover all of the costs associated with the series and so enable us to release them free of charge to listeners like you. I encourage you to show your thanks by checking them out and letting them know just how much their sponsorship means to you. The first half of this episode is brought to you thanks to the wonderful sponsorship support of the Educational Human Resource Development Program at Texas A&M University, which aims to transform lives through teaching, research and outreach. Addressing critical issues in talent, leadership, career and organisation development, the program promotes inclusive excellence across local, national and global contexts. Its modern curriculum emphasises scientific approaches and evidence-based decision-making to prepare students for today's complex and dynamic workplace environment. Graduates are scholar practitioners who connect and apply theory and research to inform policy and practice, improving the lives of individuals and the effectiveness of organisations. You can learn more about the program by visiting eahr.tamu.edu. Okay well I'm delighted to welcome Sunwan, Alexis and Brad into the episode. Thank you all so much indeed for being here. We're delighted. Thank you. Thank you. Thank you. So in terms of a good place to start, I'm conscious that we'll have listeners to the episode who may have different levels of experience around people analytics may even be using different terms. So I was wondering Alexis, would you be willing to kick us off just by talking a bit about what we mean by the term people analytics and maybe what other terms maybe being used to mean the same thing? The simplest shorthand that I go to is that people analytics describes the data functions and decision science for the people space in organisations. So this is all of the data foundations, the data stewardship, reporting and metrics, include all of these elements, as well as what I refer to as like an R&D function for making strategic investments in the people space. Maybe because of academics who want to investigate and report part definition, there are two parts in these terms as you can see and the analytic parts are linked to talk about it first. By definition in Greek it means breaking down into smaller parts and to me that means you can make it smaller, quantify almost the automate and maybe computerise. And the formal part really represents the focus and scope and so like HR analytics and people analytics, I think both terms appeared in practice probably around the same time and HR analytics tend to focus on like HR issues, hiring, onboarding, like a performance management and many failures whereas probably my understanding is workforce analytics before more to talent intelligence, external and internal and I heard talent analytics probably might focus more on like attracting, retaining, supporting talents and I definitely agree with what Alex has pointed out for people realised where like HR analytics tend to focus on HR but to be really effective and impact for it has to be connected to business and so it's not just a nature issue but operations, finance, there are many stakeholders, organizations who work together. The only thing I might add which is minuscule here and I think someone and Alexis would agree on this is there's an action orientation of this like what do you do with it? Like how like okay what do we do with this information and we have all worked in places where we've collected data and given that data back and the only connection around like how long we've been working together is the level of dust that's on the binders that is on someone's shelf and so if you're not taking action on this there's just a missing gap here and I think when folks take this really seriously the orientation around all right what do I do with this really becomes an important grounding point. You know that's so true and I've had your my teams all the time that I don't want to just hear about the what I also want the so what why does this matter and then now what what are we going to do about it? A joke that I show up every day in an invisible t-shirt that says I'm not in the curiosity business just because something's an interesting question I don't have the time or the resources to investigate every fun rabbit hole. You need to tell me what business decision we're gonna make, what'll be different, and then we'll invest those resources. - I love that, you know, and our classes at the university, you know, I tell my students that data changes the conversation. And it changes it from, I think we need to, to hear what the evidence is suggesting we might need to look at. And to the, to Lex's point here, I think we have an opportunity to use analytics in a way to really illuminate data that has been invisible before and making it visible, and then making decisions that help us really drive performance in ways that we haven't thought about quite yet. But it requires us to ask questions and have conversations around, what do I do with the information I have in front of me? - You know, it's so neat that you express it that way. One of the things I tell my folks all the time is that I secretly think people on analytics is the most powerful position in all of HR. Is people on analytics in their data stewardship role and their R&D role, they decide what is knowable by everybody else, by all the leaders, by the board of directors. We define the boundaries of what is knowable at scale. That is an awesome power. It's also a huge responsibility because let me tell you, if your mental model of this thing is wrong, or you've gotten a gender you're pushing that is not actually the best interest of the firm, bad things can happen. But to the extent that you have the business acumen, to the extent that you have the technical acumen, to the extent that you have the goodwill, wow, can this be a differentiator? Yeah, I don't wanna digress too much, but I have this to view, like people analytics is a master key in leveraging AI and leading changes for the future. But to bring us back, like there, you talked about what are the confusions. Where people use these terms, like many times interchangeably, especially between HR analytics and people analytics. And if somebody asks me, I try to be as precise and concise elevator speech as much as possible. It's about using data in a smart way to improve the business, but also HR and P Pro practices. This is interesting. It makes me wonder what it looks like in practice. Specifically, I suppose, if you were to go into an organization that you haven't been in before, what would you look for to see whether they're doing people analytics well? I probably talk to practitioners, like once or twice a month, and they work in very different organizations, places. And so my experience is, it looks all different depending on organizations, industries, and size of the company. And I am glad that the people analyst practice has matured enough to have a research form that the venture marketing. And so particularly inside 2 to 2, I think they started looking into, like what are people analytics practices are like from year 2020-2021, and they looked at, like from maybe started with 100 organizations to, I think the most recent year, close to 350 organizations. And so when they started the average size of the people analyst function and team was like 1 to 4,000, and it became down to probably around 1 to 2,500. And I heard like leading organizations tend to have a smaller ratio around 1 to 1,000 or a little more. But that's more about companies who have the people analyst team and function. More, most organizations like you mentioned therein, I have seen a lot probably more numbers of companies that doesn't have anybody or just has one person or two, and who are considering like starting a paper and the rest team or does work that people and all it's a professional is two, but doesn't have an official title. So probably that's just my experience. So what do you think Alexis? - Yeah, so first off, you're right. Inside 2 to 2 has done a wonderful job bringing together a coalition of people to do this. But I want to point out that we've seen people Jack the Tenz, Jade Gemra, Wayne Cascio who've been doing this minimally since the 70s with HR economics, et cetera. There are methodologies for linkage analysis and utility analysis, which basically is people analytics. We just didn't call it that until Tom Davin wrote that HBR article in 2010. We've also seen large P.A. teams in some industries, insurance, pharma technology going back. When I took over the team at Microsoft, the earliest hard copy evidence I saw of a people analytics team at Microsoft was in 1993. So we know that they've been around for a while in a bunch of these leading firms. To the original question that Darren asked about what are some of the signs? Some of the signs are things like regular reviews of operational metrics, well organized, strategically aligned metrics as the way we run our business, which tells me I'm a business that's interested in numbers and data. Conversations, particularly conversations with executives that lead with some sort of what does the data say? Conversations with the professionals in HR that are less likely to cite a case example of company X did practice Y, so we should too. And more likely to cite our internal research shows or more likely to cite if we do X, then Y happens, and if we do Z, then there's other things happens, more likely to cite some of those predictive analyses that people talk about on the maturity models. So one of the things that I look for is the texture of the conversation. And are people fighting for status based on their past experience and their networks and their intuition, or are the ideas that when the day ones that are backed up by data, ones that are backed up by this rate is changing in this way, this factor predicts this other thing, something like that. And so it's really, as you say, it'll look different everywhere, but the texture of that conversation and what ideas get traction and which ideas get, oh, that's interesting. And now we should investigate it to see if that's true for us. Those are the things that I look for. I'm soaking this in. I'm like, I am soaking some of this in. And so we glossed over a couple of things really quickly. The first is Alexis mentioned when she first did this at MacPerson, so I don't want to gloss over. Alexis is a, she's a heavy hitter in this area. And this is legit talking about behind the curtain what this looks like. And then Tom Davenport's article, to like that is, that was the precipice here in some ways, where it was kind of a money ball moment, if you will, for HR. Like, all right, how do we use data to understand some of the nuances that's happening in small pockets? And then where my brain goes on this is, how does machine learning and AI begin to enable us to understand those money ball moments as an equal playing field? And then what do we do with that? Like how do we begin to leverage data in ways that help us create places of work where folks just don't come to work, but they live better lives through their work. And the kinds of data that we can collect as a result of that, I think some of that stuff changes lives. And Alexis was at the forefront of that. And I just don't want to gloss over that. - Well, you are very kind and I appreciate that, thank you. Also, I want to pick up on something that you alluded to, which is the advanced analytics. One thing that's been a great frustration of mine, and one thing that I see starting off teams embrace probably to their peril, is the use of just means or percent favorable because you lose all of the nuance and information. And as analytical tools have gotten better, as mathematical techniques have been more accessible, there are libraries in R, there are all kinds of things, you can start to look at patterns that are more interesting. Money ball is a great example because you can start to look at the utility of things, you can start to look at outliers, you can start to look at something that's different than just averages. It doesn't tell you much to know what the average time to promotion is. It tells you a lot to know what is the top 10% of your popularity. and like how quickly are you advancing those people? So what is the upper bound of speed of development if you have a leadership shortage? And what does that mean about your recruiting, right? Some of these other analytical methods beyond just give me a dashboard with an average in it, give me a dashboard with account in it. Those are the things that really let you make a difference in your organization that let you push on something strategic kind of like money ball. How can I optimize my low payroll for this baseball team and get the best possible outcomes for it? - And what would that look like if we applied some of those principles into the workplace today? And then thinking about how we illuminate data and I'll go back to that statement around how do we help data that has been historically invisible? Become visible. So for example, I can be really, really engaged but also super stressed. Like those things live in the same place or what happens if I don't feel like I belong but I wanna be, like I really wanna be connected to the company but I can't find a way to connect and relationally to the company. Like how do we create really like 3D models that help us understand that data in ways that we can pinpoint opportunities for folks to really optimize the workplace. And then what if we cascaded that over to health and stress and all the other opportunities that are connected with human life? - And Brad, you made reference to the human thriving component. One of the things, my background is at Industrial Organizational Psychology. One of the things I've always appreciated about that is the dual focus on work and workers. Well, being safety, all of those things. And I think that one of the things that I appreciate about people analytics is it takes that forward. And I'll share a quick story and then I'd love to hear more from Cion Wong. One of my proudest projects was actually in a past role, kind of weirdly, the medical director for the company reported up to me. And through a variety of analyses and process changes and other kinds of things, we reduce the heart attack rate of our employee base by 50%. That's human thriving. That's kids who still have their parent alive. That's amazing stuff. And that's really an outlier. That's not normal and average. - This exceptional case is really inspired me because what I like to quickly add to this conversation is there are certain actors, companies that we should give credit to popularized like people analytics, not just fancy or technical way, but really making a real impact. So Google is another company that popularized the people analytics because people had all kinds of assumptions about what makes a effective team, what makes a effective manager. And the point I really like agree with is it's not about like a machine learning or AI, not a big data, it's about relevant data and making a real impact. And so especially for us, I know many listeners who are listening to this is like some of them know I work as an IT manager in my 20s and 30s. And I have probably used and published many multi-various statistics papers, but my huge realization of a moment is it doesn't explain the interconnectivity and structure. And so it got me into network analysis and text mining. And I wouldn't say because I like it, these are better because we have a good foundation and all the tools we have, qualitative research, quantitative, I always start by project with the interview, but this framework like Alexis, the wonderful case you shared, if we do the job right, like connecting it to more beach data set, then we can connect whether it is like a hiring talent or support L&D, we can do a much better job, not just to improving or making good changes, but touching, tackling the experiences, development within Conking. - So I've really appreciated the way that the three of you have talked about it because it makes me realize that people analytics presumably looks a little different based on like the size of the organization. So if somebody is in a major global company, then they probably have the resources for people analytics, it's probably quite advanced, at least one would hope. But then at the other end of the spectrum, we've got people who are presumably in smaller organizations, say 152, 100 people, where there may not be people analytics in place right now. So what do you think about those folks? Where would you recommend they start as they think through their people analytics journey? - Yeah, I'd love to talk about that a little bit because I didn't always work at giant companies. In fact, I spent a bunch of time really in my career in a very federated organization, had a bunch of small sites that really operated as pretty independent units. And so I spent a bunch of my time in businesses that were 150 people that operated independently. And the insights that we could use from our attrition and our hiring, the insights that we could use that actually were incredibly pragmatic about who performed well at certain tasks and how to deploy the talent we had in that organization in the most efficient and effective way. And that's actually an example. This is more feasible now than it was 20 years ago when I did it. But this happened to be a main, the one I'm having mine happened to be a manufacturing organization. And we built an agent-based simulation of the entire operation and dramatically reduced cycle time. By looking at the roles, the tasks, the skills required, the business process, and sort of through an auto-enobtonization model and said, "If these are the people I've got, if this is the equipment I've got, if this is the standard I'm trying to meet, how do I do this?" Because we had people who were working crazy amounts of overtime, they were farming their health, farming their families, et cetera. And we figured out ways to make that plant more efficient. We were able to hold people's annual wages, so we gave them a base rate increase, so they made the same annual wages, but they didn't have to work the overtime to do it. We reduced cycle time, we increased customer attention, we reduced waste and environmental hazard. And there was all kinds of stuff that we did starting with people analytics, starting with deconstructing those jobs into skills and tasks, and then optimizing the way we configured those into teams and business processes. This is people analytics work. And really, it's hard to do that for 100,000 personal organizations, 'cause you got too many things going on. 150 personal organizations, you got like 10 things you gotta do. So do the heck at home, right? Do them really well, and you can make a profound difference. - Probably many of us have chosen this field for the love of helping people. But if you look at your experiences, my experience is probably our default operation mode is doing everything analog. And so when we think about one-boarding experiences, team work experiences or rewards or L&D, career promotions, like it's not difficult to see where we keep adding things more, and it makes our work very difficult. And it is true I find when people talk about people analytics, analytics, like our advanced technique, fancy techniques comes to mind first. But like as I said, it's not none of those, it's based on relevant quality data for the good cause and right cause. And so you don't necessarily have to have great team or all the financial resources to address like challenges or needs within the organization or within the people team. So whether it is a nutrition or turnover is one common earlier people like apply, people analytics, but it's not just one earlier, whether it is a career opportunities managing career, like when repeating issues, problems continue. And like analog intuition experiences, no longer work, then it's probably a good time to consider like how we can enhance our efficiency and effectiveness by leveraging data and what analytics cannot provide. I don't want to gloss over what someone said here. I think if people, analytics had a heartbeat, it would be around helping people. Like, how do we help people? And what does that look like? And how do we leverage data in ways that enhance the life of the organization, and the people, and the ecosystem, and the community that is around us? And so he is so right about that. That is, this is so much not about ones and zeros. But at the end of the day, like, we're looking at our teams and our folks, and we're giving them information that we hope will leverage optimization and thriving and engagement and all the things that we talk about in HRD. And in a small organization, this is so possible. This is, I mean, I actually think it would be harder to do in a larger organization because there's policies and there's probably some thick manual that we have to work through in terms of guidelines. And if you're in a 250 or 300 person organization, we're starting with a business problem and thinking about, all right, how do we solve for that? And then using data that we have at the ready. All right, if we don't, if we haven't collected anything yet, what do we have? What do we know? And then linking that data to outcomes across the organization and then partnering folks about that, making that visual and actionable. And then at the end of the day, I think it's just about being curious. Like, how do we be more curious about some of the challenges and opportunities that we have? And so oftentimes, I feel like we frame an organization as we have a problem here. And I wonder what it would look like if we reframe that as we have an opportunity. We have an opportunity to optimize. We have an opportunity to increase this by 5%. We have an opportunity to help folks live different lives just by coming in here and being a part of this community. How that might change the narrative in the old museum. [MUSIC PLAYING] We'll be back in a moment with more from Alexis, Brad, and Sunguan. First, though, here's a reminder that today's episode is brought to you thanks to the wonderful sponsorship support of Concordia University, Wisconsin. Unlock new career opportunities with the PhD or Doctor of Business Administration from Concordia University, Wisconsin. Our flexible online programs are designed for ambitious professionals seeking to lead, innovate, and drive meaningful change in their industries. With expert faculty, research-driven coursework, and specializations in financial and economic management and organizational performance in change, you'll gain the skills to excel in executive leadership, consulting, or academia. Take the next step in advancing your career. Visit earnyourdoctorateonline.cuw.edu today. Right, let's now return to the episode. It's interesting to listen to you there, because it makes me realize that there's probably a fair number of people for whom people analytics kind of means what data do I already have? And how do I make them most of this data to pull reports together and send them to stakeholders? And in contrast to that, the alternative is to think about what organizational problems are there where people analytics could help solve that problem. And I would imagine that is the viewpoint that you would recommend people come from. There's a phrase, I also call it just to refer to it as dust bull empiricism, where you just start with a whole bunch of data and see what's there. And the problem is that, A, often the data aren't as interesting as you think they are. So for example, your human resources information system might tell you how long someone has been a manager, but that doesn't tell you anything about whether that person is any good at that job or not. And the other is the problem of spurious correlations. There's a guy who did cartoons, and then he made a book, and I bought it, because I just wanted to give the guy a couple bucks for thanks for doing it. But it basically showed wonderful preposterous examples, like drowning deaths in Norway, correlates at 0.97 with pounds of margin consumed in Oklahoma, or something just ridiculous, right? So you can find all these relationships that sometimes mean nothing, quite famously when unsupervised models first got sexy, somebody threw in unsupervised model together and determined that basketball causes the flu, because they both tend to spike in the fall. And actually it's because people are indoors and other kinds of things, but the model said the strongest relationship was between basketball and the flu, right? And so you can really find some things that will lead you quite badly astray, or just leave you with nothing burgers, right? Like there's just no useful information here. Like the, oh, our managers have been in roll an average of 14 years. I don't know if they're any good. I don't know if they're creating psychological safety. I don't know if they're creating space for innovation. I don't know if they're getting any kind of development out of people that are helping them do their jobs better. I don't know if they're preparing people, rescilling. I just know they've been sitting there a long time, right? So there's information that has the shape, but there's data that has the shape of information, but it's not actually telling you. There are absolutely people who say you're sitting on mountains of data, turning it into something. And that's harder to do than it sounds like. - There's a concept called like a knowledge hierarchy that many people might be familiar with. At the bottom, the largest shape is data, and above that is information, and above that is knowledge and insight. But what I really find is data is exploding, and information is probably increasing as much, but the good knowledge and wisdom is not in the shape, probably only tiny of it. There's too much noise. And even in order to better understand that data, you need a good knowledge and insight. So my perspective is it's an iterative cycle, not as a hierarchy. And so I totally agree with you Alexis. You can have all kinds of data, but if that is not relevant to the problems and goals and opportunity, you try to really leverage your data. It's wrong, like it's positive pieces, or it's a rabbit hole you are getting into, I think. (laughing) Years ago, I read a book, and I'm forgetting right now what the book was, I apologize. Years ago, I read a book that had a line in its introduction that said, "We are swimming in oceans of data, and what we need is a thimble full of insight." And that just obviously has stuck with me for a decade or more at this point, but it's so true. There's enormous amounts of noise and extracting the signal from that, turning that into insight is really hard. Yeah, there's a faculty member here in the Academy of Human Resource Development, Jeff Allen, who talks a lot about wisdom. And I would encourage any listeners to dig into his work and his research, but really thinking about wisdom is more than knowledge in some ways. And I think Jeff would say that context and perspective matters here. And I think about the idea of being in a room with a thousand people and also being incredibly lonely. But you're around so many people. We have so much data that saturates our being, our information and coming into us at all points and all ways that sometimes making sense of that information requires us to be wise about the kinds of data that we interpret and the kinds of data that we infer and then use and put that into practice. And I think Jeff would say that here, the data here and the wisdom around this is really rooted in values and how we use that data in ways that help us leverage life for the better, for the positive. I wanna riff for just one brief rabbit hole on the wisdom point. I am a super AI fan girl, right? Like I've been coding AI stuff for a long time, love it, super powerful, really aware of its works, right? Like it's not a total fantasy, it's super useful. But from the talent management perspective, one of the things I really worry about is the opportunity to develop wisdom. So if we have, I've got kids who are college age, we have a whole population of people who are graduating in full world where the entry level roles that give you access to people more senior than you appropriately, that give you the time and the trenches to genuinely absorb how something is done. If those jobs are now being done largely by some kind of an automated system, where will the opportunities be to develop that first wrong on a ladder towards wisdom? This won't be a problem for a decade. It might be a problem in two decades. So how do we think about that from a strategic workforce planning sampling? How do we think about it from a talent training and development standpoint? How do we think about that from an educational standpoint? How do we think about it from a society standpoint? These are big problems that actually people analytics has a role in where there are strategic workforce planning and training and development opportunities. Maybe a bigger role than almost any other segment in society. People analytics is not just about method, but this good conceptual theoretical, like the logical frameworks. That's how I try to absorb and incorporate in adopting and utilizing this framework. 'Cause I can think of many good frameworks and many good approaches and many good questions, such as to the point of it, because we really think about what this or AI analytics mean for the future generations. We hear like a good principle, such as asking a good question is a far more critical than like you find the answers and knowing and seeing that AI is getting smarter and smarter. Like there are also many excellent conceptual frameworks in that kind of frameworks where you can really apply like these are principles. My hope is that AI and data analytics will provide the space to be more human, to ask good questions and to give us the space to be reflective. One of the things that I think have happened over the last decade or two is there's just been the squeeze that has happened, where you've been required to not only understand the data and run the data and understand the information that's coming out of it, but also be in a space of reflection where you can ask the question that you need of the data. My hope is that in the next decade or two, and I talk to my daughter, I have a 14 year old daughter, she would be super embarrassed and she will be when she listen to this, when she's 25 in college someday. I think, and I tell my daughter this all the time, your future is in content creation and asking good questions, and then also in being kind, like just being a really good human. And if we can do those things, then the world is your oyster in some ways. But my hope is that the use of AI and things like chat GPT and automation and machine learning will condense the time that we need to analyze information in a way that we can use that information in such a great way to spur humanity forward, that we get to do these things, we get to love on folks, we get to have joy, we can spend an extra five minutes with our partner on the front steps of our house to have a cup of coffee in the morning. I think humanity is going to be better as a result of the data analytic process, but through AI and machine learning, and that that will spur joy and love and hope and happiness for so many people in the future. And that is the world that I hope for my daughter. - I find that quite a powerful vision that we want to be in that place. For people who are looking to get to that vision, they're aspiring to that vision. What's the first steps on that journey how would somebody begin to move towards that vision? - So I've seen a really funny cartoon a few times now by some of my more advanced analytics friends. It's like, oh, you do like regression and then you get into optimization models and it goes all the way up this path to really sophisticated analytics and then it drops down and says Excel spreadsheets. So if you're literally just starting out and you have no budget and you have no colleagues getting good at managing a spreadsheet and visualizing the data. So you're not just building like default pie charts please for the love of God. You're not just accepting the defaults that come out of Excel or Google Sheets or whatever spreadsheet tool you happen to have at your disposal and you are learning how to use a little bit of sophistication and how visualization is presented a little bit of sophistication and how you make segments that matter to your business that you manage the data in a way that will resonate and be relevant. That alone will take you from zero to something, right? You don't have to invest millions of dollars in zero workday or any of these. You don't have to invest a fortune in somebody who knows how to deal with gradient descent boosts to find esoteric findings. There's a lot you can do with a scatter plot. There's a lot you can do with stacking your bars in a bar chart in a way that is reasonable and informative. There's a lot you can do with the default functions that sit right inside Excel, which most organizations will have. So if you really were starting from zero and you needed a tool, I would say, get a little bit smarter data visualization. They're fantastic online free tools. You can look at for that as well as, my God, Edward Tufti has been working on data visualization for decades, well developed and there are some pretty reasonable principles that you can absorb in half a day and then get smart about your business, understand the segments that matter, understand the strategies that matter and figure out how to pull that out of a spreadsheet. 'Cause I'll tell you, even my advanced PhDs spend a lot of time in a spreadsheet. It's absolutely doable and you can get a long way with just those simple tools. - So since I am teaching a new course, graduate course in people analytics, I only require like a basic step as a prerequisite. And I give the same advice to students, start with the good descriptive statistics, understanding of your data and data visualization. But I also introduce these resources for my students and trust me, like this is close to the end of the semester and they are building a good, really solid, optician predictive models. But when it comes to tools and platforms, many companies already have access to human resource information systems or human capital systems like Workday or SAP. And also, yeah, I cannot agree more. Exeter is a very popular, you can perform many things and maybe Google's fellowship, but for data visualization tools like Power BI and maybe Tableau, you don't need coding or technical skills. But I also emphasize, it is important to note the road like short-term, mid-term and your destination. And if you start your people analytics like investment then having an HRVP business knowledge, I can recommend but also some competency in data and tech and data analysis is absolutely very critical. And the good news is there are lots of free and low cost options on the web. So there are online courses, free courses from Kursela or UDME or index. And also, podcasts, there are excellent podcasts. I mentioned Inside 2-2, David Green, Josh Berstein and Chris Rene is like, I forgot the exact name but also I don't wanna forget core network's directionally correct. And so you will get a good exposure to good, great use cases. There are conferences, sign up. So one of the huge conventions were practitioners meet and lastly, I don't wanna forget LinkedIn. Like this is amazing community, mostly inviting. And so they just started, I think the name is called Society for People Analytics and they arranged meet-ups at major cities all over the US. And so those are available. Yeah. Darren, I would add, there are oftentimes local groups that folks can get involved with. around people analytics and data analytics. And if you are looking to get involved in some way, I can't recommend SIAAP enough and connecting to the conference that they're gonna have in Atlanta, having been to SIAAP conference before and a Tableau conference, both of those are absolute game changers. They're just building community, building a network, and just being around folks who are asking the same kinds of questions that you're asking, LinkedIn is such a great opportunity, getting badges through Microsoft. If you're at a university or affiliated with a university, sometimes those things are oftentimes free for you, just to begin to explore. And I think being curious here is probably the biggest, the biggest trait to success around how do I begin to think about analytics in a way that helps me and then helps my organization. And our conversation here is really centered on analytics in the mathematical sense. It's centered on data sets, it's centered on regression, it's centered on graphs, and I want to make sure that people are being mindful that often the most powerful data, the most powerful analytics, is data that's qualitative in nature. I can help you find the rich areas to dig in, I can help you add texture, it can be enormously influential in terms of sharing the impact of something that's either a risk or an opportunity. So getting smart about the marriage between qualitative data as a meaning layer and quantitative data as like a magnitude layer becomes a really important way to help move things forward with the people at a little extreme. - And I took seriously, half joke, half seriously in my class, like when it comes to doing the project, my experience tells me, it is project management, communications, storytelling, you would find more than 50% of things that influence the success of any projects, any people in the analytics project. - Well, sadly we're coming to the end of our conversation, but I wanted to say a big thank you to all three of you for the way that you've approached the conversation. I feel like I've got a much clearer understanding of what we mean by the term people analytics. I've got quite a powerful vision as well and also some concrete steps that could be taken to move towards that vision. I'm sure there's a lot more that we could be talking about, so maybe we get back together again for a future episode, but for now I wanted to say thank you all so much indeed for your time today, it's been wonderful to have this conversation with you. - Thank you so much. - Absolutely. - Excellent, thank you for pulling us together. (upbeat music) - Thank you so much for joining me for this episode. It was wonderful spending time with Alexis Finck, Brad Schuck and Sun Wanyun. If you enjoyed this episode, check out our other 48, which contain conversations with over 100 leading scholars from around the world. To learn more about the series, check out hrdmasterclass.com. And to learn about the Academy of Human Resource Development, check out ahrd.org. Support the future of human resource development. Give to AHRD a 501c3 nonprofit today. Your donation powers research, scholarships, innovative programs and vital operations that keep our HRD community thriving. Every tax deductible donation makes a difference. Join us in ensuring ahrd remains a beacon for research, collaboration and professional growth for decades to come. Visit ahrd.org/donate and invest in the future of HRD. Together we can make a lasting impact. Also please check out our episode sponsors, the Educational Human Resource Development Program at Texas A&M University and Concordia University, Wisconsin. We're going to take a short break for the summer but we'll be back in the autumn with six further episodes exploring different HRD how-to questions. Until then, stay safe. This is Darren Short signing off from the HRD Masterclass. (upbeat music) Human Resource Development Masterclass podcast is brought to you by the Academy of Human Resource Development and is a production of allbypodcast.com. As a fan of the series, you can help make season six happen. A HRD is crowdsourcing funding for the season and you can donate today at givebutter.com/hrd hyphen masterclass hyphen six. 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