Lybra Clemons – Move Don’t Prove With Data Informed Decisions

Connect with Lybra:

• 

Connect with David:

• 
• 
• david.turetsky@salary.com

Podcast Manager, Karissa Harris:

• HRDataLabs@salary.com

 

Production by Affogato Media

Resources:

• Consulting practice
• HR Data Labs Season 7 Episode 22
• HR Data Labs Season 7 Episode 20

• “Data informed goes along the lines of ‘we want to use data to move the needs’... versus to prove something.”
• “This idea that everyone wants to be back in the office five days a week... this is where I think it’s the opposite of data driven; where there’s a lot of data and people just ignored it.”

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 alongside my friend, partner, confidant and Salary.com employee, Dwight Brown. Dwight Brown, how are you?

 

Dwight Brown: 0:56

I'm good, David, how you doing?

 

David Turetsky: 0:58

I'm okay. I'm okay. Dwight, I don't know if you know this, There's gonna be a small eclipse today.

 

Dwight Brown: 1:04

What? I didn't, I didn't hear anything about it.

 

David Turetsky: 1:08

A full eclipse. Yeah, apparently people are losing their mind that the sun is going to get obliterated by the moon. And we, you know, people won't know how to deal with it.

 

Dwight Brown: 1:19

There's going to be chaos in the streets.

 

David Turetsky: 1:21

Yes, yes. Well, hopefully, people will be hearing this after the eclipse. Hopefully everything will be fine. But today, we actually have a wonderful guest that hopefully you're hearing this because we will have gotten for not, but I'd like to introduce Lybra Clemons. Lybra, how are

 

Lybra Clemons: 1:38

I'm good! Happy eclipse day! you?

 

David Turetsky: 1:40

Happy eclipse day.

 

Lybra Clemons: 1:42

Is that a thing? I think I made that up.

 

Dwight Brown: 1:45

It is now! Now that you made it up, it is.

 

David Turetsky: 1:49

It's totally good. And you know what? We're gonna say that it came from Lybra.

 

Lybra Clemons: 1:53

Yeah, it did. Trademark it. Put it on insta. But I remember, I remember like, I do remember was it the fifth or the sixth grade was one of the first ones. And we had to make the glasses and we had the out of the cardboard box. And I just remember everyone saying, don't look up, don't look up! You'll be blinded for life, you'll be blinded for life! So when I think about this eclipse, and I think there has been one since then. I mean, I'm not a scientist. But I do remember that one. I think it was fifth grade. Yeah. It's been a few since then. Because I remember I was in California, when there was another one. And it was during a yoga class. And we were all like, Oh! It was really cool.

 

David Turetsky: 2:39

I think we can actually just get in our zen like pose and realized it. It's just part of being in a solar system that has this kind of dynamic.

 

Lybra Clemons: 2:48

Yeah.

 

Dwight Brown: 2:48

Yeah, exactly.

 

David Turetsky: 2:50

But Lybra, why don't you tell us a little bit

 

Lybra Clemons: 2:52

All right! Let's see what's interesting about me, about you? besides the fact that in fifth grade, I remember my first solar eclipse. Well, contestant number one, I have been in the HR, DEI

 

David Turetsky: 3:07

Sure. Well, that's excellent. Well, Lybra, space for almost 20 years. Although I'd like to think I've preserved pretty well, someone sees me will automatically think I'm like 30 something but I'll take it, had been working major corporations, mostly in financial services, early on in my career, and then transitioned out to Silicon Valley and worked in fintech and also SaaS. So I've had a myriad of wonderful experiences, focusing on DEI, but also in the HR, learning and development, talent management, which is really my sweet spot for many, many years. So I've seen the growth, the the evolution, worked at companies that were had been around and established for goodness, like over hundreds of years, and those that started, you know, five years before I joined, and so you get the breadth of experience and the breadth of fully understanding how the workplace and how HR and data and DEI and L&D and Talent Management have all evolved and how they settle in different companies under different leadership. we asked every one of our guests the most magical question, which is what's one fun thing that no one knows about you?

 

Lybra Clemons: 4:30

It's so funny. I'm like, I feel like I'm a pretty open book. But I will say this. There was a time and I think a few people know this. There was a time in middle school where we did these, like, projects on various, they weren't serial killers, but like, they were just prototypes of folks and I remember reading this book about Charles Manson. And that sounds so sick and demented. I'm like, I can't believe I'm saying it outloud but I became obsessed with Charles Manson. So I started reading all of his Helter Skelter, like all the books. And then this is back then when you couldn't just go on like a Netflix. But anytime there was a documentary on PBS years and years later, I would watch it and I was fascinated. And I wasn't fascinated by the the killing and the of all kinds of things and racism. But it was fascinating to understand his story as a young, a young boy growing up. And then being you know, he became a musician, was doing work with the Beach Boys and decided he was going to create this commune, and a community and his quote, unquote, sense of belonging, which, haha, that's a word that a lot of people are trying to use all that together, you know, in creating this cult. But anyway, I don't want to get on a tangent, but it was something that I became super, super obsessed with. I was like, all about Charles Manson. So I am not a serial killer. And there's a disclaimer here. But I was just really fascinated by him.

 

Dwight Brown: 6:06

You didn't write him letters in prison or anything, did you?

 

Lybra Clemons: 6:10

No, that I wasn't doing! I was, it was more, it was the phenomenon less about him, it was a little bit about him. But I don't praise him for any, you know? It was just a very. Just I was hooked.

 

David Turetsky: 6:24

I've killed a lot of boxes of cereal, if that makes sense.

 

Dwight Brown: 6:27

Oh man, you are on talking probation for the rest of the episode.

 

Lybra Clemons: 6:38

No, joke probation for the rest of the year.

 

David Turetsky: 6:44

Okay, well, why don't we transition over and start talking about our topic then. So our topic for today is data driven versus data informed and when to use it to move rather than prove. So Lybra, our first question is, what is the problem? What do you mean by data driven versus data informed?

 

Lybra Clemons: 7:10

Oh, there are a lot of leaders, and leaders are the ones that set the tone for companies, that are all about data driven decisions. I'm sure people have heard that all the time. It's like they want as much data in order for them to feel very confident in the decision that they're making. And I think that has been something that his grown and developed into a lot of the values of a lot of companies, like we are data driven, we're data driven! There is great merit to that, because you don't want to, what you don't want to do is just make a decision just just to make a decision.

 

David Turetsky: 7:50

Right.

 

Lybra Clemons: 7:51

And so that's what I mean by data driven like it is driven, like the only way that they're going to make a decision is they have enough enough enough. And I don't even know when enough data to prove the point they want to make. And then there's this alternative way of looking at things which is data informed. And that is, I have data that will help inform my decision. But I'm also looking at other variables in order for me to make a decision. And data is one way of looking at it. But leveraging your expertise within the room, having a decision making model that is based off of values that are promoting equity at all costs. And using the data. And I am taking this using data to move not prove because it's not mine, it came from Alex Booth, who is an amazing Chief of Staff of mine years ago, who came up with the concept of using data to move and not prove. And I think that data informed goes along those lines of we want to use data to move the needle. And I hate that concept. But it is to really move and to make informed decisions versus to prove something. And I think data gets oftentimes manipulated in a way that stifles innovation, creativity, diversity, but also for leaders who struggle so much with making decisions, because oftentimes, they're not in a position to do it, whether it's lack of experience or lack of confidence, or whatever it is. It's overusing the data driven as opposed to the data informed. So I hope I gave you some difference between like data informed data is one variable. There are other things that you can use. There's ways and values that help you think about making decisions, and there's risk. And I think that's the difference between data driven and data informed. Oftentimes data informed there's a little bit of a risk you're putting yourself out there, it's informing, but you're also leveraging other ways to make and other things in order to make a decision. I typically wouldn't say all the time, but sometimes it does. Those decisions oftentimes do have a more effective outcome, especially for marginalized groups and marginalized people. Whereas data driven is all numbers, all data, and you can make data say anything. I don't care what anybody says, you can come up with, like, oh, the clips hit five people, 10 people, whatever, but you can make data work for you. And so I think oftentimes, when it's overly data driven, then it to me stifles innovation at times and it creates more panic, and it's not a lot of risk in my mind.

 

David Turetsky: 10:46

Well, one question I want to ask you is, does it have to have to do with processes that are data rich, or data poor? I mean, we're not talking about quality yet. I'm talking about quantity perspective, is it about necessarily, is it about when we have too much information or not enough good information to be able to help those decisions?

 

Lybra Clemons: 11:08

where I feel like data informed. That's the art and the science. That is the art the science of like really figuring out like, way too much data is overwhelming. I've been there, I've seen it, where the data shows the data shows the data shows, and then you're stifled and you're unable to actually make any decisions that make any sense. So I think it is finding that and figuring out when you when data is enough, when enough data is enough. And you rely on people and expertise and other things and also risk and be like what if we?

 

David Turetsky: 11:54

Cause you're also talking a little bit about the noise versus the really important concepts or the important information that will prove or disprove your theories based on their quality, based on the story that it's telling, but also fitting your narrative. I mean, we all we've all been in situations where we've had to look at data that doesn't fit our narrative and say, Wow, is this if this is true, then maybe we're wrong. And either some people go back to the well, or some people use it to your point before, in ways in which will tell their story!

 

Dwight Brown: 12:30

Right.

 

David Turetsky: 12:30

Because they don't care what the data says! They're gonna say, Well, this is a mistake. But or this is not what we're talking about, but!

 

Lybra Clemons: 12:37

Yeah.

 

Dwight Brown: 12:38

Yeah, it kind of becomes robotic, almost, if it's, if it's strictly data driven. And there's a time and a place for data driven. It's not that there's that there's not but if that's what you do day in and day out, it's just robotic decision making that doesn't take into account all the other variables that go into making a lot of these decisions.

 

Lybra Clemons: 13:00

Exactly,exactly. And it doesn't, you're starting to just prove things, you're just trying to prove a point, right versus move things. And I think in this in this day and age, we're, you know, companies, and leaders in my mind, are looking to move whatever the dialogue, move the narrative, shift the narrative, disrupt the narrative. And so oftentimes, it's like finding that balance. And I think that's when, and it's the quality of the data too, because there's some not so great data out there. Like I said, I'm an adviser to a HR tech company, that's all about data. And that's something that we've been talking about, it's super, I do care about it, but it's like the quality of it, you can get it from anywhere. You know, it's like that interesting commercial that came out a couple years ago. They were like, Oh, they made some point. And they were like, where did you hear that from? The internet! You know, you can pull anything from the internet, like, I could be an aspiring dancer on the internet. So I just felt like, we've got to figure out a way garbage in garbage out, like, what's what's legit? And what are you trying to solve for? And I think that has, and then this goes back to me, the most important part is who not what, who has the data? Who's informing the decision? And what are the decisions that are going to be made. And so to me, the quality of the person that's why leadership to me is so important is finding those leaders to really, really understand what they're trying to prove.

 

Dwight Brown: 14:34

Yeah.

 

David Turetsky: 14:35

But like you're talking about data quality is important. And so if I can find one tiktok video of you dancing, then that means you could be a dancer!

 

Lybra Clemons: 14:43

I could be a dancer.

 

David Turetsky: 14:44

And it's an the internet is in many ways, it's a positive and a negative to the story because we can find any data we want that tells our story, because it could exist somewhere on the internet, because the internet is the source of all truth of course.

 

Lybra Clemons: 15:00

Yeah.

 

David Turetsky: 15:01

But also, we've been talking in this podcast for a very long time about HR being the source of quality or the lack thereof data for many, many years. And we have to own that, because to your point before, garbage in garbage out, and we are kind of a wash in that, aren't we?

 

Lybra Clemons: 15:18

100%! 100%. And that's why, to me, it goes back to the leaders. It's all about the who, who has access to the data, who is making the decisions? Who is the one responsible? Is their integrity? How are they thinking about things, and that's, it's this mindset of data informed versus data driven. And so when you have a head of HR that is constantly paralyzed around whether or not they have enough data, to support their point of view, versus getting a very strong, Chief People Officer CHRO, who has a point of view, who is innovative, who is creative, who is thinking about data, and leveraging that data to inform the decisions. I think those are two different leaders. And I think that is very, very telling. But the onus is on HR, because HR is one that is capturing a lot of this data, and the leaders are the one that's actually driving the narrative. I mean, we've all done it got to present to the board, we got to present to, you know, to the internal teams, we got to talk about our narrative, whether we're laying people off, or what's it going to look like, or how we made the decisions about total rewards packages. And so we have to be very thoughtful than I think the onus is on these leaders in HR to really understand the art and the science, but to be a little bit more progressive, and have a lot of integrity and be thought leaders. And I think I've seen a shift in that, where we're seeing more thought leaders who really do have this idea behind pushing a narrative that works that is data informed.

 

Announcer: 16:58

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David Turetsky: 17:09

So let's go to question number two, because I think a lot of what you're talking about does fit well into this, which is, so what are the major issues, though, when someone is not data informed, but they use decision making around the data?

 

Lybra Clemons: 17:23

Right. So listen, I let me just be clear decision making should be done around data. But there's a way that you actually have to draw the line. And so I've seen, I'll give you an example. And this is a pretty controversial one. But I go back to it. This idea that everybody wants to be in the office five days a week, you've got to be kidding me. Data driven data informed. And I actually think that this is where I think it's the opposite of data driven, whereas I feel like there's a lot of data and people just ignored it. But I don't know that the companies that decided that they decided and use data as the the reason or the decision maker to bring a bunch of employees back in the office to me as an example. And I think it hurt people because in the end, I think that a lot of employees, that's not what they were saying. And I'm not I don't know, while there are some conversations about oh, I want to have access to my leaders, I want to have access to this did not mean, I wanted to be required to be in the office five days a week.

 

Dwight Brown: 18:40

Right.

 

Lybra Clemons: 18:41

So I think those are, that's where I think it was data driven on the other side, versus being data informed to say, Okay, here's some options. Here's some ways we could do this. Here's an opportunity to actually think of it differently. So I think it is really hurt a lot of employers, I think it's hurt the workplace. I think it's hurt a lot of companies that are really probably hemorrhaging very, very strong talent when you have options to go other places.

 

Dwight Brown: 19:10

Well and I've, I've always been concerned, as soon as that groundswell started coming up again, there you hear you heard leaders saying, our productivity is down. But you never heard, you'd never heard them say how they got to that point.

 

Lybra Clemons: 19:29

Exactly.

 

Dwight Brown: 19:29

So you know, data driven, okay, so you got your productivity numbers. We're going to globalize this across all companies. Come on, there's something, there's something wrong there. And so they, but they used it, David, you talked about fitting a narrative, essentially, they used something. And they said This fits my narrative.

 

Lybra Clemons: 19:51

That's exactly right. Yeah. Dwight, you nailed it. I mean, that's basically what happened. And I saw it. I saw it firsthand. I saw it on the other side too, but I saw it firsthand. And I think it is let's fit a narrative. But I bet I in there was a lot less of the thought leadership, qualitative.

 

Dwight Brown: 20:12

Right.

 

Lybra Clemons: 20:13

I think a lot of companies miss the qualitative, the conversations the hearing from the loudest, and the not so loud people and striking a balance, that art and science, before making a data informed decision.

 

Dwight Brown: 20:28

Right.

 

Lybra Clemons: 20:29

And so I think you're seeing a lot of people really struggling within that. And we're, it's, I think, what's gonna be very interesting, four or five years out from those decisions being made, what the quality of the talent is going to be like, for those who ended up staying or those that are like, I'm out are those that are like, back in the office and I'm just as, I'm more upset, or I'm happier, I don't know.

 

Dwight Brown: 20:53

Right.

 

David Turetsky: 20:54

Right. Well, I think we were all kidding ourselves when we said we were happy to go to an office five days a week. And I think we're kidding ourselves pretty much saying that we're happy to be home five days a week, I think there's a balance there. But to your point Lybra, I think it comes down to being able to measure the right things. And to be able to have those things actually just tell the story, but also to do work outside of the data to be able to make and this is what your point was from the very beginning. The data is informing your decisions, it's not making the decisions for you.

 

Lybra Clemons: 21:28

Exactly.

 

David Turetsky: 21:29

Well, I have a question for you that kind of comes up from this. The world of artificial intelligence, yeah, had to come into this conversation, had to. It's coming into everything.

 

Dwight Brown: 21:38

That's reality, though.

 

David Turetsky: 21:39

I mean, it is. So the world of artificial intelligence wants to be able to use all this data to be able to solve all the world's problems. If we're trying to use data informed versus data driven. Doesn't the AI take on a role of being a consultant here? Rather than being the answer?

 

Lybra Clemons: 21:58

Yes, it does. I don't think it's the answer. So I sit on an AI, HR tech company, I sit on the advisory board of that, and, and I did that purposely. One, because I believe in the founder and I think he's quite fantastic. But also, as an HR executive, and a potential consumer, I needed to fully understand what that looked like and what that meant. I think I go back to the original point, as I see us entering into this world of AI, and we're already in it, it is not all consuming. I think a lot of people go straight to the end where it's taking over everything and we're the Jetsons! But I do believe that, you know, just like the internet, when it was first introduced, you know, we grew up in a time when we didn't have it, and then it's taken over everything that we're managing through it.

 

David Turetsky: 22:47

Yeah.

 

Lybra Clemons: 22:47

I do think there's data out there. And I think, again, it's not the what, it's the who. And so if you're leveraging an HR tech, AI tool, somebody is leveraging it, and somebody's making the decision. And so it goes back to the leader, it goes back to the HR leader, the CEO, the executive that has access to that tool, and leveraging it for good or for evil. And so it's very similar to all the all of the tech new technology that entered into our world, it's on the, the responsibility is taking, it's basically on the person. And so you can leverage that tool to confirm your narrative.

 

David Turetsky: 23:32

Right.

 

Lybra Clemons: 23:32

Or you can leverage that tool to help you in, you know, make different decisions to you know, move, you know, something. So I do think there's positives, I think I'm not an anti AI person at all. I'm actually, and I'm just a realist, it's happening, it is happening, I need to understand it. But I also need to understand who is leveraging it, what it's leveraged for? I don't know, if you're asking David in terms of the integrity of the data, that's something that AI has to actually slowly get into, because it's as good as what's out there.

 

David Turetsky: 24:07

Absolutely. Yeah, absolutely. Well, it's also based on the request that you ask as well, because if you're asking an open ended request, it's going to use whatever data is available. But if and then this goes back to being able to, to use the appropriate prompts to be able to get the AI to really fundamentally understand what it is you're actually asking for and what data it needs to go after.

 

Lybra Clemons: 24:27

Exactly.

 

David Turetsky: 24:28

So I agree with you, I agree with you. It seems like this is a a tool that kind of elevates raw data into a more consumable form, but it doesn't obviate the need for being able to understand what that is.

 

Dwight Brown: 24:41

Yeah, I mean, it's, it's very close to, you know, now we do Google searches to get our information. And now we've got AI to synthesize that but a lot of that is a lot of what's out there in cyberspace anyway, and, and it's very well known that oftentimes AI is wrong with what it comes up with

 

David Turetsky: 25:00

By the way, my son did a search last night for a paper he's doing for school. And the result that came back was an AI response. It was came from Google. And Google's Bard, I guess is the name of it?

 

Dwight Brown: 25:13

Yep. Bard. Yeah.

 

David Turetsky: 25:15

Gave gave the response. And he was like, Dad, this is okay, right? And I'm like, Well, you know, it's taken it from every other Google Search you've ever done. Right? But that's up to you. As you're saying, Lybra, it's up to the consumer, to be able to make that determination.

 

Lybra Clemons: 25:33

Right. And this is where there needs to be more support and coaching and development for people who have that information in hand. There, you know, that's the critical part.

 

David Turetsky: 25:47

And that brings us to question number three. Hey, are you listening to this and thinking to yourself, Man, I wish I could talk to David about this? Well, you're in luck, we have a special offer for listeners of the HR Data Labs podcast, a free half hour call with me about any of the topics we cover on the podcast, or whatever is on your mind. Go to Salary.com/HRDLconsulting, to schedule your FREE 30 minute call today. So what can people do to be able to look like practical examples of what can they do to get ready for that, though? And be able to to be more informed? I think what you're saying is we become a

 

Lybra Clemons: 26:27

Well, I mean, one of the things is to be a little bit of a skeptic, not everything you like I said, I'll pull it off the internet, you know, like, there's like, listen, someone could there's 50,000 things out there telling me that I should I should eat eggs, or I shouldn't eat eggs, right? At some point you go to a doctor, like eggs are good for you, don't eat eggs, like, what is it? So there's a bit of you just making the best decision for yourself, and your health and all the other things and weighing it. I am hopeful that schools are emphasizing this idea of self discipline, just that, that we're emphasizing this idea of being self reliant and being very thoughtful and doing your own personal research outside of the internet, but doing your own qualitative, and asking a, you know, research asking specific questions or learning more about it, getting history. I think, what's little lazy, and we've tried to go for whatever the most, the happened in, I guess, in the generations after the internet is everyone's, we're 100% reliant. And I want people to highest ranked answer is? start to question. And that's what I that's the difference between people who are 100% reliant on something or some tool, it's saying yes to everything that you say, versus being a little bit like hold up. That's thank you for that information. Let me go on and do my own thing and figure out how to make the decision. And those are the two different leaders you're going to get in the workplace. Those are the two different HR leaders you're going to get and look that will ultimately tell you what kind of company you will end up working for. So I go back to as a leader, there's a obligation to question and ask the questions and do your own personal beyond just oh, let me go on to do another Google search. But start to ask the qualitative, ask other people get their in, you know, their input, and a diverse group of people. That's why diversity is so critical, so that it can inform you to make a decision that is to me a little bit more balanced. And so my recommendation is that I just hope leaders do that. And I don't know what muscle because I think there are a lot of newer leaders and I say that HR across the board that typically rely on other external things and other people to help them make a decision that make their lives easier. That doesn't do anything for innovation, as I said earlier, it surely doesn't disrupt some of the work that we've been doing that hasn't informed major change. And I I'm just a little concerned that as

 

Dwight Brown: 29:09

Convenient. leaders who aren't thinking about this and being thought leaders and tapping into other sources of truth, it becomes more and more of a problem.

 

David Turetsky: 29:29

Yeah, whatever really the most convenient answer is instead of what's the right one, And so I don't know if you remember this, but

 

Dwight Brown: 29:33

Right. 30 something years ago, 30 plus years ago, I used to have to do multiple regressions by hand.

 

Lybra Clemons: 29:43

Yeah.

 

David Turetsky: 29:43

We used to have to prove it, you know, that we knew what we were doing. And even single linear regression is easy. It's rise over run. But when you have to do multiple regression, you have to write it out. You have to write it out longhand. Well. Kids these days. They don't do that stuff, right. They either use, they use their Chromebooks. They use Excel, they, they don't, they don't prove it. And so to understand the process that you go through to solve an equation to solve for x or to solve for y in that case, you need to be able to do it longhand to be able to prove that, you know, and we've kind of lost that, haven't we? I mean, I don't see my kid. I know he's using his Chromebook, or his iPad. I know he doesn't do things longhand. So So are you saying that we really need to ask people to kind of go back to their beginnings and kind of do it longhand?

 

Lybra Clemons: 30:37

I just want us to develop leaders who are starting to think differently and to, to your point, be a little bit more critical of a process, and also cosign on those types of leaders. Because what's also happening is the leaders that are not lazy, but the ones that continue to do the same thing over and over again, get lauded and praised and promoted and continue to rise the ranks. And I'm not seeing a significant shift in how businesses run, how decisions get made, especially HR decisions that are getting made. And those HR systems are the ones that are creating such inequities in our in our workplaces every day. And so you need someone that's constantly questioning and I agree, you need to go back to that. I remember in college, we had to take a course called logic. We had to take it, like it was required.

 

David Turetsky: 31:36

Right.

 

Lybra Clemons: 31:36

That was the hardest, it was like, and I took the LSATs too. Like, it was like the logic side of the LSATs on steroids. But I remember you're right, having to prove your point having to understand the logic, it was all of it. And I I'd like to think that as a human, I'll never forget that. I don't even remember what was in it. I just remember my brain hurting. You know what I mean? It hurt, it hurt. I was like, kill me now. But to

 

David Turetsky: 32:00

Oh, yeah! me, it's allowed me to exercise a muscle. And it gives me a little bit of a different perspective on things. I do need data. But I allow myself to think outside of it, so that I can continue to question it. And I don't know that a lot of people are being asked to do it. Not that they don't want to I don't think they're being asked to do it. It's very hard Lybra. When the data is all there, and it's arrayed in front of you, it's hard to question it and to say, I need to go into my CEOs office right now and tell a different story than what the data saying. Even though they pay me for this, I need to be able to have the reasons why I say I don't believe this. I might get back well, buy a survey that tells your story!

 

Lybra Clemons: 32:53

Exactly. Yeah, that's exactly what happens. And that's that's the hard part about being an HR leader. You know, if it's not to the CEO, it's also to the board, we got to tell the story, we got to tell them why everybody's leaving, or why or why is not leaving, or why we need to pay them that or why we needed you know what I mean, and it's hard.

 

David Turetsky: 33:11

But even if you're not the CHRO, even if you're somewhere in the ranks in HR, or HRIT, or payroll, or wherever you are having these kinds of thought processes about telling stories that don't necessarily come directly from the data, but come from the anecdotal sources you have around you, where you actually have people telling you what the real deal is, even if the data doesn't support it, you have to have that, I guess that fortitude to be able to stick by yourself and be able to tell the story anyways. Right?

 

Lybra Clemons: 33:40

That's it. And that's, that's the hard part. And that's where you need to that's why you just wanting to cultivate the types of leaders that understand that and that buy into that value system. Because not everybody does. And that's okay, but you need a mix. But you definitely need people that are allied with that. And that's hard. I mean, even just every most companies, but I would say most of every major corporation does a survey, they survey their employees. And every time it comes back with these numbers, and I am in a different companies. And I've seen how different folks respond to those employee engagement surveys. They accept the data or not, then there's the qualitative data that most people don't even take time to read because it's like 50,000 pages. And so that's another example of we kind of got it but let's think of it this way. But some people want straight data, they want all of it they want to this is the narrative. This is what we're sharing this what needs to go in our you know, in our reports, our you know, our yearly engagement survey reports, but it is up to the leaders at the top and you know, to really start to think about how to take this data, and form some of the decisions that get made in order that you are moving the data along, as opposed to proving a narrative that may or may not evolve the employee base to the next level.

 

David Turetsky: 35:18

A lot of times it comes down to the employees saying, Well, you're not listening to me. You haven't done anything, you've haven't told us the story.

 

Lybra Clemons: 35:26

Every year!

 

David Turetsky: 35:27

Yeah, every year, they go, Well, you we've said what's on our mind, but how has that changed what you do? You know, nothing changes. So why bother? Or why telling the truth in those surveys?

 

Lybra Clemons: 35:37

Well, that's the thing. That's what they end up saying, it's, I'm not even I'm not filling it out. They don't listen to me. It's kind of like, Yeah, I'm not even voting. My vote doesn't count. Like it's all of it.

 

Dwight Brown: 35:49

Right?

 

Lybra Clemons: 35:49

You know, all of those types of and that's when people don't feel heard. And so that's when they start to question data. So it's just a cycle. They're questioning every single decision that gets made they question the leader, they question the data, all of it, but I agree with you. It's like, no one's listening to me. That's not what I said. I put it in here. You're not listening. You're trying to and nothing. Nothing changes.

 

David Turetsky: 36:14

Right. Right.

 

Lybra Clemons: 36:15

Which is unfortunate. And it's hard to change, by the way.

 

David Turetsky: 36:18

Yeah, yeah. Yeah.

 

Dwight Brown: 36:19

It's hard to change. Yeah.

 

Lybra Clemons: 36:20

It's hard to do. I am not by any stretch of the imagination, criticizing a workplace or HR leader, because I'm in the seat, I'm done. I know what it's like, you know, they're like, Well, nothing's changed still this and they're still discriminating. And I get it's hard to do. But I also think the onus is on the conversations that happen when you get that data. And if there is a major policy or practice that can come out of it to show we can listen, that can be data informed. That would be somewhat game changing. But it's rare that you see a major policy or practice that comes out of it.

 

David Turetsky: 37:02

Which is the reason why people get mad when we don't listen.

 

Lybra Clemons: 37:07

Yeah, that's hard. Yeah.

 

David Turetsky: 37:17

Lybra, thank you very much. You've been awesome. This has been wonderful. I think I've learned probably more in this last, you know, half hour plus than I have all day and and again, today's eclipse day. So thank you so much. It's been awesome.

 

Lybra Clemons: 37:32

I appreciate it!

 

Dwight Brown: 37:34

Yeah. Appreciate you being with us.

 

Lybra Clemons: 37:35

Yeah, this was great.

 

David Turetsky: 37:37

And thank you all for listening. Take care and stay safe.

 

Announcer: 37:40

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