David Turetsky – WorldatWork 2022 Series: Talent Intelligence: The Evolution of People Analytics

Connect with Dwight:

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• dwight.brown@salary.com

Connect with David:

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• 
• david.turetsky@salary.com

Podcast Manager, Karissa Harris:

• HRDataLabs@salary.com

Production by Affogato Media

Resources:

• WorldatWork Conferences
• Compensation Consulting Services
• Talent Intelligence: The Evolution of People Analytics WorldatWork 2022 Presentation
• HR Data Labs Season 4 Episode 2
• HR Data Labs Season 4 Episode 4

• “At the end of the day, what do I want to do for my managers? I don't want them to fail, I want them to make better decisions.”
• “Ask better questions. You have the data; you have the capability; it's there. You will deliver better insights, and you will hopefully get better decisions made.”

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 for 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, that 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 the HR Data Labs podcast. I'm your host, David Turetsky. Today, we have a very special episode. It was recorded live at the WorldatWork conference for 2022. The title of the presentation is Talent Intelligence- The Evolution of People Analytics. As some of you may know, I'm a pretty passionate speaker. This is the first time we're actually taking the podcast on the road at a presentation that we're giving at a major conference. So pretty good audience, very responsive, good feedback after. Hope you love the presentation and if you do, please do me a favor, hit subscribe, send it off to your all your friends. But also just give us some feedback on our website. Thank you very much take care and enjoy. What we're going to try and do is set context for making decisions. We've been making decisions since we were really young. We make decisions based on a lot of things. We make business decisions based on a lot of things every day, whether it's coming to a presentation, whether it's getting dressed in the morning, whether it's sending your kids to school, whatever. There are a ton of pieces of data that you sift through, and you find context to make decisions. This new world that we live in with iPhones and iPads and Siris and Alexa's and hey, Google's, you're drowning in data to help you make good decisions. So the context is, how do we help set managers up to make the best decisions they can make for their people. And what we've done is in the past, we've leveraged things called analytics. In the comp world, we've been doing analytics for probably 50 years. And we're going to talk about that. We've been using very similar analytics in the past 50 years. I'm not denigrating it. But what we're going to talk about today is this new world of context and how we are flooded, drowning in other data, that what do we do? We ignore it. It's noise. And for those of you who do statistical analysis, noise sucks, right? By the way, I get really passionate, sometimes I use four letter words. I promise I'll stay away from the bad ones. My friend Lena is here. She's gonna keep me on honest, okay?

Audience Member: 3:28

Do you have a jar?

David Turetsky: 3:31

I have a really full one at home. Yes, my kids keep it. So what we're going to talk about today's context, and I'm gonna set some context for you now. So I'm gonna first talk about how we make decisions. And yes, you're gonna say, I know how to make a decision. I want to take us back a step. We're going to talk about what people analytics is, what it has become. And I built people Analytic Systems. I've built some of the most popular people analytic systems in the world. And they suck. Again, four letter word sorry. Because I'll talk to you about why and why it's going to evolve. And then I'm gonna talk to you about what this new thing called Talent intelligence is. And hopefully, when we leave today, we'll have a common understanding because I want to learn from you and that's the reason why we're gonna do the polling. I want to hear from you what you have to say about all three things that we're gonna talk about today. Okay. All good? We're on the same page? All right, let's keep going. So when I started making this presentation, one of the first things I want to do, I sat down with my nine year old and I asked him how he makes decisions. And I can't tell you because I don't understand a word he said. It had something to do with the iPad and games and Minecraft and stuff like that. And beg borrowing and stealing to do more. How do you make decisions? I want to hear from you. I'm gonna open the poll in a second. I want one word, not a diatribe, not a three minute an explanation, one word, how do you make decisions? If you hyphenate it, it's fine, I won't count that as a phrase. Okay, so I'm going to open if you can go to your app. I'm going to open the polling. Hopefully I don't screw this up. Okay, one word. That's it. What we're going to do is we're going to get a word graph out of this. I hope. All done? All done. Okay. So now you should see, that's awesome. Three big ones: data, facts and information. So you all use data to make your decision, right?We're going to talk about what's the difference between data and facts. What's information though? A collection of data facts, right? But what's fascinating is, a lot of times, we will substitute those words, but they're don't exactly mean the same thing. And if you watch TV, I hope you don't. But if you watch TV, and you watch news, there's a lot of data thrown at you, you have to interpret what's a fact. I'm not making a political statement. I'm just saying, I'm not. I'm not going to get into which channel you watch. But a lot of times, we're left to figure out what's actually a fact, based on what's being said, okay? The way that I can interpret what my son said was, and this is the way I do things, I learned about something. I listened to people about it. I try, and I fail. And if anybody's ever heard me do karaoke, you know you. What's the best part about failure? You learn so quickly what you shouldn't have done. I have failed so many times. And I have so many people in my life who've told me I failed. And the best part about it is, it was an incentive mechanism to do better next time. Because if I had succeeded, what would I have learned? Nothing. I would have done the same thing over again. And would it have worked? Maybe, maybe I got lucky. Maybe I didn't, maybe I got I was onto something. If anybody is a Tampa Bay Lightning fan. Yeah, well, you should be happy. You beat the crap out of the President's Trophy. If anybody doesn't know, hockey, I'm sorry. But this is a big deal, because they just swept the President's Trophy winner, which is the team that came out with the most number of points in the National Hockey League, swept them wasn't even close. Last night was just awful. For anybody who wasn't a Tampa Bay fan. Why because if you're in the playoffs against them, and I'm a Rangers fan, you're gonna lose, I know, I'm gonna lose. If I don't lose in this round, I'm gonna lose the next round. But what's fascinating is they've won two times in a row, they've won the Stanley Cup, which is the ultimate in hockey two times in a row, they have a pattern, they know how to win, the team has very rarely changed, their lineup has very rarely changed, they've learned something. They know how to make the right decisions to put the right people on the right lines to score goals to win games. It's fascinating to watch. And you see it happen with a lot of, if you like sports, a lot of your sports teams, or the people that you might follow on social media, they have a winning formula they learned. But I like learning from failure. And so that's why I'm in New York Rangers fan. Because we faill a lot. We've only won the cup once since 1940. And that was 1994. I got to see it. In fact, Steve Brink was with me during one of those playoff games, weren't you? Yes, you were. So I learned. Eventually, they will win again. Someday, hopefully not before I leave. What's fascinating about humans is we balance things. These are just examples. We balance things. We balance reason and logic, we backed balanced facts and data. We balance emotions and rules and behaviors and instructions. My favorite example of this is driving How many of you drive the speed limit all the time? Come on. But that's the rule. Right? When you took your driving classes, they said always follow that rule. Sorry?

Audience Member: 9:22

Within 10 miles an hour.

David Turetsky: 9:23

Okay. Well, that's not the rule. If you ask a policeman why you got pulled over, he said, Well, I was only going 75 And it was 65. And they'll say, Sir, that's 10 miles over the speed limit. But thank you for being honest. But we balanced these things to be able to make the right decision. I need to get to that class on time. Okay, I didn't shower I'm not ready. I'm tired. I looked disheveled. I didn't eat I'm gonna fall asleep during the class. But I'm making the decision to get up and go to that class even though I will fall asleep and probably fail anyways, right? These are decisions, even though we know the consequences of those decisions. They're bad. But we do it because we have to balance things. Nothing is easy anymore. We have to make decisions within context. So I asked my son to show me the work for one of the math problems he did. And it was a very simple math problem. Jackson, spelled the way my son likes to spell Jackson. Your son's name is Jackson, I can give you the slide the actual slide. Jackson had five groceries, he splits those into two bags. And he showed me his work. I was very proud of him. Okay, spelling is nehh. But but he showed me the work. When you're young, you have to show how you make this decision. What were your thought patterns? When Why did you get from here to there? In words, and in pictures? Show me how you get there. Has anybody after ever asked you how you got there? When you bounced a check. Show me your work? What did you do? What was your balance before? What were your outstanding checks? What was your balance after? Show me your work. No, they trust you as an adult to make the right decision. Right? Well, nine year old can't balance his account, obviously. Nor can he manage his time when he's got Minecraft and he only has two hours to play a night. But the thought process is starting. And we've made these decisions. And we've never had to show our work. But as we go on in time, and we make more complex decisions, do we show our work? Do we explain? And the answer is yes, you do. You don't make big purchases at work without showing your work. Without giving a presentation, you don't make a pay decision because you think that's the right answer. You show your work, right? Who does market pricing using salary survey data? You usually have to show that to someone so that they can use it to make the right decision, right? You're showing them your work, they're going to show you how they make a decision with that, right? And so making a decision utilizing facts. This is a simple example I've been using literally since 1990. It hasn't changed because the math hasn't changed. What we're looking at is a plant accounting manager in a fictitious company. And we're looking at fictitious market data. And what is someone see in this? Are we at market are under market? Under a little bit, but we're under right? And so if you if a manager came to you and said we're under market, can you give this person an increase? Your first inclination would be what? No, right? No, there's not. There's nothing wrong here. Right? Not really that much. But you could justify maybe a couple percent if you're really because if this person is critical to your manufacturing operations, right? But it's not that simple. There's more work to be done here. There's more in this analysis, because you're not taking into consideration that we give an incentive, and at the median. We're giving the same incentive. So now from a total cash perspective, how are we doing? Basically the same? But now we're looking at a more complete picture of the decision. Right? If someone said the base salary is low, you'd say, you're right, they're a little low, maybe we should make him give them an increase. But you then the next thought should be okay, well, wait a minute. I'm in total rewards. What do we do on an incentive for them? And then the next thought should be, well, wait a minute, we have a long term incentive program. We give this person plenty of stock, even at the median of the market, only at the high end of the market. Did they give something? Why? Because we treat our employees like crap, and we have to pay them like, like a lot more money! Again, second, I'm sorry, second curse. But right, but you see, so now what's the picture gonna be in total reward? In total compensation we're above the market. If you had made the decision to give this person an increase, are you kicking yourself now? It's about context. You've learned and we in compensation have learned to show a complete picture, to do the entirety of the math so that we're looking at the entire picture. We don't stop at base salary. Anybody stop a base salary when you're doing market pricing? It's okay. Don't worry about it. We're not going to look at each other's hands. I see you. Thank you for being honest, sir. I appreciate it. And in some situations, it's okay. Why? Because we don't give incentives to a lot of jobs, right. We don't! Maybe we pay overtime, but that's not let's not talk about overtime. It's not a competitive issue. But still having this analysis means that we're okay. If I had just stopped the base salary for this job, I would have made a mistake. How many times do people stop? A lot? I'm sorry, I'm considering all of you didn't raise your hand liars. Sorry, I had to say it, the four letter word. And we look at the context of what people analytics is. Sometimes we look at the surface of a problem. It's unfortunate, I built a platform on people analytics that people don't use, because they don't understand it. And I had to build a consulting practice around it. Because people don't understand it. It's not in their DNA, to understand what is people analytics.

Announcer: 15:45

Like what you hear so far? Make sure you never miss a show by clicking subscribe. This podcast is made possible by Salary.com. Now, back to the show.

David Turetsky: 15:55

So our next poll, go back to your thing. What is people analytics? Right? You see it? One word, just one word. Don't put metrics, please. Don't put metrics. That's another word for analytics. Okay, again, data information. But the biggest one insights, exactly. What people analytics is, is insights, a vision or a view into what's happening into your organization. A vision a view. By the way, there's plenty of seats down here. You don't have to stand up there. I think the fire marshal would be really mad at me if you were standing on the stairs. A vision a view, it's something. Okay. I defined it. But of course, I have to go back here and actually show you that. A way a way, of explaining a business problem, from an HR perspective. If anybody's that, does anybody do people analytics here? Is that one of your jobs? Oh, good, good, good bunch of you. I'm sorry, I don't mean to minimize your role I love you all. Keep doing what you're doing. Hopefully, you'll be energized. To change a little bit. You know what that's like? The company just spent a couple million dollars building an analytics platform. And then they go to managers and the minute the manager say you wasted $2 million. That's firing, that the person who have signed off on that can get fired for that. I helped her not get fired. But that's, you know, then we need to spend a little more time on it. So I asked some of the biggest thinkers you may know Richard Rosenow? He's like one of the godfathers of people analytics. I asked him about it. And he said, decision support, blended with many other functions when the people analytics team works with other groups. He's seeing the bright side of this. For a lot of us, we don't have that luxury, especially in people analytics. When you do that, I promise you, you make magic. You help them get better on the other functions, and you get better too. And I've had him on my podcast. He's a really good guy. And he he's a really big help in the in the people analytics world. Our friend and ethicist John Sumser. Said, it is an analysis of the overall workforce. Okay, I'm summarizing, paraphrasing, but that's what he said. And if you haven't heard his presentations, or if you haven't heard him talk about HR ethics, he's a joy, love him. And then my friend, Mark Miller, I wanted to get an HR technology perspective. He said it's an endeavor, what's the thing about endeavors? You almost never get to the end of them. There's something you try you do, but you don't get to the end. And he said, it goes from simple data management all the way to information craftsmanship. I love that I bolded it and put it in all caps. Because what that saying is someone cared enough to think about the end user that they made it look and made it feel and made it useful to them. It was useful to them. He also called it actionable insights. So a friend of mine who will remain nameless, who had a group named after him in a big organization said in 2015, that there is a maturity curve in the world of analytics. And if you are at level one, all you do, and I'm serious, he said, all you do is reporting. Anybody think that reporting is just an all you do? I promise you it's not because even those of us who are in HR analytics today, we spend a lot of our time across the spectrum, probably not beyond descriptive statistics. But we work at different levels. He was saying that the world was at predictive statistics. I think it was somewhere around 70% in 2015. Do all of you 70% of you believe you're at prescriptive or sorry, predictive statistics right now? No, I love him. He's very smart. He's wrong. Why? Because there's not a one size fits all in every organization. We all do reporting, we all do descriptive statistics. And some of us get to do predictive, predictive analytics. Predictive analytics, predictive statistics are like turnover probability, who might lead my organization over the next six months or a year? It's useful. It tells us things about people, it tells us who might leave organization, what is the profile of someone who might leave organization, what jobs might leave my organization? It doesn't tell us who it can't predict to. This isn't the Minority Report with Tom Cruise? Yeah, thank you. We don't know this is only 2022. Anybody using prescriptive statistics, you're giving people options on what they know. Yeah, right. Exactly. Maybe. My friend, Steve Goldberg, he let me use this from Twitter, he said that, when we look at something at the predictive analytics level, the case for it is extremely compelling. It's useful. But the problem with it is, it's very, more apt to be wrong. But even if you're wrong, what's the worst that can happen and treat people better? You hope they stay? Maybe you give them an increase? Don't do that. Do not give people increased because you think they might leave? You're going to be giving increases to everybody who lately? What's this? In the world of people analytics, this is useful, right? This is the number of hires full time versus part time. Over the last five quarters, some managers find this information useful. I haven't met them yet. But we produce dashboards with this is one of those key metrics, right? And if you produce this, I'm not, don't worry about it. We're all friends here, or we will be by the end of this presentation. Okay. Nobody's admitting to that. That's pretty bad. But here's my other favorite. Everybody does this one, right? What do you learn about your world from this statistic? Nothing. I know nothing other than a bunch of people are leaving, they're leaving at different rates. There may be a trend up. But if you looked at the hires number, I was also hiring people. So maybe I was getting rid of bad performers. And I'm filling them with people who I am hope are better performers, right? It tells me nothing in isolation. Nothing. It's useless. And when you give this to managers, they go great. So what are you going to give me more money? Are you going to make the working conditions better for my people? No, there's nothing here. These are the standard bearers. Sorry, if I spit on you, I apologize. These are the standard bearers for analytics for people analytics. This is what we build those damn dashboards and send the managers and then they go, so what? Well, that's the reason. So what, who cares? When we have access to all of this data, and I beg you, anybody who has access to this kind of datasets, in the people analytics, just raise your hands. I love you all. Thank you. You're chefs. You have access to all of this data. You have all of these things sitting on your shelves, like cute, cute, and I'm gonna use cumin for anything? Cilantro? Yeah, thank you. Okay, well, I appreciate that. I have not figured out how to use it. But you've got these great ingredients. I want to pick on the last one on the left hand side for internal statistics or internal data on innovation. What does innovation tell us about our company? Oh, if you could just tell me what kind of statistic might be measuring for innovation? Sorry, creative ideas, right? New products, R&D spend. If our R&D spend is going up, I have hiring to do, I might actually have to have new support. If we're going into AI or robotics, I might actually have to retrain folks, there's a lot of things you can do a lot of things you learn when you start looking at innovation in your organization.

Some other things: 25:20

inventory. If I have warehouse workers, and I have no inventory, what do I have? Idle workers! I'm paying them still! Well, you're probably not paying them, you're probably gonna have to furlough them. Especially if you're in no baby formula manufacturers here, right? Sorry. But if you're baby formula manufacturer, you have nothing on your shelves. What are you doing with your workforce, your work, warehouse workers? They're not! they're sitting at home, so you furlough them save money. And when that stuff starts coming in, you're gonna need them back. Or maybe you keep them on and you talk to him about what's happening. But you can do something about that, you know, because you have the data, you know, when it's going to happen. Okay. The external data, why is it important to know what's going on in academia? What skills are we training the kids the next generations on? What things? Are we teaching them? Do we have those skills in our organization? And should we be recruiting, or pre recruiting or getting internship intern Good lord internship programs at those institutions. We used to do that with Georgia Tech at ADP. We actually had a people analytics think tank, where we hired interns, and we gave them real practical HR problems when I worked at ADP. And we had them work through those problems. And they learned about what we were doing in the people analytics world. And it was fascinating to hear their unique perspective on it, given their background. They use things like Twitch and what's that other platform you know, where they talk to each other while they're playing games. Discord, thank you the Discord lady's gonna be mad at me. But we learn things about how they work as well. So that when we start hiring them, they don't go, Wow, you guys use email? And Teams? What is that? I can't do video on teams like I can on Discord. But these things are critical. And they're important. And they give you context for what's happening in your organization. The stuff on the right gives you more context, because it's telling you what you're not doing. And it's telling you what's coming. Vendors, entertainment, weather. Weather. If you're in agriculture, whether it's easy. If you're at an entertainment weather is also easy. Anybody in the entertainment space? Do you guys count the number of cars in your theaters or parking lots? Baseball? Okay, well, sorry. Yeah, exactly. That's a bellwether? Are we doing okay? Are we not? Do we have to do fan promotions? Do we have to get more marketing out there or not? It also informs what's happening on the people side. This stuff is critical. This is what brings everything together. And this is what people analytics forgets. Because think about these things. All of these things are what we should be caring about. Are these statistics that people analytics measures. Changes in the organization. My favorite one? No. When there are reorgs, does HR know first or last? Typically? Okay, it was a loaded question. Sorry. But seriously, if we had predictive analytics about when changes in the organization, because it's tough to talk to CEOs and CFOs about these things, but if we knew when those things are happening, we could inform managers and talk about how that relates to their organizations. These things should be informing what's happening in our people analytics, so we can inform them. Unfortunately, none of these things are in people analytics today. Unless I'm wrong and the people who do people analytics, you any of you do any of these? Okay, but it's, it's basically after it happens, right. Right. Okay. So, the lady up front said that they do report on it, but once the decision has been made, and then how do they deal with that? Right, they have to. What I would love is if we were asking these questions instead on hiring. Why do we need a role? Have you ever asked the manager that question? What are we missing? Could we hire a gig worker instead? What would happen if you said that to our manager?They would say what? No, definitely not. We need to have this person on board. But you only need them for this project. It's projects only two weeks. Right? What sources are we using? are the skill sets required available in the market? No. How are we going to get it? When does this roll become unnecessary? Has anybody ever asked that question? Excellent. There is a field in your HRMS for end date, by the way. And it can be pre populated, but no one does. And by the way, if you tell somebody that they only you only need him for two weeks, what are you doing for them? You're setting their expectations about should they take the role or not? Turnover! What are the trending skills in the market for which we compete? Why would I ask that question? If someone's leaving, is it because that skill is trending? And they're being poached? Exactly. I worked at Morgan Stanley, our desks used to get recruited all the time, all the time. And you know, when we found out? After they tended their resignation, and then we had a conversation with the head of that group? And they'd say, yeah, yeah, it's all going on. Why didn't you tell us? We could have done something about it. We don't know. Can we find replacements if we experienced turnover? Oh, sorry. There was one other on there. What innovations changes to our product, or changes to SOPs are required to react to turnover? They may know what I mean by that. If we're now using AI, to do something that we used to use people for? Who's going to run the AI? And if we're replacing someone, could we replace them? Or could we place them now into managing the AI? Because they're the experts in what it's doing? It's the one that's going to train them? Because does AI know how to do what it does without them? Come on? Does the AI know what to do if you just put it there? No, you have to train it on what to do you program it! Does anybody remember this? Someone says, I need you to market price a job. So you find an appropriate survey. You match you exchange data with that survey company? You go through QA process, and then you present. Okay, it's simple. Just a simple example. AI now can do this, most of it. Are you worried? Bom Bom? Don't be, because a lot of this is art more than its science. And you guys are hired because of your brilliance and being able to solve problems. That right now the AI can't or doesn't know how to. But don't sit on your laurels. Because it will. Because AI is being used today to do magic. Right? Some companies use AI to do the initial matches. They suck. Again, sorry. But they do right? Anybody see that AI is being used to do initial matches. All right. That's a good thing. It's giving you a leg up. The problem is, it's it's sometimes very wrong. But that has more to do with our job taxonomy than anything else. The advances in technology today are incredible. The reason? Cloud computing is cheap. When my dad started doing this in the 60s, it was expensive to store data. The process of actually computing the data wasn't that expensive. But storing data was really expensive. You know why? Because they stored them on tape, or on cards. And those cards sometimes got ruined, and they'd have to do things again. And sometimes those tapes became demagnetized. And it was really expensive. It didn't become super cheap until very recently. It's super, anybody know how cheap it is? Remember when you used to buy those eight gigabyte, sorry, the eight megabyte little thumb drives, you can't buy them anymore. They start at like eight gigabytes. Now. That's a lot more than eight megabytes. I don't remember the math. But now it's also extremely cheap to actually compute. Amazon is one of the world's largest storage facilities for data. And if anybody's never used Amazon for that, it's ridiculously cheap. Like, if you do mailings to your clients using it, it costs pennies where it used to cost hundreds, if not 1000s of dollars to do those emails. Because they have the scale to make it cheap. And now they own that market. The worst thing is, or the best thing, if, depending on your perspective is the acceptance of cloud computing. We're all okay with our data being held without us knowing where the hell it is. Does anybody know where your pictures are stored? When you take a picture on your iPhone? Does anybody know what location it's in? We don't care! As long as somebody else isn't looking at it. It's not related to us, right? But that acceptance of giving up some things has made this stuff really cheap and ubiquitous. Why does that matter? Because the thing at the bottom, artificial intelligence and machine learning are cheap. For those of you who don't know, you can actually literally download AI and ML, of course, you have to know it, you have to program it, you have to learn it. But you can actually go on Coursera and learn it very easily, and probably not very cheaply. Anybody ever do that? Go do one of those learnings on Coursera. It's really cool. And it's really, really fast. So the the most important consumer based one is like Siri. And so this is what consumers use. We're not asking what's my bank balance? We're not asking when should I pay my credit cards? We're not asking when's the optimal time to refinance my mortgage? We're not asking should I date him or her? Because they're much more important questions than these, except for that. Number two, playing the podcast that HR Data Labs, it's really good. The host is really dynamic. People tell me they love him. But seriously, what are we asking our AI? They're stupid, simple questions, play a song by Willie Nelson. Really, that's the most important question you got to ask it right now? Or what's going on with the weather? But that's what we don't know. We don't have context for asking better questions. And therefore we don't just like before, are people analytics aren't asking more deep in depth business questions. Why? Not because we don't have access to the data. Maybe we don't. But we should ask for it. We should make friends with those people who have access to that data. Ask them for it. Tell them you want to start thinking about how we can merge the data to give managers better decision making power. But but definitely listen to the HR Data Labs podcast. It's a lot of fun. When you use AI, it is very clear. Remember Lieutenant Data from Star Trek Enterprise I think it was or next generation? When you talk to him. And by the way, this is before Siri, he gave you just facts. He didn't really care about reason. He used logic, he was more like a Vulcan. Sorry, I'm going down a bad path here. Totally geeking out on this. He didn't care about emotions. Although there was one episode where he gained emotions. Remember that? But he follows the rules, and he cares about instruction, he doesn't care about behaviors, you won't find him with behaviors because he's programmed. He follows instructions. And that's what AI does. And that's why it's predictable. When I asked Steve about AI, he said one of the greatest sources of get benefit would be if we could predict when there were errors in payroll, that would save us a lot of money. I love my friend Steve, it is not going to save us a lot of money. Because you know when why we lose a lot of money when it comes to payroll errors. Missed punches. Can I predict a missed punch? Well, I can if I know that a bunch of missed punches are coming from one manager or because the clock is down, right. But that's not predictive. You know why it happens? Human error, people forget to clock out or they forget to clock in or in California they didn't take the break or they didn't clock in and out for their break. And we have to fix it. Because you can't pay them unless you have all those things. He's right though. We could use our AI and ML to be able to reduce our errors so we save some money. It's not going to change the world though. I told him I would say that so he knows that I'm picking on him right now. So okay, let's use our app again. Well, and we're keeping you on your toes. What is talent intelligence? What does it make you think about in the context of what I've set it up to be? What is it? What's the one word that comes to mind? Please don't say, insights. And don't use the word intelligence. You can't define a word by the word. Okay, ready? Let's hit next and see if we got something. Knowledge. I love you guys. I knew I'd like you guys. Exactly. If we take all this stuff together and set context, we give people an understanding of things. Now they have knowledge and what was important for them when we remember we talked about how we make decisions, learning, right? So they might fail the first time, if we give them knowledge, then the next time they do it, they'll be okay. This is my definition, you're not going to find this in a book, there is no book, I'm writing the book, I will write the book. I haven't written it yet. I wrote a blog post, which got published today, I think. Blog post is a good start. It's the crossroads of data and processes. And it borrows those advanced analytical and compute computing innovations to set the appropriate context for people. I want to set the appropriate context to give managers all the information they need. All the stakeholders, they need all the information for stakeholders, it's beyond just the intersection of large datasets, we talk I haven't used that BD word yet or that phrase. I haven't used it and I don't want to. Because a lot of times you'll hear people talk about analytics, news BD, it's not about BD. It's not about whether large datasets or small datasets, I don't give a shit about the datasets. Sorry, another one for the swear jar. It doesn't matter. How many of you use Excel spreadsheets for your processes? It's okay, you can admit it. It's okay. I have built so many 1000s of my lifetime, hundreds of 1000s. It's okay, but that's where they're stored. What if we were to leverage all of those insights in all the innovations you put in those Excel spreadsheets, all of that data, to set context and to be able to come together? It's beyond the large datasets, but use it utilize this next generation thinking about how we ask questions, and gather the right evidence. At the end of the day, what do I want to do for my managers? I don't want them to fail. I want them to make better decisions, businesses decisions, people are part of those business decisions. Every day. Every day, whether I hire or I fire, whether I lay off, whether I merge, whether I divest people at the heart, the heart of that. And if I give them good context for making that decision, then I have now become part of the profit chain. I've now become part of not a cost center. But I've been part or I got a seat at the table. How many times we talk about that thing. Richard says it generates and collects new data to inform stakeholders. And love Richard, by the way, I'm not going through these, because I only have a few minutes left. And I want to leave time for questions. I have 19 minutes left, but I want to leave time for questions. My ethicist says it's an understanding of individuals built on aggregate aggregated data around the web. A little interesting. His comment comes at what's being used right now, in the term talent intelligence to talk about recruiting. There are some companies that actually have kind of leverage that term to talk about recruiting and be able to use different tools, ML, AI and datasets to be able to find better candidates. I'm talking about beyond sourcing and beyond recruiting. And my friend Mark says it's the organization's people are the organization's sorry, talent intelligence are the understanding of the organization's only differentiator in this case, he means people but it's also their culture and how it integrates with the people. Oops. So here are my examples of a good talent intelligence question. I'll let you read it for a second. Are there good? Are there skill gaps in our organization? Can you ask your people intelligence sheets or your people analytics tool that right now? Can you ask your talent or people intelligence? Can you ask your people analytics tool? Any questions right now? No. But if you ask this question of your people analytics team, do we have skill gaps, they might be able to get it for you. But it would take a long time to get it right. That's a shame. We need to make decisions. Now. We may have to invest in training now. We can't wait two months or three months for a study to be done. Have we lost any key employees lately? There's a lot built into that. What's a key employee? It's not your top five people, by the way. It might be the people who have the highest productivity on your line. It might be your if you're if you're in financial products, it might be your accountants who know how to unwind financial products. Or it might be your best salespeople. Are these are those key? Damn right they are. Because without them, you're not making money. It might be your manufacturing people. And if you're in retail right now, it's the people on the front lines. Right? Which departments and locations have turnover of staff that will be hard to replace, and why did they leave? And the next one is a big one. It's a multi part question. Why can't I ask a worker in my shop to work an extra hour for tomorrow's shift? Does anybody ever get that question? Anybody working retail? Anybody? Any retailers here? That matter? Your managers in your shops are asking this question every single day, especially when they can't hire people. Because when they're managing their shifts, what are they bumping up against? The ACA hours, right? They're trying to keep people under 30 hours typically, am I wrong. There's a lot of things to worry about overtime. Do you want to pay overtime to your staff? In some cases, yes. In some cases, no. Right. But you definitely want to put a lid on it. So there's a lot in this question right here. And the worst part, it's about tomorrow shift. I only have a few hours left to do that. Right. And then I have to call people after hours. And I don't like doing that. AI should be able to read the government regulations and say, in this jurisdiction. Key, because it's different between different cities, the overtime rules, the overtime rates, the ability to ask people to work longer differs between New York City and Rochester, New York or in California anywhere. Right? Right. In this jurisdiction wants a part time employee, this is totally fictitious. No idea. This is right or wrong. Works 30 plus hours in a week the organization is required to do you want to do that? Because if so, it's going to cost the organization this amount of money. Wouldn't you love to have this to give your manager a decision to say it's going to cost this amount of money, then they can make the business decision. What are they going to do? Are they going to immediately post that shift? Are they going to post a wreck online? Hire another person? What are they going to do? Now they have context. They're going to do what they have to do to maintain profitability. And what's best for the employee and sometimes the cheapest thing. But there's more. Instead, there are four other workers not bumping it's a bumping up against their limitation. Why? Because we have access to all of the data from the time system. And the point point of sale system. We know all of this information, we've got it all together. We can also set context that way. Other managers in the situation, have done this. And then is been successful or has failed. Key. It's read history. It knows what happened. It knows not only the context for the decision you're asking, but it's telling you what the outcomes were. Has it changed employee engagement. Have people been happy about it? Have your customers been happy about it? What happens when you ask people to do too much overtime? Ever run into a barista who's worked too hard? Seriously, that cup of coffee does not taste what you hope it would taste. And that experience you have cuts into your appreciation for the brand. Am I wrong? Have you ever had anybody if not to pick on Dunkin Donuts? But anybody ever go to a Dunkin Donuts? Have you ever had a cup of coffee? When that that caraf has not been cleaned? It's disgusting. And you go, why do I do this, but then tomorrow, you're gonna go right back to another Dunkin Donuts, right? But it does tarnish the reputation. That's important. It sets context from beyond just the employee piece of it. But it gives a manager a better understanding of the business context. How should you proceed. And then your choices are outlined below. With the table of costs as a comparison, borrow it from Amazon, steal it from them, I'm sorry, if Amazon's here, I am not telling anybody to steal any of your proprietary AI, artificial intelligence or whatnot. But you've seen it. Other people have done this. And here's what it costs. And here's how much they liked it. Go into any store, Home Depot Target, you can scan the UPC code, and find out if people liked it. Frankly, you can also scan to see if it's cheaper somewhere else. Right? Right. Has anybody ever used that? What context is setting for you? Should I spend my hard earned money? Of course go back to rationale and money, right? Those two things are usually not congruence. But here's what I'm setting up for the manager. I'm giving them the opportunity to make a decision with all of the information. They have all the insight, it's all there for them. Can they make the best decision possible without failing? I don't know. I don't know them. But I know if you're trying to set the right context for them. And you're asking the right questions. And you're giving them the right answers. Or they're asking the right questions, and you train them on that and they're giving you the right answers. Then guess what, you've done a stellar job. And you should applaud yourself for it. So to me, there are steps required to make this useful. And if you've ever heard my podcast, one of the first things I talk about is clean data is the benefit of benefits people analytics in ways you'll never understand. Unless you look at the analytics before you clean your data. Has anybody ever shown your managers data that's not clean? What's the first thing they say? It starts with a four letter word. Crap. This is crap. Right? Because they say this person transferred out of my organization three months ago, why are they there? Or these job titles are all wrong? How could you know what my market pricing is? You don't have the descriptions do you because those job titles are wrong. Clean data is the enemy of everything we've just talked about talked about. The problem is that you don't own them. All you can do is assume they're clean for most of them. You're an artist, you're drawing on a canvas, that the paints don't necessarily belong to you. You're trying to weave a story and set context for a manager. And you have to make a ton of assumptions. But your HR data is up to you. And the processes that make that data should be up to you. Because if you're a consumer of them, and you don't complain about them, it's your fault. See something say something, not just getting on the subway. And I'm serious. If you see bad data, tell somebody about it. Tell your friends in HR IT. We'd love to hear from you. If you know that your processes are throwing bad data, don't let those processes continue without your input. Does anybody know what an action reason code table is? Okay, some of you. Has anybody ever looked at your action reason code table and not laughed? It says things like action termination. Reason, resignation. What does it tell you about someone leaving? Why did they leave? A will they resigned? That's what I know. No, they didn't resign. They hated their boss. They hated the working conditions their pay sucked. They had a better job offer, their spouse left the area not with them. Happens, happens, happened to me, deeper story over beers. But seriously, action reason code table, it's one of the simplest things to fix, go look at it. And I promise you, you will be shocked. Why? Because your managers are making decisions based on those action reason codes. Because when you run an analysis like term reason, has anybody ever looked at a metric called term reason, and not left. A lot of times when I'm working with clients, the first thing that we audit, is that that table and they look at it and they say, I'm sorry, like, don't worry about it. The most important step is you're looking at it right now. And we're going to fix it. Identify the data champions throughout your organization, who are the people who know where the bodies are buried in data? And how to make them your best friend? Is anybody a CFO here? I will give you a hug for being brave enough to raise your hand. Okay, there's two reasons why I say that. One reason is, is anybody's ever done a headcount analysis and got called to the CFOs office? Because they are the arbiters of the truth? Because they will call you to the carpet for having the wrong headcount data, because they get from the GL, the number of people that work at your company, right? Does any business sound familiar to you? Yeah. I've literally been in a very large organization and called to the CFOs office, because they told me that my numbers were wrong. And I said, No, they're not sir. And I didn't lose my job, because I then followed that up quickly by saying, Your numbers are right. And mine are right, but they're looking at the data differently. Know your data, no, who in the organization knows their data? And then say, if you talk to Sandy, in accounting, she'll tell you, you count data differently than we do. Our definition is this, your definition is that we're FTEs, you're butts and seats. That's why our numbers are way off. Thank you. That's useful. Now get out of my office. Seriously, but you have to know those people so that you can get some help, and then be able to get access to that data. And then know, what are the failings in that data. So when you bring it into your analyses, and those questions, you have a much better appreciation for why they're good or bad. I've got five minutes left, update your data competency. And this is where I was talking about whether it's Udemy, or other places, or Coursera. There are great courses in analytics, learning data statistics, you don't need to be a data scientist. But if you take some courses, you'll learn some new advancements, and how all that works. So you'll be able to ask better questions. The first thing they teach you in data science, is to think about the data differently. Yeah, of course, they make you set up R and they make you run through transforms of big data sets. But at the end of the day, they're trying to make you think differently about how all that data works together. And what they want you to do is they want you to learn, they want you to show your work, and document it for other people. Why? Because somebody else looks at your analysis, they want to see all your assumptions, they want to see where you got the data from. And they want to see what transforms you did on the data to get to the analysis and the results, in the context that you set. In it, they're they're not very expensive to do, I think there's $50, a course on Coursera, that may have changed because of inflation. And again, look for those data stewards in your group. Tap someone who wants to learn more about this stuff, and let them grow. Let them be the next generation that can take this on and carry the flag for you when you're gone. Because you got promoted because you did such a great job listening to this. Yeah, that was a joke. At the end of the day, what I want to end on is you already saw it. So I apologize, ask better questions. You have the data, you have the capability, it's there. You will deliver better insights, and you will hopefully get better decisions made. But those questions need to change. It can't be about how many hires and how many terms. It has to be, why did we get those terms? What's the business context? Did we lose a significant desk? Did we lose a leader? Did something happen? What happened? And I promise you if you do this, I can't guarantee it. Of course, people will start asking you new questions that you didn't anticipate. And they're going to leverage the excitement that you generate by asking these new questions. And they're going to innovate, and they're going to ask better questions. And at the end of the day, it's going To start and mushroom, and it's become bigger, because when people analytics, one of the problems is no one asks those questions. No one cares how many hires we had? Ask recruiting, they know. What's the best recruiting source? Is a better question. Okay, there's a metric for it, I promise you. But that's not as popular why? Ask better questions. Any questions that you guys have? Yes, sir. The first thing I do is find out what data is available to you. And then think about it, whiteboard it, go to the board, go to this wall right here with non permanent marker. And no literally go to a place where it's safe, you don't get fired for it, and start thinking about the thoughts of what do people want to ask? What would managers want to know? If I brought two datasets together? I'll give you a simple one, sales and turnover? If our sales are going down, are we seeing or are we losing people in the sales organization? Right? The theory, it's a hypothesis, we're gonna use the scientific method to ask a question, we might not have a high enough end to be able to actually know statistically, but you'll see a pattern. And when that pattern leads, then you're gonna be able to uncover other patterns. So start with one thread, start with something that's a burning platform, a burning issue in your organization, then ask for the data. What's the worst they could say? No. And say, Hey, listen, I'm trying to produce analytics, for our managers that do this. Is there anything you can give me? I mean, there's some publicly available data for most companies, right? Some of the better data is being held by other people. That's why you have to make friends with them.I'm being very serious, make friends with them, tell them what you want to accomplish. And they're gonna say, you know, that's really interesting. You say that, because I was hoping to get the total payroll that we spend on this group. Now, you may have to get some approval so that because payrolls very dear, but why not? In the era of transparency, why not start talking about sharing data? Not at the PII level, by the way, we don't do that we never give out personally identifiable information. If you leave WorldatWork, and yeah, listen to this guy and tell him intelligence, but he didn't say anything about not giving up PII. I just said don't give out PII. Because you'll definitely get fired for that. It was a good question, though. Start simple. Thank you very much. I appreciate you're paying attention. You're all very wonderful for saying that long. Thank you.

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