[0:00 – 7:05] Introduction

• Welcome, Al Adamsen!
• Today’s Topic: The Past, Present, and Future of People Analytics

[7:06 – 20:25] How HR Has Grown and Developed to Today

• Giving managers the “why” behind their data
• Anticipating the needs of your customers

[20:26 – 29:56] Observations on the State of HR Today

• Predictive vs descriptive analytics
• The importance of carefully reviewing the data that feeds AI

[29:57 – 42:41]  Looking Forward to the Future of HR

• Getting in Front of HR Challenges
• Having Frequent, Actionable Conversations with Your Workforce

[42:41 – 44:15]  Final Thoughts & Closing

• Episode summary
• Al shares his closing thoughts

Connect with Al:

• LinkedIn  

Connect with David: 

• LinkedIn
• Twitter
• david@turetskyconsulting.com

Resources:

• PAFOW Website

• “If we are doing our jobs as people analytics professionals, we’re actually going to our internal customer first, identifying what they want or need, and then creating a product for that appetite.”
  
  
• “What people analytics does is it creates accountability where accountability did not exist before.”

Announcer: 0:02

Here’s an experiment for you. Take passionate experts in human resource technology. Invite cross industry experts from inside and outside HR. Mix in what’s happening in people analytics today. Give them the technology to connect, hit record, core their discussions into a beaker, mix thoroughly. And voila, you get the HR data labs Podcast, where we explore the impact of data and analytics to your business. We may get passionate and even irreverent, but count on each episode challenging and enhancing your understanding of the way people data can be used to solve real world problems. Now, here’s your host, David Turetsky.

David Turetsky: 0:46

Hello, and welcome to eternity labs podcast. I’m your host, David Turetsky. And welcome again to the HR tech conference, HR data labs podcast as always, I have with me Dwight Brown. Hey, Dwight, how are you? Awesome. And we have with us a special very special guest today, we’ve been trying to get Al Adamson on the HR data labs for quite some time. We tracked them down, we ran after him, we tracked him down at the conference floor. And we got him here today. So we have al Adamson from PAFOW. We are so excited to talk to you today. How are you?

Al Adamsen: 1:19

Doing outstanding, David, thanks for having me.

David Turetsky: 1:21

It is completely a pleasure. So tell us a little bit about your background and tell us how you started. When what it is well,

Al Adamsen: 1:29

Well PAFOW (Puh FOW) stands for People Analytics and Well thank you very much for doing Well that I really Future of Work. And when I came up with a naming convention, I did not know it would become a foul because it sounds like you know the Batman thing. After cringing for a couple few months, I just embraced it. People had a smile on their face when I said it. So that was roughly 2014 2015 really. And so without going too deep into the whole history, I was a practitioner in this space in early 2000. So I created the quote unquote people analyst capability at GAP Inc. It was then called human capital analytics fast forward, we rebranded ourselves employee insights from GAP, I joined a vendor that was serving us their informal info HRM. And they were acquired by success factors. A couple years later, I had gone on to connect to create a workforce analytics practice. And then when 2008 give or take, you know, the world’s falling down. And, you know, the idea of strategic use of data wasn’t a priority, just keeping the wheels on the bus. At that time, I made a conscious decision in my career given teaching my kids to facilitate peer groups, my kids were eight and six at the time, roughly. And so I want to be around with them at that stage. And I don’t want to be flying all over as a consultant. So in partnership with Brian Hackett of the learning forum, we created a workforce planning council report analytics council did that for a number of years started speaking at a number of events around the country. And with our council, we were asked to bring in vendors occasionally say, hey, what are they doing? What are they doing? What are they doing? And then we said, well, what would be like if we joined a conference with these vendors and with the peer group, and it’s like, oh, well, now we got an event. And we started saying, well, there’s got to be some naming conventions, they’ve got to put a stake in the ground and call it something. And that’s about all so just bring that home, but bow given COVID and we had done events and San Francisco for a number of years, Philadelphia, London, Sydney as COVID hit in early 2020. And now we’re pretty much a media company. So we have an online show. We do online learning experiences, both live and on demand. And I have a podcast as you know, people data for good. And so I put a stake in the ground around that naming convention, that mantra, right, and it has a definition is promoting the ethical and responsible use of people data analytics and AI for the benefit of individuals, first and foremost, teams groups, including diversity groups, organizations and society at large. So I’m passionate about it. Obviously, I have going on 20 years in the space background in economics, so as a consultant with Ernst and Young, so I think systematically I think about systems. And probably the most important attribute that I carry out at least I like to thank is I come in with a beginner’s mindset, a growth mindset, be really curious and compassionate about where the world is where individuals are, they struggle and try and do what they do. 

David Turetsky: 4:58

We appreciate it because we care about the practitioners, we care about the employees, and we care that the function of HR learns about what it is to actually be able to measure outcomes, and be able to relate that to their business. So thank you for everything you do. So I’ll put you on the spot. What’s one thing that no one knows about you? 

Al Adamsen: 5:23

It’s funny, because I was asked this question a couple of days ago, and I was surprised by my answer, and I feel like I should give a different answer. So I’ll say this is that I am an avid beach volleyball player. And I love beach volleyball, because I get to hang out with people who are old and young men, women, boys, girls, people from all over the world. So I travel in my job professionally. And so I’ve played in tournaments in Australia, and played in Europe. And it’s Yeah, it’s just, it’s a wonderful community. And I feel very blessed at this stage in my life to be able to do that, and something I can do with my kids. So yeah, that’s something that having grown up played football, basketball track, you know, you’re I am playing beach volleyball, and most weekends.

David Turetsky: 6:20

And most people when I asked him about sports, we’re not young anymore. They say, Yeah, I did it until, and I play hockey, and I play hockey twice a week. So it’s, it’s good that you have that competitive thing still

Al Adamsen: 6:33

Absolutely life long athlete And to your point, I played college football is something people don’t know, either. I don’t show it. The thing is, I called football terminal sport, you walk off the field, the last time you’re done, you don’t go to a park and say you want to play pickup football. You don’t do that. But with volleyball, it’s something like I said, it’s extremely inclusive, it’s fun.

David Turetsky: 7:01

And you need a ball, then you need a net. Yeah, you don’t need pads. That’s really cool. So the beautiful part about your background is especially how it all came together as then this should resonate with our listeners is then you have great context for people analytics in the past and how we used to do it, and where it’s coming from, and then how that translates from a context perspective to what we’re doing today. And then you’ve heard everybody talk, and you’ve talked to about, and of course, you’ve talked about this, where’s the future of it going? So let’s start with context. Tell us what your thoughts are, when I asked you, where has people analytics come from, whether it’s origin stories that you have, or what your thoughts are around where it came from?

Al Adamsen: 8:00

Well, for those who want a quick history lens lesson, I wrote an article a few years ago called people analyze three Dotto, where it actually one Dotto was, effectively event based research. So I want to figure something out, therefore, I’m going to go and grab all this data, create a hypothesis tested against the data, and then present that in a deck. And, you know, it’s like defending a dissertation. And so if that is in fact, people analytics that’s been going on for 100 plus years. And so Hawthorne studies and all that was that depends on how you find people. And then people want to dato aligns with what I would call the business intelligence revolution, where in the early 90s, you had Cognos and Rio and Hyperion and other technologies that were aggregating data from all across the enterprise, including HR, and at the time, Norton and Kaplan, of Harvard Business School came out with the balanced scorecard as effectively, okay, we’re gonna manage the business with these four lenses of financial last and customer market lens, internal operations, LEDs, and what they call the learning and growth lens or quadrant. And I’m like, Alright, cool. And at the time, I was with Ernst and Young and, and we were building some services around this model. And I kept looking at the learning and growth product. And I’m like, I don’t know what that means. As much as I read and like, talk to clients and try and figure it out. So me being me, I said, I’m going to change that. And I’m going to call leaders managers in the workforce, and who are they? Well, they’re the customers of HR. And so HR has all these processes and things that they do and who sets the agenda for HR well, leadership. So now we have this causal framework, right? So and then, you know, is there data along these relationships across that framework? Yes, yes, and yes. And so without going through that whole model, the business intelligence promise wasn’t really delivered, particularly for HR given the uniqueness of HR data. So now you come into the early 2000s. And there was a group out of Australia uniform info HRM. At the same time jackets and the Saratoga Institute, they were creating tools, solution models where, okay, this is the way it can be done. Now, there’s solutions that are being brought to market that actually do this. So the core value proposition there was taking HR data or people data, as we now call it, aggregated an event driven basis, but on a recurring basis, rather, and presenting it through dashboards with user rights and so forth, which is now pretty commonplace. However, at that time, we would send at the time when I was at gap, we would send inform our supplier data once a quarter, you know how long it took for them to refresh that data? Six months, roughly 21 days, three weeks? So you’re talking about Okay, after three weeks, you know, who is interested in that data? Nope, nope, nope, no. And so we worked. So we’ve got to do this monthly, we got to get a refresh rate down to five days, you know, and now that refresh is almost daily ohms instantaneous.

David Turetsky: 11:15

Let me ask you a question about the cause when it was first created, and I had actually worked on some dashboard projects a long, long time ago as a practitioner. One of the comments that some of the managers said is, why are you sending me this stuff? I never asked for it. It’s not giving any value. And they were right, we were sending them a trend of headcount in bar charts. And not that a birth chart versus a line graph matters. But they said, Tell me something insightful about my business. Don’t just tell me headcount. Yeah. Okay. Sometimes headcount can relate to cost. But what do I do with this? What are the answers? What outcomes? And it took us decades? To try and make that leap? And of course, we’re talking about the past now. But I guess I asked you because you did this as a practitioner? Did you provide training to your managers? Did you give them the why, when you did that?

Al Adamsen: 12:10

Well this is a something that I’m very passionate about, because I see the world and I say this compassionately, not critically, it’s kind of backwards on this. And let me explain what I mean, is that, okay, we’re generating this insight, and we’re going to go train people on how to communicate that insight downstream, I find that largely backwards, it’s like, if we are doing our jobs as people, analysts, professionals, we’re actually going to our internal customer first identifying what they want, or need, and then creating a product for that appetite for the for that knee. And as opposed to the big aha.

David Turetsky: 12:51

But that’s a more modern concept, though. Because in the past, when I worked as comp with hrs, I asked them for a bunch of data, I would put it together into these elaborate graphs and charts. And I used to do a roll up of how much compensation did we need for the CFO to get to the median 75th percentile? And I’d give him numbers, and he’d be like, wow, I need to pay that much money to get us to the median. Yes. And that was appointed one. That’s why I listened to him, and I did it. But there are a lot of times when it would be, we would push that we’d ask you to offer data, and we push out information. And the manager would come back and say, Why?

Al Adamsen: 13:32

Exactly, no, I love where you want that because there’s, again, a couple nuances that need to be appreciated, because if I’m going to create a solution, whether it be an automated solution, or just a deliverable that they use for a board meeting, I want to know what they need to know what’s going to be as early as possible so I can craft, you know, towards that end. That being said, one of the things I strongly advocate for HR business partners or any change agent, is go to your ultimate decision maker, your internal customer, and ask, you know, how they view the world where they’re assessing my policies, and also be attentive to what they’re asking for what they’re not asking for, that they shouldn’t be asking for. Because we still have to bring our domain expertise and our innovations to the fore. So yeah, it is, in fact, you know, a two way street. So that is something that, you know, takes creativity, it takes courage, it takes, you know, obviously, domain expertise. And the good news is, I mean, with the proliferation of data that’s available now, and the quality of the analytical tools and we can provide insights that, you know, leader to not grow up where they don’t know it exists, you know, so that’s really exciting for us as analytics professionals. The one thing I want to come back to real quick before we get too far afield, and you touched on it, David, is that these dashboards and metrics that we were pushing out and people asked to Dotto they would go out and like well So, you know, what am I supposed to do with it right? And in the early 2000s, John Boudreau, and others and I had the great pleasure of working with John, at the Center for effective organizations when I was enrolled at GAP, he and others were putting forth this curve where you’re going to do reporting, and then you’re going to do descriptive analytics and your downstream you can get to predictive and then over the years, I would say, well, who’s there are predictive like no one’s Well, we’re not there yet. I’m not there yet. And so I was kind of up in arms and I love john have much respect for him on many, many levels personal and professional, I also was calling something out that I saw as a practitioner is that we need to do advanced analytics earlier in the game to actually create the story around what these metrics mean. Sure, you know, that does doesn’t happen, because we wish it to happen, then we can bring data we can do not to put my geek hack on on but the structural equation modeling, we can do any number of analytical applicator disciplines techniques, that is to better understand the historical impact of a set of metrics, and the potential future impacts of set of metrics. So that combination of both doing the advanced analytics along with the dashboarding, and so forth, I became an evolution like in the knocks, if you will, and then we’ll wrap this up is over the last 10 years or so, yeah, people have been talking machine learning AI and all that, but I would people want to three Dotto to me is really operationalizing analytics, not only for executives, but for people, leaders as well as individuals themselves, which gets really exciting because after all, they’re generating the data. And we can get into this whole ethical discussion about who owns the data at the end of the day and where the world is going with that. But I’m just really happy that we’re at this stage. And we’re the level of discussion is very appropriate, around Okay, whose data is it? You know, why are we doing this? Should we do this? So I’m excited.

David Turetsky: 17:10

Well, I think one of the things you’re going to see that happening now, it’s not a future thing is transparency. Companies need to be more transparent, the SEC is forcing it to public companies. And that’ll then happen to private companies as well, because people will say I want to gravitate toward where I feel appropriate. And I like knowing more than not knowing until transparency to me becomes more of a democratic or democratization of not just data, but of the end result of data, the insights that are being generated, exactly to your point. Because if we just think about one group, the leader or the executive, and we forget about the managers and the employees, then we’re going to have a difference and understanding and not perfect information. And there’s going to be generating a lot of demand then from those other two groups to get it. And people are going to normally when they feel anyways.

Al Adamsen: 18:02

I love it and I cite, what Salesforce does, they do not do turnover predictions, because they don’t believe that is going to elevate trust, they have a trust metric, it’s going to compromise trust in any way, we’re not going to do it. And so they have governance structures around that. So I celebrate that. Microsoft’s similar is that they have a Trust Center. So anything that is going to compromise the trust of the worker, and how their data is going to be used, they’re not going to do it. So having those guardrails and the fortitude to say no, not only individually, but systematically because the rules are a great thing. But not many organizations have that level of discipline, obviously, Microsoft and Salesforce are special cases.

David Turetsky: 18:48

So the one thing I want to add to that one point is, then the level of data integrity gets higher, because people will know what their data is, or have a hand in agreeing to update their data, and it will be better for everybody

Al Adamsen: 19:03

And you might even desire. Yeah, and you know, hey, I’m gonna, I’m benefiting. It’s not only for the organization, I’m getting benefit, and my peers were benefiting. So I have a kind of citizen responsibility, and not only to the organization, but to myself, you know, so I can be seen, I mean, we know that, oh, my talent profile, and you know, my core HR, there’s only 26% deal filled out, right? You know why? Because they don’t have confidence, right? That they’re going to be found this can be used for a virtuous purpose. Whereas, okay, who’s going to update their LinkedIn profile, they are going to be looking for a job. And we know that they’re going to do that if their knowledge worker, so because they trust the system, they see the personal benefit, as well as the organizational benefit.

David Turetsky: 19:47

But one of those benefits and we’ve been talking about this with a few of our guests over the last couple of days, his skill profiling and then Career Career frameworks that enabled them to understand where they could go if the person doesn’t have all their data. I filled out on their talent profile, then the career framework can’t really represent what would be the appropriate ways they could go. And I’m not talking like predicting it. I’m saying that if I don’t know what skills you have, I can’t tell you what gaps to fill in order to get to that other career. Even if it might be right next to me, I may not know because I don’t know what you do have and what the assessment would be testing for. So now let’s transition from the past. today. What are you seeing people do? And how is people analytics? Or has How has it changed? from the past? To right this very moment? What examples you not to mention in player names going to want to, but where have you seen and what have you seen? That gives you? happiness? sadness, depression, what have you seen that’s giving you those emotions around where people analytics is today?

Al Adamsen: 21:05

What makes me sad, I’ll start there is that there are still business cases that have to be created to justify investments and people analytics, I find that absurd. It’s like, you know, who created a business case for market analytics, you know, to understand customers? And in my mind, workers are the customers of leadership. And do you, as a leader want to understand your customers, those you’re serving? Right? If you’re truly a servant leader? Yeah, I would think you do. And you not only do you want it on a one off basis, because there’s a problem, you want that to be a way of doing things. That means you have systems and processes and technology and data and analytical engines that generate insight. So you can actually see not only where you’ve been, but where you’re likely headed, but and that is a matter not only of responsibility, in my view, but it is a matter that or opportunity, rather, as a matter of responsibility.

David Turetsky: 22:06

Do you do you think there’s some missing or misunderstanding that they think that there’s something happening around maybe workforce planning? And that’s enough? Or what’s the disconnect between executives and people?

Al Adamsen: 22:20

I think there’s two things, I think they grew up without it. So having done this for 30 years, you know, I got retired five years, you know, I’m alright, you know, there’s gonna be somebody else. That’s kind of the cynic in me, you know, coming out, but I’ve seen it long enough to believe in some organizations, that’s, in fact, you know, the case, because what people analytics does, it creates accountability, where accountability did not exist before. But I have seen leaders to press insight, because it did not reflect well on them. or others look at DE&I. Yeah, exactly, exactly. And there’s been a lot of excuse making around it. So it takes a evolved courageous leader to say, this is the way we’re going to do things on the good news, the adoption of people analyze technologies, and the commissioning of people, analytics professionals to do this work is going up. And it’s actually gone up significantly over the past year with COVID. Because of the focus on well being retention and all this, like, What the heck’s going on? I got it. Well, what do you mean, we can’t spin up that survey and do this analysis? Well, we don’t have anybody to do it. And so am I gonna outsource that? Do I want that person in house, so the operating models to get this work done, have evolved, particularly they’ve accelerated significantly over the past 12 months? What I will say, and this is hovering between the current and short near term future states, is that the idea that people analytics is a luxury and that is something that we’ll eventually get to is going away. They understand the essential nature of this work. I just don’t think there’s a broad understanding of what that actual work looks like. And some are scared of it. Some have said I’m going to hire a data scientist and he started doing stuff and they don’t have ample resources to actually do their work we’re all land with this is it was really interesting to hear Josh Bursin speak day before yesterday word yesterday was yesterday. He did not say the words people analytics. He did not put those two words together. He did not say digital transformation. Yeah, go back two years. I mean, people I looked up this whole segment and you know, digital transformation was something we’re all in obviously COVID accelerated that but I call that out not in like, Josh, what are you doing? I actually celebrate it because people aren’t listed becoming embedded. A lot of the technologies that are being adopted now. And that doesn’t mean in the way I view it, it’s like, you know, we don’t have electrical conferences, you know, conference on electricity, but we use it. And we there’s still a role for electricians. And that’s kind of what in my view are people on which professionals are going to be is that they’re going to be the experts to know how to put in the wires and make sure everything works. And if something breaks down, they’re going to go in and fix it. And if we’re going to expand another wing, to this building, then they’re going to be able to connect everything. And they’re going to think systematically that the work is not going to go away ever. It’s just a way of doing things moving forward.

David Turetsky: 25:39

So Josh, I love you. But about four or five, maybe five or six years ago, he did a maturity model around people analytics, where he talked about reporting analytics. Yeah. Well, the reason why is because I was building the DataCloud at the time for ADP. And when we were designing it, we were designing it based on the needs that our clients had. So reporting was a huge concept still, because basically, most of our clients would build a lot of their analytics starting with reporting because they, they trusted those queries, they knew where the data was coming from. And then we had built an analytical platform that was standardized across every client. And to get them to transition was big. And he was coming out and saying, descriptive analytics are passe, you know, you need to go and do, you know, not predictive analytics prescriptive, and most of our clients we see are going to prescriptive, and I said, What’s wrong with my company than that? You know, and we, it was ADP at the time, you know, we had 1000s and 1000s of clients, and they were all still in reporting. And I had to transition that conversation with them to say, there’s nothing wrong with reporting. There’s nothing wrong with descriptive statistics. Yes, you might want to get too prescriptive, predictive, or prescriptive. But don’t fear that Josh is saying, and again, justly love you. But I think it was a little premature to even talk about predictive and prescriptive because there are a lot of clients who are still in the reporting world, and still today are, and I’m talking six years ago. And so I don’t like talking about top maturity models, because you can’t make that it just

Al Adamsen: 27:17

And I talked to that real quick. So I have, for 15 years, I’ve had what I call an analytics, maturity progression. And I use those words intentionally. Because, yeah, we can say that I’m just playing with words. And it’s, in fact, a maturity model. But it’s not that you leave one and just go to the other, it’s an end. And I call that out, that reporting is always going to use this, you’re going to always have descriptive statistics. And predictive as never been the goal. Me it’s the appropriate insight at the appropriate time. Like we talked about analytics analyst is process our product or deliverables, insight, leaders could give a rat’s tail how it’s produced. So they just wanted to be accurate, they just wanted to be accurate, they want to trust it. And, and even with predictive and prescriptive, and whenever I hear it, I go like this, because it’s like, we’re not doing analytics for analytics sake, it’s not for the sake of the researcher, or the analytics professional is for our customer. And predictive analytics is based on what’s called in statistics, the frequentist approach. So I have a bunch of historical data, I’m going to analyze all this, and it’s going to predict what’s going to happen in the future. What does that do? that assumes the past good, I suppose good. And that’s, and that’s what we desire in the future. And, you know, particularly in this rate of change, we don’t, it doesn’t allow for disruption. And prescriptive is like actually blind, predictive. It’s not, it’s not entering the human. So I really I caution, people who just use those terms, because they’ve seen it in a book somewhere, or seen it in the maturity model. And so I don’t really want to have that. They just throw those words,

David Turetsky: 29:12

Right. And that’s why when I talk to people, especially on the floor, and they talk about how the AI is doing or will do and I say, look at the basis for what the AI is looking at. Have you gone through and made sure that it corrects for history? Because history sucks. Sorry, but history sucks. And if you guys haven’t gone through your data, especially with a fine tooth comb looking at some of the decisions have been made in the past, especially around promotion opportunities, where you’re giving hours out to whom and why, who’s who is and who is not getting promoted. And why or who’s leaving and why. Then my advices you know, don’t use your past.

Al Adamsen: 29:53

I couldn’t agree more.

Announcer: 29:56

Like what you hear so far. Make sure you never miss a show by clicking the subscribe button now, this podcast is made possible by Turetsky Consulting and listeners like you. Thank you for your support. Now back to the show.

Al Adamsen: 30:11

Can I talk about the future with you, because that sets the stage for what I am excited about that I see here on the floor more and more, because having done the work, I had been content, or just had blind acceptance of the data that was available to me to do the work. And I was like, trying to make magic out of something that was, you know, I’m trying to make a gourmet meal with, you know, stuff, ingredients that are very basic and just weren’t going to get me where I wanted to go. And so that’s kind of how most people analytics professionals have existed, since we really started to form a discipline. And what I’m seeing that excites me is that we’re now getting in front of the challenge. In other words, why our HR technology is adopted in most organizations, as a depends on the organization size and complexity, of course, but let’s say they have 30 information systems that touch an employee or worker, they all generate data, okay, we’re going to aggregate that data, and we’re going to analyze it, you know, produce deliverables, produce dashboards, all these things, okay, we’re doing people analytics. But I wasn’t as the people most professional involved in the selection of those technologies, those technologies, by and large, were implement or selected and implemented to stand up a process or improve a process. But they weren’t done because leaders wanted to know something different, nine out of 10 times. So what’s the better solution? Well, if I’m a people analytics lead, or someone who’s involved in not only the analytical process, the identification of what I call appropriate data, then I’m going to be involved in the technology selection, I’m going to be involved in the process design to ensure that the data that I need is being generated, so I can then consume it, analyze it, generate the insight and push it downstream. So that’s what I see happening more and more I package it like this appropriate questions lead to appropriate technologies, which leads to appropriate data, which leads to appropriate insights, which leads to appropriate action, I use this term appropriate, obviously, intentionally, right? Because appropriate starts with appropriate questions from a change agent called people athlete or HR business partner, whoever, to that end, customer, internal customer. And, again, going back to what we talked about before, what he or she saying, what are they not saying and then go, designing processes, that data architecture that that technology ecosystem to generate this stuff that doesn’t happen on accident, you know, so that consciously conscious creation of the ecosystem and the underlying data, I see that more and more, which is a fantastic thing, I would love to see that continue. And the final thing I’ll say on that point is, surveys used to be, you know, once a year, once every other year, and now, we’re going quarterly. And now that we’ve COVID, you know, there’s almost continuous dialogue, continuous listening, you know, with workers, you know, for the sake of what, what, you know, what does that data get juxtaposed? What’s the nature of those questions, and being really thoughtful, you know, again, appropriate, what are you trying to achieve? And are you actually having a discussion with your workforce at scale, that’s not only meaningful, but actionable, that they see it, that they have confidence that they’re okay, we’re going places, and they’re looking out for me, and we’re going to succeed as an organization, business wise, and good things are gonna happen. So

David Turetsky: 33:56

And I think right now, there’s such disruption in the business world, whether it’s COVID, and working at home, whether it’s the great resignation, and we’ve had lots of discussions about this, or whether it’s the changing work profile. And yes, this is kind of like another thing that we’ve talked about for years where gig economy versus not whatever. listening to your employees and being able to change the culture, or the espouse culture is critical. You will lose your brain trust if you’re not listening to them in a way that enables you to be able to react. You can’t. I’ll tell you a quick story at Morgan Stanley in 19. I think it was 1994. The CEO said we will not do casual Friday, when Goldman Sachs muirlands. All of our competitors were introduced in casual Friday with an investment bank, you know, remember barbarians at the gate. You know, Ty stewed every day, every Why. And the lower level people were cheaper suits in the upper level were the most expensive suits from the best tailors and got back on that they fit in a matter of moments. Because of all the people who said, this better change, it started as a groundswell that they actually changed and reacted, it took weeks. That doesn’t happen anymore. The immediate backlash because of social media is immediate. And so I guess the question back to you is, is that, are there the back channels for like social media? Or is this happening through us asking, and people listening? And having those mechanisms to listen like engagement? studies?

Al Adamsen: 35:41

Yeah, it’s both. It’s both. And there’s so much around? How do I say, the well being of an individual, and how that at scale relates to the culture of the organization. So I have an article that I wrote years ago called feedback is garbage. And it’s effectively a challenge to define what feedback is, and what I advocate, either defining feedback as such, or just calling it something different, is okyou, eyes, observations, questions and ideas. Because I believe people that after the basics want three things they want to be seen, they want to be heard, and they want to be empowered. You know, they don’t want to be invisible, they don’t want to be ignored, they want to be told what to do. You know, they want to be empowered, offered ideas and things like that. So if I feel heard, by responding to a survey, or having a discussion in focus group or something like that, or if I put something out in social media, I see that there is a mechanism by which to see the tone of the chatter, and oh, man, and there’s a great story out of IBM, where IBM, Uber and lifts weren’t reimbursable. And there was a big uproar about that. And they actually caught that in social media chatter about I think it was internal chat, but whatever, you know, they, they were then able to see it and act on it. And so that’s it, you know, listening and having the fortitude to act quickly. So you know, that, I would hope in years to come, it’s going to become the norm. But many are not taking advantage of those technologies. What I will say, too, and I’d be interested in your thoughts as well, if we talk about appropriate data, I’m interested in not only the skills, because skills are getting a lot of play on the floor here, but to other things capacity, like because we’re all constrained by time. And there’s a lot of people who are suffering. And we as people, analytics professionals have the wherewithal to shine a light on that suffering as well as what to do to alleviate it. And so, I also am interested in the intentions and capturing the intentions of individuals. And there’s some vendors who are getting at that, too. It’s like, Hey, I have, you know, my kids and in school, and I want to, you know, be around and coach their teams, or I have them taken care of an elder parent or something like that doesn’t mean I’m less of a former, it means I got other things going on in my life. So I would really love for us to listen to workers in a very compassionate way, as opposed to all about productivity. And that is going to elevate trust and commitment to the organization as well. So

David Turetsky: 38:32

Remember, in the past, as HR, we don’t care about what people do in their homes, we can’t ask them as a manager, you can’t ask about them. You can’t ask about their relationships. You can’t ask personal questions, not on hire, and certainly not regular conversations, because the decisions you make me either disenfranchised or, or support people in the wrong way. Like someone says, My mother’s sick, I need money, is there any way I can get an advance? Well, a compassionate manager may do that one off, but then also puts the company at risk. And so we used to not do that. And we used to not allow that. But I think what we’re seeing now after COVID is maybe the equations change. And maybe we have ways of being able to do things differently now still within the law, as compassionate people to be able to change that. I don’t know. Back to the other question you asked. So I guess I guess I don’t know that way. But the other question you asked about time and and how can we measure how much time people are actually spending? It is a cop out. And I will call HR nit to the table on this. If they said they didn’t know. We know when people are working. We know it from their logins. We know it from the the messaging systems, which know when you’re available, and when you’re not. If you go and touch your spacebar, we know so it’s horse crap to say that we don’t know about Unless somebody punches a clock, we don’t know when they’re working. And they’re not. That is afoul of a lot of things. But it’s also, it’s disingenuous to say, unless someone puts in their timecard. I don’t know what their time is yes or no. And I think that we’ve done a lot of O&A on emails, right? And that O&A is very valuable. Have we ever done anything to say, if you send an email at 10 o’clock at night, that that was working time? I don’t know. Have you ever heard of that? Oh, that’s great. Has it ever gone back to Kronos for getting payment?

Al Adamsen: 40:38

For an hourly worker? That’s actually a great question. I don’t have an answer right. Now, what I will say, is this is that we, to your point, have the ability to access such data and juxtapose it against all kinds of other data performance data, engagement data on down the line? Should we do that? For what purpose? What are the ethics? You know, how does it impact, you know, different groups, diversity groups that, you know, I’m thinking, the nature of people analytics has expanded way beyond, you know, surveys and comp data, and you know, or just, we have access to that data, I believe that we can do a lot of great things with that data. And this relates to workforce planning, it relates to the work that I’m doing around the future of work, I’m talking a lot now about what I’m calling perpetual work design, some call it perpetual work transformation, or the fact that we’re always designing the organization, you know, we can’t, okay, this is the way we’re structured. And this is where we’re gonna make. But using data to inform, okay, we have, you know, X number of employees, we’re going to have y number of contractors, in fact, but over the next year, we’re going to outsource this, which means we’re going to free up these employees, and they’re going to have the ability to be repositioned to do something else. So we’re going to think about the training that we’re going to apply to, you know that Not to mention, oh, we’re going to have robotic automation take over this process. And that’s going to free up the capacity of others. And so what are they going to do moving forward? So having that forethought, and planning, I think is going to be something that’s going to evolve over the next few years, and become a mainstay AI that takes new governance structures. And the last thing I’ll say on it, is who is most qualified to facilitate that discussion and bring relevant insight? I would

David Turetsky: 42:40

I think we could probably talk all night, but I say people probably have to stop. Yeah. I think we’re, I think we should call that a day. We’ve unpacked a lot of stuff here talking about the past, present and future of people analytics, and we’ve scratched the surface. Awesome conversation. Thank you very much.

Al Adamsen: 43:10

David & Dwight I thank you for having me. Thanks for doing what you do happy to do it again. And,

David Turetsky: 43:15

And you as well, it’s really cool to talk to people who not only have done it, and not only think about it, they get paid to do it, and they get paid to push the envelope and make things better. And so I appreciate that. And thank you. And thank you for listening. And hopefully, if you like this conversation, you’ll hit subscribe. And also if you have a friend who thinks this might be interesting, send it over to them. And stay tuned for more HR data labs at HR tech conference and stay safe. Take care. Bye bye.

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