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Transcript

Is HR now about Math? · Humanity Working

• 0:05
  SPEAKER_01: You know, I if you look at the historical aspect of HR, it's you know always been very uh people oriented, which is fantastic.
• 0:13
  SPEAKER_01: Um and I think now with some of the introductions of reporting and and modeling, it takes that to another level.
• 0:20
  SPEAKER_01: It takes if if a good HR executive is is really engaged, then you know, they're using that insight to further their personal experiences with the employees.
• 0:31
  SPEAKER_01: Um and I think that's where it's exciting.
• 0:34
  SPEAKER_02: My guest today is Keith Good, who's the vice president of services at Zeroed In Technologies.
• 0:40
  SPEAKER_02: Welcome to Humanity Working, Keith.
• 0:43
  SPEAKER_02: Oh, it's great to be here.
• 0:44
  SPEAKER_02: Thanks for having me.
• 0:45
  SPEAKER_02: Oh, you're welcome.
• 0:46
  SPEAKER_02: Now, Zeroed In is an HR platform that provides organizations with data insights into their workforce.
• 0:52
  SPEAKER_02: And they use techniques like data mining, collective listening, machine learning, and predictive modeling to gain an understanding of what is happening inside organizations.
• 1:00
  SPEAKER_02: But don't worry if you don't know what any of those are today.
• 1:03
  SPEAKER_02: We're not going to be focusing on the technology side of this.
• 1:06
  SPEAKER_02: We're going to be more focused on what we're seeing in the data and how technology can help us going forward.
• 1:12
  SPEAKER_02: So uh Keith, let's let's start with something pretty simple.
• 1:16
  SPEAKER_02: So your your world is the the broad definition or the broad area in which you work is people analytics.
• 1:23
  SPEAKER_02: So for people that are not in this field, I'd love for you to explain what people analytics is and why it can be useful.
• 1:33
  SPEAKER_01: Yeah, so I I'd love to get into that, let me tell you a little bit about my history.
• 1:38
  SPEAKER_01: So uh I've got 20 years of experience uh in analytics and reporting, mostly specializing in the HR space.
• 1:46
  SPEAKER_01: Um and my passion really lies in generating value from data.
• 1:50
  SPEAKER_01: So what is uh HR analytics?
• 1:53
  SPEAKER_01: Well, if you think about it, HR organizations have a tremendous amount of historical data that is so siloed.
• 2:01
  SPEAKER_01: It's you know, payroll systems, it's learning management systems, it's performance systems.
• 2:06
  SPEAKER_01: And how many times have we seen an organization that they often hop from one platform to the next?
• 2:12
  SPEAKER_01: So not only you have this historical data, but it's also formatted quite different from those various systems that organizations have utilized over the cat past several years.
• 2:22
  SPEAKER_01: But HR analytics, and especially what we try to do is mine that data, bring it together, correlate it together, and then look at some of the advancements in predictive modeling and and help organizations take that data and can glean value from it by predicting what might happen in the future regarding their workforce.
• 2:42
  SPEAKER_01: It could be anything, it could be something like who might be at at risk of leaving the organization.
• 2:47
  SPEAKER_01: Uh, you know, especially if you have uh skilled resources or um, you know, people that you've spent a lot and invested a lot, you don't want them leaving the organization.
• 2:57
  SPEAKER_01: It would cost you a lot.
• 2:58
  SPEAKER_01: And let's face it, they might go to a competitor.
• 3:01
  SPEAKER_01: So there's that opportunity cost as well.
• 3:03
  SPEAKER_01: So if we can utilize that historical data, if we can utilize some of the relationships in that that the organization already has and learn from that data and to who might be at risk, there's a lot that could be done to try to scave off that that loss.
• 3:20
  SPEAKER_01: That's just one example.
• 3:21
  SPEAKER_01: Um other examples might be you know finding a person that's ready for promotion or you know, looking at uh your candidates that you're looking to hire and trying to utilize some historical data to find out who might be those those stars waiting to come into your organization.
• 3:39
  SPEAKER_01: So a variety of different things that that could be used.
• 3:42
  SPEAKER_01: But overall, it's it's really giving the executing HR team a method to kind of peer into the possible future and and react to it.
• 3:51
  unknown: Yeah.
• 3:51
  SPEAKER_02: So Keith, you touch on a couple of really interesting things that I just want to um like zone in on just for a second.
• 3:57
  SPEAKER_02: So the first thing is about this this concept of siloed data.
• 4:00
  SPEAKER_02: So for people that are in technology fields, they'll be they'll be very, very familiar with this, with this concept.
• 4:07
  SPEAKER_02: Um, and it really comes through, I suppose, really sets of historical decisions that happen inside companies.
• 4:13
  SPEAKER_02: So you're in a company, you're some vendor comes and says, Hey, we've got this great software product that will solve problem X or Y or Z.
• 4:23
  SPEAKER_02: And so you you say, Oh, I need that product, it will solve this problem that I have.
• 4:28
  SPEAKER_02: And of course, that product has a whole bunch of data that is associated with it.
• 4:33
  SPEAKER_02: And then a year later, some other vendor comes in and solve is solving a new set of problems, and you're going and you're grabbing that information.
• 4:41
  SPEAKER_02: And at no point in most cases are we thinking about oh, what value could we get by combining those two pieces of data?
• 4:48
  SPEAKER_02: And what more could we learn and what and what more could we understand?
• 4:52
  SPEAKER_02: And one of my observations is that certainly in the HR field, that whole concept, even of like thinking about the underlying value associated with the with all of these different pieces of data is something that's relatively new for a lot of HR professionals, and they've never really thought about that.
• 5:10
  SPEAKER_02: So as you're going into as you're going into organizations, how do you, how do you make the light bulb go off and go, okay, here are the four or five pieces of data, here is the magical insight that you can generate if you can just see across those different data sets.
• 5:25
  SPEAKER_02: How do you how do you get people to that point where the light bulb lights up and they go, oh, that's of high value?
• 5:32
  SPEAKER_01: Well, it's uh interesting concept.
• 5:34
  SPEAKER_01: Sometimes it's it's harder than others to try to do it.
• 5:37
  SPEAKER_01: But I would say that most executives and most senior leaders understand the value of making better decisions.
• 5:43
  SPEAKER_01: And uh so that part's kind of easy.
• 5:46
  SPEAKER_01: I think the challenging part is helping them understand, you know, the data.
• 5:51
  SPEAKER_01: They they look at that that historical 10 years of of people data and like, oh my gosh, I don't even know where it exists, the format of it, you know, all these different platforms.
• 6:00
  SPEAKER_01: But in reality, the other concept I like to bring out is history has its uh way of repeating itself.
• 6:06
  SPEAKER_01: So, you know, understanding that, you know, people can then say, yeah, you know what, there could be some real value to all that.
• 6:12
  SPEAKER_01: And um, so those are two mechanisms.
• 6:14
  SPEAKER_01: I think you know, people uh generally do understand the value of some of these interesting predictive models that are out there and what they can bring to light and and value they offer.
• 6:25
  SPEAKER_01: Um so we try to hone on that, but then you know, try to say, you know, that that heavy lifting of of cleaning that data and bringing it together, you know, let let vendors, let others uh that have that expertise, that history in in doing that, let them help you out with that because it is a challenge and it can be you know overwhelming uh to a lot of organizations.
• 6:47
  SPEAKER_02: It's fairly obvious to see that in a large company um that everything you're talking about could be of could be of high value.
• 6:54
  SPEAKER_02: So a company with like multiple thousands of people and things like that.
• 6:57
  SPEAKER_02: But of course, almost every large company starts as a small company, and at the point at which it's a small company, the HR leadership is gonna be like, and and the managers, frankly, in that organization and the leaders inside that organization are gonna say, I I I know my people, I don't need this, I know my people, right?
• 7:15
  SPEAKER_02: So at what point, and I'm not expecting you to say, you know, 478 people are not like that, but at but at what point does a company get to a point where it's needing the types of services that you guys are offering?
• 7:32
  SPEAKER_02: And what should what should leaders be doing if they're growing if they're in a company that is growing right now to prepare themselves for that eventuality?
• 7:42
  SPEAKER_01: I think in general, organizations that have a thousand or more employees could definitely benefit because they have that historical data, they've had that growth, they have, you know, there's not one person that knows all a thousand people in the organization.
• 7:54
  SPEAKER_01: So I think that's kind of a fair uh starting point.
• 7:57
  SPEAKER_01: However, for the smaller organizations, I think there's still a lot of opportunities that that can be had.
• 8:02
  SPEAKER_01: And that I think uh an example might might prove that to be you know evident in terms of we've we've worked with an organization, it's a fairly small organization, and they had people that were certified in certain tasks.
• 8:16
  SPEAKER_01: And their work came in uh from all different areas, and and the the certifications that the people held were sometimes geographically, sometimes nationally.
• 8:26
  SPEAKER_01: Uh mostly it was in the accounting and and auditing aspect, right?
• 8:30
  SPEAKER_01: So you have these highly skilled people that had different certifications, and the work that came in, um some was hard, some was less hard.
• 8:39
  SPEAKER_01: Uh, some had to be go and follow people that had certain certifications to be able to work on.
• 8:44
  SPEAKER_01: So there are aspects of generating models that could be used to help score the work coming in, and then um identify and work to uh score or identify the resources, uh, what their current bandwidth is.
• 9:00
  SPEAKER_01: Um, and then there's a theory called a backpack theory, which is how much stuff can you put in a backpack, right?
• 9:05
  SPEAKER_01: And there's an optimal way of doing it.
• 9:06
  SPEAKER_01: Well, we took the same kind of algorithms and said, okay, well, let's score the work coming in and let's figure out how to optimize the delivery of the different tasks across this skilled workforce.
• 9:17
  SPEAKER_01: And you don't need a large organization to do that.
• 9:20
  SPEAKER_02: Um So you joined uh ZeroDin 10 a little over 10 years ago, right?
• 9:29
  SPEAKER_01: Yeah, absolutely.
• 9:30
  SPEAKER_02: Yeah.
• 9:30
  SPEAKER_02: So this is a field that that I know has changed a lot over the course of the last 10 years.
• 9:37
  SPEAKER_02: So I'd love to I'd love to do a little bit of a compare and contrast.
• 9:42
  SPEAKER_02: What's different today than it was a decade ago in this field?
• 9:46
  SPEAKER_01: I think, well, the you know, that and you would know this very well, that's really different and exciting today are are the models that are available to us there.
• 9:55
  SPEAKER_01: You know, you've got the um large language models, you've got the you know predictive models, artificial intelligence that are coming to light.
• 10:04
  SPEAKER_01: And, you know, in a lot of cases, it's open source, so we can start taking advantage of some of those really neat things.
• 10:09
  SPEAKER_01: However, there are some things that still are kind of baselines in that maturity model before we can start taking advantage of some of these really neat things that are out there, there's still things that you know need to happen, that that basic block and tackling that needs to be there.
• 10:24
  SPEAKER_01: One, it's unfortunately some data cleaning, it's it's aggregating that that data together.
• 10:30
  SPEAKER_01: Two, it's having that platform that people can go to for a single source of truth, whether it's simple things like headcounts or tenure or turnover, you know, that that still needs to exist.
• 10:43
  SPEAKER_01: And I would say that to take advantage of some of the newer capability and some of these new modeling, you know, you want to have those those basics, that foundation in place, right?
• 10:53
  SPEAKER_01: You don't, you know, when you build a house, the key element is is the foundation.
• 10:57
  SPEAKER_01: And then you build the stories on top of that.
• 10:59
  SPEAKER_01: And you think of um machine learning and AI as just another uh story in your building.
• 11:06
  SPEAKER_01: And um you have to have that solid foundation to continue that that building up.
• 11:11
  SPEAKER_01: And that foundation comes with good, good, clean data, uh uh mechanisms to report out, whether it's your current reporting and the reporting that's been around, that that block and tackling reporting, and then it's growing on top of that that that platform to be able to identify you know some modeling, uh some predictive models, as well as I I think where things are going to get exciting in the future is you know uh AI has the ability to do really two things.
• 11:40
  SPEAKER_01: One, summarize, um, you know, take a whole bunch of data and then you know, hey, summarize, you know, my last uh four quarters of turnover compared to uh new hires and and our employee headcount.
• 11:55
  SPEAKER_01: Well, it can look through that tremendous amount of data and then respond back to it with a pretty decent summary um written and explained fairly well.
• 12:03
  SPEAKER_01: That's one aspect.
• 12:04
  SPEAKER_01: And then the other aspect of is drilling in deeper and and having that AI say, okay, well, you know, I want to learn what's going on in this organization for this job type.
• 12:15
  SPEAKER_01: And they can um AI can take that that data and drill in and then provide another explanation for what someone might be seeing.
• 12:23
  SPEAKER_01: So it's that summary and explanation that that becomes, I think, the the future of what we're seeing.
• 12:29
  SPEAKER_02: So Keith, I want to I want to challenge you a bit on this on this whole aspect of data-driven decisions around people.
• 12:39
  SPEAKER_02: And I'll bring a couple of examples that we've seen in our field when we've done interviews with people with people about how data is used in inside organizations.
• 12:50
  SPEAKER_02: So the first, I think I would refer to it almost as like uh almost like the Big Brother effect, as in Orwell's Big Brother, not the TV show.
• 12:59
  SPEAKER_02: And um and what we've uh encountered is um levels of concern about how data-driven decision making is potentially replacing you know human human judgment um inside inside organizations.
• 13:19
  SPEAKER_02: So a good example that that we've seen fairly regularly is people is people that spend periods of time during the workday on the grid and spend periods of time during the workday in quotes off the uh off the grid.
• 13:36
  SPEAKER_02: Doesn't mean they're not working, doesn't mean they're not they're not um creating value.
• 13:42
  SPEAKER_02: But for example, let's say it's that it's the human that knows they need to spend two or three uh hours in a day or something along the lines of shutting everything down, sitting in front of a whiteboard or sitting on a you know in front of a traditional offline mechanism to think, right?
• 14:01
  SPEAKER_02: Um in order to move themselves materially forward.
• 14:07
  SPEAKER_02: What we're starting to see is is increasingly people are modifying the way in which they're working to be seen to be digitally present and to be seen to be digitally active.
• 14:23
  SPEAKER_02: And it's they're they're potentially, they've admitted to us even in interviews, right?
• 14:28
  SPEAKER_02: I'm doing worse work, but I'm doing it in order to stay, uh in order to appear as though I'm doing work.
• 14:35
  SPEAKER_02: And the ultimate example of that is the mouse jigglers and and all of that and all that kind of stuff, right?
• 14:40
  SPEAKER_02: That that people spend a lot of time trying to figure out how to make how to make it look like they're busy, right?
• 14:46
  SPEAKER_02: So that's the first piece of it.
• 14:47
  SPEAKER_02: And then related to that, um what we see is concern um more broadly from employees and from managers and so on, as to what do I what do I need to do as an employee to look good in the numbers versus just focusing on getting uh on getting work done and trusting, as it were, that the analytics will show that I'm that I'm doing good work.
• 15:19
  SPEAKER_02: So I I'll pull those two things together and say it's the what we see from employers, uh from employees and from managers in particular is a concern that we're using this data in sort of a fairly one-dimensional way.
• 15:37
  SPEAKER_02: We're missing strong talent and we're changing behaviors of people that have a concern about this creepy underworld of data analyzing what they're doing.
• 15:48
  SPEAKER_02: Um, I don't know exactly how to turn that into a question, but I'm just curious as to what your thoughts are about all about all of that.
• 15:55
  SPEAKER_01: Yeah, I I have a lot of thoughts on this.
• 15:57
  SPEAKER_01: I wish we could talk about it for the rest of the week.
• 16:00
  SPEAKER_01: But um absolutely, I think there's there's two two things to to unpack there.
• 16:04
  SPEAKER_01: I think, you know, first is is with those um individuals out there that are kind of just filling a seat, you know, working and and doing kind of what they they do.
• 16:16
  SPEAKER_01: Um I would say that from a um HR analytics perspective, there's also a lot of uh effort out there to reduce bias in any of its uh predictive modeling.
• 16:27
  SPEAKER_01: And in fact, you've got states like New York State that have actually um written um legal uh laws now that it says that if you're doing a predictive modeling in the HR perspective, it has to be audited for bias.
• 16:44
  SPEAKER_01: Um so you know, there is I I would argue you you don't need a law to do that.
• 16:49
  SPEAKER_01: I would say that most people that are, you know, anyone that's worth assault in analytics would be doing that anyway.
• 16:56
  SPEAKER_01: Um, but it's kind of interesting that there is that that that effort in there and and you know driving that.
• 17:03
  SPEAKER_01: Um so bias is important, and I think that you know, we've always had that type of of work uh going on where you know um people that you know could they be more productive?
• 17:17
  SPEAKER_01: And I think actually uh analytics can help determine that and help can can find where there might be ways to help people be uh more engaged and you know where you can focus your resources to to help that productivity.
• 17:32
  SPEAKER_01: Um so that was one aspect.
• 17:34
  SPEAKER_01: The second aspect is more futuristic, I would I would say, and and I think where you know we need to go with our our workforce is creativity.
• 17:42
  SPEAKER_01: Creative, you call it think or thought.
• 17:44
  SPEAKER_01: I I like that approach where people can go and think, but what's gonna set us apart from other workforces and from the machine learning as well and the AI is our ability to be creative, our ability to either use numbers or use facts in the past or and and do something creative.
• 18:02
  SPEAKER_01: Um, and I think it's the people that can tap into their creativeness with some of that historical data and with some of um an understanding of of value, those are the ones that are going to be successful.
• 18:16
  SPEAKER_01: So that gets back to the the humanity of of work.
• 18:19
  SPEAKER_01: It's it's showing our creativity side in order to produce value.
• 18:26
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• 19:57
  SPEAKER_02: There's data that can show a good element of of what of how humans are proceeding and progressing in the workplace.
• 20:08
  SPEAKER_02: The data is incomplete, right?
• 20:11
  SPEAKER_02: It always will be.
• 20:12
  SPEAKER_02: So for example, if I am inside a uh an organization and I'm doing a learning and development program within the organization that is part of an approved learning and, you know, this is part of an LMS that the organization has, that will get picked up.
• 20:30
  SPEAKER_02: But if I am uh attending a uh physically attending a community college after hours and it's not recorded anywhere in my work in my work environment, that will not get picked up, right?
• 20:42
  SPEAKER_02: So there will be a picture, a picture is generated, and that picture is partially um is a partial picture of the human being inside the organization.
• 20:53
  SPEAKER_02: I think it's incumbent upon organizations, if they're going to build high trust organizations, um, to share with employees um how, as best they can and in very meaningful ways, how data is being gathered, how data is being analyzed, and what, if you like, the rules of the game are, right?
• 21:17
  SPEAKER_02: And because if you don't share that, then employees are gonna guess and they're gonna start doing things based on the degree of trust they have.
• 21:25
  SPEAKER_02: And the degree of trust they have in you as an organization won't necessarily be very high if you're not sharing stuff.
• 21:30
  SPEAKER_02: So it's always wise to be transparent in the organization as to what you're doing with data, why you're doing it, what the purpose of it is, why you believe it to be helpful to the um to the organization and to the people inside the organization.
• 21:47
  SPEAKER_02: And then also to recognize when you're measuring people that that picture that you do have is incomplete.
• 21:53
  SPEAKER_02: And there are there are other elements that can be brought into the picture and give employees a chance to share that, right?
• 21:58
  SPEAKER_02: So for example.
• 22:00
  SPEAKER_02: If an employee is doing something at a community college after hours that wouldn't be picked up on a s uh on a system inside the organization, have them be able to share that information and have that to be considered as as part of the overall evaluation as to who they are.
• 22:13
  SPEAKER_02: I think this there's we we we have a tendency, I think, inside organizations to hold all this information really close, really close to our chest versus actually share how it's used and use it as an opportunity to increase trust inside organizations.
• 22:29
  SPEAKER_02: What are your thoughts on that?
• 22:31
  SPEAKER_01: Well, I I'll even challenge you now on that and and say, you know, especially some of the the younger uh workforce, I would say that that should be an assumption.
• 22:41
  SPEAKER_01: That should be an assumption, and I would argue that many of them already know that.
• 22:44
  SPEAKER_01: So, you know, you you've got people in high school today that realize whatever they post online is going to be available when they go for their first job interview.
• 22:53
  SPEAKER_01: So, you know, I think the assumption should be that as an organization, you're looking at maximizing your assets, and data is a valuable asset, and it could be private data or public data.
• 23:06
  SPEAKER_01: Um, so I I would argue that organizations are somewhat negligent if they don't tap into that.
• 23:13
  SPEAKER_01: Yeah.
• 23:13
  SPEAKER_01: And yes, I think people, it's always good that they communicate that, absolutely.
• 23:18
  SPEAKER_01: But I think people should always have that assumption that, hey, I work for an organization and they're doing everything possible to gain as much value for the organization, for their clients, and for the resource and for their employee resources.
• 23:30
  SPEAKER_02: Yeah, I think we would, I think we'd actually agree on that.
• 23:33
  SPEAKER_02: I think the I think the main issue here, though, is that as an employer, um, it's beneficial for you if that picture is as complete as possible.
• 23:42
  SPEAKER_02: And as an employee, that actually is true, is generally true too, right?
• 23:47
  SPEAKER_02: So find the more transparency there is as to as to how this is gathered and how this is measured and and uh and how it's used and so on, the easier it is for those things uh for those things to uh to happen, right?
• 24:00
  SPEAKER_02: And so for example, I'd uh the employees that are worried, for example, about this concept of like, I don't know, going offline during a day, and will that uh and will that impact how they're perceived inside an organization and so on, they should know.
• 24:13
  SPEAKER_02: Does that or does that not?
• 24:14
  SPEAKER_02: Right.
• 24:14
  SPEAKER_02: In some cases, in some cases it will, in some cases, in some cases it won't.
• 24:18
  SPEAKER_02: So I think there's a lot of work to be done inside organizations to get the technology side of this is going to be solved faster than if you like the human uh the human aspect of this and how we respond and how we uh and how we we delve into all the all of the different aspects of it.
• 24:36
  SPEAKER_02: It's really, really fascinating field.
• 24:38
  SPEAKER_01: It really is.
• 24:39
  SPEAKER_02: Yeah.
• 24:40
  SPEAKER_02: So um, as you say, changed uh it's changed a lot.
• 24:44
  SPEAKER_02: I'm curious as to what you think um this all means in terms of how HR itself is practiced, right?
• 24:52
  SPEAKER_02: Historically, HR has been, as you pointed out, like lots of data silos.
• 24:57
  SPEAKER_02: And because of lots of data silos, incomplete pictures of what's happening to employees inside organizations.
• 25:05
  SPEAKER_02: You're helping some of that go away.
• 25:07
  SPEAKER_02: What do you think is the ultimate way in which the practice of HR will shift as we develop bigger pictures of the of the humans inside our organizations?
• 25:18
  SPEAKER_01: Oh, I I think that's a a great question.
• 25:21
  SPEAKER_01: And I think there's a lot of opportunity there.
• 25:23
  SPEAKER_01: And that's what excites me about working in the field that I'm in.
• 25:28
  SPEAKER_01: You know, I if you look at the historical aspect of HR, it's you know always been very uh people oriented, which is fantastic.
• 25:36
  SPEAKER_01: Um and I think now with some of the introductions of reporting and and modeling, it takes that to another level.
• 25:44
  SPEAKER_01: It takes if if a good HR executive is is really engaged, then you know they're using that insight to further their personal experiences with the employees.
• 25:55
  SPEAKER_01: Um and I think that's where it's exciting.
• 25:57
  SPEAKER_01: It doesn't mean that HR has to become mathematicians and you know, just looking at the data.
• 26:03
  SPEAKER_01: No, I don't I don't think that at all.
• 26:05
  SPEAKER_01: But I do think there's value that's gonna add to what they can do and um help their um interpersonal and and their communications with their their employees.
• 26:17
  SPEAKER_01: So I think that's where the excitement is.
• 26:19
  SPEAKER_01: Also, uh get back to that creative aspect is you know, uh, I think HR has been very um standardized in in the in the past.
• 26:29
  SPEAKER_01: It's been, you know, this is what HR does, you know, it's the same things over, it's a it's the pay, it's the benefits, it's the training, it's right.
• 26:38
  SPEAKER_01: Now this is turning upside down.
• 26:41
  SPEAKER_01: It's it's you know, figuring out where that those those things can really be utilized to be much more efficient as opposed to just a standard thing.
• 26:50
  SPEAKER_01: Um, and uh so I I'm excited about it.
• 26:53
  SPEAKER_01: I'm excited about where where HR is going.
• 26:55
  SPEAKER_01: And you know, not so much to to be at a seat of the table.
• 26:59
  SPEAKER_01: I I think that that statement's all gone.
• 27:01
  SPEAKER_01: I think what will help them is identifying these areas where they can be add more value.
• 27:07
  SPEAKER_01: The value gets them to the table, not just you know, these practices.
• 27:11
  SPEAKER_02: Does that make sense?
• 27:12
  SPEAKER_02: It does.
• 27:13
  SPEAKER_02: And as uh and I think what's interesting here is I think you're actually highlighting both an opportunity and a danger, actually.
• 27:19
  SPEAKER_02: So the on the opportunity side, I think you're exact I think you're exactly right, right?
• 27:23
  SPEAKER_02: We're using this data uh as a tool in order to be able to be more effective and creative and create a larger, you know, a better picture of the humans that are working inside our organization, that can only that can only be of help.
• 27:41
  SPEAKER_02: I think the danger is that um for many people that have worked in this field of HR, there is a there's a gap inside organizations between what many HR leaders want to be and want to represent inside their organizations and what they actually do.
• 27:56
  SPEAKER_02: So particularly inside uh larger organizations, it is, and I know this because I've worked in a couple of them, yeah, it is not uncommon for the employees' attitude towards HR to be they're the people I see on day one, and I don't want to see them ever again until the day I leave because I know the moment that they're in a room with me, it means that I'm being escorted out the door.
• 28:17
  SPEAKER_02: Right.
• 28:17
  SPEAKER_02: And so it's a and so there's a there's there has been historically a lot of distrust between employees and HR based on the idea that HR is is working for the man and they're not working for me, right?
• 28:33
  SPEAKER_02: Right.
• 28:34
  SPEAKER_02: And so I think that this is why that this use of data is such a fascinating thing because used right, it can help mitigate some of those challenges.
• 28:45
  SPEAKER_02: Used wrong, it can only perpetuate the idea that HR is big brother HR and HR is making uh like number-based decisions about me with no and they don't know who I am, they don't know anything, they don't know in quotes know anything about me.
• 29:04
  SPEAKER_02: All they see is a bunch of data points.
• 29:06
  SPEAKER_02: And so I do think this really points to the idea that HR, you don't get to be strategic HR just by saying it, right?
• 29:17
  SPEAKER_02: To your point, right?
• 29:18
  SPEAKER_02: You get to be strategic HR by being it.
• 29:21
  SPEAKER_02: And so therefore, this is a real opportunity for HR to be to be much more strategic and actually start living the people side of Chief People Officer better.
• 29:32
  SPEAKER_02: So I I I I love the way you framed it because I think it it really points to the idea that there's a bit of an inflection point here.
• 29:40
  SPEAKER_01: Yeah, it is, and I it reminds me of a story I heard was working with a company that or an organization, a municipality, and um we helped them uh analyze some some data um and it was quite interesting.
• 29:54
  SPEAKER_01: We were focusing on on the gap of positions um within the year that someone may have left and before someone hired, and taking the look at that budget amount that otherwise would have gone unrecognized, and then utilize that amount to to do end-of-year bonuses at much higher rates.
• 30:14
  SPEAKER_01: So, you know, there is a positive side for the employee to understand, hey, HR is doing this great stuff with this data, um, you know, and it's and it's helping my my bottom line.
• 30:25
  SPEAKER_01: So um, yeah, uh there's two two edges to that sort.
• 30:30
  SPEAKER_02: Last question for you.
• 30:31
  SPEAKER_02: So we've talked a lot about data.
• 30:35
  SPEAKER_02: We've talked a lot about the the value that it can bring, and obviously we've um we've discussed the the flip of that, particularly as in terms of how employees might might regard the use of this and the fears they might have uh uh associated with data being used extensively in their organization.
• 30:53
  SPEAKER_02: Just broadly thinking, as somebody who's in this field, um where do you think is the right balance between um gathering all this data and acting upon this data and then using more traditional, the the traditional, if you like, art of this process, which is you know, how does a manager feel about their employee and how do employees' colleagues, you know, um feel about working with them and sort of gathering all of that intel together.
• 31:26
  SPEAKER_02: Um I'm thinking of this almost along the lines of of sports as an as an analogy, right?
• 31:32
  SPEAKER_02: And these days, anytime you bring up like Moneyball or the Oakland Days, somebody at some point in a discussion will point out, well, you do realize that the Oakland A's haven't won the World Series since all that revolution started, right?
• 31:45
  SPEAKER_02: So uh with the with the point being that there is a balance here between doing what the data tells you and um and using judgment and art and experience and all of that kind of stuff.
• 32:00
  SPEAKER_02: So what do you think that how do you think that applies in this in this field?
• 32:05
  SPEAKER_02: What is the what is the balance between cold hard data and the art of performance management?
• 32:15
  SPEAKER_01: Uh I think it's an easy response.
• 32:18
  SPEAKER_01: I think uh the decisions are always a responsibility of an individual.
• 32:23
  SPEAKER_01: However, it's every decision now has the opportunity of being of receiving help from analytics.
• 32:30
  SPEAKER_01: So, you know, I wouldn't are I wouldn't say that there's this decision or that decision.
• 32:35
  SPEAKER_01: I would say everything is still owned by a by by people, by HR, but there's endless amounts of help that can come into it.
• 32:44
  SPEAKER_01: And we're just at this starting point.
• 32:47
  SPEAKER_01: You know, yeah, we've got new models for flight risk and rising stars and you know things, but we're still early in this.
• 32:54
  SPEAKER_01: And um I I would would would never lose uh give up control of the final decision, though the models are going to get better and stronger, um, but still the the ownership of the decision is is still you know relies in in people.
• 33:10
  SPEAKER_02: Keith, great place which to leave it.
• 33:12
  SPEAKER_02: Thank you so much for your time today.
• 33:13
  SPEAKER_02: I appreciate it.
• 33:14
  SPEAKER_01: Yeah, great.
• 33:15
  SPEAKER_01: Thank you for having me.
• 33:18
  SPEAKER_00: Humanity Working is produced and edited for Billion Minds by Matt Neal.
• 33:21
  SPEAKER_00: Our interviewer is Paul Slater.
• 33:23
  SPEAKER_00: Music is by Dolo Records.
• 33:25
  SPEAKER_00: If you like this podcast, please subscribe, like, and recommend us to others.
• 33:29
  SPEAKER_00: It helps us reach more people.
• 33:31
  SPEAKER_00: If you want to learn more about how Billion Minds gets companies ready for the future of work by building adaptable, resilient employees, look for them on LinkedIn or visit their website at billionminds.com.