Lybra Clemons - Move Don't Prove With Data Informed Decisions

[00:00:02] 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

[00:00:20] 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

[00:00:38] data can be used to solve real world problems. Now, here's your host, David Turetsky. Hello and welcome to the HR Data Labs podcast. I'm your host David Turetsky alongside my friend, partner, confidante and souri.com employee, Dwight Brown. Dwight Brown, how

[00:00:56] are you? I'm good, David. How are you doing? I'm okay. I'm okay. Dwight, I don't know if you know this. There's going to be a small eclipse today. A what? I didn't hear anything

[00:01:07] about it. Apparently people are losing their mind that the sun is going to get obliterated by the moon and people won't know how to deal with it. There's going to be chaos in the streets. Yes, yes. Well, hopefully people will be hearing this after the eclipse.

[00:01:27] Hopefully everything will be fine. But today we actually have a wonderful guest that hopefully you're hearing this because it will have gone for naught. But I'd like to introduce Libra Clemens. Libra, how are you? I'm good. Happy Eclipse Day. Happy Eclipse Day.

[00:01:42] Is that a thing? I think I made it. It is now. It is now that you made it up. It is. It's totally good. And you know what? We're going to say that it came from Libra.

[00:01:53] Yes, 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? It was sort of the first ones and we had to make the

[00:02:08] glasses and we had to make like the, like 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

[00:02:21] 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 when I think it was the fifth grade.

[00:02:30] 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, that was

[00:02:38] cool. I think we can actually just get in our zen like pose and realize that it's just part of being in a solar system that has this kind of dynamic. Yeah, yeah. We're good. Exactly. Yeah. But Libra, why don't you tell us a little bit about you?

[00:02:52] All right, let's see. What's interesting about me besides the fact that it's fifth grade, I remember my first solar eclipse. Well, contestant number one, I have been in the HR DEI space for almost 20 years. Although I'd like to think I've preserved

[00:03:12] pretty well. Someone sees me. You do. I think I'm like 30 something, but I'll take it. I've been working at major corporations, mostly in the financial services early on in my career and then transitioned out to Silicon Valley and worked in the

[00:03:29] Fintech and also SAS. 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 evolution

[00:03:54] worked at companies that were have been around and established for good knows 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

[00:04:07] fully understanding how the workplace and how HR and data and DEI and L and D and talent management have all evolved and how they settle in different companies under different leadership. Sure. Well, that's excellent. Well, Libra, we ask every one of our guests

[00:04:26] the most magical question, which is what's one fun thing that no one knows about you. 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

[00:04:44] 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 it sounds so sick and

[00:05:03] demented. I'm like, I can't believe what I'm saying. And I laugh, but I became obsessed with Charles Manson. So I started reading all of his health or scope, like all the books. And then this is back then where you

[00:05:14] 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 killing and the reason with all kinds

[00:05:27] of racism. But it was fascinating to understand his story as a young a young boy growing up and then he became a musician, was doing work with the Beach Boys and decided he was going to create this commune in a

[00:05:45] community and his quote unquote sense of belonging, which that's a word that a lot of people are trying to use all the together and creating this cults. But anyway, I don't want to get on a tangent, but it

[00:05:56] was something that I became super, super obsessed with. I was like all about Charles Manson. So I am not a serial killer. I visit disclaimer here. But I was just really fascinated by him. You didn't write him letters in prison or anything, did you?

[00:06:10] No, that I wasn't doing. I was it was more of it was the phenomenon less about was a little bit about him. But I don't praise him for any, you know, right? Yeah, just I was hooked.

[00:06:24] Hmm. I've killed a lot of boxes of cereal, if that makes sense. Oh, man. Oh, man. Sorry. You are on talking probation for the rest of the episode. No, no, no, no, no. For the rest of the year. Yeah.

[00:06:44] 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 Libra, our first question is what is the problem?

[00:07:07] What do you mean by data driven versus data informed? Oh, there are a lot of leaders and leaders also 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.

[00:07:22] 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 has grown and developed into a lot of the values of a lot of companies like

[00:07:41] we are data driven, we are data driven. There is great merit to that because you don't want to do what you don't want to do is just make a decision just just to make a decision. Right. And so that's going to be by data driven.

[00:07:53] 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

[00:08:06] 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

[00:08:29] 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'm taking this using data to move not prove because it's not mine.

[00:08:47] 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.

[00:09:04] 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

[00:09:25] 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

[00:09:41] informed data is one variable. There are other things that you can use. There's ways and values that help you to think about making decisions and there's risk. And I think that's the difference between data driven and data informed.

[00:09:56] Oftentimes data informed, there is a little bit of a risk. You're putting yourself out there. It's informing, but you're also leveraging other ways to make another thing in order to make a decision. And typically I wouldn't say all the time, but sometimes it does.

[00:10:10] 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 know what anybody says. You could come up with like, oh, the eclipse hit five

[00:10:30] 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.

[00:10:46] Well, one question I want to ask you is, does it 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 from a quantity perspective. Is it about necessarily, is it about when we have

[00:11:00] too much information or not enough good information to be able to help those decisions? Yeah, I think that. So that comes up a lot. So there's a lot of information and there's little information, right? And it isn't just the quality is the quantity. And I agree with that.

[00:11:17] That's where I feel like data informed. That's the art in the science. That is the art in 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,

[00:11:30] 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're relying on people and expertise

[00:11:50] and other things and also risk to be like, what if we? Because you're also talking a little bit about the noise versus the really important concepts or the important information that will. That's right. Prove or disprove your theories based on their quality,

[00:12:07] based on the story that it's telling, but also fitting your narrative. I mean, we all have 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.

[00:12:22] And either some people go back to the well or some people use it to your point before in ways in which we'll tell their story because they don't care what the data says. You're going to say, well, this is a mistake,

[00:12:34] but or this is not what we're talking about. But yeah, 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.

[00:12:47] But if that's what you do day 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. Exactly. Exactly. And it doesn't you're starting to just

[00:13:03] prove things. You're just trying to prove a point versus move things. And I think in this in this day and age, we're, you know, companies are and leaders in my mind are looking to move whatever the dialogue, move the narrative, shift the narrative, disrupt the narrative.

[00:13:23] 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 advisor to a HR tech company that's all about data.

[00:13:41] 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.

[00:13:53] And they were like, where'd you hear that from? The internet. You know, so it's like, you know, you can pull anything from the internet. Like I could be an aspiring dancer on the internet. So I just feel like we've got to figure out

[00:14:06] what you're garnishing and garnering with. Yeah, 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 the decision

[00:14:21] 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 as finding those leaders to really, really understand what they're trying to prove.

[00:14:34] Yeah. But like you were 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. There'd be a dancer. And it's and the internet is in many ways

[00:14:47] 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.

[00:15:00] Yeah. 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

[00:15:14] garbage and garbage out and we are kind of a wash in that, aren't we? 100 percent. 100 percent. 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 decisions, who is the one responsible?

[00:15:30] Is there integrity? How are they thinking about things? And that's it's this mindset of data and form 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

[00:15:49] versus getting a very strong Chief People Officer, C.H.R.O., 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.

[00:16:05] 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. We've got to present to the board.

[00:16:18] We've got to present to, you know, to the internal teams. We've 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 tortable awards packages. And so we have to be very thoughtful.

[00:16:32] Then I think that 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.

[00:16:46] 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. So let's go to question number two, because I think a lot of what you're talking

[00:17:13] about does flow 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? Right. So listen, I let me just be clear. Decision making should be done around data,

[00:17:28] 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.

[00:17:46] You've got to be kidding me. Right. 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

[00:18:01] the companies that decided that they and decided and use data as 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

[00:18:23] a lot of employees, that's not what they were saying. Right. 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 and wanted to be required

[00:18:37] to be in the office five days a week. Right. So I think those are that's where I think it was data driven on the other side versus being data informed to say, OK, here are some options. Here are some ways we can do this.

[00:18:53] Here is an opportunity to actually think of it differently. So I think it has 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

[00:19:06] hemorrhaging very, very strong talent when you have options to go other places. 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.

[00:19:23] But you never heard you never heard them say how they got to that point. Exactly. So, you know, data driven. OK, 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.

[00:19:41] 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. That's exactly right. Yeah. Do I you nailed it? I mean, that's basically what happened. And I saw it. I saw it firsthand.

[00:19:59] I saw it on the other side, too. But I saw it firsthand. And I think it is let's fit a narrative. But but I and there was a lot less of the thought leadership qualitative. Right. I think a lot of companies miss the qualitative, the conversations,

[00:20:17] the hearing from the loudest and the not so loud people and striking a balance of that art and science before making a data informed decision. Right. And so I think you're seeing a lot of people really struggling within that.

[00:20:35] And we are it's I think what's going to be very interesting for 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

[00:20:48] or 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. Right, right. Well, I think we were all kidding ourselves when we said we were happy to go to an office five days a week.

[00:21:01] 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, Libra, I think it comes down to being able

[00:21:11] 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.

[00:21:25] It's not making the decisions for you. Exactly. I have a question for you that kind of comes up from this. The world of artificial intelligence you had had to come into this conversation had to it's coming into every podcast we do. It is. Yeah, it's a good question.

[00:21:41] 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,

[00:21:52] doesn't the AI take on a role of being a consultant here rather than being the answer? 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.

[00:22:07] 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 need to fully understand what that looked like and what that meant.

[00:22:22] I think I go back to the original point as I see us entering into this world of AI or 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

[00:22:35] and where the jet says, but I do believe that. And it, 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 taking over everything that we're managing through it.

[00:22:47] Yeah. I do think there is 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 is making the decision. And so it goes back to the leader.

[00:23:07] 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 of the new technology that entered into our world. It's on the risk.

[00:23:22] The responsibility is taking is basically on the person. And so you could leverage that tool that confirm your narrative. Right. Or you can leverage that tool to help you and, you know, make different decisions to, you know, move, you know, something. So I do think there's positives.

[00:23:43] 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.

[00:23:57] 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. Absolutely. Yeah, absolutely. Well, it's also based on the request that you ask as well,

[00:24:11] because if you're asking an open-ended request, it's going to use whatever day is available. But if and then this goes back to being able to use the appropriate prompts to be able to get the AI to really fundamentally understand

[00:24:23] what it is you're actually asking for and what data it needs to go after. Exactly. So I agree with you. I agree with you. It seems like this is a tool that kind of elevates raw data into a more consumable form, but it doesn't obviate the need for

[00:24:39] being able to understand what that is. 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.

[00:24:54] And and it's very well known that oftentimes AI is wrong. What it comes out with. 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.

[00:25:09] It came from Google and Google's barred, I guess is the name of it. Yeah, it's a part. Yes, it's part gave gave the response. And he was like, Dad, this is OK, right? I'm like, well, you know, just take it from every other Google search you've ever done.

[00:25:24] Right. But that's up to you as you're saying, Libra, it's up to the consumer to be able to make that determination. Right. And this is where there needs to be more support and coaching and development for people who have that information in hand.

[00:25:44] Very, you know, that's the critical part. 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.

[00:25:57] 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 forward slash H.R.D.L. consulting

[00:26:12] to schedule your free 30 minute call today. So what can people do to be able to like practical examples and what can they do to get ready for that, though? And be able to be more informed?

[00:26:27] Well, I mean, one of the things is to be a little bit of a skeptic. Not everything you, like I said, I pulled it off the Internet, you know, like there's like, listen, someone could there's 50,000 things

[00:26:39] out there telling me that I should I should eat eggs or I shouldn't eat eggs. Right. You know, at some point you go to a doctor and they're like, eggs are good for you. Don't eat eggs. Like what is it?

[00:26:50] 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 to just that that we're emphasizing this idea of being self-reliant and being very thoughtful

[00:27:16] and doing your own personal research outside of the Internet, but doing your own qualitative and asking, you know, research, asking specific questions, learning more about it, getting history. I think what's happened in, I guess, in the generations after the Internet is everyone's 100 percent reliant.

[00:27:36] And I want people to start to question. And that's what I that's the difference between people who are 100 percent reliant on something or some tool that's saying yes to everything that you say versus being a little bit like, hold up, that's thank you for that information.

[00:27:54] 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.

[00:28:03] And that will ultimately tell you what kind of company you will end up working for. So I go back to as a leader, there is an obligation to question and ask the questions and do your own

[00:28:18] personal beyond just, oh, let me go on and do another Google search, but start to ask the qualitative, ask other people, get their input and a diverse group of people. That's why diversity is so critical so that it can inform you

[00:28:33] 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.

[00:28:45] 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,

[00:29:01] it surely doesn't disrupt some of the work that we've been doing that has an informed major change. And I'm just a little concerned that as leaders who aren't thinking about this and being thought leaders and tapping into other sources of truth,

[00:29:18] it becomes more and more of a problem. I think what you're saying is we become a little lazy and we've tried to go for whatever the most the highest rate answer is. Yeah, the most convenient answer is instead of what's the right one.

[00:29:33] And so I don't know if you remember this, but 30 something years ago, plus 30 plus years ago, I used to have to do multiple regressions by hand. Yeah, we used to have to prove it, you know, that we knew what we were doing and even linear regression is easy.

[00:29:49] 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.

[00:30:02] 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.

[00:30:19] 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

[00:30:33] to their beginnings and kind of do it longhand? 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.

[00:30:53] 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,

[00:31:11] 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.

[00:31:27] I remember in college we had to take a course called logic. We had to take it like it was required. Right. That was the hardest. It was like and I took the LSATs too. Like it was like the logic side of LSATs on steroids.

[00:31:44] 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 remember who's in it. I just remember my brain hurting.

[00:31:59] You know what I mean? Oh yeah, I totally know. It hurt. It hurt. I was like, kill me now. But it to me has allowed me to exercise a muscle. And it gives me a little bit of a different perspective on things.

[00:32:14] 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.

[00:32:28] I don't think they're being asked to do it. It's very hard, Libra. When the data is all there and it's a rate in front of you, it's hard to question it and to say, I need to go into my CEO's office right now

[00:32:40] and tell a different story than what the data is 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

[00:32:52] or get out of there. That's exactly what happens. And 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 is leaving

[00:33:06] or why everybody is not leaving or why we need to pay them that or why we need to, you know what I mean? And it's hard. But even if you're not the CHRO, even if you're somewhere in the ranks in HR

[00:33:16] or HRT 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.

[00:33:32] 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? That's it. And that's the hard part. And that's where you need to,

[00:33:44] 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 aligned with that. And that's hard.

[00:33:59] 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've been in different companies and I've seen how different folks respond to those employee engagement surveys.

[00:34:25] 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.

[00:34:43] They want all of it. They wanna, this is the narrative. This is what we're sharing. This needs to go in our reports, our yearly engagement survey reports. But it is up to the leaders at the top and to really start to think about how to take this data

[00:35:03] 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. A lot of times it comes down to the employees saying,

[00:35:21] well, you're not listening to me. You haven't done anything. You haven't told us the story. Every year? Yeah, every year they go, well, we've said what's on our mind, but how has that changed what you do? Nothing changes.

[00:35:34] So why bother or why telling the truth in those surveys? Well, that's the thing. That's what they end up saying. I'm not filling it out. They don't listen to me. Kind of like, yep, I'm not even voting. My vote doesn't count. Like it's all of it. Right.

[00:35:49] You know, all of those types of, and then 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.

[00:36:06] 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 changes. Right. Which is unfortunate. And it's hard to change, by the way. Yeah. Yeah. It's hard to change.

[00:36:20] 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've done, I know what it's like. You know, they're like, well, nothing's changed. It's still this and they're still discriminating.

[00:36:35] 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.

[00:36:54] That would be somewhat game changing, but it's rare that you see a major policy or practice that comes out of it. Which is the reason why people get mad when we don't listen. Yeah, it's hard. Yeah. Libra, thank you very much. You've been awesome. This has been wonderful.

[00:37:21] I think I've learned probably more in this last, you know, half hour plus than I have all day. And again, today is eclipse day. So thank you so much. This has been awesome. I appreciate it. Yeah, I appreciate you being with us. Yeah, this is great.

[00:37:37] And thank you all for listening. Take care and stay safe. That was the HR Data Labs podcast. If you liked the episode, please subscribe. And if you know anyone that might like to hear it, please send it their way. Thank you for joining us this week

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