38 - It Depends... On The Data

How do you benchmark and use data to solve strategic problems for your organization? Where do you start? It depends! In this episode, we speak to Jessica Collins, Director of Organizational Culture and Analytics at Anchor HR & People Analytics Instructor at York University, about tackling People Analytics at any stage.

Release Date: March 23, 2022

[00:00:00] Speaker 1: Morning, this podcast is about the realities of working in people operations. This is not a stuck up PC compliance-based, or employment law podcast about stuffy outdated HR practices. Shit will get real here and we assume no responsibility.

[00:00:16] Speaker 2: Just another day in the office.

[00:00:17] Alexa Baggio: There's nothing better than like a bunch of people who work with me kind of getting around a table and-and sharing these stories. We have this like out-of-body experience in HR where you're like thanks for getting here. It's not that bad.

[00:00:26] Tyson MacKenzie: It's not-not that bad. It's not.

[00:00:29] Alexa: Come hang out with Tyson and I on this podcast, we'll make you laugh.

[00:00:31] Tyson: This is the People Problems Podcast with, Alexa Baggio and Tyson McKenzie.


[00:00:39] Alexa: What's up Tyson?

[00:00:40] Tyson: Not too. Um, okay. I did wanna tell you something though today. So-

[00:00:43] Alexa: Oh boy.

[00:00:44] Tyson: -I went on a little excursion today, so we walked to the library that said my little-

[00:00:48] Alexa: Oooh.

[00:00:49] Tyson: -my little area that I live in, this tiny little area. And so obviously I've got my five-month-old with me. We're looking at the kids' books and stuff, and one catches my eye and it's called The Day The Crayons Quit.

[00:01:00] Alexa: [laughs]

[00:01:01] Tyson: And I open it up and it's adorable. It's all of-- Each of the color of crayons writes their resignation letter to, like, the little boy who's coloring with them. So the white one's like, "I'm quitting because you color with me, but why even bother 'cause I'm white." And like-

[00:01:19] Alexa: [chuckles]

[00:01:19] Tyson: -it does-- I don't even show up on the paper." And so stuff like this anyways. [chuckles] So I'm obsessed with this book. I wanna go- I wanna buy it. I think I'm gonna buy it. And I look it up on Amazon and it says something like, "Age-appropriate analysis for kids to talk about the way that each of the crayons quit and why they quit." And I was like, "Wait, is this like the first HR training book?"


[00:01:42] Tyson: It was-- It's so cute. Anyway-

[00:01:44] Alexa: It's the-

[00:01:44] Tyson: -I'm gonna get it 'cause it's-

[00:01:45] Alexa: Amazing.

[00:01:45] Tyson: -so cute. Yeah.

[00:01:47] Alexa: Amazing. I'm gonna have to check that out, The Day The Crayons Quit. That's adorable.

[00:01:50] Tyson: Yeah-yeah.

[00:01:51] Alexa: I wonder if-if the author has a background in HR.

[00:01:53] Tyson: I know.

[00:01:54] Alexa: Yeah.

[00:01:54] Tyson: I need to know more. I need to know more-

[00:01:56] Alexa: Yeah.

[00:01:56] Tyson: -'cause it's just too cute.

[00:01:57] Alexa: We'll have to hunt them down. And if they're-

[00:01:59] Tyson: Yeah.

[00:01:58] Alexa: -listening to this podcast, we'd happily have you as a guest.

[00:02:01] Tyson: [laughs] Yes. It's so good.

[00:02:02] Alexa: Because that-that sounds super fun. That's very adorable. Very cool. I-- in similar news I was excited, but then kind of unexcited to see that Nick Kroll on Netflix. The guys who made Big Mouth came out with a new show called Human Resources, but it's not actually about-

[00:02:15] Tyson: HR.

[00:02:16] Alexa: -HR. I think it's-

[00:02:16] Tyson: Is not. No.

[00:02:16] Alexa: -humans as resources because if you've ever watched Big Mouth it's about all these like hormone monsters and kids in puberty. And so it seems like a spinoff of the monsters and it's kind of an egregious comedy that's very not PC, but it's very-- At least Big Mouth was very funny. So I got all excited that like there was gonna be a funny cartoon about HR and that it wasn't actually about HR, so-- Oh, well. Awesome. Anything else new? All is- all is well in above the border?

[00:02:42] Tyson: Yep-yep. It's so snowy as ever. So-- [laughs]

[00:02:47] Alexa: Cool. Well, I'm excited that maybe next time we record together although it will be past- it will be past this time. By the time this episode drops it will not be so dark out when we meet because it gets dark at like 2:00 PM in the States right now. So daylight savings. Cool.

[00:03:00] Tyson: I did get a ring light though, but I realized that I don't have enough USBs to hook it up. So I'm-- I need to figure that out. [chuckles]

[00:03:07] Alexa: You need to figure that out, Tyson.

[00:03:09] Tyson: I know, I know.

[00:03:10] Alexa: You-- And you can put some crystals on it or whatever-whatever you normally do. All right. Before you launch into talking about crystals, 'cause I opened that door. Let's move on to POPS in the news.


[00:03:28] Alexa: Our article today it's in HR brew, but it's-- this topic has been like in quite a few places. So I think this actually might just be a synthe- a synthesization of a few things, but it is called Biometric Monitoring is Booming in the Workplace Raising Ethical and Legal Questions for HR. Subtitle is Workers Have Few Legal Protections When it Comes to Biometric Data, but There are Some Notable Exceptions. So the article here basically is talking about this new landscape of like employee biometric data. And it's interesting because it says the biometrics market reached, uh, basically $28 billion in valuation in 2021 and is expected to reach nearly 75 billion by 2027. So think of things that get monitored, like eyeballs, heartbeats, physical movements, palms, if you saw the recent Amazon store they released in DC. Just all kinds of things that people are using to basically track your time, whereabouts, work, whatever it may maybe.

And it's basically a way of sort of gathering information about workers. The problem is as the article mentions that it's a way of gathering information about workers who largely don't have well-recognized privacy rights. So this is a little bit of a new frontier in the legal zone. Uh, and the article does a pretty good job of-of articulating. And I will obviously not do it justice 'cause I never do, but articulating sort of the-the pros and the cons for this being possible. So the big one that they talk about is there's a group Kronos that actually think is based here Boston, where I am.

[00:04:51] Tyson: It's based here. yeah.

[00:04:52] Alexa: Who said they have a, you know, these screening clocks and it ma--

[00:04:55] Tyson: So, clocking in?

[00:04:56] Alexa: Yeah, it's clocking in basically.

[00:04:57] Tyson: Okay.

[00:04:58] Alexa: And it says, they're basically trying to mitigate the th- the threat of "time theft" which-which occurs quote, "when, uh, an employee is paid for work they have not actually done or for time they were not actually at work". And so they're quoted as saying without biometric technology in place employees can clock in for one another or buddy punch. So I guess they're-they're basically trying to combat the-the issue of like, "I-I clocked in for, you know, Tyson, but Tyson wasn't actually at work."

[00:05:25] Tyson: Or like the old wiggle-wiggle the mouse so that your Slack turns green. [chuckles]

[00:05:29] Alexa: Oh, I-I didn't know that trick, uh, adorable/obnoxious.


[00:05:35] Alexa: Um, yeah, I guess there's lots of ways to fake that stuff. But the-the interesting thing about this, I think is that it's one of these, like, you know, policy is finally gonna have to tech-- catch up to technology-

[00:05:44] Tyson: Mm-hmm.

[00:05:44] Alexa: -because these technologies are actually in a lot of places. Like they're in hospitals, they've been in warehouses, they're in city, government offices, they're now in supermarkets. Like it's all the things that you think of from like a consumer perspective and an efficiency perspective and a logistics perspective, and then people go, "Oh, wait, oh, shit, we're also collecting data on employees."

And in this particular case, Kronos actually did not get permission apparently, or there's some lawsuit against them for violating the Biometric Information Privacy Act. Which I guess passed in 2008 and requires employers to inform employees in writing of biometric data being collected and its intended use with written consent to do that from their workers.

And I-I'm gonna venture to guess that this practice was already in place and then the law happened and then they never went back and retroactively fixed it. So-

[00:06:28] Tyson: Right.

[00:06:28] Alexa: -just a really interesting article to start thinking about, like this stuff is basically not going away. And I know everyone is very up on sort of consumer privacy rights and we talk about-- I always get the letters wrong, but like, you know, in California and the UK, there's all these- there's all these new laws around how you can and cannot use consumer data, uh, and you need consumer consent. And we maybe haven't quite covered all our bases as it comes to new technologies in the workforce. So it-it is a fascinating article. I definitely recommend it, but it's also just a, one of these, like "here we go style" conversations 'cause this-- you can't put this genie back in the bottle. What do you think, Tyson?

[00:07:01] Tyson: Right. Yeah. The vibe that I got though was when we were chatting about the Amazon warehouses and they track like every time someone goes to the bathroom or if they stopped-

[00:07:09] Alexa: Oh, yeah.

[00:07:10] Tyson: -to chit-chat and how they were kind of using that as a way to performance manage people which I don't think that this particular thing is used for-- should be used for. The only biome-biometric experience that I personally have is like the-the security. So I don't know if you've seen those where you touch it as a security measure. So your-

[00:07:27] Alexa: For like--

[00:07:27] Tyson: -your screen POPS up. It's kind of like a-a two authentication sort of thing. A two-factor authentication.

[00:07:32] Alexa: Oh yeah. A two-factor authentication.

[00:07:34] Tyson: Yeah. So it POPS up and it's like touch your biometric sensor and you just touch it on your computer and it's-

[00:07:39] Alexa: Okay. Yeah. I have a-

[00:07:40] Tyson: -just-- it's like your password.

[00:07:41] Alexa: -uh, a MacBook that you like touch the-- you touch the-

[00:07:43] Tyson: Yeah. I got that-

[00:07:44] Alexa: -thing and it opens your MacBook.

[00:07:44] Tyson: -I've got that too, but this is like a separate little piece that you put in the MacBook. So it's like a two-two thing. Yeah. So that's-that's pretty much the only experience I have, but I don't know. I think that there's--

[00:07:54] Alexa: Yeah. I'm one of those people that doesn't worry too much about my private data 'cause I figure most of it's already out there anyway, if people-- If ever really wants to hunt me down, they're going to although that doesn't mean it's the right thing for everybody. Like I have clear, uh, which here in the United States is like a retinal scan that allows you to-to skip-

[00:08:08] Tyson: Whoa.

[00:08:08] Alexa: -the line at the airport and I'm a convenience whore. So they scan my eyeballs and-

[00:08:11] Tyson: Yeah-yeah-yeah.

[00:08:11] Alexa: -TSA knows who I am and I'm clear-cleared to go. And I never wait in line at the airport and it's the best thing ever, but I consented for that. I also paid for it. [laughs] So, um, and-

[00:08:20] Tyson: Right.

[00:08:20] Alexa: -this is the opposite problem.

[00:08:22] Tyson: That-- So that's what I was gonna say. So cool. Like this is new technology, we can take advantage of. It probably has a lot of really great potential here. So the problem with some of this stuff though is that it's so confusing that people don't understand. Like they don't even understand, like, what having my eyeballs could mean or how-

[00:08:38] Alexa: Right.

[00:08:38 Tyson: -that could be abused or the information can be used in a way that is, you know, negative. So it's about not only writing a policy, but making it so that people really understand it. So obviously-

[00:08:49] Alexa: Yeah.

[00:08:49] Tyson: -we talked about this, no legal jargon, and also like about the-the biometric aspect of it. So like why-why is it unique for someone to have your fingerprint versus like, I don't know your address where you live, like-

[00:08:59] Alexa: Right.

[00:09:00] Tyson: -there's a lot of information up there that people don't-don't really understand.

[00:09:03] Alexa: Yeah. I also think there's gonna be a future of this and-and-and this doesn't,-- This article does not go into this 'cause it's much more about the sort of legal landscape being sort of new and-and needing some more thought and expertise. But the idea of what I think people are calling now, which is like body as a service, which is this idea of constantly monitoring things about your health and your personal body. So like I wear an Aura ring. I have-

[00:09:23] Tyson: Oh, yeah, yeah.

[00:09:24] Alexa: -I have watch that tracks my runs, I wear a heart rate monitor.

[00:09:26] Tyson: Yeah.

[00:09:26] Alexa: Like you basically kind-- You could-- If a- if a random person was-was watching those things every day as I wear them would probably be able to tell me if I was about to kill over maybe a little bit before it happened. But I can't imagine in-- a world in the future where we are not continually tracking more and more employee data for things like health outcomes, right? So, hey, I wanna reduce my health insurance premiums across my population. I monitor, you know, the heart rate and the blood pressure and the such and such and such and such of my employees, which is a just a- just a derivation of existing wellness programs, you know, to get further and further into the predictive ability to-to not only keep people out of the hospital, but also make them well, and there's going to be, I think, a real conversation coming up about how far the employer can go to encourage that kind of behavior and keep their costs down versus what is just seen as, like, purely invasive. And like, I can't work there if I'm not willing to be monitored on all fronts.

[00:10:20] Tyson: Yeah, see-- I didn't see it so much from like a, oh, we want to like keep you well.

[00:10:24] Alexa: Because it's cheaper if you're healthy, just to be fair.

[00:10:26] Tyson: Yeah. Okay, so sure. So there's that side of things, but, like, imagine there was a perfect sort of heart rate pulse, whatever, that showed when you were actually enjoying work or engaged in your work? So imagine, like, there was, like, a sensor that somehow, and I don't know what it is. Like some scientists would have to figure it out, but, like, your heart beats a certain way and, like, your skin is at a-

[00:10:45] Alexa: A dopamine monitor or?

[00:10:47] Tyson: -cer-certain temperature, something to know when you are actually, like, engaged and then you could see all your little employees and, like, who's engaged and who's not engaged. Imagine?

[00:10:56] Alexa: I can't, I don't want to. I want to introduce our guest because this is a real-

[00:10:59] Tyson: Alright.

[00:11:00] Alexa: -this is a real rabbit hole. Right, yeah, I'm thinking of, like, contact lenses with engagement data. Like you might--

[00:11:07] Tyson: Amazing.

[00:11:07] Alexa: Too much, too much, too much, too much. We need a whole episode on that. All right. Our guest today who has been patiently waiting is Jessica Collins. She is the director of organizational culture and analytics at Anchor HR. And she's a people analytics instructor at York University. Jessica leads strategic consulting projects related to people, analytics, change, and performance and she gets excited about solving complex challenges in practical ways. She's energized by understanding the strategy-related barriers and then going deep to find solutions that get to the root cause with a fit for an organization's culture. With people analytics, she loves solving puzzles with data in order to better understand support and people. Jessica, thanks for being here.

[00:11:45] Tyson: Welcome.

[00:11:45] Jessica: Thanks so much. Thanks so much for having me. I'm looking forward to this conversation. Um, my mind was racing with all the possibilities that-- going down the rabbit hole with you.


[00:11:54] Alexa: I was going to say, we talking about data so any-

[00:11:56] Jessica: That's--

[00:11:56] Alexa: -any thoughts on biometric screening?

[00:12:01] Jessica: Yes, definitely a conversation in-in people analytics and that's always a challenge. Right? If you can think it, you can do it, but is it ethical? Is it in the interest of-of our employees, of our organization, of our society? It's always a continuous conversation and, you know, legislation has not necessarily caught up with technology. So it's in some-- In a lot of cases, it's up to organizations to make those decisions.

[00:12:28] Alexa: Yes, which is sometimes great and sometimes terrifying. Yeah, so tell us a little bit about Jessica before we jump into the wild and wonderful world of people analytics, which I know is a hot topic these days. Uh, talk to us a little bit about your journey into that space. How did you get to be a professor of people analytics?

[00:12:48] Jessica: Yeah, I-- so when I first went to school, my bachelor, uh, business administration, I first thought I was going to be in marketing. I loved consumer behavior, that kind of study, but then I went-- got into the courses on organizational behavior, that got me even more excited. And when I thought about, you know, where do we spend all of our time? Where could I really have a meaningful impact? That's really where I got interested and-and so I specialized in HR. I went on to do a master's in, um, HR at the University of Toronto.

And from there, yeah, I worked in corporate HR departments at, um, larger organizations and now I'm-I'm in consulting. And so I get to work with a huge variety of-of industries and sizes of organizations on huge layer of different kinds of, uh, projects, like you said. So change performance enablement is a big one, and people analytic-analytics more and more on its own can be a project, but really embedded in a lot other projects as well.

[00:13:51] Alexa: And just so people know, is there a required set of learnings or education for people analytics, or-- I-I feel like this is one of those things, like, eventually you'll be getting degrees and, you know, I'm going to get a specialty skill in-in people analytics because it's sort of a-- it's like a philosophy of, you know, I think a combination of data and math and since it's--

[00:14:11] Tyson: Economics.

[00:14:12] Alexa: Yeah, economics, all those things.

[00:14:13] Tyson: Yeah.

[00:14:13] Alexa: So, us liberal arts degrees like to know if there's an educational [laughs] component of this.

[00:14:19] Jessica: I-I think it's exactly that, it's really a combination of so many different disciplines and I think it is heading in that direction. Um, so I-I did actually take a certificate program, the first one in Canada in people analytics and that is where I'm now teaching. So it's at- it's at York University.

[00:14:37] Alexa: Uh, the pupil that becomes the teacher, how-how symbolic. I love it. Uh--

[00:14:42] Jessica: Yes. Yeah. [laughs] So that's how that happened.

[00:14:43] Alexa: Yeah, so do us a favor, for-for people who are like, "Oh, people analytics, that's just, like, data about people, like, break-break the discipline down for us. Like what is- what is-- Yeah, what is it that, you know, sort of map it out for us a little bit for-for those who are not intimately familiar.

[00:14:56] Jessica: Yeah, and it is-- Really simply, it is using data to better inform decisions. I think sometimes we overcomplicate it and in a lot of ways, um, some of the things that HR functions are already doing, I would call analytics, right? If we're using, um, if we're using people data to inform talent decisions, that is people analytics. If we are even, you know, the basic KPI reports that either functions are putting together, obviously you want to be using it in strategic way. You're prioritizing what your KPIs are and you're making decisions based off of that, that's analytics.

If you have a question, you know, why is our turnover rate increasing, and you dive into the data and do analysis and make recommendations based off of that, that's people analytics as well. So it all falls in there and you'll see a lot of different models where people will say, you know, here's the-the level of maturity so you could have the-the more basic, descriptive, um, analytics, which is those-those that are reporting. And then you might get into really more advanced techniques that apply that modeling statistics and-and more predictive analytics. Uh, but you really are using all of those. So even the most advanced people analytics teams are doing descr- uh, descriptive analytics as well. You need to do all of those.

[00:16:18] Tyson: I feel like it was maybe three, four years ago, all of a sudden it was like the-- one of our, like, top, like, things that we needed to put on our, like, development pra-- plans for the year was, like, being data-informed in HR. So, like, everyone just put that on the list of things that we had to do but I think that it's so often fell short because no one really knew, like you said, like, there's-- yes, there's collecting the data, but then it's following through the analysis, the prediction of, like, what that data means. And that's where I think a lot of people in HR sort of fall flat is we collect all this data, but then we don't know what to do with it. We don't know how to tell the story with the data.

[00:16:56] Alexa: Or it's the interpretation and the actioning of the data.

[00:16:59] Tyson: Yeah, yeah.

[00:17:00] Alexa: Uh, data for data's sake doesn't get us very far. So-so, Jessica, for-for people who-- So let's-let's-let's walk across the, like, life cycle of-of an organization here, right? So I imagine that people analytics at a- at a startup or a younger company is a bit more sort of as you said descriptive, it's probably a bit more topical, it's probably, like, lar-largely buckets and-and demographics and some- and some sort of big buckets. And then you get to larger-scale organizations where it's more predictive, it's more possibly prescriptive, et cetera, so-so walk us through sort of, like, the life cycle of how people analytics looks as an organization scales and maybe just good examples of, like, what's important maybe in your data at each of those-those sort of, uh--

[00:17:41] Tyson: And what data we should be collecting at each of those stages, right?

[00:17:43] Alexa: Yeah, wh-what's important and what does it look like at sort of, like, let's start with startup.

[00:17:47] Jessica: The longest question of all time. [laughs]

[00:17:48] Alexa: So, let's-let's go-- Yeah, this is like a-- well-well, it's an Alexa question, which means it's a lot of-- It's me working it out in my head in real-time, but let's-let's start with like, okay, you're a young organization. You're-you're a startup that's planning to grow, but maybe you're, you know, 20-30 people and maybe sub-- you know, definitely sub-100, like, what-what sort of the-- what does your- what does good people analytics look like there?

[00:18:05] Jessica: Yeah, really just starting off with, for your organization, thinking about what's your strategic priorities for your organization, for your business, what are the questions there? And then looking to see, do you have data to be able to measure and answer those questions? And oftentimes at the start of stage, you might not have the data yet and that's fine. Keep asking those questions, having the conversations around that and the insights. You might not have the quantitative data that not-- may not be even doing employee surveys and all that.

The common data sources that we see in HR, but just having those questions to be able to, um, have those conversations is really important at that startup stage. And then you can map out, okay, here are our priorities. Um, and here's-here's the data that we do have, what do we need to start collecting? And it is really, you know-- Sometimes this is not a nice answer, but it is really unique to each organization. So I can start--

[00:19:07] Alexa: It depends, it depends.

[00:19:08] Jessica: It depends, yeah.

[00:19:09] Alexa: It depends.


[00:19:11] Jessica: It kind of depends and really starting with-- yeah, start with the strategy, from there and what are the key questions that we have?

[00:19:20] Alexa: What are some things that you see in young organizations that either they-- like, you know, it's data they wish they had, that's kind of hard to get or things that are-are sort of common that people wind up going and collecting?

[00:19:30] Jessica: The-the common ones when we think about KPIs for HR in point engagement, turnover, um, and especially if you're able to use something that you can compare across, especially when you're starting out, you don't have a lot of internal data to be able to benchmark. Really my recommendation is always mostly be looking internally for benchmarking, but when you're starting out, you want to be able to benchmark externally. So if you can use ENPS employee, um, net promoter score. So you can use that one question to be able to compare against all, uh, externally, that's a good practice when you first start out.

[00:20:08] Alexa: Well, let me ask this, does the answer change as you get bigger, or does it always just depend? Like, are there things large organizations are starting to do or-or things that you can do just like, you know, law of large numbers, you can get more stuff right and do more things with more data that larger organizations are capable of doing that adds a layer here?

[00:20:25] Jessica: Mm-hmm, yeah, absolutely. The larger organizations, the more advanced people analytics teams, uh, there's a few key things that you see. So they're definitely using more systems, so you'd see a greater number of systems and they're also more integrated and you're able to have more real-time access to the insights. And so it's not, you know, one person generating those reports and then-- manually, and then sending them out maybe a month later because it takes so much- so much time.

It's really giving that access to the insights to the leaders, to that people data. Um, so that's what you really see in the more-- the larger organizations and that helps too with the people analytics team, when you have that system to be able to give that direct access to-to leaders, um, or even employees if you get to that stage, be able to have that continuous insights access, then you can have the people analytics team really more focused on projects and really diving deep into those research, um, projects.

[00:21:31] Alexa: Do you have any powerful examples of-of this? Like-- sorry, sorry, Tyson.

[00:21:35] Tyson: Just a quick- just a quick point on like what you said, like the integration piece, I think is super important as you're looking to scale and grow because what usually happens is you're just picking sort of this system, that system, you know, we're might use a Google this year.

[00:21:47] Alexa: Yeah, you're cherry-picking data.

[00:21:48] Tyson: You-you are cherry-picking data, and then you get to the point where you are large and you are looking for like that, you know, high-level people analytics, but all of your data is all over the place and the reports that you're pulling are all so different, that it's almost more work than anything to get at any connection between data points. So, especially as you're looking to scale, I think it's so important to consider like what systems can be integrated and like how to get the most out of, like, one system, for example, versus like cherry-picking all the-- like a whole bunch of-

[00:22:17] Alexa: Often-oftentimes a lot of those systems are not even your decision though which sucks, uh, but there are some groups, like, I think it's called Noetic and a couple other guys that are trying do a lot of this stuff, which is like, be a topical layer for plugging in like all the things so that you can actually get some good data across-across platforms. I mean, they're insanely expensive, but that's-- you know, there-there will get to be better, I think in less expensive tools as this field develops.

[00:22:39] Jessica: Yeah, yeah. Now, as you get bigger as you're scaling, there's-there's a few-- So Visier is definitely the leader in people insights, there's a few and again, it depends on what you're looking for. The main thing that they do is bringing all that-- all those data sources together and make it really, really accessible and tangible. So Visier, for example, is designed for HR, so you-- it's-it's so easy to understand, we talk about that data literacy kind of presents it in a way that's more easy-- easier to di-digest and understand what you're looking at.

[00:23:09] Alexa: So-so let's go back to examples, 'cause I think we need to drill into some specifics here, like what are some examples, Jessica? Uh, I think there's probably two things, just examples of people that you've worked with being like, oh my God, like we just needed some data to help us fix this or tell this story. And then I think there's al-also probably a conversation here about like the-the steps that we talked about earlier that are like, it's important to not only get the data but to actually interpret it correctly and action it correctly. So we'll get to that second. My-my first series of questions are just like, what are some of your are favorite stories about how data and people analytics, you know, has been used to-to really help change and transform?

[00:23:46] Jessica: Yeah, I think-- so, key-key use cases for-for me, a lot of the main questions and problems that I'm solving are around retention, um, DEI, hybrid work, future of work, so those are probably the three key areas. And I think you raise a-a point where the data, so-- the data isn't there often, but we will find that with-with retention where you kind of-- You can see there's a trend, you can see something's happening and maybe you have some-some theories of what could be going on, but the data is not-- just not there to be able to dive that deep into it, or another key challenge is we really-- To the integration, we really wanna be able to connect the people data with the business data.

And being able to-to do that effectively and also have, you know, stakeholders be open to do that, uh, definitely, um, that would be an example of maybe a-a key challenge that I had, uh, in one of my projects. Where, you know, there's a-a-- turnover was increasing and so we kind of looked at that and-and looked-- for all the data that we could get and-and we put together, um, a model. But in terms of how meaningful it could really be, the data wasn't quite there yet. So kind of came out of it saying, and here are the- here are the recommendations or the insights, but a lot of the recommendations are around, like, he-here's the data that we can- we can start building and-and experiments that we can do and-and more insights that we can gain because the-the data that's just in your HRS or-or sales systems in terms of, you know, how-how-- the performance of-of the team, it just-- it wasn't there enough to be able to-to answer the questions that we had.

[00:25:33] Tyson: That's such an art too, right? So if you're looking at something like retention, you can't just look-- So let's say, okay, we do exit interviews with everyone and their main reason-- everyone's main reason for leaving is compensation. So that sort of sends you down that rabbit hole. So you pull up all the comp information and then you start looking at the data of the comp ratios. Okay, but hold on a second, all the comp ratios were really super high, so how come people are saying that, you know, they-they weren't getting paid enough when the comp ratios shows were so high. Okay, and then what's the next level, wait, hold on.

We've been comparing them to the wrong job in the market, the corporations are all outta whack. Like it's a whole rabbit hole that this data can take you on and you can't leave any stone unturned, right? 'Cause then you're gonna wanna be looking at maybe the performance of these individuals. Well, you know what, they were all performance-performers, so that's why we weren't paying them a lot. So it's okay that they're- that they're leaving, right? So it's this type of information, this storytelling that I think we're gonna get to, that is so interesting about all this data. But also I think that there's such an obsession for HR to be collecting data, data, data, data, data, that reading that story and understanding that story is-- it's an- it's an art, it's a skill that we need to learn, right?

[00:26:42] Jessica: Absolutely, and-and even, so there's the storytelling at the end and one of the key skills that-that people analytics professionals bring is asking the right questions.

[00:26:52] Tyson: Yeah.

[00:26:52] Jessica: So to your point of kind of where you're diving in, you can get into so many different areas, is the scope the right size. You can be super big in terms of, are we attracting and retaining the right amount [laughs] or how do we solve all these problems or super zeroed in is, should we increase the pay to, uh, decrease our retention? I mean, that's such a narrow scope. Um, we really wanna be in the middle and asking more open-end questions to be able to-to do an effective people analytics project.

[00:27:26] Tyson: Yeah, and usually people-people analysts, is that what they're called? People or analytics specialists, they're so good at that, 'cause I'll go to them and be like, I just need the retention number and they're like, uh-uh-uh, and I'm like just gimme the number and they're like, no, no, we gotta- we gotta peel all the layers back.

[00:27:41] Alexa: Yeah, why are you asking me that? What are you solving for?

[00:27:45] Tyson: What are you doing? What are you up to?

[00:27:46] Alexa: What are you up to over there?


[00:27:48] Alexa: So we just-- actually, it-it's fascinating 'cause this in some ways is a lot of like, this is like the product research user research group of HR, right? I-I love to, um, I'm currently sort of working on a book in this fashion, but like about how existing customer functions really are HR functions and should be HR functions and so people analytics is like the user research group. And so it's funny, we just did a-a survey of POPS, just a survey that was like 150 or something, I don't- I don't know the exact number, 'cause I didn't look at the-the raw data. Uh, but just people in the people function, the HR function and-and one of the things we asked them was like, what's your chief pain right now?

Kind of the magic wand question, right? Like if you could- if we could wave a magic wand, what would be the biggest pain that-that we could solve for you right now? And we made the mistake of two of the available responses were, one was attracting and one was retaining [laughs] and it was like, we-- just, everyone said one of those two things. And it was like, it didn't actually tell us anything about what we were trying to get at because it's just like, of course everyone who works in people is trying to either attract or retain employees, like tho-those are the two buckets that basically make up the profession.

And so we sort of read the results and were like, "Oh man, we should not have put those answers in this survey, like they just never should have been available, like we should have left it open or we should have given like more specifics or sort of made-- forced them to choose one of a, like some other options that were subcategories of that." 'Cause like, you know, 150 people being interested in-in retracting and retaining talent, doesn't tell you anything, given that they all work in people ops. So we kind of failed on our own survey and I went, oh man, like of course, like those are the two hot-hottest topics in the market right now, uh, so it just goes to show like-

[00:29:23] Tyson: And I think it's just-- that's what the-- everybody is obsessed with it-everyone's obsessed with attraction and retention.

[00:29:27] Alesa: But it's-but it's been-- so just to be fair, that's one of those buzzwords or those two buzzwords, that's-that's been two-- Attraction and retention attract, retain it's forever.

[00:29:35] Tyson: It's forever.

[00:29:35] Alexa: It's like we've got to find new words to talk about this industry because like it's-it's an impossible-- they're impossible standards and they sum it up, they-they oversimplify, right? Like they-they oversimplify the issue, right? Like retention. So for example, what's the retention number like, oh it's 60% like, oh, well why is it so bad? And it's like, well actually it's not that bad, you're retaining 95% of the good employees that you want to be retaining.

[00:29:58] Jessica: Exactly.

[00:29:58] Alexa: That are high performing, you know, comped in the right ra-- in the right range. Though, uh, you know, drain on managers, you know, you're promoting regularly. Like you're-you're actually-- you-you don't have a retention issue. You have a, you know, a basically new hire turnover issue and-and, you know you're-- you've got a bunch of people leaving that weren't a good fit which is actually a good thing.

[00:30:19] Tyson: Do you find that that happens ever, Jessica? Like do you ever, you know, uh, managers like or you're working with the company they're like, "Oh my gosh, we have an issue," and then you're like, "Hold on a second," and kind of do exactly what Alexa just stated there? [chuckles]

[00:30:32] Jessica: Yeah, absolutely, it's like that is one of the- one of the main benefits of external benchmarking. That is so as I said before I-- Generally prefer the internal benchmarking but that is one of the benefits of external benchmarking. Where you can say, "Well, this is actually, uh, what's going on in the market and-and we know what they're looking at," and also diving into a deeper, well, which groups are staying and why.

Um, and even as we think about kind of where-where HR is heading and where leadership is heading and-and the culture of your unique organization or your-- um, your industry you might think-- you-- this could be-- it could be a higher performer. You are encouraging their career and their best interest is to move on to another role and that's the truth, and they may like come back.

[00:31:18] Tyson: Yes, love a good boomerang.

[00:31:21] Alexa: A boomerang.

[00:31:22] Tyson: We do not celebrate boomerangs enough, love a boomerang. [chuckles]

[00:31:25] Alexa: Yeah, yep, you go get- you go get some experience on somebody else's dime and you bring that back to me.

[00:31:30] Tyson: And come on back, yeah, love it, love it.

[00:31:31] Alexa: Come on back with all your newfound glory. Uh, yep, love a good boomerang. Awesome, so what are- what are some like-- You don't have to give company names or client names obviously, but what are some examples of-of-of like stories of either just cool things that you've seen that are, yeah, sort of specific in this or problems maybe that you've solved that you just were like, "Oh, wait, this is like sort of not what people think of when they think of people analytics?"

[00:31:54] Jessica: Yeah, so, I mean-- so there's two key-- two types of what brings you in people analytics and there's-- So there's the project that I'm talking about. There's also a health, uh, organization that I'm just building a data-driven culture, right? So, they might just come and say, "Where do I start? Uh, what-- Where am I at? [chuckles] And that kind of- that kind of work is really exciting. So just coming in and assessing, uh, you know, what the data is-is like, what their strategy is like and thinking through the roadmap.

And, uh, you know, I'm in co- in consulting but I have this long term relationship so I get to se, you know, the-the progression as I think about, uh, one organization that their culture is data-driven but HR wasn't quite there yet. I really focused on operations, keeping things running in terms of HR processes and so they just wanted help in that respect.

They had somebody working in, you know, managing the HR areas but in terms of, like, for overtalking about data literacy, really having that appetite for insights and understanding and feeling confident. Bringing that to the leaders and leaders, the most important part leaders wanting to, uh, hear from HR and seeking data from them. Um, where they're getting to that stage of kind of mapping up how to get there.

So now they have somebody running, um, people analytics. They're doing a-a DEI analytics project right now. And so they've really come-come afar length in, uh, in just have few years.

[00:33:27] Tyson: Yeah, that's amazing, the addition of people analytics teams like on the HR team, yes.

[00:33:31] Alexa: Yeah, exactly. That's what I was gonna say, is there are a point at which you start to say like, okay, you should have at least a person, and then maybe you should scale back? Like wha- like what's the-the, like, people analytics function that you see, when does it get introduced? Like, when does it start to blossom? What's the right structure?

[00:33:48] Tyson: It depends.

[00:33:49] Alexa: Oh my God. Put your sweatshirt away, Tyson.


[00:33:54] Tyson: I'm gonna send you one of these.


[00:33:59] Alexa: Why don't we drink on this podcast?


[00:34:03] Tyson: I know.

[00:34:03] Jessica: I would say key factors are-- So the number of systems, right. We talked about as you get more advanced, you are-- you're working with more systems. So you might need a bigger team instead of managing-- working with more data sources or a system in terms of analyzing it or presenting visualizations. So you need a bigger team to be able to do that. And then the people analytics team might also be responsible for more in terms of actually producing the report. Sometimes the people analytics team is responsible for that, so they can be leaner.

[00:34:33] Alexa: Are you ever influencing the systems? Are people ever like, "You know, let me work with the systems team or the, you know, whoever-- Systems is also a snowflake, it's handled differently in every organization. But, like, are you ever influencing the systems across the organization that you're buying?"

[00:34:47] Jessica: Yeah, yeah, and, uh, and also integration, right? So there's a lot of, um, employee experience, uh, systems that are actually built up to also have operation systems, or sales systems, and some of them are already built up for that. That's great, and so really being a good partner with IT, um, and the business to be able to be in-in the room to help make those decisions, uh, is really important.

[00:35:12] Tyson: Yeah, makes perfect sense, like you should be consulted-- Like people analytics should be consulted when we're purchasing these just for again, like, maybe we're not that advanced right now but, like, what could the future look like and what the-- Like what opportunity does this system have to collect data? And then I know this is such, like, an administrative type thing in HR but we do a lot with reports, and, like, there's nothing that pisses me off more than when you have one report that's, like, sorted by employee number.

And then another report that doesn't have employee number but has, like, employee last name or, like, when the names are merged on one report. It would be, like, last name, first name and then in the next report it's, like, last name, first name in once cell. Like, little things like that are so irritating, like, it drives me nuts 'cause I spend all the time-

[00:35:53] Alexa: Yeah, claims-- Yeah, just sloppy.

[00:35:57] Tyson: -like sorting and, yeah, it's really sloppy.

[00:36:00] Jessica: Yeah, and honestly in people analytics it-it sounds really exciting but I would say 80% of the time it's data wrangling, data cleansing, and structuring the data just like that. And then you get to fun stuff where you're actually presenting the insights and-and making the recommendations but a lot of it ends up being bulk paperwork.

[00:36:20] Alexa: Fun.

[00:36:20] Tyson: What are- what are some of the things you- what are some of the things you're looking for? Like, when-- Okay, so let's just like play it out, like what-what it could look like?

[00:36:26] Alexa: I-I wanna talk about-about the-the part where you present and give actions.

[00:36:30] Tyson: Yeah, like that's what I mean. So, like what do you look-- Like what are you looking for in that data that then can drive the next steps? The storytelling aspects I-I suppose.

[00:36:39] Jessica: It does come back to the-- if you start off with the good questions and you come back and you look for the-the insights that support the-the answering those questions. And then it comes-- I would say too within HR, we already have a lot of these transferable skills. Uh, so storytelling is often we'll talk about that, we need to have storytelling for-for people analytics.

I would say a lot of people in-in HR have developed this skill, it's just not with data yet. Uh, so it's really just moving it over and understanding how to use evidence to support the arguments and it really is your starting off with, uh, your, here's the recommendations, often and then having that supporting evidence to be able to hold a story of how you got there.

Especially when you're talking to people about the stuff you do, you know, you're gonna start off with, you know, here's the-- here are the key points and then- and then here is all the supporting evidence. And you might have, like, a nice visualization to really make that point that doesn't need to be really fancy. Uh, it really just needs-needs to have, uh, an effective message.

[00:37:43] Alexa: All right, so give me some specifics. What are some times that you saw some-- you came up with some data or maybe someone else that you coached them or- or whatever. I mean, I know you-you do both sides of the-the isle here. So my-my question to you would be like what are some stories where you're like, "Okay, you saw some data and you thought it means A and actually that's not what that-- or how I would interpret that or that's not-- like that's not where I would take that insight."

[00:38:07] Jessica: That happens a lot, that happens a lot [chuckles] and it often comes back to starting off with having that average knowledge, that instinct and so coming back to well that's, like, how I'm interpreting the data. Uh, so in terms of some examples, I think especially when we bring in demographics, we need to be really careful about how we're presenting that.

And so I think about a model around, um, retention where we-we did look at demographics of kind of who's staying. And-and so really being very careful about how we're presenting, so here-here are the key demographics and-and different factors that contribute to who's staying, who's at risk of leaving. And that's-that's really aggregate, that's to help us make decisions to improve our retention efforts.

That's not to say, okay, this is the specific person that we're now gonna then recruit but the opposite of what we're doing. And so making sure that when we were communicating that to leaders, having that discussion to make sure they were understanding, you know, we're looking for specific demographic, uh, in our new hires.

[00:39:17] Alexa: Is that a correlation, that causation style argument? You know like, "Hey, there are some similarities here but it doesn't mean that, you know, we specifically need to go recruit a certain kind of person?

[00:39:28] Tyson: Yeah, walking that back sometimes can be difficult too.

[00:39:32] Jessica: Yeah, and you're presenting, uh, you know, a full model. And so if you think about you're-you're more used to seeing something like correlation and saying, "Okay, well this is what we're talking about retention and-and we're-we're obviously-- when we should be attra-- uh, hiring the people who are gonna stay," uh, and-and that's not the intention of-of the model.

[00:39:51] Alexa: Yeah, is-- but is the int-- is the interpretation, no you should be hiring like people that are leaving or like what's the-- like be a little more specific if you can be like, yeah, okay like a certain--

[00:40:00] Tyson: Was that like saying, like, we discovered that--

[00:40:01] Alexa: Person A, B, and C is leaving. So we're just gonna go hire people, you know, D, E, and F because they stay. What's the- what's the actual interpretation of how you should think about that.

[00:40:10] Jessica: Yeah. So the actual interpretation in that scenario is-is really diving in deeper. Why are these segments at risk of leaving? It's not that they're, uh, a bad hire. It's maybe-maybe our processes are not supporting these-these demographics and making them feel included or-or giving them opportunities to, uh, be promoted or X, Y, Z. We didn't know within the data we had. We were just-- We were presenting the-the model. And so we got there but, um, you know, in-in the initial discussion, we had to really spend a lot of time explaining the model.

And-and I think on my side, really learning, um, how to do that storytelling. You know, we don't have to go-- dive into here's the full model to really get to here's the insights. And-and I think that that would've made that conversation a lot more effective and really getting to thinking of what your audience is to be able to have those conversations.

[00:41:06] Tyson: Yeah. I think- I think maybe an obvious example could be 30-year-old women leaving the workplace and it's like, oh, you know, they're not like invested in work. They're not committed. They don't care about their job but really it's because daycare is too fucking expensive. There's for mat leave benefits. They don't have anyone to help them. Like they want to work but for some reason, they can't because let's say that that's typical women who are, you know, about to have a kid. And then there's a lot of judgment made about hiring women. Oh, she told me in her interview that she just got married. Let's not hire her because you know what that means, right?

[00:41:41] Alexa: Yeah. That's super fucking illegal.

[00:41:44] Tyson: This is how this happens. But these are how these things happen. Like you think, you think, oh my, we don't do that anymore. It's 2022. But I-I have people who work-- I have a girlfriend she recently got married and everyone at her work was, oh, when are you gonna have a baby? When the baby's coming, blah, blah, blah. And she turned down opportunities to be-- for promotions because of that. And then, yeah. Well, guess what? She, you did have a baby but like it's still-- it's those types of judgments I think that can happen when you're looking at data. And then it's making sort of assumptions about a certain demographic of a person when that's not what we're really supposed to be doing that.

[00:42:17] Jessica: Yeah. And-and, you know, insights can help with that too. So think about with women, we often see engagements higher but then, you know, retention could be lower. And so thinking about-- and performance might be higher too but then promotions aren't. So thinking about, how do these two square away? These could be great employees.

[00:42:39] Alexa: Yeah. Yeah. Exactly. To your- to your storytelling aspect like how-how is it actually worth and do the cost analysis here along with all the other layers of-of this is like, is it actually worth attracting more of those people and paying for mat leave basically and benefits for these people? Because we know ultimately they're gonna wind up being better employees, more loyal, more engaged, more creative, you know, all the things that maybe that particular demographic is for you or is for your product or is for that role.

And that's one of the things I think that-that people analytics, I think is probably on the verge of a sort of highlighting for a lot of organizations is like it's not the same as saying, oh, this person's a 30-year-old female looking for a job with some recent demographic and lifestyle changes that sort of trigger some things. It's like, is this person, in the long run, the kind of person you wanna be attracting and-and a larger fit for your organization.

And sometimes there are different scenarios you have to take into account. So like the answer might be like actually it's a winning strategy to do nothing but recruit women in their early 30s having kids because what you're gonna get on the back end is a 15-year employee who's 30% less expensive than everybody else who quits in the meantime, right? Like you-you just-- you could actually figure that out with this kind of data and this kind of storytelling which would be fascinating and-and is so much more multi-layered than just like, oh, we don't hire people that we think are about to be pregnant.

It's like, well, that could actually be a losing strategy. Uh, you know, someone could use that to your advantage, like to their advantage, for sure. So I think that's the kind of stories that this stuff will-will start to tell over time, that'll get really, uh, really popular in this space. So, all right. I hate to do it kids but I gotta move us to our People Problem for the evening. Tyson, what do we got today?


[00:44:25] Tyson: So I'm totally taking advantage of the situation. This is my People Problem. Okay.

[00:44:28] Alexa: Aw.

[00:44:28] Tyson: So, in-in order [laughs] in order--

[00:44:32] Alexa: That's not fair. You jump the line. That's not equitable.

[00:44:35] Tyson: No. I'm sorry. But I just have to take advantage of this situation having Jessica here with us.

[00:44:39] Alexa: Listeners beware, we will put ourselves first.


[00:44:43] Tyson: [laughs] Okay. So let's-- I wanna talk about engagement surveys.

[00:44:46] Alexa: Yeah.

[00:44:46] Tyson: So, we do these engagement surveys every year. They're expensive, they're time-consuming and we get all sorts of amazing data points, right?

[00:44:54] Alexa: Like what?

[00:44:56] Tyson: So you have to-- Well, like all-- like, you know, um, how people are feeling from like, we have wellness perspectives, they're, you know, whether or not they're considering to stay. So let's just say that the data is accurate when people are filling those surveys out but we've got a lot of data. Okay. So you've got the one manager who looks at it and is like, okay, whatever and it falls flat.

That's one problem. But where my question comes from is the manager that takes the data and wants to sit and splice and dice and cut and paste and look at all the different things. And then he gets to a point where then he's like, but I need more information and more data. And it's almost like this loop where it's like they can't get enough information to do anything. How would you move that manager forward?

[00:45:46] Jessica: Yeah. No, it's a great-- [laughs] it's a great question. I often see common challenges are kind of on two ends of the stream. It's either, you know, hesitance resistance, so not ready yet, or I want everything. [laughs]

[00:46:01] Tyson: Yes. More.

[00:46:02] Jessica: Yes. In the future, future setting, if there's investment, if there's a budget for it, amazing to be able to get software where this-this leader can get direct access. They can slice and dice as-as much as they want, right? But I-I think with your example, you're thinking they're coming to HR and they're saying, can you update this report? Can you do this for me?

[00:46:24] Tyson: Or even they-they, even if they can do it themselves but when they get to an endpoint in their slicing-slicing and dicing, they're like, I still can't do anything 'cause I don't have enough information. It's like, ah, we just need to do something.

[00:46:39] Alexa: Well, doesn't it-- but doesn't it come back to, you know, as Jessica, so nicely primed this conversation up, you know, about 45 minutes ago, doesn't it all come back to like what-what is the goal? Like what are you solving for?

[00:46:49] Tyson: To be more engaged though. Like, remember, I know put-put your personal feelings of that engagement aside.

[00:46:54] Alexa: No, no, yeah. But what the-- This is why I hate engagement-- the word engagement. Uh, even though I play in-- I-I-I, you know, I play in that space. Like it's just what the fuck does that mean? Like engaged in what, engaged with what, engaged in doing what? Like you can't be engaged all the time with everything.

[00:47:09] Tyson: So I think- I think, like, usually-usually, it's-it's defined as like, are they gonna stay? Are they gonna go above and beyond stay, say, what do they say about the organization? Stay, say, and strive is the definition-

[00:47:20] Alexa: All right. Okay. all right.

[00:47:20] Tyson: -of engagement. Okay. So let's just go with that. So going back to Jessica, like-- [crosstalk].

[00:47:24] Alexa: Trust was another one, like did they trust the organization, was from-from a prior guest, I think?

[00:47:29] Tyson: Yeah. So yeah. Like how do you- how do you move that manager almost beyond the data like-

[00:47:34] Alexa: Yes, exactly. The story.

[00:47:34] Tyson: -or-or like that sort of thing? Yeah. Movement.

[00:47:39] Alexa: Out of the analysis paralysis.

[00:47:41] Tyson: Yes, yes, yes, yes.

[00:47:44] Alexa: Yeah. I'm gonna tell-- I'm gonna venture to guess that person is a high S or a high C on their disc, uh, assessment. That they are-- and always need a lot of data to answer-- to do anything. Factfinder is a personality type anyway I digress. [crosstalk]

[00:47:58] Jessica: And you will- and you will see this more in certain types of companies too. With much more-- who are more data-driven companies. And so the-the leaders will wanna really get into the data. Um, especially if the main source of data about people is the engagement service. So this is the only source of data they have as they really wanna dive into that. And I think it really is having that dialogue with them, getting to their unique pain point, their problem they're trying to solve. So yes, this is the overall employee engagement survey. This is what we're trying to find out more information about. But this specific leader is probably looking to solve a problem. This is- this is probably something underneath there.

[00:48:40] Alexa: I'm gonna venture to guess they're fearful of fucking up whatever the thing is they're trying to decide about. I'm again gonna venture to guess there's some sort of other roadblocks there. That's like, okay, what, wait, there's not enough data in the world to make you happy right now. What are you scared of fucking up? What are you looking for? [laughs] Why are you looking so hard? 'Cause I think if you say it like that and then you say, okay, well we do have data that indicates, you know, if the options are A, B, and C or you're trying to solve for X, Y, or Z. Like that kind of clearly lead us towards one answer. Why are you still hesitant? What are you worried about?

[00:49:10] Jessica: And if we think about what the engagement survey is ultimately within that their own team, this is the feedback from the employees. And so yes, it's a-- it's more, uh, it's confidential. And so it's a different source than it's, you know, quantifiable which is amazing but it shouldn't be the only source of data for that leader about-- from their employees. Right. They should be having those ongoing conversations, one-on-one, as a team.

They should really have a better understanding about their team outside of that survey. And so kinda having that dialogue, coaching them through that. It might not be in the survey itself but then there could be other ways if they do need more information, maybe it's assessments, let's say, they're uncertain, you know, if they have-- about the team's capabilities or it's process mapping if they're unsure about the teams having the-the tools to be able to do the work that they need. Whatever it is, there can be som-- another-another way that you get the data that they need, based on their unique problem trying to solve.

[00:50:10] Tyson: Yes, almost it out of the data.

[00:50:13] Alexa: Maybe they just don't like the answer.

[00:50:13] Jessica: No, but I-I-I--

[00:50:13] Alexa: Or they just don't like the answer and so they're like, "Oh, I need more data." [laughs] We're going to do more surveys.

[00:50:21] Tyson: I find these people, though- I find these people are often like the overachiever managers, though, that were probably really good at their, um, their skill, and like, maybe not as good at like the management side of things because what I've done in the past with, like, situations like this is I take them out of the data, right? So kinda like, exactly, you said, Jessica.

And I just say, "What do you think is wrong with the team?" Like, you-you don't have your head in the sand completely? Like I know you're a good leader. So what do you think is-- like, you don't need all of this data necessarily to like tell you so I like your sort of your-your advice there, Jessica, in terms of taking them out of maybe that specific survey and that data, and having them look bigger picture, um, elsewhere.

[00:50:59] Alexa: Also, like, because of the fact that like this is just one survey from your employees, like it's-it's biased in that way, and that it is one survey from your employees. So like you don't have more datasets. I almost feel like is this true that maybe good people analytics and good data-- Like the sign of a good people analytics function and-and data function in your organization is that no-nothing like surprises you guys?

You would be like, yeah, like, the data generally just tells us kind of what we already know, uh, and keeps us kind of honest but is there-- it's kind of like, you know, people say, like, you know, people who quit, and certain things that happen culturally like they shouldn't be a surprise if you're in a- in a healthy organization. Like you should kind of be able to see it coming if you've got like the right checks and balances, the right like sort of pulse on things, um, that maybe data is kind of an additional pulse.

It's like you, you know, if-if something is like really hitting you from left field, or is like, you know, for this particular manager, maybe like, "Oh, I didn't see that one coming." That it might be, you know, sort of a sign that something else was going on?

[00:51:52] Jessica: There's definitely a part of that but there's-there's absolutely surprises, there's absolutely. So the data either confirms what you already are aware of but, um, as an individual, we just don't have access to the same amount of information at all. It might be more anecdotal or just based on our own experience, our own perspective and so being able to have more data and analyze it in a different way, there's absolutely surprises.

So just yeah, still being open to that. Having that mindset to still be open to it is really important for people analytics.

[00:52:26] Alexa: Don't let personal bias-bias the rest of it. Um, cool. All right, Jessica.

[00:52:31] Jessica: And not look-- not looking to find the answer you think?

[00:52:35] Tyson: Oh, yeah, that's- that's really--

[00:52:35] Alexa: Oh, yeah. I was a statistic, I studied statistics in college so you can make it a number say anything you want.

[00:52:42] Jessica: Yeah, yeah.

[00:52:42] Alexa: And literally anything. It's amazing. Anyway, I love it. I'm actually kind of a stats nerd but awesome. Jessica, if people like what you have to say, where can they find you and reach out?

[00:52:51] Jessica: Yeah, I'm on-- mostly on LinkedIn. Um-

[00:52:54] Alexa: All right, Jessica Collins.

[00:52:54] Jessica: -I'm on Twitter, yes. Jessica Collins.

[00:52:58] Alexa: What's your Twitter handle?

[00:53:01] Jessica: Good question.Uh, I think that's @JesCollinsHR.

[00:53:06] Alexa: Nice. Jes Collins HR. All right. Well, we'll hold you to that. I believe you. You seem confident.


[00:53:11] Alexa: Exactly. Let's go with LinkedIn. Everybody else just says LinkedIn so-

[00:53:15] Jessica: Find me on LinkedIn.

[00:53:16] Alexa: On it, I love it. Thanks so much for being here, Jessica, it's a true-- This was a true blast and yeah, appreciate the insights.

[00:53:22] Tyson: Thank you. Hell, I love that insights. Get it?

[00:53:24] Alexa: Yeah, thank you for getting that joke. This episode was executive produced by me Alexa Baggio with audio production by Ellie Brigitta of Fair Harmonies. Our intro music was also done by the wonderful Ellie Brigitta of Fair Harmonies. You can find more information about us and future episodes and peopleproblemspod.com or follow us at--

[00:53:40] [END OF AUDIO]

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