How To Master People Analytics and Deliver Insights That Actually Work
Matthew Hamilton, VP of People Analytics & HRIS at Protective Life, dives into the challenge of delivering actionable insights to leaders, known as the "last mile problem." He highlights the importance of storytelling with data, balancing context with emotional engagement, and experimenting with new approaches like generative AI. Learn how evolving strategies and upskilling teams can drive meaningful change in people analytics and leadership.
In today's episode of the HR Leaders Podcast, we are joined by Matthew Hamilton, VP of People Analytics & HRIS at Protective Life.
Matthew shares his unique journey from flying helicopters in the army to leading a cutting-edge people analytics team. He discusses the "last mile problem" in people analytics—delivering actionable insights to leaders—and why it's not just a sprint or a marathon but more like an ongoing race against a "zombie horde."
🎓 In this episode, Matthew discusses:
How to tackle the "last mile problem" in people analytics.
The importance of data storytelling for HR leaders
Why simply providing data isn't enough—leaders need context and emotional engagement.
The value of experimenting with different approaches to upskill teams and deliver timely insights.
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Matthew Hamilton 0:00
People, analytics and, you know, the implementation and rolling things out. It's definitely not a sprint, because it's not that you move fast and then, boom, you're there. But it's also, I think, it's not a marathon either, because with a marathon, it's maybe a more paced, it's longer, but there is a defined ending point, like after, after 26.2 miles, you're done. You know, it's long, but you're done at some point. And I was like, so rolling out data, insights, people, analytics, it's not like a sprint. It's not fully like a marathon. To me. It's more like running from the zombie horde.
Chris Rainey 0:31
So a number of wave comes. You mean, like it's
Matthew Hamilton 0:35
never ending, right? Sometimes you got a sprint, sometimes you can slow down, you can pace yourself, but you can never stop. When you stop, that's when you die.
Chris Rainey 0:48
Matthew, welcome to the show. How are you? Hey, good
Matthew Hamilton 0:51
morning. How are you doing?
Chris Rainey 0:52
Nice to see you. It's been a while. When did we last year, actually? Well, that
Matthew Hamilton 0:55
was a few months ago. Yeah, busy, yeah. How
Chris Rainey 0:58
was the summer?
Matthew Hamilton 0:58
It was good. You know, it went by fast, which is, I actually won't complain, having the kids back in school is helps with daily structure. You know,
Chris Rainey 1:09
everyone knows my daughter went back to school yesterday, and my wife and I, like, it's yes, they did for we're like, Yeah, this is good. Yeah, it's a lot, right? It's a lot. It's this
Matthew Hamilton 1:21
amazing, yeah, routine works well for them, so 100% exactly, and
Chris Rainey 1:25
us parents are trying to keep them entertained all day. Is like, yeah, you run out of ideas.
Matthew Hamilton 1:31
I reflected at some point, I'm like, when I was working from home one day with with them at home, and I was like, how did, how did we do this during the pandemic? Because it was
Chris Rainey 1:41
three years. Yeah, straight, yes, crazy, yeah, yeah, man, I think we've all got PTSD and we just blocked it out, like, like, it never happened. I suppose you haven't built an algorithm yet to predict how to keep them organized.
Some analytics of that. But, um, before we jump in, tell everyone a little bit more about yourself personally, and sort of the journey to where we are now in this current role.
Matthew Hamilton 2:04
Yeah, so, so leading people analytics and hrs here at Protective Life, the company, were life insurance and retirement products, financial services based in the US. So we're we're we. Our CEO likes to say that we punch above our weight. So we're a small company that maybe not a lot of people know about, but we, we really do have a big impact in the market. And we're the, we're the the US arm, where the the US own subsidiary of a Japanese life insurer. So we got sister companies all across, mainly Asia Pacific, but we are the North American branch of the Daiichi Life Insurance Group.
Chris Rainey 2:45
So that's quite an interesting story, though, because, if I remember correctly, the founders, could you share that story? Because obviously, of how the I'd say
Matthew Hamilton 2:55
we've been, we've been the US subsidiary here since 2015 we were acquired, but Protective Life ourselves as a company, we've been around for 100 do the math, right, 117 years now, I think, founded and based here in Birmingham, Alabama. You know, we kind of fly under the radar in some ways, but, but, yeah, wow.
Chris Rainey 3:16
And as me thinking seven years in business was long, yeah, I've
Matthew Hamilton 3:24
been here for, let's see, five years now, leading people analytics and hrs. I've been in the people space now for probably eight, going on nine years. Had worked at a commercial and consumer fortune, 500 bank before that, building out the people analytics function, then got recruited away to come here. So I've been kind of in this journey for like, say, going on nine years now, which is sort of a career pivot for me, which I think is not abnormal for a lot of folks in people analytics now, or at least it's for a while there. You didn't tend to start a career in People Analytics, you would find your way into it from somewhere
Chris Rainey 4:04
else or another function,
Matthew Hamilton 4:07
right? Yeah, a lot of folks from finance or risk or it, in some cases. For me, it was, it was really weird. Actually started my career in the military. I flew helicopters in the army. Yeah, yeah. So very career pivot,
Chris Rainey 4:23
oh, net, like, that's a new one. And I've done, I've done 1000 episodes, that's a new one. Yeah,
Matthew Hamilton 4:28
I say I'll never have, like, I love my job, but I'll never have a job as fun as flying helicopter. I
Chris Rainey 4:35
think you're, that's, that's probably when you peaked too early, you
Matthew Hamilton 4:39
know. And I had the vision of the, you know, a full 30 year military career and retired things, you know, family, family reasons, things kind of changed, and ended up getting out. And I kind of searched around for a while, worked in commercial aerospace for a little bit, and then, and then found my way. I wanted to get here to Birmingham, where we're headquartered. For. Only reasons. So I kind of pivoted into the HR space based upon the military background leadership development leadership experience. That's what got me into HR and the leadership development function. And then a couple years out into that, I got tapped to build out the people analytics capability at the bank. And I I joke, although it's not too far from the truth that the executive of operations has eyes landed on me and said, Well, you have an engineering degree. You understand math. A lot of HR folks to
Chris Rainey 5:27
say that, like, what is your background outside of the helicopter like, in terms of the analytical nature of the role, like, so you, yeah, engineering beforehand. Okay, cool. That's a great combination. So you, you are quite a rare you have the the analytical side, combined with your experience with leadership and bringing that together, and the commercial, obviously, understanding of organizations that is quite unique, yeah, to bring together. So I know
Matthew Hamilton 5:57
that exact path, I think again, but that's the
Chris Rainey 5:59
whole point. Sometimes in life, right? You you taste different things and have those different experiences and jobs, and you never really know how that's setting you up for success in the future. I never far be doing this job. If you ask anyone that knows me, they're like, and we kind of explain what we do, I'm like, but it's the combination of all of those lived experiences that gave me the right skills to be able to do this, and I never knew that at the time to be able to do that. I know. So let's jump in. I know. I know, when we last spoke, one of the things that came up, which you're really passionate about, is what we're calling sort of the last mile problem, delivering insights to leaders before we jump into that. I'd be great if you can give everyone some context of what you mean by that, and sort of break that down, and then we can kind of jump into the meat of
Matthew Hamilton 6:47
it, yeah. So, so the last mile problem, in a kind of terminology, comes out of logistics, supply chain realms. And so you think, you think of what it takes to get a product from a from a manufacturer, into a customer's hands. And so I'll use to kind of paint the picture. I'll use Amazon as sort of, sort of an analogy for this. And to be clear, I have never worked at Amazon, so I may say, you know, I'm going off of things I've read, or assumed I may get something totally wrong and how they actually do things. So if there's any, if there's any audience members who actually work or worked at Amazon, and I, I get this wrong, forgive me, but, but, you know, I think back to the early days of Amazon in the in the aughts, for example, they, they didn't have a didn't have close to the number of fulfillment centers warehouses that they do now, like, if you actually had the other day, was curious to Google to See, like, a time trend of the number of of warehouse facilities that they have. And it's, you know, it grew for a while, and then in like, the mid 2010s 2015, ish, it just sort of like, like, exponentially skyrocketed the amount that they're building or have so so if you think of, you know, the last mile, right, it's getting the product to the customer. And so you saw, over time, Amazon would expand their had expanded their their footprint, and gotten more fulfillment centers out into more states, closer to customers that that helps cut down on the, you know, the distance, if you will, to get a product to a customer. But the the challenge is the last mile is literally once you've got the product into the town of the person getting it to their front door, or to their door, to their mailbox, to their front door, and that tends to be the most challenging and difficult and expensive aspect of the supply chain, right? So, like flying a product on a cargo plane costs money, but you fit a lot of product in there, it's efficient. But once you get it into the town, getting it out to all these different locations, specific customer locations, gets really expensive and challenging. And so that's the hard part to really for a supply chain to slim down costs get more efficient. And so you see, Amazon has, over the years, experimented with all different kinds of approaches, right? So they used to be, I think they were exclusively ups. Was their deliverer for a while, and then I'll totally mess up the order and the timing, right? But over time, they expanded from having UPS doing their deliveries to using the postal services also to having their own delivery fleet of vehicles to building out Amazon lockers where customers can go, picking up from a location in a town themselves. You know, they experimented with, I don't know how far they ever got with the drone deliveries.
Chris Rainey 9:31
I remember, you know, around that, yeah, they got definitely, and they were at first, to get licensed to Yeah, and
Matthew Hamilton 9:37
yeah. So they experiment with all kinds of things. And what did they land on is they use a lot of those. They use a lot of different ways, in different contexts, for different reasons, for different urgency of product to deliver for different locations, right? So it's a mix, because no one size fits all for closing that that last mile. So pivot this into the space of people. Analytics and getting insights into the hands of leaders, right? So, so the companies that have been going down this journey, a lot of the early phase of that supply chain, or that delivery chain of getting insights to leaders, is the things like building out an infrastructure so you have some kind of, you've presumably built some kind of people analytics platform, either, either a home built, or a, you know, a vendor delivered, could be a purpose built thing, like vizier one model or embedded analytics, like, if you're a workday customer, but whatever it is you've you've built something out. If you haven't done that, right, if you're still doing people analytics and excel, right, the last mile is not a problem. Yeah, right. You have other things to do first, but once you've built that out, and then you have this infrastructure where you can develop and deliver insights efficiently and and, and hopefully you've gotten, or getting, or are getting to the point where you have direct user access for your leaders, right? So you're, you're cutting HR or the people analytics team out, largely cutting them out as the middleman for, you know, generating a deck or printing something to a piece of paper or something where you where people have actually secure access themselves to get to those insights. That's where the last mile then really starts to become a challenge. Of, well, I've got all this stuff available, I've got all this insights available, but how do I efficiently, at scale, deliver it to, you know, dozens or hundreds of leaders. How do I get them to consume the right thing at the right time in the right way that they're going to understand? How do I make sure that they're looking at it when they need to, but they're not wasting brain power or time or energy when it's not useful? So those are all the kinds of problems related to that last mile of getting the in, the data, insights into the hands of leaders. And so it's a, it's a challenge that that I face in figuring out what works because different like going back to no one, no one size fits all different things work for different people. So it's a, it's an interesting challenge to kind of figure out, well, let's try all you know, let's try these things, and it's going to work for some people and it's not going to work for others.
Chris Rainey 12:07
Let's start. Let's start, then more with what hasn't worked. Because I feel like sometimes help, and hopefully we can help our listeners avoid some of the pitfalls and mistakes that you've seen, that you've personally, maybe have made it, or you've seen other organizations make so what are the things that you're seeing that are not working? Yeah,
Matthew Hamilton 12:27
so So it's hard. I don't think there's anything, anything proactive that you're doing. I don't think that nothing, that anything doesn't work at all. What it really comes down to is for each company, kind of figuring out what works for more people, right? Because there may be something really well, but only five people engage with it, well, then it's not efficient for me to spend the time on it for five people, unless that happens to be like the CEO the CFO, okay, maybe then I'm gonna still right. So what I would say is, what doesn't work is, if you, if you have that whole infrastructure and you have the ability to put insights into leaders hands, well, I'll say the one thing that doesn't work is just putting it out there. So if you just put it out there and assume that people are going to to consume it, right? So it's like, what is it? You can lead a horse to water, but you can't make them drink, right? So if you just put it out there, and actually, I've used that, that that metaphor, to say more like, in some some cases, it's not that we've led the horse to the water and they're not drinking. It's that we've pointed them. We've said, Hey, water's over there, and you need to go over there, right? So that, that is one thing that definitely doesn't work, where you just, you throw it out there, and you're just relying upon people to go, access it, consume it. Why
Chris Rainey 13:47
is it? Why doesn't it work? Yeah, and again, it
Matthew Hamilton 13:51
does work with some people, but in general, that's a much harder path to get to where you want to be, of people leveraging using those insights. And I think part of why it doesn't work is leaders are just busy, right? There's a lot going on that they're having to deal with varies for different leaders based on what their job, their function is, but, but leaders are just busy overall. And in a lot of cases, people data and people Insights is a newer thing, and so they may not be used to using and consuming and considering some of that. So it's, it's different than the norm for them, and they're just busy overall, so they're not going to take the time, they don't have the time to engage and do what, what I, you know, in a perfect world, would want them to do. So I think that's part of it. And then, and then related to this, I think it's also that we people can assume that people need to be motivated and that we need to that they need to want to do something and so. So I actually think in a lot of ways, leaders often have more intrinsic motivation than we give them credit for. And. Um. So, like a lot of leaders would want to leverage the data insights we have about people, but it's not as easy as we people in my role tend to think it is for them to do so. And so the kind of, one of the things that kind of guides my thinking on that there's a researcher professor at Stanford, a guy named BJ Fogg who has this his model of behavior change. And I won't try and describe it as perfectly as he does, but essentially he he's talking about the behavior change to get somebody to do something that's a function of their motivation, their ability, and a trigger like or a prompt, something has to prompt or initiate them to do something. And so then it exists on this kind of two dimensional access of the motivation and the ability. And the problem in business is too often we try to, we spend our energy trying to motivate people to do something, to inspire them, to get them to want to do something. But that goes back to what I said. I think they actually not always, but often have better intrinsic motivation than we give them credit for. The The challenge, though, is the ability, right? So if somebody has high motivation to do something, but they have low ability or it's hard to do, then the likelihood that they're going to do what we need or want them to do, to access the insight, to leverage it, to apply it, is is harder just because it's hard, right? So I've actually pivoted over the last few years, and it's not that I never spend any time trying to motivate or inspire people, because sometimes that is useful, right, to give people ideas or get them to think about things that they hadn't thought about, but, but I probably spend more energy focused on the ability so lowering the barriers for users to use data.
Chris Rainey 16:42
What does that look like from a practical point of view? Yeah, yeah. So,
Matthew Hamilton 16:46
so maybe one of the first things, and this, this, I guess this would probably apply in in lots of cases, but is the, the upskilling, the data literacy of your team. So that could be the HR team. Could you could be your leaders, right? So one, there's, there's generally some effort needed to help upskill the literacy, the data literacy of people to understand and know how to use data. We could do an entire we could probably do multiple podcasts on the data literacy side of
Chris Rainey 17:15
things. Could you just give us an overview of some of the ways you've done that? Like, briefly, like, whether it's like courses, whether it's like different platforms, like, what some of the things that you used at founders worked in terms of upskill, yeah,
Matthew Hamilton 17:25
yeah. So, so you've used different things at both of the companies, but with the HR team, do a lot of virtual learning. So so live instructor led virtual learning, where we work it into things,
Chris Rainey 17:40
or use external, external company for that be great if you can. Prefer we
Matthew Hamilton 17:45
have in the past a little bit but, but we're small enough that it tends to be more, maybe not more efficient, for us to do it, but we get that more granular control and get concepts focused relevant to us. But there are some good ones out there in the market, that you know that, especially when you get bigger and you get need to get more efficient, that you know that could be leveraged. So, so upskilling data literacies. So that's like creating, think of the fertile ground for somebody to be able to cognitively, be able to use what we put in front of them and interpret it the way that we need them to. So, you know? So, so that's one way, the getting things out and making things easier. So I think about putting the right insights into the right leaders hands at the right time, right? So, if you, if you're distributing something to everybody on a periodic basis, maybe that's overkill, right? Like, maybe I don't need to be looking at the analysis on, you know, this thing every month if it's not relevant to me at a certain time. So, so we've played around in our platform with ways of delivering using criteria or triggers to deliver something to a leader.
Chris Rainey 19:01
You mentioned trigger in the method earlier, right? So you want to link that each of these things to the trigger? Yeah, I know, using a different context now, but you mentioned earlier, like, because if it's not linked to their challenges or a business objective, I'm assuming that makes it, you know, they're not going to be as engaged in the process,
Matthew Hamilton 19:23
so that, you know a trigger could be that they've come to, yes, I got a problem with this, right? Like they could actually be the trigger themselves. The trigger could be that we know, I'm just coming up with a hypothetical here, like we know when certain conditions are met, say in in employee listening through engagement surveys like what like we know from prior experience when, when teams tend to have certain indicators show up in their employee listening data that that tends to be a precedes something with retention problems. For example. People. So that could be the trigger of, well, if we, if, if a leader, if we start to see data coming up in the in the engagement data, and the employee listening that we anticipate, that you may need to be preemptively starting to deal with some retention issues, for example. So it could be a lot of different things, but, but that idea of the trigger of being something when it meets a criteria that we've set to then share an analysis or an insight with a leader, but only if it's relevant to them, right?
Chris Rainey 20:27
It's such a pain, right in companies now, where they just send you everything and then you open nothing, because nothing's right, nothing
Matthew Hamilton 20:37
I will do give sort of the counterpoint to that. Sometimes there are things where I have said it is useful to look at things so like, we have an executive dashboard that all our senior executives get. Technically, they have access to it on demand at any point, but we push it out every month as a reminder, like, hey, go check it out. And a lot of the metrics on there don't change on a super frequent basis, right? But that's one where maybe the counter is, I say it is useful to get into that and just look at it, even though it may not be changing frequently, because you need to develop a sense of what's normal, or like, what's right, so that when something is different context, yeah, you have, yeah, you're able to recognize that, hey, something, something's deviating from the norm here. So, yeah. So
Chris Rainey 21:27
you mentioned before, one of the main, you know, mistakes is just putting out there and hoping that everyone's gonna grab so any, any other kind of common mistakes that you see people make, or that you've made in the past that people should avoid. Yeah,
Matthew Hamilton 21:42
so you had used the word before a couple times context. So that's another one, I think, is when you, when you get something out into the leader's hand, some analysis, some some insights, not providing the right amount of context. And so that, you know, think, if you're, if you're, if I'm putting an analysis in front of in front of somebody, that's a data and visual heavy thing, without a lot of context explaining that sets the background for why this was done, how I should be interpreting the results that I'm seeing. That could create a lot of problems, because you can't guarantee. You can't know that every data consumer is consuming and interpreting and applying what you've given them in the way that you think. And I not to give a specific example, but they're like, there have been times where I've been on a call and somebody has looked at a chart, for example, and they they verbally talk through it, and I'm like, in my brain, I'm like, you totally just totally misunderstood, and don't even understand what that's telling you. And so that partially does link back to the data, upskilling the data literacy. But so so putting it out there, you know, one problem, putting it out there and just assuming people are going to consume it. Two, putting it out there make knowing or making sure that they do consume it, but not giving the context necessary, and kind of holding their hand and making sure that they're interpreting and understanding it the way, you know, the right way, or the way that you intend, that could lead to problems if people are, yeah, and then they're doing something, and, you know, I may be sitting here in my office not knowing what they're doing with it, to imagine the problems that that
Chris Rainey 23:23
might create miscommunication. It's interesting because I probably had, what, 200 plus people analytics leaders on the show. And one of the things that kept coming up over and over again is, and it surprised me, if I'm being honest, when I asked him about key and this goes with the point you just mentioned skills of people analytics leader, and it was storytelling, and it kind of threw me off. What do you mean storytelling? And to your point is providing, here's the data, but also telling the story so you have that context, so people understand. Because if you just throw too much data at people, Ben, you know, it's just overwhelming, but if you don't, but so there needs to be this balance of context, storytelling with the data. Is that something that you've evolved how you present that over the years to ensure you, yeah, yeah,
Matthew Hamilton 24:12
completely. I so. So I was at a conference. Let's see. This is several years ago, 2818 ish, 19 ish, somewhere in there. Cole, no spammer. Nak, she was the keynote speaker at one. And so if you're familiar with her name, or people who Google, or if you can figure out how to spell it, but she was
Chris Rainey 24:31
about how I was about to say, Yeah,
Matthew Hamilton 24:34
but she wrote storytelling with data and data, storytelling, consulting and training and all that, and, and, and so she was a keynote speaker to conference I was at, and she was just like, wow, like, she kind of walked through a mock board presentation in a in a bad way, right? And that stopped, and then went back and did it again in the in the using a lot of the right thing. Things of data, storytelling. And it was just like night and day, different and so so that that was, maybe that wasn't my first thing, you know, exposure to that concept of storytelling, but that really opened up my mind of of effective ways to get around, you know, or to get at doing that. And it's interesting. So actually, completely unrelated to my, my role here, just sort of a personal passion thing that I do at the company. Also, when we have our intern class come through every summer, I'll do the training session for them on, we call it the informed intern. So I'll do a training session on presentation skills for them, nice. And that just relates to some other things that I, that I have been involved in. But I think presentation skills are an important thing, right? Because if, if I've got a great idea, and again, this completely separate from people analytics, but if I've got a great idea that I want to share with somebody and get them to to embrace it themselves, or to do something, to take some action, if I can effectively communicate that to them, Well, it kind of does. No You know, there's no good done that. I've had a great idea if I can't get that across to others and when, when I'm one of the things I do when we're talking, when we're doing this presentation skills training. As I talk about the the three, the three, I'm totally going to mess up the three methods of argument, Logos at those and ethos. And so talking through the the that ethos, the the ethical appeal and the Logos, the the logical appeal, the the tendency in business, I think, right, is to go to the the logos, to making a logical appeal, right? So we we present things using facts and data and charts and figures and all this, and very, you know, very rigid like that, and we fail to make the emotional appeal too often. Yeah, yeah, you know where you're tugging at heartstrings, you get things people care about, right? So think visual imagery, stories, things like that, examples, analogies, quotes, and that's what you know, no presentation of data should involve only the data, right? You have to weave together the data, the logic, the insight, with the thing that's going to make it resonate and get people to care about it and connect it with themselves or their job or their team or whatever it is, yeah, so yeah,
Chris Rainey 27:23
and that's how big, yeah, and that's how we we've been, we've been built to do so for 1000s of years as human beings, to tell stories, and they live on now as legends and myths and fables, right?
Matthew Hamilton 27:38
Stories were passed down before, writing, yeah, yeah, in modern form. So exactly.
Chris Rainey 27:44
So I, whenever I look back at memories from business or even parts of my life, I don't remember the data. I remember the feeling and the way I felt and and the visualizing the location where I was, when I felt it. If that makes sense, those are the things that you know, really stick with people. So if you to your point, if you can connect those two things together, very powerful. Yeah, very well,
Matthew Hamilton 28:12
that feeling. So I've explained that to people, like, if you're going in and doing a big presentation, and so people could apply this in the in the analytics space, or or other, you know, other professionals, you're doing a presentation on something, the audience is not going to remember everything that you said. There's, like, there's no way their their capacity is, you know. So if you're going in and you're presenting on, here's the 10 things of blah, blah, blah, well, they're not going to remember 10 things. They're going to remember the key takeaway. So, like, What's your main message? They need to, they need to be able to take that away, and so you got to have a very crisp, clear key takeaway, right? Because, if your key takeaway, if it's something that you can't get across in the space of two or three sentences, it's too complex, that key takeaway and then the emotion, how they felt when, when they heard it, if you, if you nail both of those, having a clear and concise main message and positive emotion, they'll come back to what you said, right? So they'll have the deck or their notes or executive summary or whatever, right? They'll come back to get the details later. But if they walk out not caring or not being clear on what the message is, because they're confused because you weren't crisp enough on it, they're not going to come back to the rest of your data. Yeah. So
Chris Rainey 29:24
I think that's why TED Talks work so well, because if you see any great TED talk, it's exactly what you just described.
Matthew Hamilton 29:32
Yeah. So I mentioned the reason I do our presentation skills training for several years before the pandemic, actually, my outside of work activity, actually organized our local TEDx event. No
Chris Rainey 29:47
model, right? They have a specific model of it. So funny.
Matthew Hamilton 29:53
And I've actually found the Be it a TED talk or just other presentation. Shorter tends to be better. Like, people think, oh, I need to now. Like, shorter is harder. It's much harder to get across 90% Yeah, but better also, because it forces you to get really focused. And Chris, yeah, I was, like, totally orange off the data here.
Chris Rainey 30:15
I was about to say, on that point, we've covered quite a lot. And so let's go back to we started with the last mile problem. What would be your key takeaway and key point that you do want people listening to remember then when they think about this last mile problem? Yeah.
Matthew Hamilton 30:29
So what I think the key takeaway is you got to experiment and try different things. You'll probably have to do multiple things over time like you're not going to find one thing that closes the gap, closes that last mile for everyone. So you're going to have to experiment with different things. Some of them may work better than others. Keep the ones that work better, scrap things that are, you know, less efficient, and take more work and aren't touching as many people. But you'll probably have to keep doing multiple things, and you'll probably have to continue to evolve it over time. There's, you know, some, especially because people settled into complacency. So you'll probably need to continue to reevaluate over time what's working, what's not, continue to evolve and change something that we haven't gotten into yet. But it's, I see it in the near future, and I see more possibilities. Is using generative AI to help deliver some of that sure to get it out into people's hands in a more efficient, but still very effective way. So, yeah, so that's, that's, I would say it's the it's a living, breathing problem that you'll never fully solve, so you got to keep working to solve it again and again and again. I
Chris Rainey 31:41
absolutely love that perspective. And you have to have that mentality, because everyone, a lot of people, have that we have to get it right. And this is how it should look. No, it's not a there's no end. Yeah, there's no finish line for that project. There's an ongoing living and breathing, as you said, as new technology like generative AI come through, as new, you know, business challenges arrive. You know, whatever it may be as well, it's a living and breathing so don't get too caught up in perfection or worrying about getting things wrong. It's through that failing forward and that, that constant pursuit of disrupting yourself that's not that's going to be part and parcel and very normal, yeah,
Matthew Hamilton 32:21
one of my go to examples or analogies I've used. I'd spoken at a conference years ago, before, before the pandemic, and was talking about people analytics and, and, you know, some people, you've heard people say, you know, it's not a sprint, it's a marathon, right? And so I was like, Well, okay, people analytics and, you know, the implementation and rolling things out. It's definitely not a sprint, because it's not that you move fast and then, boom, you're there. But it's also, I think it's not a marathon either, because with a marathon, it's maybe a more paced, it's longer, but there is a defined ending point, like after, after 26.2 miles, you're done. Your European audience, I can't convert that to kilometers, so I have to do that themselves. But, you know, it's long, but you're done at some point. And I was like, so rolling out data, insights, people, analytics, it's not like a sprint. It's not fully like a marathon. To me. It's more like running from the zombie horde.
Chris Rainey 33:16
So a number of wave comes. You mean, like
Matthew Hamilton 33:20
it's never ending, right? Sometimes you gotta sprint, yeah? You always have to. Sometimes you can slow down. You can pace yourself, but you can never stop. When you stop, that's when you die. Yeah,
Chris Rainey 33:28
I love that analogy. I love that energy, that that is, you know, very much a misconception. And what would you say is one of the other big misconceptions that you hear and see around people on licks.
Matthew Hamilton 33:44
I don't know. There's probably, I don't know if there's one big misconception. There's probably, probably lots of little ones that happen. Maybe, if I can generalize it out to big misconception is that people sometimes think that the data is going to give them the answer, and and so that's one thing that I've definitely seen is in in most cases, not in all cases, but in most cases, people analytics actually never gives you an answer. You know, maybe there are certain things where it's, you know, if you are our CEO and said, what's our head count, okay, I can give you an answer, right? Like, so there, maybe there's some straightforward things where there are is an answer. Well, some companies
Chris Rainey 34:21
can't even give them that. So, yeah, not as easy as it sounds, everyone.
Matthew Hamilton 34:28
So when you get the HR and finance arguing over,
Chris Rainey 34:32
so, go on. Carry on. So, but yeah, but
Matthew Hamilton 34:35
yeah, so, but, but more. It's this misconception that people analytics can give you answers to questions like, Are we hiring the right people, you know, the right people. What's the right person? Do we have a metric or or an attribute, or, you know, do I have a badge in my profile at work, I
Chris Rainey 34:53
saw one recently. I saw one recently, similar to that was, it was, it was an article, and it said, can peep and legs really tell why? People leaving your company?
Matthew Hamilton 35:01
Yeah? No. Like, I can tell you, you know, I can give you the reason that was coded in the HCM of why somebody left. You know, that's not wrong, right, but it's, you know, it may help narrow the focus, yeah, but there's definitely more so to really take that in. So there's a lot of cases where people analytics can deliver insights, but there's a lot of assumptions that are going on. So an important thing, and this, this actually goes back to the talking the data literacy before the fourth pillar of the model that we or the framework that we use for data literacy is is data skepticism and curiosity. And so I talk about having a healthy data skepticism, understanding what something's telling you, but more importantly, understanding what it's not telling you, what assumptions are you having to make, to interpret some to interpret an analysis. And if those assumptions don't hold true, then your your interpretation of it may not be true either, but, but, yeah. So. So the the common problem, I think, is, or a common problem can be that people are over their expectation of what you can get out of people analytics maybe not in line with reality, because they think it can give you answers. It can, you know, I'm sitting here saying things where I like, maybe sounded like I'm underselling the capability that I work. No, you
Chris Rainey 36:20
know you're giving no, this is really important. That you're saying, Yeah, you
Matthew Hamilton 36:24
got to understand, like, what are the limitations, right? And so, so I think I've said before that People Analytics doesn't give you the answers. What it helps you do is ask better questions. So like, if a question's asked and I can narrow in, and I can, I can weed out what's clearly not relevant or not important. I get more focused. Then I ask another question, and I then weed more out. So it's, it's sort of this iterative process. People will say, you know, peeling back the layers of the onion of getting more and more to knowledge, to insight, but at some point, yeah, there's assumptions that are being made that you have to realize, you know, nothing can fully tell it. I can't, I can't go into our analytics platform and say, What was the turnover rate of our of the right people, you know, but we can put some assumptions around, well, what, what do we define as the right person? Or, you know, in sending these kinds of things talking about, you know, do we have the right people on board? What's our quality of hire like, we can put some constraints and assumptions saying, Well, this is how we're defining who, who's the right type of employee, or, you know, the reasons why people are leaving. And there's all kinds of things, but you narrow in, but then at some point you're, you're, there is a little bit of a faith of, well, this is we've looked at all of this, and we've weeded out the things that clearly aren't relevant, and we're focused on things and this, all indications are, this is what's going on the the analogy, I use a lot of analogies. I
Chris Rainey 37:48
know it's your master analogy. I'm gonna have to go throw in your last analogy before then. And then we'll and then we'll say, goodbye, go for it. Yeah.
Matthew Hamilton 37:56
Well, and the reason is, I found that that simple concepts or analogies or things that I can link back to, that I can take a maybe more complex people analytics concept and link it back to something that you just understand as a human being or as a you know, as a business person, tends to help bridge that divide of like, oh, I don't get that to Oh, okay, yeah, this makes sense. So, so the the idea of of weeding out the stuff that doesn't matter, focusing on what what does, but that there's still some assumptions going on. The analogy I used to talk about that is how astronomers, when they're going out you they cannot observe black holes, right? Like, you can't, like, by definition, you can't look through a telescope and see a black hole because it's gravity. Is, is light? Light can't escape, right, because of its gravity of a black hole, so you can't actually see it. Yet, astronomers know where black holes are, and how do they do it? Is they they see all the markers around it, so they can look at other stars and other other solar systems and see how their gravity is being affected by the black hole, so they can kind of look around it, and they can see, okay, based upon all what we are observing here, we are pretty confident that there is a black hole here, right? And so it's similar in the people analytic space, we can't actually look at some things because they're they're not measurable, they're not actually observable. But we can start to look at all the markers around something so, so going back to the are we hiring the right people? So we could put some some markers around, you know, understanding our involuntary turnover in the first, you know, couple months of hire, the performance in the first year, all kinds of things like that. We could put markers around and go, Well, if we are seeing all seeing all these things, that's giving us a pretty good confidence that we are, hopefully are or aren't, hiring the right type of people. It's so
Chris Rainey 39:49
interesting because all of this comes full circle to what we just spoke about earlier, which is also the storytelling. You know, whenever all of your analogies is what I'm referring to are stories. These, right? Because sometimes we can turn complex into, you know, very easy to understand by by telling it in the context of a story as well. But listen, I could talk to you forever, but I have to let you go at some point, because you've got a job to do as well. And I've got and I've got a door, I got a pickup from nursery. But before I let you go, where can people reach you? You know, where can they follow you if they wanna reach out and say hi, LinkedIn, yeah, best, yeah.
Matthew Hamilton 40:23
LinkedIn is the best. I'm not really much of a social media maven. No
Chris Rainey 40:27
worries. Well, for everyone listening, as always, the link will be in the description. So to connect with Matthew, Matthew, I appreciate you coming on share this journey and experience, and I wish you all the best. Until next week, I appreciate you. Bye.
Victoria Klug, HR Director Eastern Europe at Beiersdorf.