Trends in People Analytics: A Fireside Chat with Turetsky Consulting

Connect with Dino:

Connect with Karissa:

Connect with IBE:

Connect with Lori:

Connect with Dwight:

• 
• dwight@turetskyconsulting.com 

Connect with David:

• 
• 
• david@turetskyconsulting.com

Resources:

• Turetsky Consulting LinkedIn
• HR Data Labs Season 2 Episode 18
• HR Data Labs Season 2 Episode 16

• “I think sometimes we get too fascinated with the science and technology of it, and we forget the human component. … When I would consult clients talent management, I would always say, ‘Let’s not forget that we’re all human.’” -IBEJesus Prince
• “If you’re only using change management to force a bunch of people to use something, I think you’re missing [the point].” -Dino Zincarini
• “If we spend more time listening before we buy and build a technology; and then make sure that what we design is attuned to our users, it’s better.” -Dino Zincarini

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 the HR data labs podcast. I’m your host, David Turetsky. Like always, we find people inside and outside the world of HR to talk about what’s going on in HR technology, people data and people analytics. Today, I have a very special group of people with me. And by the way, Dwight Brown, you’re hosting as well. So you’re going to be included in that too. But we have the entire Turetsky Consulting Group, IBE, Karissa, Lori, and Dino. Hey, everybody. Hello. Hello, there. Hi. So today, we thought we would do something special. It’s a special day for us. We’re going to have a roundtable discussion around things that we’re thinking about in the world of people analytics, we have three very interesting topics, and we’re going to plow right ahead. The first question that I have for the team is, I’ve heard the word used or the phrase used Talent Intelligence. What is Talent Intelligence?

IBEJesus Prince: 1:52

Right. Good question. So I heard the phrase for the first time today, and I decided to do a little bit of digging. My background is in talent management. So I hear a lot of words, a lot of buzz words. And I’m always trying to understand what does this mean? How does this tie into the big picture? For those who might not be too familiar with talent management? It is pretty much the whole reason why we do talent management is to attract, develop and retain top talent. That’s what I did for years. I know that inside and out. So when I hear talent intelligence, I’m like, Is this just a spin on what I’ve always known to do? Or is it really something different? And what I found from different sources was that it is a focus on talent. But business intelligence, so you’re looking at your data, you’re using technology, to draw conclusions and kind of find ways to, I guess, in, in a way, make your talent better. But the one thing that I saw that was different that I wasn’t expecting was that one source said that it comes from the competitor. So it’s not so much your internal talent intelligence, it’s the intelligence of your competitors. And when I saw that, I said, That’s interesting. That’s my first time seeing something that said that we need to look at our competitors talent as opposed to our own interesting.

David Turetsky: 3:14

So becomes more like an index of how I fit against other people in the competitive space. Right. Interesting. Yeah, I’ve been in talent management a long time, too. And one of the things that I think is fascinating is there’s a lot of ways that we can measure talent. And unfortunately, what has typically happened, especially in the compensation and assessment space, we start to get very technical, we start to go into skills. And we start to talk about how do I measure the skills necessary for a job versus assessing someone and finding out how much they have and what the gap analysis is, and then how to train that away, so that a person can do their job more effectively. And we’ve talked to some companies on this actually about assessing people on their entry, and then closing any gaps we might have for that person, as they develop in the role so they can be the best they can be in that role. So to me, when I think about the term talent, intelligence, I think about how much do we know about our people? And how much have we understood about what the needs are that they might have? And how do we set up a plan for them to be able to achieve those things? So I’m fascinated to talk about this, because we’ve been in the world of talent management for a long time. Talent Management exists around recruiting around compensation around performance and OD a lot of OD things, organizational development. And there are sciences around them. Right there. They’re definitely people who take it to the, to a very large extent, using data for regression analysis to find how do we know that this person is going to be successful in this organization and trying to come up with different types of assessments and quizzes and other things, but to me, it’s about making people or understanding where people And how to get them to the right place so that they can perform the best. And so they can have a good career and understand where their career needs to go, and what training they need to get there.

IBEJesus Prince: 5:10

Yeah, I think sometimes we get too fascinated with the science and the technology of forget the human component. Sure how humans first. And that was one of the things that when I would consult clients on talent management, I would always say, let’s not forget that we’re all humans. And that’s the biggest part that we need to leave with. When we’re doing assessments when we’re doing our check ins and progression and development. We’re still humans. That’s right.

David Turetsky: 5:35

That’s right. And we may have more needs outside of just normal training are normal assessments. Has any of you ever taken an assessment? Have Ever Have you ever done an assessment on either on an application or when you’re applying for a job?

Karissa Harris: 5:48

I’ve done assessments on Indeed, which have been really all over the place, some of them I feel are very pertinent and can help employers know whether this is the right candidate or not. Right. And some of them feel completely arbitrary. Some of them felt very common sense. Yeah. Like, this is basic math. Yes. All right. This is the way that we do the alphabet in America. Yes, yeah. But some of them I do feel like could have been possibly insightful for the employers. I don’t know how insightful since a lot of those didn’t hire me. So their loss.

David Turetsky: 6:29

But one of the fascinating things about those assessments, especially if you’ve taken them recently, they start to ask much more technical questions. So if you’ve been on Indeed, or you’ve been on LinkedIn and tried to apply for a job, you get assessments, they should be much more pertinent to the skills that you’re trying to utilize in those jobs. And I found it fascinating that those assessments are getting much more, much more targeted, and much more specific about certain skills, and descriptions of those skills. Yeah, I’ve taken assessments that have asked me about Excel for executive level positions. Yeah. Do you have basic Excel? Sure. Okay, then prove it. Here is five questions about Excel. Okay, great. I’m sure it’s useful. But I think Karissa there are exceptions. And there are actually much more targeted assessments that are actually doing some good.

Karissa Harris: 7:22

How do you feel about personality assessments with fit for culture for organizations?

David Turetsky: 7:29

So how many of us have actually taken a Myers Briggs personality assessment? I’ve taken a lot of them. Lori, you didn’t take one apart as part of work scape or ADP?

Lori Craig: 7:40

I may have, obviously it didn’t… haha

David Turetsky: 7:46

Don’t. No, you have a great personality. And it fits in. What’s really fascinating is that if you look at the six of us who are on the podcast, right now, we all have vastly different cultures. But we have extremely good cultural fit together, we work very well together, right? Had we all taken a personality assessment, I wonder what it would actually show. And if we should do that, afterwards, Dwight, we maybe should find a good one that we can take. And then maybe in the show notes, we’ll put how we did. But one of the fascinating things is that they actually do predict Karissa, they actually do predict whether a team will be successful together for no other reason that if there are people on the team who work in different ways, and those may clash culturally, you will find that they have terrible times working together. Now, there are some people say that opposites attract, definitely not in the working world when you’re trying to achieve common goals. So sometimes that actually is true that personality assessments actually do help.

Karissa Harris: 8:49

One thing I think is really fascinating, because I love personality tests a lot, I’ve taken a lot of different ones. And my favorite one is the Enneagram, which is nine personality types. And then there’s like subtypes, so it’s pretty complex. And one thing that the Enneagram really, if you study it, really drives home is that you may be this type, but if you are an unhealthy version of that type, you’re not going to work well with anybody because you’re an unhealthy person. So the idea of we can personality type people, and everybody could be the right type that they should work together. But if they’re all toxic, or they’re all unhealthy as people, then it’s not going to work anyway. So a personality test can only tell you so much because your personality may be one way. But if you’re in a really unhealthy space in your life, and you’re going to be cruel to other people, or whatever the case may be, then the personality test isn’t going to really guarantee that you’re going to work well with others period.

David Turetsky: 9:45

That is very true. It’s like a state of mind is very important when you’re going into these things.

Dwight Brown: 9:50

I think, you know, assessments are very, very helpful in many ways. They help us to be able to really identify skill sets. And personalities like we’ve been talking about. But I think it’s also very necessary that everybody understand what the limitations are of the various assessments that we’re giving people or taking ourselves because it really gets back to something that IBE touched on that. At the end of the day. There are still people behind these things. Sure. And he, we can never create a total model of people based on ones and zeros, you know, we’re not built up of ones and zeros. And so it’s necessary when thinking about the sorts of things in the organization and whatever part and in whatever function of the organization that we also be very clear on what the limitations are, and we adapt accordingly.

Lori Craig: 10:47

I think maybe this is the completely wrong way of looking at it. But I look at them as a baseline kind of similar to this isn’t the same but like SATs, right when kids are preparing for college and they take the SAT so just personally use my daughter as an example. She’s an incredibly bright woman and does very well yet on the SAT, she came out of it thinking I did great. And she did just fine, right she wasn’t in didn’t overachieve. So there’s caution to be used, right? Because if you’re using the SAT as an example, that is not the whole of her, right, it’s one, it’s a piece of her, there’s so much more. So I feel the same way, obviously, with these assessments.

Dino Zincarini: 11:35

I guess for me as a, as an analytics guy when I hear about this assessment, and Lori, I’m a little bit with you there because I’m one of these people that that really freezes when there’s a test. I don’t like them, I get very intimidated. And I generally don’t do as well as I would like to do. I hope that is not a reflection of my general intelligence. But I’ll leave that to others to assess. But the point there is, as an analytics person, I would encourage companies who use assessments as opposed, I’m not going to say whether you should or you shouldn’t I don’t know. But at least measure whether it’s materially impacting your business, we know the nice thing about these things is there’s a lot of data. And instead of just accepting what a vendor, for example, might say, Oh, this test is an indicator of performance. Go ahead and try it measure it, does it really indicate performance, have a control group that does not use the test and control others that do and and see over time, it requires some patience and some investment. And I don’t think a lot of companies do that a lot of teams don’t do that is to question their vendors and to actually measure for themselves whether some of these tools have impact. And maybe we should be doing more of that. Because sometimes that’s the only way you can really figure out if things work or not for you. That’s a great point.

David Turetsky: 12:47

Yeah, it is great. I agree. And by the way, on that point, there have been lots of studies that showed that there are certain groups where these types of assessments are extremely valuable, like sales groups, or call center groups, where there are certain skills that are very, Oh I don’t want to say the word easy, it’s not that they’re easy to model and easy to test. But they’re easier to assess. And thus the measurement of them, and then the testing of the efficacy of the results leads to higher performing teams. So it’s not every group that can do this. And it’s certainly not every culture and every company that can do this. So to your point, measure, do the scientific method and see how it turns out and use it as a signal. Don’t use it as the word. Yeah.

David Turetsky: 13:43

This brings us to our next conversation, which is about artificial intelligence in the world of HR. Now, we’ve had this as a full podcast, and we’ve talked about AI. And the reason why that’s interesting, given the last topic that we have is that AI uses signals to be able to make recommendations or to give decisions, or to make at least suggestions. And there are certain companies actually there’s a certain car company right now that’s in the news around their AI, and how it may be trained wrong. But let’s talk about HR in the world of HR and how AI can impact the world of HR and for good or bad.

Dino Zincarini: 14:29

And so, yeah, I’m sorry, I’m, I need a bit of a break while I get my soapbox ready because I do my usual spiel right now. Big skeptic and I think more people should be skeptics. I think AI is cool, and it’s also scary and it requires us to know what we mean by ai ai is ultimately about data. And all AI is the same in that it is an algorithm that has to learn and it learns by looking historical data. So the data you train the AI on will inform whether you what the AI is all about. It’s kind of like people, right? You go to a bad, you know, school and you might not be a great you know student, when you come out, it’s it’s kind of the same thing. And so if you’re going to use AI, you better know the data, because that is going to inform the kind of AI that you got. And I think there because it’s such an abstract and difficult thing to understand a lot of people will rush to it, because it’s new and exciting. I remember HR tech A few years ago, every, almost every company there have the letters A&I and the name, and yet, what is it really doing? What is this a I think there’s a lot of opportunity for AI, don’t get me wrong I, especially in automation, I think, right? That is one area where it’s really powerful, where, especially for repetitive tasks, AI can see the repetition and model that and maybe teach the application to do some of it itself. As opposed to the end user having to constantly repeat a process where I get a little bit more squirrely with AI is where it starts to make judgment calls deciding who to interview, for example, that’s where things get a little weird. And so I would just encourage everybody to be a healthy skeptic with some fair stuff.

David Turetsky: 16:17

Well, we’ve talked about this in the past, which is one of the really great uses of AI could be finding holes in data sets, right? Finding incongruous data, and being able to call it out and say, is this really true, and then helping the end users who don’t want to pour through millions of records to go through and actually find those holes? that could lead to errors in outcomes if we they weren’t found, right. So whether it’s in algorithms, whether it’s in predictive modeling, whether it’s in AI and using a AI to train something else, use the bots to find the bad data first, before you go and do anything more higher level on that stuff.

Dino Zincarini: 17:01

Yeah, I agree completely. That kind of a hybrid approach where AI makes the human more productive, but does not take the human out of the equation is a much more realistic, I think, objective?

Dwight Brown: 17:11

Absolutely. One of the areas that I’ve seen and heard recently is looking at the use of AI for candidate filtering for applicants for a position. And, you know, I never cease to be amazed at our capabilities of developing hacks for everything. And so one of the, I was talking with one person, and I said, you have the, I put my resume out there and is looking for all these keywords, I’m supposed to have all these keywords in my resume to filter it to the top of the pack. And I just can’t do that. So what I did was, and very small, white font on my resume, I just took every buzzword they had in the job description. It was invisible to the reader, but the AI picked it up. And that’s a that’s another limitation that’s out there. And it was AI continues to develop. So too, are the hacks or Dino also talked about. There could be errors and algorithms. So there’s there there are those sorts of things to to be thinking about as we employ AI more and more in our processes.

David Turetsky: 18:20

Yeah, Dwight, for any anybody who’s tried to applied for a job in the last year and a half, and then gotten a rejection notice less than 10 minutes after hitting the submit button. There’s not a human on the other end, he’s just waiting and dying to get that resume and poring through it for all of about five minutes and then hitting a button that’s not happening. So there is artificial intelligence that’s filtering through the resume and the application and saying, No. And so to Dino’s point, one of the things that we would hope is that they’re using they meaning the TA staff, they’re using the AI to put in people in buckets and then do a review themselves. Unfortunately, we know that’s not happening and the AI is just rejecting people wholesale.

Dwight Brown: 19:10

You lose great potential.

IBEJesus Prince: 19:13

I would venture say past a year and a half. I think that happened to me back in 2015.

David Turetsky: 19:19

Oh, absolutely. I’m not saying this is late breaking news. This has been happening for a while, but it’s getting more rampant. Now. I think the AI is actually taken over a lot of the filtration to the extent at which what Dwight is talking about the hacks. People are making hacks to this stuff. And it’s not just putting, you know, your skills up front on the resume. It’s literally embedding either in the metadata or in hidden font or whatever. Putting that stuff, actually either in copying the job description into your resume. There are lots of hacks you can do and it’s really disappointing.

IBEJesus Prince: 19:51

It is Yeah, I actually use the hack and I actually got an interview. Once I realized that keywords was the problem. I didn’t do the white font I didn’t know about that. But actually worked it into my resume and I got an interview and I got the job, though, by

David Turetsky: 20:05

By the way, Turetsky Consulting does not use AI.

IBEJesus Prince: 20:07

Oh no it was not Turetsky. It was many many years ago.

Dwight Brown: 20:11

We’re much more personal than that.

David Turetsky: 20:13

Yeah.

Announcer: 20:17

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: 20:31

So let’s go to our third question for the podcast, which is, how has the business intelligence and analytics market changed in general? And how does that apply to HR?

Dino Zincarini: 20:46

I think, you know, having I’ve been in this space pretty much my whole career unintentionally, by the way, but nevertheless, that’s where I’ve been. And so I’ve seen a bunch of stuff happen. And one of the things that I’ve seen happening for a little while now, and I’m really excited about it, is that analytics and business intelligence, whatever you want to call it, is getting closer to the business applications that it’s designed to measure. So what I mean by that is, for example, I don’t know if you follow tech much, but to Tableau, one of the biggest business intelligence tool companies out there was bought by Salesforce, one of the biggest business application vendors in the world. And that’s what I mean is this has been going on for a while I myself worked at business objects, which was bought by SAP. Analytics are tied fundamentally to the business processes that are measured using those tools. And the industry was born separate from those business applications, right, Tableau was founded as an analytics company only without any data without any application and measure. And the idea was, you can put it on top of anything, and that’s true. But that is work for you, the client, that is an effort you have to do, which is technical, primarily technical, to get that analytic tool to do what it needs to do. And I find that the effort because it is so hard, and it is so technical, it ends up being all about wiring everything up. And the focus isn’t where I think it should be, which is adoption. And adoption, isn’t just about training. It isn’t just about change management, if anything, change management, to me is a bit disappointing, because it’s after the fact it’s like, oh, we’ve got the tool, we’ve got the thing, we just got to convince everybody to use it. And by convinced I mean a force, right? And in reality, it’s the other way around the change management should happen to the very beginning. are we solving a problem that people care about yes or no. And instead of talking, which is what we do with training and change management, we should be listening? What problem do you really have that data can help you solve? And you know, our users don’t know, all we can do is listen to the problems and and say, Hey, I see an opportunity here. I think if we give you this, you could do a better job. We talked about talent intelligence earlier, if as a recruiter, you knew what the market was paying for a particular job right now, in this moment, could you do a better job of recruiting? Probably, okay, so my job is to give you that data, if we oriented that way, spend more time listening before we buy and build a technology. And then make sure that what we design is attuned to our users, it’s better. And so when these technologies, these analytic tools are part of the business applications that they are measuring, it’s easier to remove those technical barriers, and therefore easier for us as business users to make sure we’re solving a real problem and not focusing instead on wiring everything up.

David Turetsky: 23:42

You know, remember back to the beginnings of HR dashboards, right? People would put together these PowerPoints or these Excel spreadsheets that had four or five different metrics in them. they’d send them out to their users. And they’d say, there you go. Here’s your headcount. Here’s your turnover. Yeah,

Dino Zincarini: 23:57

Yeah I solved your problem so here you go. Oh, yeah.

David Turetsky: 23:59

Yeah. Did they ever ask the business question? What is going on in your business? And how can I help you? Did they ever ask the questions? Right question. So I guess my comment back to you is, has the evolution of business intelligence been created? Has it evolved? Because we’re now able to ask better questions. We’re now being trained, or at least part of us are being trained to ask better questions about from the data to try and help the business.

Dino Zincarini: 24:27

I hope so. And I think the answer is yes. Especially as roles like HR analyst have evolved. And they’re less and less about just getting a bunch of data out of a system and putting it into another system, but rather, understanding the business. Why does the business need data? Where could the business use more data, and then figuring out how to get that delivered as that role gets more sophisticated? I think we are getting closer to this. And that’s why I’m happy that the technology industry is starting to solve more and more Have those technical hurdles, so that HR departments and IT departments spend less of their energy and money on those technical problems, which I think are distractions, and instead get to doing what exactly what you said, understanding the business problems we’re trying to solve, and finding ways to have HR data, help solve those problems.

IBEJesus Prince: 25:24

When you mentioned change management at first, Dino, you said I forgot exactly what you said. But it wasn’t a good thing. And I was like, No, I love change management. But you explained further that people are performing change management in the incorrect order, they roll something out, think fix what they think they’re fixing. And then like you said, they say this is change management, we’re forcing you to do this. I’m a big promoter of change management. Because I believe in requirements gathering, I was on the call, I love requirements gathering, I just feel like it helps you build the foundation to whatever you’re building or fixing. And if it is shaky, good luck down the road, you know, that thing’s gonna come tumbling down.

Dino Zincarini: 26:06

Yeah, I should probably qualify my statement. Cuz now all the change management people hate me. What I meant, what I should have said was change management without proper understanding of the business problems that you’re trying to solve is not really the way to do it. Right? If you’re only using change management, to force a bunch of people to use something, I think you’re missing it. And I think with the analytics world, this is kind of a problem, we build it and expect them to come and then they don’t come. And so we just give them more and more training, thinking that’s the problem. It’s like why maybe the problem is you’re giving them something they don’t really think they need. So that’s the thing.

Dwight Brown: 26:43

Yeah. And I think that I would echo the training piece of things. And that’s one piece that I’ve seen morphed over the years is so much more emphasis on giving the users their dashboard, but also spending the time to be to teach them how to look at the data at the right questions to ask from the data, and really understand it deeply. And the benefit that we get from that is they when they come back to us to modify their dashboards or reports, or whatever that is, they start to think more deeply about both those business questions, but also about the data that we are or can provide them. Right. And I think is they understand more about the data behind the data and the meaning behind the data that’s there, that we end up with this great feedback loop that ends up happening to help refine the processes around dashboarding, and reporting and business intelligence in general.

David Turetsky: 27:40

Well they become better consumers, right, guys, better consumers now they can ask for better products,

IBEJesus Prince: 27:48

When I think about HR, and I think about talent, I think this is starting to be pretty evident, especially post COVID, that talent is just like any other competitive edge, when it comes to business, when it comes to an organization, they have to understand that talent is just like, you come up with this brand new product, or you sell something or whatever it is, that’s your competitive edge, your talent is right up there. And so it is aligned with business objectives. That’s one of the things that I came to understand once I got into talent management, just how much how closely they’re tied to each other, and how much they impact each other. They’re not separate. They’re not, you know, with the HR, HR data doodles, we see that constantly how people try to separate HR and business and it’s like no, right? Because without your workforce, what do you have unless you’re self employed?

David Turetsky: 28:42

And you know, even if you’re self employed, it’s still the same equation.

IBEJesus Prince: 28:45

It’s still the same equation, you still have to have those skills or whatever it is. Yeah.

David Turetsky: 28:55

So we’ve talked a lot today about the things that are going on in the world of people analytics, we brought up three fascinating topics, one, which is around talent intelligence, then we talked about the use of AI and HR. And then we talked about the evolution of business intelligence and analytics. And I guess I just wanted to ask the Turetsky Consulting Group if they had any last thoughts before we close today, IBE, anything?

IBEJesus Prince: 29:21

Yeah, more than anything. I’m really glad to be here. I’ve learned so much just from joining the group. I’m always in student mode. So the things that I’ve learned from talent and the things that I’m learning now is just a kid in a candy store. So pretty yeah.

David Turetsky: 29:37

Thank you. Karissa….anything?

Karissa Harris: 29:40

This was fun! It’s good to talk to everybody. And everybody has a lot to share. And that’s always good, get different perspectives. Lori?

Lori Craig: 29:50

I echo Karissa’s sentiments. I listened to these every week of course, as an employee, but it was nice to partake and it was an easier conversation than I had thought it would be so thank you.

David Turetsky: 30:03

You’re welcome. Dino any last thoughts?

Dino Zincarini: 30:05

No. It’s always fun to see when we put a bunch of people together what comes out of it, which I think was the original spirit of this. So absolutely enjoy that.

David Turetsky: 30:13

And Dwight, the co-host.

Dwight Brown: 30:15

Yeah, I would echo what everybody else has said, I’ve learned so much from you guys over the time that I’ve been working with you. And I always appreciate the variety of thoughts and perspectives that each of us brings to the table. And this was just yet another illustration of that. So

David Turetsky: 30:31

Outstanding. And thank you for listening. And I appreciate your time. And we would love to hear your feedback. If you have any feedback, please go to turetskyconsulting.com/podcast and let us know. Also, if you liked the episode, please hit subscribe. And if you know of somebody who might find some value in the conversation, please send it over to them. But thank you very much. Take care and stay safe.

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