[0:00 – 2:10] Introduction

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

[2:11 – 9:26] Reflecting on Past People Analytics Trends

• The importance of learning how to sell
• Every big story needs a big idea
  
  

[9:27 – 16:16]  Insight on the Current State of People Analytics

• Investing in the business intelligence infrastructure to support data science
• Why smaller people analytics applications are taking off
  
  

[16:17 – 24:36] The Future of People Analytics

• Learning more about how successful teams work together
• The three “horizontals” of people analytics that will shape its future
  
  

[24:37 – 25:40] Final Thoughts & Closing

• Episode closing

Connect with Zack:

• LinkedIn

Connect with David: 

• LinkedIn
• Twitter
• david.turetsky@salary.com 

Resources:

• Visier

• “Whether you’re recruiting someone to work with you, raising capital, or getting someone to pay for the thing, you got to get people to believe and see it.” 

• “Just with Facebook and Google alone, you have like $3 trillion of value on understanding the consumer, but think about how much money is spent on the employees, right? And every problem is solved by people working together.”

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, pour 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 the HR data labs podcast. I’m your host, David Turetsky. Today we have with us Zack Johnson from Visier. Zack, how are you? I’m great. Thank you for having me. My pleasure. And as always, we have Dwight Brown from Turetsky consulting now. salary.com great beer. Awesome. So Zack, why don’t you give us a little bit about your background, what you do for Visier. And we have one question for you. They’re gonna love. Awesome, something that no one knows about you. But first, tell us about yourself. And then tell us one thing that no one knows about Zack Johnson?

Zack Johnson: 1:16

Yeah, sure. Thanks. So first, my name is Zack Johnson. We established that part. But I’ve been working in people analytics now for about 14 years. Which knowing today’s topic, I think we’ll have some fun because the world that people noticed changed a lot. And first I started as a founder CEO, did that for about eight years. And then I wound up joining the Visier team about five years ago to found their embedded analytics business. So I’ve been growing that now for a bit. And it’s exciting. Just bringing companies people analytics at scale. That’s great.

David Turetsky: 1:46

So tell us one thing that no one knows about you.

Zack Johnson: 1:48

That nobody knows about me? Llike no one anywhere? I think my favorite color is pink. That’s great. I really like pink. I think it’s really cool. It’s a really cool color. That’s awesome.

David Turetsky: 2:02

Yeah, that’s the first time anybody’s told us their color. We’ve heard of their astrology signs like adventure that they’ve taken. That’s really awesome. Thanks. So the topic that we talked about before is people analytics yesterday, today and tomorrow, and kind of understanding where it had been, you got a lot back since you’ve been doing it for 14 years. And then what is what are you seeing today, especially as part of Visier, you know, what you hear from not only the embedded clients or the clients where you’re helping them in bed, but also other clients that are with Visier? And then what do you think about the future of analytics? So let’s start with what have you seen in the past? Where was people analytics?

Zack Johnson: 2:50

So what’s? What’s crazy is it used to be in like coffee shops and like breweries, and it was like nerdy PhD students. Like, that’s totally where it was, right? That’s kind of where I got my starting at anyways. But like, it was really people who were asking, why don’t we do this questions? And would this be cool type questions, right? So like, like, why don’t know, what makes one team more successful than another? Why don’t we know what makes one individual advance faster or things like that. And so once upon a time, it was very much always about asking a question. And then it was about finding a sponsor for that question. So someone had an organization who had enough discretionary budget to say, that’s a really good question. Let’s go figure it out. And so you think of it as like people are building a lot of Formula One cars back in the day, right? Super fancy. Over and over again. Yep. super fancy, super high tech. But like the moment the limited application it was applied to was over, it’s like, this thing’s utterly useless. Yeah. And so I think there was so much rework for like, 20 to 30 years, right? Because it seemed that earliest schools, people and like stuff was like, you know, Raytheon in the early 90s, and stuff like that. But I think there’s been a transition to privatization scale, because scale is the only it’s like, you can’t just go to the gym wants, like, I wish we could, I mean me too….deeply,

David Turetsky: 4:14

Especially with the ice cream at the HR technology conference.

Zack Johnson: 4:18

But yeah, it’s changed the change quite a bit. But it was very much I’d say data science driven versus BI driven. Big difference between the two very much came out of academia. And so there’s some still some luminaries in the space who like, are our major influencers, but he’d been doing this for a really long time coming by coming out of academia. Sure. And it was very much sponsored by the most experimental progressive organizations.

David Turetsky: 4:39

One of the fun things that when I was a practitioner, and I’m a comp practitioner from old was that we asked hrs to do a lot of it for us, but it wasn’t analytics. It was surely reported. And I think a lot of times it has gotten confused where they were, they were kind of crossing the line or we were kind of crossing line wasn’t really analytics, but there were analytic. There, there was insights that were being generated. But to your point, they were small, they were point solutions. They only lasted for a little bit. And they were not productized. Totally. And so we kept experimenting and failing and succeeding. But we never learned from it. We just kept doing it over and over again and expecting. I didn’t know, we were expecting, I think we were just expecting the job done and do it again, at some point in the future. And I guess the question is, lessons learned from the past? Did we do anything right? Do we do anything wrong? And what lessons did we learn from the past?

Zack Johnson: 5:29

That’s an awesome question. And I think the people building the space, learn how to That’s actually really important, right. And it’s something where technologists sometimes look down on sales, but like, whether you’re recruiting someone to work with you, or raising capital, or getting someone to pay for the thing, you got to get people to believe and see. And so I watched a lot of people who now are majorly successful and influential in the space, have to grind it out for 10 years to get people to become believers. And that’s how Mark because you have to build a market. And so I think that’s a big one, I think people really learned how to describe the value, how to set expectations and manage promises, how to dot i’s and cross T’s because there was no governance in 2008. There, there was no GDPR. Like, it was the wild west. And I remember, like, just some of the things I saw companies do are crazy. And selling is there’s evangelism they’re selling, but selling is very much about like connecting people with value and helping them make good decisions. And I think if we had all the tech we have now, when we brought it back 10 years ago, you’d go nowhere. Just because it’s not a technology problem. It’s a belief problem. And I think there’s, it’s a really important lesson for building any market.

David Turetsky: 6:46

I love what you’re talking about. I totally agree. I think some of the times the best people, the best thought leaders, the best could have been influencers, had no skills to sell what they were doing, they created these, and I worry about this with data scientists too. Whereas they’re so brilliant at doing what they do, that their skills don’t extend beyond the project. They can’t articulate. They can’t get those leaders who need to sign off on the funding to be able to understand fundamentals. Like as a consultant, one of my jobs, and one of the key skills that I grew from Towers Perrin back in 1989, was being able to translate whatever the statistical analysis I was doing to people 100% Yeah, it’s a little challenge. And going in front of the CEO, you have to add skills not just to be able to sell it to them, but to relate it to the business problems are trying to solve totally, and Susanna totally agree with you. I also think that in for those HR people who are listening, it doesn’t mean you have to take a class in sales. What it means is you have to be able to take a class, if you can, in communications, and or writing, being able to find the audience. And to be able to sell to the audience, you need to be able to translate what you’re doing. And I don’t know if you know, data scientists, I’ve known a lot of data scientists, and they have a lot of problems being able to communicate the brilliance in their mind, not just on paper, but to other people.

Zack Johnson: 8:24

Totally means just storytelling, right? It’s just about connecting. And one so a pro tip for anyone in the audience that’s really helped me as you just bring people along in your journey is every big story needs a big idea. Good versus evil, whatever. Right? Then he parables me examples. Because the end of the day people want to Linux or something. We’ll talk about what the future some huge ideas there. But how does a meeting look different? If you have this capability? How does the decision look different? That’s the thing because then you people emotionally connect to it for a big part. But that’s in technology, sometimes we’re lacking for artists. And sometimes you need to have a couple artists around to help you figure that out.

David Turetsky: 9:03

I love that, to me, it’s, you know, the scientific method, right? Create our hypothesis, you gather facts to experiment a little bit, and then you prove your theory or not turn it into a story. You know, it doesn’t have to be once upon a time although you could. But it’s about a creative write writing exercise where you take that and you find a way to be able to translate that that’s awesome. So let’s go from the past to today. And the things you’re seeing about the way in which people are being able to either use it or not use people analytics in their businesses today.

Zack Johnson: 9:49

So it’s such a different world, like the scope and like if you think about like the curve of who has people analytics used to be like, you know, 50 companies and now like I know with us we have 1000s Just we’re busy I think one of the things that’s really important is analytics is like this huge catch all word. Yeah. Right. There’s like, like my favorite things to show people that the slide where it’s like, here’s a grid with all the things that are in, like, you have natural language generation, you have natural language processing, you have sentiment analysis, you’ve applied social network analysis, any one of those could be a full market, let alone a company, and so part of its like, I think there’s, I’ll share the ones that are most exciting to me. The first is, you’re seeing enough companies investing in the business intelligence infrastructure to support the cool data science. Most vendors here are talking about people analytics, they’re really talking about data science, like a really cool application of an algorithm or like a point kind of module. But it’s the boring stuff, like, who’s supposed to see what data and how do you derive that from an organizational hierarchy? Like, that’s not sexy, it doesn’t sound like you can charge a lot of money for it. But it’s maddening can’t do all the cool stuff that five years from now you’re going to need to compete, if you don’t make those fundamental investments. That’s a huge, I’m seeing 1000s of companies do that not 10s, or hundreds. That’s huge. The second one is I’m seeing the delightful small applications start to take off a bit. So I’ll give you an example. I was a CEO of a company with like, 10 to 20 people at any given time, right? If I gave someone a raise, like we didn’t have like a formal HR review, I don’t check why did you give somebody a raise? What would you have to do? You got to go look at the ADP, pay stubs, right? And you’re like, when did it go up? Wait a second, was that a tax thing? Or is that? Right? So So one of the things we have in our product is employee history, it’s actually my favorite product, we just show all the attributes when they changed. In one button clip, it takes like six hours of big on the phone. And like, what we find is some big companies, you have the big workforce planning use cases, and our big diversity targets and stuff like that. But we got like coffee, coffee shops and churches, like all sorts of stuff. Now there are embedded business. And like, if you save someone an hour or two and a small business, yeah, and like, it doesn’t need to be a product that can be a feature. And so it’s exciting to me, because to me, people analytics isn’t the buzzwordy products. It’s like, it’s like if you’re on Instagram, right, and you see how many followers someone has, you know, whether they’re mega famous, kind of famous, or like a normal person, right? That’s not Analytics, you know, think of it as analytics, but just drawing a conclusion about the world around you that’s directionally from data. That’s where it’s going.

David Turetsky: 12:27

Oh, and to that end, you know, using the Instagram example, it’s embedded in such a way into the experience that the person’s looking at, that they understand very clearly, analytics is over complicated. And in all deference to my friends like David Greene and other counselors, I love that they’re talking about the bigger stuff, the future stuff and trying to solve bigger problems, to me and Turetsky consulting. And what we’ve been working on for my goals are smaller issues, like data, and data quality, and job architecture and other things, which is those are fundamentally aligned with the people analytics from the beginning, then if all of the data that you’re looking at is incorrect anyways, and you can’t draw any conclusions. Or if you do, you’re drawing conclusions that are wrong. And so what I like to do when I start talking about today, and people analytics shows, when people start up people analytics, what I hope is, and this is no plug for Trotsky consulting, but But what I hope is the first thing they do is a data audit, an audit of all their processes that generate data, but also a process where they look at the data that fundamentally will underlie everything, so that they have a good foundation. And then they can start to draw those conclusions.

Dwight Brown: 13:44

So that’s, that’s the amazing thing is that there’s all this technology out there, and so much good, purports to bring you all been together having a wonderful insights, but people do forget that piece of things, the data governance side of things, right, where, you know, garbage in, garbage out. Yeah. And no matter whether it’s today, or in the future, or whatnot, until companies go through that exercise and start to put those rules in place. So getting a grasp on that. It’s not gonna it’s not going to do them the good that they want to be able to do.

Zack Johnson: 14:21

We pull out some so we’re sometimes we’re, we’re we don’t talk enough about some of the things we’ve done that are unique. Pretty much all of our customers are on the same data model. So we’ve set up a normalized system, which like, yeah, everyone thinks that’s cool, because the benchmark piece, but you start to establish norms on like, what does something and I know from my experience, if people notice, I’m sure you’re at the same thing with all the work you’ve done. It’s intuition plus the data that helps that makes magic right. It’s been explained it and there’s no better way to clean data and than to look at it in an analytical model and say, That person doesn’t work here anymore. Yeah, exactly. Some Yeah, but the problem is, hopefully you haven’t rolled it out yet. Oh, no, no. Absolutely right? Absolutely.

David Turetsky: 15:07

You know, one of the fun things that we do when we work with clients is we show them data in the pre state, we show them the analytics and the pre state. And they look at it and they go, Oh, my goodness, where did this stuff come from? And it’s the stuff that they’re actually running their payroll, and their HR and their benefits and all that other stuff on and the things that we use to show them, they’re all standardized metrics anyways. And the worst part about it is is that they, they haven’t looked at these underlying fundamental data issues like the job table, in order to be able to correct those things because they don’t need to. It doesn’t drive payroll, doesn’t drive benefits, it doesn’t drive other things. But what it does drive is a complete misunderstanding of the client, with a client with the employee, with the manager and the career path. And we’ve had lots of conversations the last few days with people talking about modern data architectures that start with a career framework. Totally which, you know, people analytics, utilize a career framework, but it’s not really necessary. Unless the job tables not right.

Announcer: 16:10

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.

David Turetsky: 16:25

So Zack, what’s the future of people analytics?

Zack Johnson: 16:29

Do I have permission to share some really, really scary big ideas?

David Turetsky: 16:32

Okay, today, what are we talking about really scary big ideas.

Zack Johnson: 16:35

Awesome. So I actually think people analytics is going to be top three single biggest society changing things in the 21st century. I’ll tell you the reason why I got into people on Linux, I remember I was like 18-19 years old. And I was working in a applied team science lab doing like, crazy social network analysis and stuff. And some of the biggest CPG companies on the planet, were coming to the lab, maybe like, we’re not sure why one team succeeds or fails, like, can you help us I remember sitting there and like, I was like, I don’t have enough money to like, feed myself, I weighed like, 50 pounds less than the right stick. Now seeing they’re like, wait a second, you’re telling me you spend like 200,000 a pop on these people, and you don’t measure how they work together? That’s the dumbest thing I’ve ever heard. And like once you see that you can’t unsee it, which is like that, like just with like Facebook and Google alone, you have like, what $3 trillion of value on understanding the consumer. But think about how much money spent on the employee. And every problem is solved by people working together. And so it is going to be a gold rush way bigger than HR to basically change how people work together and manage people.

David Turetsky: 17:38

Are you talking about assessments? Like how does one person’s culture fit in with the group? Are you talking about something different?

Zack Johnson: 17:45

So I’m talking about every aspect of work, being assisted and nudged and pushed based on inputs that happen behind the scenes to give you a toolkit? So think think like, I’ll totally talk to you about my work week, 15 years from now, when I wake up, I want to get a note that tells me I have the busiest week of the entire year by 30%, I should probably cut some meetings because those weeks don’t go so well. By the way, my team’s mostly meeting with people internally. And really, we’re behind on our numbers. And so externals, pretty important. Oh, and also, those emails have been writing, you know, they don’t really sound like all other VPs write their emails. So it’s just a reminder that like, I should just double check stuff like, all those little like, none of it requires me to go to a big analytics project or seek stuff out. But if you think about it, so employees are going to have a world where they’re not going to rely on someone looking like them, to hopefully coach them on how to do their jobs, they’re going to be there’s gonna building scaffolding. Managers, like me will actually understand like, various adjective like aspects of a team that right now are all intuitive. Executives will be able to simulate, hmm, if I doubled the size of my Salesforce in all of history, how often does that actually lead to the growth rate that I just told my board I’m going to do and then investors are able to say, while we’re actually going to invest in capital metrics that are leading indicators, because people are the ones who executed.

David Turetsky: 19:04

But a lot of those things you’re talking about? They’re partially there right now. Exactly. They’re frustratingly not there, right? I mean, even the thing you just mentioned about the investors, investors have a really cool process they go through to be able to make those determinations. Which which industries they bet on which companies they bet on. They do a lot of manual due diligence, really frustratingly manual, due diligence, but actually, they’re, they don’t work together. First of all, they don’t fit together. And even when you try using some technologies that try to fit together, they’re so frustratingly not there?

Zack Johnson: 19:39

Well, so look at look at like, like people have been marketing the consumers for like 150 years, right? took a really long time before you had Targeted Advertisement followed throughout the internet based on your psychometrics. Like it’s same way that like, took seven years to get to the Sopranos. And so we’re really warming I think it’s easy to Imagine the applications. But there’s three layers of market that are required. This is where I think people get tripped up sometimes people analytics, because first people analytics is the macro HR analytics as a subset. And so all the operational data, everything else is actually where most of the data will be. But if you think about it, like a bridge with a keystone, who are you? Who do you work for how much you pay, that’s the missing piece that everyone else doesn’t have. So it gives HR a lot of power. The piece that I think’s really interesting is the same way like the consumer web isn’t a market, it’s an economy, to bring to life and have that stuff work. The way it does today requires an economy. It’s basically three levels, that economy and how I visualize it. The first is infrastructure. So all the stuff you’re talking about job architectures, data governance, and how do you deal with sovereignty in different jurisdictions? How do you bring it together, cleaning data like that is a massive, multi billion dollar market, just like just getting the infrastructure together? Sure. Then you have the next layer, which is what I would call content and applications. So it’s all the different solutions that can be powered by that data and all that stuff. So like, their compensation solutions, their recruiting solutions, there are two management solutions there are, how do you make a development team work solutions all does. The last one that I think’s the one that I’m most unsure of how it’s gonna play out, and that that layer is your video? And what I mean by that is like, through what mechanism, whether it’s a screen, or what is really the future of management happening on, like, if you think about work, there’s really a few mediums there’s email, Zoom now, I’d argue is one of the most important mediums and work. There’s calendar, right? So just people in Linux get woven into every medium, which in some cases, well, or does a new one arise? I don’t know.

David Turetsky: 21:44

Well, even if you look at Microsoft, they’ve tried to actually get a lot of those analytics embedded inside of out there was something where they were trying to do like Cortana had an email come out every day about metrics about your day. And, in fact, very similar to exactly we were talking about before, hey, you know, you have scheduled time here and here. And here, you might want to block off some time. And they they’ve kind of gone away from that they’re actually coming out with something new, we’ve done research, and we’re gonna create just a full stack application on that. One of the funny things that I think you just said, when you talk about those three, those three kind of horizontals, not verticals, the middle that you talked about was very verticalized, and very siloed. And that that’s the thing that disappoints the crap out of me, is if you go down, and we’ve talked to ADP, or Ceridian, or workday, they’re what they’re trying to do is create a fully CMS stack that has all those things, and tries to interweave them, but they are still verticalized. Yeah, and you know, when you talk about the verticalize, the teams need to stop the vertical, pardon the expression bullshit, they need to start looking more together. Because even if we take the microcosm of recruiting and compensation, if they’re not working together, they’re working against each other, because recruiting is creating a market that compensation doesn’t want. They may be using some of the pieces from Comp comp has been very insular about. Why is it? We haven’t gotten to a stage not just today, but in the future? In the future that you’re outlining? Why can’t we break down those verticals, and actually work more like teams like you talked about before?

Zack Johnson: 23:14

Well, my favorite piece of advice is you overestimate what you can do in one year, you underestimate what you’re doing. So I think that some of that will naturally I use the parallels the consumer space, because a lot of other markets and things have gone through digitization and massive investment, stuff like that, like, you can do pretty much everything through Facebook or WeChat. Like so I think like those types of trends will protest protest. And we’ll see that in the workplace. The biggest barrier that was not technology, these barriers buying behavior. And so as long as there’s no chief breaking down silos and teams working together officer, you’re stuck with whoever has the most pressing problem and discretionary budget forging the way and or you wait for a department to feel to go through an entire hype cycle and an entire maturity curve on purchasing. And so like, that’s one of the reasons why, you know, there’s so many exciting small companies here is like, it starts with a really focused vision on a focus problem understanding that customer and like, it’s really hard for one company to understand 20 different types of customers shouldn’t do that. Well, but I think it’s actually a go to market problem, not a tech problem. You could make the tech work.

David Turetsky: 24:23

Sure, we could create API’s that could do it. The problem was that silos exist from the demand side.

Zack Johnson: 24:28

Yeah, totally. So that way, a better way to describe it that by the way.

David Turetsky: 24:35

Thank you. So we’re going to have to end now. But thank you so much for joining the HR Data Labs podcast. We really appreciate I would love to invite you back. Obviously we could talk forever and thank you, Dwight, as always great. Thank you for listening. We appreciate you joining and if you’d like this episode, please subscribe. And if you are somebody who would like it, please send it their way. Thank you, Zack Johnson from Visier, appreciate it, and we will see you soon. Take care and stay safe.

Announcer: 25:10

That was HR data labs, please visit Turetsky consulting.com forward slash podcast to review the show. Add comments about this episode, or add new ideas about upcoming shows you’d like to hear. Feel free to be creative. But please be nice. Thank you for joining us this week on the HR data labs podcast and stay tuned for our next episode. Stay safe