HRD: How To Implement People Analytics

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But there's data that has like the shape of information, but it's not actually telling you. And I think we really have to be cautious about that. There are absolutely people who say you're sitting on mountains of data, turn it in. 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 organisation that leads HRD through research. I'm your host, Darren 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 twenty twenty five. To explore this important question, I'm joined today by three experts. My first guest is Dr. Sung Wan Yoon, Professor of Human Resource Development and People Analytics at Texas ANM University.

Sangwan'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 PSYOP and was recipient of PSYOP's Distinguished Service Award in twenty nineteen. She earned her PhD in Industrial Organizational Psychology at Old Dominion University.

My third guest is Dr. Brad Schuck, an internationally recognized scholar, entrepreneur and thought leader in employee engagement, organizational culture and leadership development. He is the author of Employee Engagement a research overview published by Routledge in twenty twenty 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 all by podcast dot com forward slash analytics to learn more about the bios of our three countries.

And also to connect with our episode sponsors, the Educational Human Resource Development Programme at Texas AM 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 Programme at Texas AM University. Which aims to transform lives through teaching, research and outreach.

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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 organizations. You can learn more about the program by visiting eahr.tamu.edu Right, let's dive into the episode.

Okay, well I'm delighted to welcome Son Juan, Alexis and Brad into the episode. Thank you all so much indeed for being here. We're delighted. Thank you. To be here. 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 that 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 organizations. So this is all of the data foundations, the data stewardship, we'll include reporting and metrics, it'll include all of those elements, as well as what I refer to as like an RD function.

for making strategic investments in the people. Maybe uh because uh academics who want to uh investigate uh and report the definition. Uh there are two parts uh in these terms as you can see and the analytic parts uh I'd like to talk about it first. by definition in Greek uh it like it means uh breaking down into smaller parts.

And to m uh to me that means you can like uh make it smaller, quantify, almost automate, and maybe uh computerize. And so uh like uh Yes, and the former part uh really re represents the focus and scope. And so like HR analytics uh and people analytics, I think both terms uh appeared uh in uh practice probably around the same time and HR analytics tend to focus on like HR issues.

uh hiring, onboarding, like a performance management and many areas, whereas probably uh my understanding is workforce analytics refers more to talent intelligence, uh external and internal. And I heard uh talent analytics probably might focus more on like uh attracting, retaining, supporting talent. And uh I definitely agree with what Alex has pointed out, or people uh realized while like HR analytics tend to focus on HR

But uh to be really effective and impactful, it has to be connected to business. And so it's not just a nature issue, but uh operations, finance. Uh, there are many stakeholders, uh, organisations who work together. The only thing I might add, which is minuscule here And I think Sung Juan and Alexis would agree on this is there's an action orientation to this. Like, what do you do with it? Like how like

Okay, well 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 um 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 I think when folks take this really seriously, the orientation around, all right, what do I do with this?

really becomes an important grounding. You know, that's so true. And I badger 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 the now what. What are we gonna do about it? I 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 going to make, what'll be different, and then we'll invest those resources. I love that. You know, in in our in our classes at the university. You know, I t I tell my students that data changes the conversation. And it changes it from I think we need to to here's 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 it requires us to ask com uh d 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 for um people analytics is the most powerful position in all of HR. Because people analytics in their data stewardship role and their RD 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 got an agenda you're pushing that is not actually in 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 differential? I I don't wanna digress too much, but I ho uh have this view.

Like people analytics is a master key in leveraging AI and leading changes for the future. But to bring us back, like uh Darren, you talked about uh what are the confusions, where uh people use these terms. Like you uh many times interchangeably, especially between HR analytics and people analytics and if somebody asks me

I like try to be uh as precise and concise, elevate the speech as much as possible. It's about using data like uh in a smart way to improve the business, but also uh HR and people practices.

So 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 uh in in like very different uh organizations, places.

And so uh my experience is it looks all different depending on organizations, industries, and size of the company. And I am glad like uh the uh people analysts practice uh has uh matured enough to have a research firm that does benchmarking. And so particularly inside two two two, I think they started uh looking into like what our people analysis practices are like.

uh from uh year two thousand twenty two thousand uh twenty one and they looked at uh like from maybe uh started with a coup hundred uh organizations to I think the most recent year. close to 350 organizations. And so uh when they started, uh the average size of the people analyst functioning team was like uh one to four thousand and it became down to probably around uh one to twenty five hundred. And I heard uh like uh leading organizations tend to have a smaller

uh ratio like uh around uh one to a thousand or a little more. But uh that 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 uh doesn't have anybody or just has one person or two and who are considering like starting a people analyst team or

uh does work that people analysts professionals do but doesn't have an official title. So probably uh that's just my experience. So what do you think, Alexis? Yeah, uh so first off, you're right, Insight222 has done a wonderful job bringing together a coalition of people who do this, but I want to point out that we've seen people, Jack Fitzends, Jay Jamrog, Wayne Cassio, who've been doing this minimally since the 70s.

with HR economics, et cetera. There are methodologies for uh linkage analysis and utility analysis, which basically is people analytics. We just didn't call it that until Tom Davenport wrote that HBR article in twenty We've also seen large PA teams in some industries, uh insurance, pharma, technology, going back. Uh 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. Uh, to the original question that Darren asked about what are some of the signs. Um some of the signs are things like regular reviews of operational methods. 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 is the data? uh 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. uh or more likely to cite if we do X then Y happens and if we do Z then this other thing happens, more likely to cite some of those predictive analyses that people talk about on the maturity model.

Uh 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 uh networks and their intuition? Or are the ideas that win 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 we glossed over a couple of things really quickly. The first is Alexis mentioned when she first did this at Microsoft and 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 and then Tom Davenport's article took like that is that was the precipice here in some way.

where it was kind of a moneyball moment, if you will, for HR. Like, all right, how do we use data to understand some of the, some of the nuances that's happening in small pockets? And then uh where my 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 a lot. And Alexis was at the forefront of that. And I I don't I just don't want to gloss over that.

Well well you are very kind and I appreciate that. Thank you. Um also I wanna I wanna 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. Moneyball 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 average.

It doesn't tell you much to know what the average timed promotion is. It tells you a lot to know what is the top 10% of your population, like how quickly are you advancing those? Um, 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 a count in it. Those are the things that really let you make a difference in your organization, that let you push on something strategic. Kinda like Moneyball. How can I optimize my low payroll for this baseball team and get the best possible outcomes for it? Yeah, 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 I'll go back to that statement around how do we 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 or what happens if I don't feel like I belong?

But I wanna be ri like I I I really wanna be connected to the company, but I can't find a way to connect in 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 all 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 in 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 um 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 Xiun Huang. 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 fifty percent. Right? That's human thriving. That's kids who still have their parent alive, right? That's amazing stuff. And that's really an outlier, right? That's not normal, an average.

Yeah, these exceptional cases really like inspire me because uh what I like to quickly add to this conversation is there are certain actors, companies that we should uh give credit to popularize like people uh analytics, not just in a fancy or technical way, but really making a real impact. So Google is another company that popularized people analytics because people had all kinds of assumptions about what makes an effective team, what makes a effective manager.

And the point I really like uh agree with is it's not about like machine learning or or AI, not a big data, it's about relevant data and making a real impact. And so uh especially for us, I know many listeners who are listening to this is uh like some of them know, I worked as an IT manager in my 20s and 30s. And I have probably used and published many multivariate statistics papers, but uh my huge realization aha 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'cause uh like these are better uh because uh we have a good foundation uh and all the tools we have, qualitative research. quantitative. I always start my project with an interview, but uh this framework, like Alexis, the uh wonderful case you shared. Uh if we do the job right, like connecting it to more rich data set.

then we can connect, whether it is like hiring talent or support LD, we can do a much better job, not just to improving or making good changes. but touching uh tackling the experiences, development we've been talking.

So I've really appreciated the way that the three of you have talked about it because it makes me realise 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 organisations, say a hundred and fifty two hundred people. Uh, where there may not be people analytics in place right now. So when 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 uh I didn't always work at giant companies.

In fact, I spent a bunch of time early in my career in a very federated organization that had a bunch of small sites that really operated as pretty independent unit 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 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 um this happened to be a man, the one I have in mind 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 threw it into an optimization 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 that was ha harming their health, harming their families, etcetera.

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 retention. We reduced waste and environmental hazard. Like 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 a hundred thousand person organization because you got too many things going on. 150 person organization, you got like 10 things you got to do. So do the heck out of them, right? Do them really well and you can make a profound difference.

Probably uh many of us have chosen this field for the love of helping people. Uh but uh if you look at like uh uh w your experiences, my experiences, probably our default operation mode is uh doing everything analog. And so when we think about onboarding experiences, team work experiences, or rewards. or L D career promotions Like uh it's not difficult to see uh where we keep adding things more and it makes our work very difficult.

And uh it is true, I find, like when people uh talk about people analytics, data analytics. like uh advanced technique, fancy techniques uh comes to mind first. But uh like as I said, it's not uh none of those uh is based on like uh relevant quality data for the good cause and right cause. And so you don't necessarily have to have a great team or all the financial resources to address like uh uh challenges or uh needs within the organization or within the people team.

So whether it is a uh attrition or turnover is one common area, uh people like apply people analytics. But uh it's not just uh one area, uh whether it is a career opportunities managing career, like when uh repeating issues, problems continue. and uh like analog uh intuition experiences no longer work. then it's probably a good uh time to consider like uh how we can enhance our efficiency and effectiveness by leveraging data and what analytics can offer.

Yeah, and I I don't want to gloss over what um Swan said here. Yeah. I think the h i if if people analytics had a heartbeat, it would be around helping people. Like how do we help people? And and and 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 that is around us. And so he is

so right about that. It 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, you know, this is so possible. This is a 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 you know, if you're in a two hundred and fifty or three hundred person organization, I mean starting with a business problem and thinking about all right, w how do we solve for

And then using data that we have at the ready. All right, if if we don't have if we don't if we haven't collected anything yet, what do we have? And what do we know? and then linking that data to outcomes across the organization and then partnering fol with 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 how do we be more curious about some of the challenges and opportunities that we have? And so oftentimes I feel like we frame We frame an organization as we have a problem here. And I wonder what it would look like if we reframed that as we have an opportunity. We have an opportunity to optimize. We have an opportunity to increase this by five percent. 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. 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 a PhD or Doctor of Business Administration from Concordia University, Wisconsin.

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It it's interesting to 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 the 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 uh Iopsychologists tend to refer to it as dust bowl empiricism, where you just start with a whole bunch of data and see what's there. Uh 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 uh 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 margarine 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, um, somebody threw an 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 but the model said the strongest relationship was between basketball and the flu, right? Um and so you can really find some things that will lead you quite badly astray.

Or just leave you with like nothing burgers, right? Like there's just no useful information here. Like the, oh, our managers have been enrolled 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 uh getting the any kind of development out of people that are helping them do their better job jobs better. I don't know if they're preparing people, reskilling. I just know they've been sitting there a long time. Right. So there's um information that has It like has the shape, or there's data that has like the shape of information, but it's not actually

telling you anything. And I think we really have to be cautious about that. There are absolutely people who say you're sitting on mountains of data, turn it into something. And that's harder, harder to do than it sounds like Th there's a concept called like acknowledge hierarchy

And many people might be familiar with uh at the bottom the largest like a shape is data and above that is information and above that is knowledge. and uh insight. But uh like what typic uh I really find is uh data is exploding and information is probably uh increasing as much. But uh the like good knowledge and wisdom is not in the shape, probably only a tiny bit. Uh there's a too much noise.

And even in order to like better understand the data, uh, you need a good knowledge and insights. So my perspective is like it's an iterative cycle, not as a hierarchy. And so I I totally agree with you, uh Alexis. You can have all kinds of data, but uh if that is not relevant uh to the problems and goals and opportunity, uh you try to really like leverage your data. It's uh wrong like it's positive pieces or it's a rabbit hole you are getting into, I think.

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 thimbleful 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 there's a prof there's a faculty member here um in the Academy of Human Resource Development, uh Jeff Allen, who talks a lot about wisdom. And I would encourage any listeners to to dig into um to his work and his research. But really thinking about wisdom is is more than knowledge in some ways. And I th and I think Jeff would say that context and perspective matters here. You know, I I I I think about the idea of

um, being in a room with a thousand people and also being incredibly lonely. Like but you're around so many people. We have so much data that that saturates our our being, our information, uh, and coming into us at all points in all ways. that, you know, 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 um infer and then use. and put that into practice. And I think Jeff would say that

Here, uh, 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. Um For the for the better, for the positive. I wanna riff for just one brief rabbit hole on the wisdom point. I am a super AI fangirl, right? Like I've been coding AI stuff for a long time. Mm.

Love it, super powerful, really aware of its warts, right? Like it's not a total panacea, but it's super useful. But from a 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 into a world where the entry level roles that give you

access to people more senior than you appropriately that give you the time in 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 rung 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 standpoint?

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 analyst is not just about method, but like this uh good conceptual theoretical like a methodological framework. Uh that's how I try to like uh uh observe and incorporate uh in uh adopting and utilizing this framework. Because I can think of many good frameworks and many good approaches and many good questions, such as to the point about because we really like think about Uh what is all AI analytics uh mean for the future generations?

uh we hear like good principles such as asking a good question is far more critical than like finding answers. and knowing and seeing that AI is getting smarter and smarter. Like there are also uh many uh excellent conceptual frameworks, uh theoretical frameworks where you can uh really apply Like it is a principles. Uh my hope is that um AI and data analytics will provide the space to be more human to ask good. And to give us the space to be reflected.

One of the things that I think have happened over the last decade or two is there's just been this squeeze that has happened. where you've been 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 this.

My hope is that in the next decade or two and I talk to my daughter, my d I have a fourteen year old daughter. Um, she would be super embarrassed and she will be when she listens to this, when she's twenty five in college someday. My I think and I tell my daughter this all the time, you 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 f the the then the world is your oyster in some way. 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.

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. I think humanity is going to be better as a result of the data analytic process. B 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 I hope for my Well 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 spread.

So if you're literally just starting out and you have no budget and you have, you know, no colleagues. Getting good at um managing a spreadsheet and visualizing the data. So you are not. 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 in how visual visualization is presented, a little bit of sophistication in 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 Vizier or Workday or any of these. You don't have to invest um a fortune in somebody who knows how to

I don't know, 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 a scatter plot. There's a lot you can do with stacking your bars in a 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 smart at data visualization. There are fantastic online free tools you can look at for that as well as My God, Ed Edward Tufty has been working on data visualization for decades. It's well developed and there are some pretty reasonable principles that you can absorb in half a day.

Uh 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. Because I'll tell you, even my advanced PhDs. spend a lot of time in a spreadsheet. It's it's absolutely doable and you can get a long way with just those simple tools. So since I am teaching uh a new course, graduate course in people analytics.

Uh I only require like basic state as a prerequisite, and I give the same advice to students. start with a good disp descriptive statistics, understanding of your data. and data visualization. But I also introduced these resources for my students. And uh trust me, like it this is close to the end of the semester and they are building uh uh good, really solid audition predictive models.

But uh when it comes to tools and platforms, many companies already have access to uh human resource information systems or human capital systems like uh Workday or uh SAP and uh also yeah I cannot agree more. XL is still very popular, you can perform uh many things. and maybe Google spreadsheet, but for data visualizations, uh tools like Power VI and maybe tabular, uh you don't need like uh uh coding or uh technical skills.

But I also emphasize uh it it is important to know the road, like short term, middle term and your end destination. And if you start your people analytics like uh uh uh investment, then uh a having on HR V business knowledge, acumen, but also some competency in data and tech. and data analysis is absolutely very uh critical. And the good news is there are lots of uh free and low cost uh options. uh on the web. So there are online courses, free courses from COSLR or Udemy or LDX.

And also podcast. There are excellent podcasts. I mentioned uh uh insight two to two, David Green, Josh Bursin, and Chris Rainis. like uh uh I forgot the exact name, but also uh I don't want to forget uh core networkers directionally correct. And so you will get a good exposure to uh good great use cases. uh there are conferences uh uh side up

So one of the huge uh convention where proc practitioners meet. And lastly I don't wanna forget LinkedIn. Like this is a amazing community, most inviting And so they just started, I think the name is called Society for People Analytics, and they arranged meetups at major uh cities all over the US.

And uh so those are available. Yeah. Darren I would add, there are oftentimes local groups that folks can get involved with, uh, around people analytics and data analytics and If you are looking to get involved in some way, I I can't recommend SciOP enough and connecting to the conference that they're gonna have at Atlanta, having been to a PSYP conference before and a Tableau conference. Both of them. are absolute game changers. They're just f 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 is 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 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. It 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 layer, quantitative data as like a magnitude layer becomes a really important way. to help move things forward with the people that And I joke uh seriously, uh half joke half seriously in my class.

Like when it comes to doing the project, my experience is tells me it is uh project management, communications and storytelling. You will find more than fifty percent of things that influence the success of any project, any people analyst 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. Thank you so much for joining me for this episode. It was wonderful spending time with Alexis Fink, Brad Shuck, and Sung Wan Yoon.

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