Ask The Expert In People Analytics with Dan George

[00:00:00] David Rice: Today, and for taking part for those of you who don't know me, uh, my name is David Rice. I'm the senior editor for people managing people, and I will be your host for the day. So today's session is going to focus on people analytics, and we'll be speaking with a thought leader. One of our editorial advisory board members and award winning HR expert.

Dan George, uh, Dan's got quite a few endeavors that he's involved in, and you're going to get to learn a little bit more about that today. We have a few guests today. So that's why I include this. So if that's you welcome. 1st of all, this is just 1 of a series of monthly sessions with HR leaders that we hold for our members and we get to enjoy these as well as a slew of other benefits, including our forum.

Uh, access to all of our templates and other resources. And you can learn more about membership at people, managing people. com forward slash membership. [00:01:00] So Dan, I had mentioned there that you have a, first of all, welcome.

I've mentioned there that you have quite a few endeavors always going here. You're on the go quite a bit. Uh, tell us a little bit about your background and introduce yourself to the audience.

[00:01:16] Dan George: Yeah, absolutely. Well, again, thank you to everyone for joining today. I'm excited to have this session. I'm always open to kind of having some of these dialogues back and forth with, uh, with other professionals like you also excited to be here.

Um, my background is a myriad of different things. Uh, so. For a long time, I was a consultant right out of undergrad out of Chicago. I worked for Accenture. Um, I went back to business school, got my degree, my MBA, uh, worked at Bridgestone Americas, um, which is the, you know, Bridgestone Tires, Firestone Complete Auto Care, uh, for a number of years, uh, started my own Consulting firm after that, which is still going.

Um, [00:02:00] and, uh, on top of that, uh, I'm the chief experience officer for an organization called new orchard. And we do organizational analytics and alignment. Uh, and then, uh, I'm. I'm an adjunct professor at Vanderbilt University Peabody College, and I focus on a course called Strategy and Analytics. Most of the members of my class, between 20 and 25 students, are all leadership in organizational performance, graduate level seeking professionals.

Yeah, that's, those are kind of the main few that I do, uh, and on top of that, I'm an O& A consultant, uh, with a company called Cognitive Talent. So, yeah, got a couple, couple things that I do, but, uh, balancing the schedule is, is one of the hardest things, but it's also one of the, one of the things that brings joy, uh, to my, uh, professional career.

[00:02:56] David Rice: Yeah, I'm, uh, I'm very impressed because I have a hard enough time juggling this [00:03:00] one job. So that's right.

[00:03:01] Dan George: And I try to write, you know, some articles for you guys every now and then. Yeah,

[00:03:05] David Rice: yeah, absolutely. Got some really good stuff coming on that front.

[00:03:09] Dan George: That's right.

[00:03:10] David Rice: Um, all right. Well, with that, let's jump into the questions because we've got less than an hour with Dan and we want to make the most of it.

So, uh, You know, if you're on the call today, you might be completely new to people analytics, or you might have heard the term quite a bit, but not really have had a chance to dig into it. It's not like H. R. has a shortage of other things to do, right? But Dan, I want to start right at the top. I want to give you a chance to give us your definition of people analytics and how you see it being leveraged by H.

R. and people managers to improve the workplace.

[00:03:41] Dan George: Yeah, absolutely. You know, it's, uh, people analytics is 1 of those terms that is, is, is I was involved probably in the last 5 or 6 years before that. At least when I started back in, you know, people analytics or used to be called H. R. analytics, uh, [00:04:00] sometimes workforce analytics.

Uh, so that was back in 2006 and 7. So way back when systems were a lot more difficult to deal with and, uh, to be honest, there weren't a lot of organizations looking at using HR data to provide insights back to the business. Uh, HR, at least when I started my career almost 20 years ago, was mostly focused on just the transactional nature, maintenance and upkeep of HR records.

I first kind of got into it through, uh, again, Accenture, where I worked, and we, you know, we're implementing these massive systems, uh, yet, even though we had all this data, we weren't really using it to make informed business decisions, whether it was You know, simply about head count or turnover or compensation.

And so as that's evolved over the last number of years, and especially I'd say within the last five or six, the, you know, the focus is still the same, right. It's to make informed [00:05:00] business decisions. Uh, but I would say people analytics now encompasses a lot more than just kind of those transactional. Um, you know, transactional system and elements.

It's really about, you know, what is our head count? But, you know, what is employee engagement? What does compensation look like? What are some of the discretionary effort and performance related metrics? How do we look at, uh, figuring out? You know, ways to incorporate, you know, the way we work at home or remote or, uh, or hybrid and and all of these decisions that come with, you know, managing our people to the best we possibly can, uh, to serve the business.

And so people analytics is now kind of a much larger breadth of what it can cover. And so I think that's probably what's the most exciting aspect now, because I feel like people analytics in general is now starting to kind of ratchet up to, uh, a level that's equal, [00:06:00] uh, to, you know, financial performance and analysis, what most people might know is at PNA or, you know, market and advertising analytics.

So I'm excited that the fact that more leaders across the business are starting to use people analytics as a way. You know, to help, again, make them, make their business decisions a lot more informed as it revolves around their workforce.

[00:06:26] David Rice: So, you know, for the people who are new to analytics, you know, I think about like, where do you start on this journey? Because that's one of the things that always comes up when we, when we discuss this or we even think about it, right? It's like, what are the sort of the best ways to establish a foundation and build a strong analytics capability in your opinion?

[00:06:46] Dan George: Yeah, I mean, that's a, that's a lot harder of a question now than it used to be, uh, but I think for the most part, the fundamentals are still there, you know, the basic aspects of, you know, any data and analytics kind of starts with, uh, the [00:07:00] observations and transactions that occur, uh, I don't, oftentimes we don't kind of realize it, but the basis for any data and analytics is, you know, the Recording of observations, right?

An observation can simply be a new hire or a termination, um, a promotion, a change in pay, right? Those are all observations that get recorded, you know, in our HRIS or HR information systems. So, starting with how those observations are recorded, you know, via the processes, the business processes that we all have, that's probably some of the, you know, If I were to walk into an organization now, I would want to see what are our systems map, what's our process map, and how well are we, how are, how well are we, are we recording those observations?

Um, once you have kind of a base set for that, a lot, you know, a lot of the next step then comes with, how do you start reporting on those observations and, and transactions, uh, you [00:08:00] can confidently then provide insights back to the business about. So. What is your current headcount month, you know, month beginning, month end?

Well, what does the turnover employees, what's your, you know, monthly payroll look like? So stuff like that is, is really kind of the fundamental basis for it. Because in the very beginning, a lot of it is about building trust with not only your HR leadership team, but with the other C suite members and executive leadership team that helps kind of make those larger level business decisions.

[00:08:31] David Rice: One of the things I think I've heard come up quite a bit is, you know, there's all these tools now and they're, they're all collecting data, right? And you can try to tie it all into sort of your, your view of your people and what's going on within your organization as part of your, your people analytics strategy.

I'm curious, what are some of the pitfalls though, when integrating data from all these different sources and does it kind of cloud the view of what you actually get?

[00:08:59] Dan George: I love this question [00:09:00] because it's, it's one that comes up a lot, especially because we have so much data being collected and all these different systems.

And again, I think I read somewhere not too long ago. Don't quote me on this, but the average organization, you know, mid range size has something like 17 to 25 different systems related, you know, in and around HR. So the fact that all of these systems are, you know, creating transactional data, that thing gets.

Hopefully fed back to some sort of database. The biggest pitfall in the very beginning is the expectation from either leadership or I. T. or even, you know, H. R. itself is that all of these things have to be perfectly synced. The data has to be excruciatingly clean and that, uh, with the absence of or without, you know, without clean data, you can't possibly begin to start this journey of providing information [00:10:00] back to the back to the leadership team.

And I, I just think that's an incorrect view of it, because for most systems and data and everything else, you're really just trying to become directionally correct as you provide information back. You know, for the most part, businesses are, you know, in research institutions, so they're not looking at doing longitudinal studies that need to be, you know, statistically calibrated and whatnot.

It's really just, you know, what is our general headcount? You know, what is, you know, what are our general views on trends and all this? And so being able to, you know, Balance a very clean data system and architecture versus providing insights is probably the biggest pitfall. And again, in the beginning for me, it's all about getting that kind of directionally correct.

You know, arena that you can provide information back, make decisions without painstakingly worrying about, again, a perfectly curated data [00:11:00] set.

[00:11:01] David Rice: I know I've had the experience. I'm sure a lot of folks on this call have where, you know, clearly the leaders of your organization got the memo that, you know, data is the new oil and all that old saying.

Yeah. But how do you kind of inform or educate execs when, you know, yes, you have the data there, but there may be expecting immediate results or what they think they're going to get out of it isn't necessarily in tune with reality, um, you know, when, when the data is there to support the movement in the direction that you want to go to, but you've got to kind of.

Educate them about timelines or just what it might reveal, I guess. Um, you know, how do you sort of educate around that and get them on board with where you see it going?

[00:11:49] Dan George: Yeah. So. I've always looked at again. I'll probably use this phrase a lot. There's a balance to providing information [00:12:00] back to the organization.

And part of that's going to be built or based on trust. And that trust is usually predicated by a relationship. So, in the beginning, wherever I've worked, uh, I will try to build a network. Of relationships with, you know, people, my level of people, you know, kind of, uh, levels below me, and then obviously, you know, managing up, uh, and by creating those relationships, you build again, competency, uh, and and clarification validation that you can do the job with them, but then also kind of displaying that you're.

You know, at a certain life cycle stage within your, you know, analytical capabilities, and sometimes that takes time. Uh, but when I come, you know, come back to the balance aspect, it's all about being able to deliver certain things as, you know, kind of as fast as you can that are directionally correct [00:13:00] that, again, provides the leaders with, You know, either real time or, you know, short time, you know, decisions or information where, you know, as the further side of it is kind of constantly working on improving not only your capabilities, uh, or skills in yourself, but also within the technology that you're using to provide that information.

So the balance really comes with, you know, having the technical capabilities and systems that work, but also being able to have that relationship so that you can share those insights back with them, but then also have them. I guess, uh, how do I want to phrase this? Creating that relationship of understanding that, you know, delivery is oftentimes not something that you can turn around in an hour, uh, but, you know, level setting on what expectations might look like.

[00:13:49] David Rice: It wasn't that long ago. I can't remember, you know, there was a lot of like. Privacy and security stuff, this would come up all the time whenever you talk about this [00:14:00] stuff. And I'm curious, you know, have you seen that conversation evolve a little bit over the years as people maybe have gotten a little bit more used to this type of technology?

And so how do you ensure that data privacy and ethical considerations are upheld in in the practice?

[00:14:16] Dan George: Yeah, I mean, there's obviously some mechanisms around that. Uh, I think any good organization that's either starting or even one that's had people analytics and is looking to, you know, scale up or or kind of increase their capabilities.

Uh, a good data governance board cross functional really helps because that within that mechanism, you can create charters that have very clear policies, uh, procedures. Uh, technology is listed. So you've got. Uh, a basis and understanding across, you know, a concerned group of individuals that all have, uh, again, a, an incentive to make sure that these [00:15:00] systems and what you're providing is in science, you know, have a, you know, a level of trust, uh, and security, uh, I will say, you know, years ago, uh, The systems and security was a bit more primitive.

And so everything was kind of locked down just simply because it was too difficult to kind of provide that information to everybody. But nowadays, with the sophistication, the complexity of the systems and our ability to manage some of that security. Whether it be by silo, function, department level, uh, it's gotten a lot easier to be able to provide that information with, you know, with several layers of security that, you know, can help, uh, I don't want to say this, uh, help breed a, uh, you know, a culture that is data driven, but without, you know, kind of that risk of oversharing too much information.

Uh, Obviously inherently with anything that you're doing in datalytics that are, you know, surrounding people with again, not only state and federal laws and regulations, but just also [00:16:00] kind of the cultural norms of what information should be shared. You've, you know, you're, you have access to a lot of really sensitive information.

So, as. Individually, as a person, you've got to show and display that, you know, your, your character and what you're trying to accomplish, you know, does have a, you know, a level or a layer of trust simply because you've got a lot more information than most people do, uh, and keeping that safe is really important to, again, not only leadership, but, you know, to your, you know, Your fellow, um, your fellow peer within the organization.

[00:16:37] David Rice: I was, I was chatting with somebody before this about, and I was telling them about the session and question that they had, they weren't able to attend the day, but at their company, they're, they're suffering from a bit of, um, what they called overwatch culture. And so, in other words, staff are wary about things like monitoring, you know, when folks are online or, or secret productivity reviews or meeting attendance tracking, you know, they don't, [00:17:00] now there's all rumors, but it's believed among some of the, the staff that this exists and they'd like to do more on the people analytics front, but there's like this fear that it's not going to land well with staff and their sort of cultural climate.

I'm curious about if you have any tips for messaging this kind of thing to staff that might be sensitive to the concept of of how a company is using data.

[00:17:22] Dan George: Sure, I mean, a couple of years ago, as chief people officer with a smaller organization, and we certainly came across a couple incidences where people were either had 2nd jobs or really weren't doing a ton of work.

And so those concerns of, you know, from the organizational level is that. Yeah, they want to try to limit that as much as possible, which, you know, obviously makes sense. If you're paying someone to do a job, you want to make sure that they're doing that. But, uh, I think the flip side of that tends to be, you know, that overwatch [00:18:00] or overly diligent, you know, big brother, if you will, that can, you know, You know, create a culturally disconnected, uh, you know, team or environment where leadership is, is, is no longer helping to manage, you know, those people, but is instead kind of policing them and that.

Yeah, I think the, you know, the human spirit just doesn't really vibe all that well, uh, with that. And so your, your, your risk comes at that level of discretionary effort and, and push for your employees in your workforce to do their job, do their job well, simply because they don't trust, you know, what could happen if they don't.

So, yeah, I think the other thing I think about is, It was the Industrial Revolution and all that came out of a couple studies, maybe for like the 1920s, where, uh, the old saying came out, you know, whatever, you know, whatever, uh, gets watched, or, you know, is, uh, is recorded, you know, they're going to ultimately, you know, someone's ultimately going to find a way [00:19:00] around that.

And so that's, you know, if you're having some of these, you know, screen technology, the screen tracking technologies, whatnot, well, people are going to find ways around that. I mean, I, I think I read somewhere the other day, there's like a foot pedal. That you can toggle between screens and stuff like that.

That was wild. But ultimately, I always come back to the fact that, you know, people want to be, you know, they want to be trusted. And the only way to do that, that in my mind, and it's, it's can be hard, is that you have to have intelligent, Dedicated managers that are working with their staff, you know, on a regular basis, you know, to ensure their work is being done correctly, ensure their work is being, you know, done diligently, uh, and a lot of that centered around again, back to the relationship aspect.

So, managers are either spread too thin, uh, or don't have enough training to where they're, you know, kind of not matching the incentives of, of their staff. [00:20:00] You're going to have problems related to that, but I think again, the best run organizations, you know, have a layer of trust. Uh, and so if I mean, if I'm making an overly, you know, over suggestion or recommendation here, my biggest thing is instead of spending money on technology that tracks employees spend money on training managers to work with, you know, to figure out how to work best with their employees and to have that relationship in which, uh, people feel great about working and doing the work that they do so that, you know, they can ultimately perform at their best.

[00:20:39] David Rice: I mean, that's great advice. I couldn't agree more. Um, there's a question here that's popped up in the chat and it's sort of, I find it interesting because we do sort of get into at times. I think this sort of scoreboard watching mentality that's mentioned here. So how often is too often to be looking at people analytics data?

Should it be a regular [00:21:00] cadence or only as an input to decision making? Something in between, you know, both kind of thing. Um, I'm curious what your thoughts are on this. Sure,

[00:21:11] Dan George: I mean, it's kind of like if you want to relate it back to other aspects of your own life I mean, how often do you look at your bank account?

How often do you, you know, track how well you run? It really depends on the objectives that you're That you're seeking to achieve. I mean if you're, you know, going for financial independence You're probably going to be looking at your bank account a little bit more often, your expenses, and trying to figure out You know, how do you reduce that or make more money?

Um, if you're looking at running a six minute mile, you're obviously going to be tracking your running activity there to try to accomplish from, you know, what you're eating, how much sleep you're getting, uh, that could be on a daily or weekly basis. So really depends on what you're trying to accomplish. I would say overall, uh, [00:22:00] just like finance and, you know, other You know, functions like marketing that, uh, looking at it at a monthly basis is, you know, dedicating an analysis to, uh, you know, month end or month beginning basis for understanding kind of what trends are occurring, I think is a, a really great place to start.

Uh, but then if you know certain areas that you're. You know, trying to enact change or, you know, achieve an accomplishment, then you're going to look every, you know, every day, every week, every other week, you know, whatever it is that helps you, uh, to, uh, redirect, refine, adapt yourself to getting closer to that goal.

Um, but, yeah, overall, I would say for the main metrics for HR turnover, head count, that kind of stuff monthly is, is just fine.

[00:22:50] David Rice: All right. Um, you know, we're in this era where there's all these different tools, like I mentioned, and they're all starting to incorporate a I'm [00:23:00] curious, what is your take on using a I'd analyze and to gather insights from maybe non sensitive employee data?

Should this become a common practice? Or are there considerations to be aware of? This might not be a good path for your organization, maybe or your industry.

[00:23:17] Dan George: I mean, there's always. Considerations that, you know, balance out how you operate in your daily career. Um, I'm a big user of AI. Uh, I would say I use it almost every day.

Um, I have actually AI embedded in my, you know, in my email, uh, service provider. I've got this special one that helps me write emails that, uh, I would say the best thing out of that particular use is that I can ask the AI questions. And it can pull that information from emails, so I'm not just keyword searching, I can actually ask the AI a question, like, when am, you know, [00:24:00] when am I meeting with David?

And it will give me all the times that I've met with you, or all the times that we have a planned meeting in the future. So stuff like that is just. Lightning fast responses as it, you know, kind of pertains to, you know, actually people analytics or HR data. Uh, I think, yeah, if there's a way to have, uh, analyze data, uh, help you fix data sets, reengineer data, uh, the, the use cases are almost endless, uh, in that sense, but I would just take a closer look at, you know, What typically takes the most time?

What can you actually accomplish by using it? What prompts tend to work the best with what you're trying to achieve again? For instance, New Orchard, we we use, uh, the chat to analyze some of our some of our documents and linkages at a much faster rate than we can. And I would say that sped up. [00:25:00] Yeah, our ability to provide reports and feedback to our clients and customers much faster than ever has been before.

So they're asking me an opinion on something that might take me 45 minutes or an hour. I can usually turn that back around in 20 minutes. So, um, you know, I'm shaving a. You know, two thirds off my normal kind of delivery time. But we've also understand that, you know, there was very little risk for us and using chat GP to do that.

Um, simply because we, you know, we've kind of created and carved out our own, our own space to, to do that. So we've got kind of a semi locked down, uh, version of chat GPT that we created. So that's, that's been. Insanely useful. Uh, just being able to do more with with less resources. Uh, but yeah, I'm huge proponent.

I'd say as much as you can try to figure out where it makes sense for you. Uh, obviously balance that with, uh. You know, a very reasonable and healthy respect for the [00:26:00] fact that, uh, sensitive data shouldn't be loaded into it and, uh, you should be careful about what it spits out to like you've, you've got to look at, you know, the results and see if it's actually something you want to share.

I, I've seen a lot less hallucinations probably the last couple of months that I have, you know, in prior quarters.

[00:26:21] David Rice: Yeah, I think it's getting better. I mean,

[00:26:23] Dan George: yeah,

[00:26:24] David Rice: in the beginning, it was, I was like, man, it's dressing up a lot of things as facts or they,

[00:26:30] Dan George: I'll say another thing, my biggest learning over the last probably six months has been, if you're going to ask generative AI, a big question, break it down.

Like your, uh, this is not my quote, but, you know, bring it down. Like you're talking to like a seventh grader, uh, you know, the sense that it's like, If it's super complex, ask it one question, layer on another question, layer on another question, layer on another question, you can refer back, [00:27:00] especially if it's in the same channel.

And that's really helpful for, again, not only limiting hallucinations, but also getting it to, or making sure that, you know, it understands what you're trying to accomplish. And so that's been. That's been terrific. I, you know, I just think there's so many amazing ways that it can help you do some of that larger.

You know, work a lot faster.

[00:27:30] David Rice: Yeah, absolutely. Um, part of what we're all getting good at, right? Prompt engineering. Yeah.

[00:27:35] Dan George: I mean, I, I have learned a lot about how to ask the right questions.

[00:27:39] David Rice: Yeah. Yeah. It does make me ask much more targeted things. Um, there's a question here in the chat that I want to get to, cause this is a good one.

It says my org has limited technology to move forward. The analytics piece of people analytics. I have been with my org for a year now. And with current requests from leadership, including the CHRO, [00:28:00] I have yet been able to try, try to move from reporting to analytics. What is your advice on impacting change in this kind of a situation?

And they note that it's sometimes difficult to educate leadership that there is a difference between reporting and analytics.

[00:28:17] Dan George: Absolutely. Um, and thank you, Amy. Uh, we have we know each other from past experiences. Um, but such a great question. I've earlier on in my career. I spent a lot of time, especially when people analytics workforce analytics was new educating leadership and that's just always something that when you're in an innovative space, And you were literally sharing new information with people that never knew this type of technology or capability was, was possible.

Available, you're going to have to spend a decent amount of time just educating, getting them comfortable with, you know, not only the [00:29:00] reporting aspect, but then, you know, again, gradually shifting them into analytics. I think, like, with anything, it's the whole baby step approach start trying to introduce a little bit more analytics ahead of kind of your basic reporting.

Uh, I think a lot of that can start, uh, with when you do or send a report. Provide your own analysis on top of that and then start to sprinkle in, uh, an additional metric here there or something that is in line with a story. Um, that. That the organization is currently prioritized or is experiencing, right?

So if you have head count or let's say, turnover issues, you know, start looking at the different demographics, uh, selecting different dimension, uh, dimensions to cut and slice that data. And then, you know, see if there's any linking trends that, you know, are kind of occurring with what you're hearing, [00:30:00] you know, quote, unquote, at the water fountain by adding those kind of little snippets here that you're going to begin to create a longer storyline that you can have, you know.

Month over month, and then eventually quarter over quarter, and as you kind of introduce new, you know, metrics along with your analysis, you've now generated, you know, somewhat of a, you know, of a, of a story trail, and that in turn can help not only the executive leadership team become more interested, because, again, humans, Um, you know, a lot of companies are compelled by stories.

Uh, and so they they want stories. And so having just a report on financial metrics is fine because, you know, again, financial metrics are inherently, you know, less human, right? But since people analytics are so much about the workforce, the human element aspect of it, building in a story case to that of how the workforce is changing, what trends you're seeing, how [00:31:00] you might fix or correct that.

Uh, all of that kind of builds that interest. Because it resonates with leadership. And then if you could, you know, on a regular cadence or, you know, even introduce little things that, you know, you might change or fix something that, you know, they might, you know, that they might not consider or, uh, something that, you know, just you're hearing or seeing, I think all of that kind of.

Is the snowball effect is you kind of constantly refine and add little elements so that you can move from basic reporting into analytics because leadership is curious about what you're thinking, what you're recommending and, you know, ultimately, uh. Driving for them better decisions.

[00:31:51] David Rice: So, well, we're a little past the halfway mark at this point.

I always like to add in, like, a little random questions to give everyone a [00:32:00] chance to breathe and process some of what's been talked about. So I'm going to ask you just a random sort of off the wall question. Would you qualify cereal as a soup?

[00:32:11] Dan George: Cereal as a soup? Uh, um, No, I mean, I think cereal, again, I just watched that Kellogg's Post Netflix show, right?

Where they, they went head to head. Uh, and I, I didn't realize there was such a fight for breakfast. You know, back then, uh, I would say cereals, its own category. I don't think it's a soup.

[00:32:35] David Rice: Okay.

[00:32:36] Dan George: That's, that's my take on it. Although now that you say it alphabet soup, Ooh, that's kind of like a cereal. No, I'm still cereals.

It's something I'm going to stick with that.

[00:32:50] David Rice: Okay. Um, all right. So we have a, we'll get back to it. We'll start with a question from a member. Um, This was in [00:33:00] our community chat. Uh, I've seen our org fall into the trap of tracking every metric they can, they can get their hands on. Is there sort of a minimum or maximum number of metrics that you'll look at at any given time to avoid gathering too little or too much data?

[00:33:15] Dan George: Oh, yeah. Uh, analysis paralysis is one of the largest pitfalls for a scaling people analytics function. Okay. Everyone gets excited. You might have gone from one or two kind of customers. And, uh, you know, I say customers as in, uh, you know, functional heads or HR heads, right? You've grown it enough to where there's, you know, people from different aspects of the business starting to ask you for things.

They like the information they get back. So all of a sudden, you went from, You know, probably 5 to 10 metrics that you were pulling together each month to now it's again, if you've got 17 to 25 systems, uh, and people are starting to connect that. Now you've got and, you know, [00:34:00] maybe 12 people that you're kind of dealing with on a maybe monthly or bimonthly basis.

So now you're starting to kind of manage 15 or 20 metrics. As with anything else, you're, you know, we're all human. We can only handle so many priorities at once. Uh, having your boss or your leader help you prioritize that is, is key because you just can't run all metrics at all time. Uh, there are some organizations that are, you know, much more mature and complex that have been able to automate a lot of that.

But for those, Functions and people in this professional discipline that are, are still kind of either new or growing your biggest, uh, how do I want to phrase this? Your half of your job is limiting what you should be focusing on so that you don't, you know, either, you know, burn out or get to that analysis paralysis point where you've just got so many metrics and [00:35:00] so many.

Objectives that you're trying to meet that you don't do anything. Well, the the best thing you can do for yourself Is work with your leader? Have again working with the relationships that you've created with these other customers that you have And just level set expectations on a lot of this because again most information uh outside of Your monthly reporting aspects aren't necessarily needed every month, uh, and so, and, and if they are, they should have a really high impacting business case attached to it.

On the flip side, again, being able to create that level of demand, um, is really great for scaling, but managing, being able to manage your priorities, being able to manage, like, the scope of work that you can, you know, accomplish, At the degree or the, you know, the, the level of effectiveness is, is going to be your biggest tool to continue to expand again, not only your, you [00:36:00] know, your career or your team, but just the insights that you can be able to provide back.

So, you know, it's was a like, you know, Every good leader goes through a change of saying yes to everything, to no to everything, so at some point you're going to have to say no, but when you do say no, make sure your relationship with those leaders where you're saying no, they understand why you're saying no, and then set expectations as to maybe getting them to a yes sometime in the future, or reprioritizing your projects.

[00:36:32] David Rice: Yeah, I can't agree with you more on fine tuning it and honing in on what's really important. I've seen people just become sort of numb to the insights that come out of this stuff because they just have had to track everything. And it's, it's clouded their ability to almost interpret what they see.

[00:36:48] Dan George: Yeah, I think that's where actually it's funny like Tableau, um, And some of these other tools, like even Power BI, right?

They have a specific screen size and you can't go out [00:37:00] of that, right? So PowerPoint, you know, all these, like, you know, and you can only fit so many metrics. On that page, uh, for the most part, I would say, if you want to try to regulate yourself, make, you know, set up your dashboard, you know, to the point where your metrics fit on 1 page.

If you're having to manage more than 1 page, it better be for, uh, you know, an objective or achievement you're trying to accomplish, uh, But I want to say like even the past organizations if it can't fit on the page, then you know You're probably looking at too many. Um, and so that's a that's kind of an easy way to kind of regulate it

[00:37:42] David Rice: We had another comment from amy here says great advice on storytelling and baby steps How can we create more curiosity within the finance team in particular?

We know finance sometimes but just likes Looking at numbers and percentages in something like excel Yeah[00:38:00]

[00:38:02] Dan George: A lot of HR professionals tend to back away from the finance function, and there's a lot of reason to that, you know, depending on the organization and its culture. Sometimes finance is put on a pedestal. Uh, and so, you know, again, every organization has, you know, You know, a degree or, you know, a level of incentive and then responsibility to manage its money really well.

Um, and obviously, salary, wages, and benefits, you know, HR spend is, uh, is one of those costs that every organization tries to, you know, to limit or just manage, you know, to, to the degree that it matches. Yeah, the profitability and margins that the business is seeking. So in that sense, understanding what incentives finance, you know, leaders and peers have, you know, makes a lot of sense.

So in terms of trying to, uh, you know, partner with them, [00:39:00] I've said this already a couple of times, build a relationship with, you know, a finance counterpart that you can. You know, have a deeper understanding of what they're doing, but then also you better be sharing what you're trying to accomplish as well.

And having them understand what you do is just as important as having them understand or having you understand what they do. And so wherever you can align those incentives is probably going to be The, you know, the best match for, you know, what you can provide back to them. So let's if finance, you know, FBA teams is kind of looking at, you know, what are the margins that we have, you know, over the last couple of months?

Um, you know, where can we reduce costs? Well, you know, we had maybe a bloated head count and, you know, a region or, or geography, or we've had kind of higher spend on this. So understanding that if they're, uh, you know, Objective is to, you know, have a higher margin, right? [00:40:00] Try to figure out how to help them reduce costs.

Try to help them figure out, okay, where, where can I help you? And by, by helping you kind of figure out where there are issues and challenges with again, head count or turnover or something like that, they can then help direct you as to, you know, what are the, what are the areas that, what are the levers that you can kind of work together on to, to fix or change?

Um, and so just sending them numbers, like I kind of said before, leaves everything up for interpretation. The better relationship that you're going to have with them is going to be based on, again, story set, recommendations that you're, you know, that you're seeing, uh, within your own data set that you can then provide back to them.

Uh, having your opinion shared with them is going to not only create a deeper relationship and understanding, but also is going to, quote unquote, give you, you know, probably a seat at that table, uh, especially if what you're providing back is useful to the And it helps, you [00:41:00] know, organizations align to their incentives of whatever they're trying to accomplish.

Maybe that's too generic. I don't know.

[00:41:10] David Rice: I think, I think that was good. Um, this next one is sort of focused in on talent acquisition KPIs specifically. I want to know what are your, what do you feel the top five talent acquisition KPIs for people analytics are? Or from a people, people analytics perspective are?

[00:41:29] Dan George: All right, let's see. Um, number, let's see if I can try to organize this correctly. So I would say the number one is going to be time to hire. And that could be time to hire time to fill. Uh, I think the second one is going to be Number of candidates per application. So are you getting enough candidates to apply?

And if you aren't, that could be a number of reasons that, you know, again, it could be for job descriptions, [00:42:00] you know, not the right advertising or marketing or channels. Um, so time to hire a time to fill. Uh, candidates per requisition number of requisitions. Per, uh, per recruiter, I think is another really good 1.

and then I think, uh. 1 that we often kind of miss, uh, that's. In and around that time to fill, uh, is the time to interview. So there's a metric that circulates around. Okay. So from the initial screening through the interview process, how long does that does that take and there's a couple elements to that because it's you've screened the candidate.

Now you've got to be with. You know, one of the people from the hiring function, the hiring manager, and then maybe typically, you know, a skip level above that, that time frame is really important to the candidate experience. And I think we often forget about how often do we, how often are we able [00:43:00] to align those interviews within a consolidated amount of time to where we can get back to the candidate and provide them feedback.

Um, I think, uh, the, the final step that I would look at is, uh, an offer acceptance rate. Uh, so again, there's a lot that goes into the offer acceptance rate, but what ultimately you're saying is that in an offer acceptance rates, that's like lower than 80%. So, for, uh, every 5, right? You're either not finding the right candidates, you've graded an experience that has, you know, turned off a candidate, you know, or your offer and, uh, your, your, your salary, wages, benefits, whatever, you know, offer package doesn't meet the expectations, uh, of that individual.

So, uh, if you're seeing kind of a lower offer acceptance rate, one of those aspects is [00:44:00] off. And again, town acquisition has some of the largest impact to any organization because of time to fill, you know, goes from, you know, let's call it 45 days to 55 days to 65 days. You know, you're talking a number of weeks where there's a vacancy in that position.

There's higher, higher degree or higher level of probability of burnout. And so being able to reduce ultimately time to hire by finding the right candidates, scheduling, giving them the right candidate experience, and then being able to offer. Within their expectations and have a higher offer acceptance rate.

Now you've just kind of lined up the whole value chain, not only for the candidate experience, but also for the organization.

[00:44:45] David Rice: Okay. So I know that we are getting down to time, uh, only about 15 minutes left or 12 minutes left. Uh, And I know some folks may need to start peeling away, go to the next meeting, kind of that kind of thing.

Uh, if that's you, I just want to say, [00:45:00] thank you for joining us today and being a part of this conversation. If you love this type of content, you can RSVP RSVP to our next event. We have a lean coffee session with Dr Liz Lockhart Lance. These are agenda free events where you steer the discussion. So we talk about and find solutions to the things that matter most to you.

Uh, Michael just posted a link in the chat, so you can check out that session there and RSVP there. If you're a guest today and you want to know more about becoming a member, check us out at peoplemanagingpeople. com forward slash membership. Um, So with the time that we have left, please do get your questions for Dan in the chat.

Uh, we get them all as many as we can and definitely before we actually close. One of the questions that has come up, uh, that was there just before I started that, it said, uh, what are the key technical skills that are sought after in PA roles? And I think by PA, they mean personnel administration.

[00:45:58] Dan George: Personal administration or people [00:46:00] analytics or people

[00:46:01] David Rice: probably right on topic, right? Yeah, right. I thought that

[00:46:06] Dan George: 1, right? It's interesting now, because I think the generality of people analytics, uh, now encompasses such a large breadth of differentiated kind of skill groupings that it's. It's potentially hard to, like, kind of pinpoint a general people analytics skill, uh, set for kind of the entire, what I'll call the value chain of people analytics, because there's, you know, there's not only just data engineering.

Um, but, you know, HR system management, data engineering into the analytics and display. And then there's finally kind of this, you know, consultative, uh, HR business partner, talent insights, you know, uh, role. And so between all those roles. Those definitely require different skill sets. And I would say, I think [00:47:00] the question was on technical, right?

Um, because, you know, and here's kind of what I'll say, having an understanding of HRS systems a lot easier than it used to be used to, you know, need to learn how to at least code in one of the languages that the systems, you know, was based on, that's Not an issue anymore, as most back end systems are now configurable, so you don't actually have to write any code.

If you're looking for, like, the code writing stuff, I mean, packages in R and SPSS, you know, any kind of statistical tool, you're never Having the fundamentals in statistics and math is never going to hurt you. But, again, I also realize that those are curated over years. But, again, There's no time like the present to get started on just doing a little bit, uh, you know, every week.

What I will say, some of the bigger skill sets that are coming down the pipeline that have kind of reared its head a [00:48:00] little bit more is this storytelling aspect. The data analysis, visualization, and presentation aspect. So there are certain people that are really good at, you know, kind of telling stories, but they're, you know, to do that, oftentimes you have to understand how to analyze data and relate it back to again, what the business is trying to achieve.

And so, while less quote, unquote, technical, these are skills that you have to refine. And the experience behind that is not often kind of sitting in a class or reading a book, oftentimes it's kind of getting out there and experiencing. How do you tell stories? Uh, I mean, I've one person in my past. That was, um, yeah, there wouldn't classify them as a comedian.

That wouldn't even classify them as kind of a generally silly or hilarious person, but they took improv. Uh, simply to be able to learn how to think better on their feet because they got so nervous speaking with executives and [00:49:00] their means to an end was never to jump on stave and tell jokes to, you know, 40 people in a room.

It was literally to be able to have a level of comfortability standing on stage talking to people that either they don't know or. You know, could potentially have control over their career and being able, being comfortable speaking on their feet. Uh, and so I think those types of skill sets, whether or not you call it technical or not is up to you, but those are some of the best ones in, you know, in terms of advancing your career.

But again, getting back to the actual technical, you know, aspects. Any kind of experience in, you know, managing some of the systems that are out there are great. These are just indelible, uh, stepping stones to, you know, being able to manage, uh, people analytics practice because again, even if I talk to, you know, VPs and SVPs of people analytics these days, some of the largest organizations, many of them have backgrounds in, you know, basic system, uh, [00:50:00] management and configuration and just being able to understand kind of how it all works is, is really useful.

[00:50:08] David Rice: I know, you know, uh, there's been a lot of talk in the last year about moving towards becoming like skills based organizations and people analytics folks. They have a role to play in this. Obviously, I'm sort of helping, uh, organizations identify skills and sort of where the, the workforce needs to go. I'm curious, you know, because we had talked previously and this was months ago, but, you know, you would kind of talk to me about the, some of the challenges that with that.

And I'm wondering where you think, you know, that we're now a little past halfway through 2024 and this was something that a lot of people were thinking was going to be this big thing for 2024. And I'm kind of curious, where do you think we are on that? And what are some of the biggest challenges that remain?

[00:50:53] Dan George: Oh man, this is such a good topic. Uh, so about a year ago, May, 2023, I went to a conference. [00:51:00] And with a bunch of other people, analytics professionals, we took several hours out of a day to kind of whiteboard session. We had, you know, skills and skill groupings and skill taxonomies. And we had everything kind of written out and we're like, all right, how do we look at this?

How do we do it? And to be honest, it's it's 1 of those things that. I just kept staring at the whiteboard and I was like, this is potentially one of those endless exercises that truly never gets you where you want to go. So in my, in my opinion, which is probably not as popular as, you know, some of the other ones that are out there.

And it's like, I'm, I don't know if I'm a big subscriber to this, like whole, like overly managed skill network. You know, I think there are things that you like to do, you, you want to learn to do, but ultimately You know, even skills have a half life. And so managing like this, you know, [00:52:00] you know, some list of skills that I have is great.

Like, uh, for instance, like I used to be able to code in SAP ABAP. Uh, I used to be able to, uh, quickly and easily pull together a statistical analysis. Like, I don't do a ton of that anymore. And so could I relearn it, you know, faster than I could have when I first learned? Sure. But I don't use that very often.

So like my half life or some of that stuff is just gone. So, you know, my focus has kind of always been to what are the skill sets that you need to do your job really well right now? What skill sets do you feel you could be interested in the future? Because again, our as humans, our interests, our incentives, our motivations change over time.

So I would say you can certainly manage a list, uh, that is apparent to, you know, whatever current role responsibilities you have. But insinuating that you're either always going to have that skill or it's always going to be an interest is is just [00:53:00] a I think a misguided view because things change so fast these days that it would.

I just think it costs organizations too much to manage a skill based org than it does to. Again, I don't know, I don't know what a new phrasing for it could be, but it's, you know, kind of managing the, you know, in the moment skill sets, uh, as it relates to it, because again, no, there's no one standardized taxonomy.

So, from 1 to work to the next, it's all going to be different, uh, from 1 month to the next, it's going to change slightly and from 1 year to year, it's going to change. I mean, look, 2 years ago, you know, Gen AI was around, but it wasn't, wasn't doing much for anybody. Right. You know, again, now it's like something I use almost every day.

Um, and so that skill set has come out of nowhere. And I would say even in the last two, two, three quarters, you know, for me, it's been a huge thing that I've been constantly working on. Uh, but have [00:54:00] I, you know, coded in, uh, OpOp or SAP? No, I haven't even done that much work at Excel, you know, kind of recently.

So it's, you know, even getting back in that could be, could be difficult. So, you know, the. The push for the, you know, the entertainment of this skills based or I think might be a bit misguided, uh, and probably a trend that we won't hear much about this time next year.

[00:54:27] David Rice: Yeah, yeah, I kind of agree with that.

It seems like it's not quite taking shape. Somebody wanted to ask this would be kind of our final question. Uh, you know, you've had a lot of success in your career. What advice do you have for aspiring people, analytics professionals?

[00:54:42] Dan George: Yeah. Um,

I would say the biggest thing for me, you know, I've allowed myself to be curious and a lot of different realms. Um, [00:55:00] if, uh, you know, coming out of undergrad, you know, I would have said that this is what I would be doing. I would have. I think I would have been surprised, shocked, even. Um, but I've found a real passion for things that I just never would have considered in the past, something, uh, uh, of an interest.

And so I think, you know, for me having that level of curiosity and making sure you spend a little bit of time on yourself and what you want to do, you know, whether that's, you know, networking with other people to discover that or reading books or, you know, online stuff, like take some time for yourself to really find out what.

What you're interested in doing and, you know, see if it's something that people will pay you for, um, and ultimately kind of connecting those two is going to probably bring you more joy than, you know, sticking to some sort of very rigid career path with, you know, a probability of, again, career paths, you know, linear career paths is just not as, [00:56:00] not as prevalent as it, as it used to be.

So. Find out what you really like and, um, hope, hoping what you really like kind of gives you, gives you joy and, uh, and hopefully someone will be able to pay for that. Uh, but yeah, I would say that's, that's probably my biggest advice to most people because I think in the end of it, you know, when you, when you look back on your career, are you going to say, hey, I did this right because I, you know, I followed what I wanted, really wanted to do, or did you say I followed a distinct path?

So, um, yeah, that's what I would suggest. Maybe not articulated perfectly there, but close enough directly. Correct.

[00:56:42] David Rice: All right, folks. So there you have it. That's everyone in the audience. Thank you again for spending this hour with us. And thank you for contributing to the vibrancy of our community. Please take a second to fill out the feedback survey that we posted in the chat so you can let us know how we did today and submit a topic that you'd like to see us cover in a future [00:57:00] session.

We always love some crowdsource suggestions. So, of course, Dan, big thank you for for volunteering your time today. This was fun.

[00:57:10] Dan George: Thank you for having me. I've had a lot of fun. Appreciate it.

[00:57:13] David Rice: Awesome. We appreciate the expertise as always. Until next time everybody, check out the podcast. Be sure to keep an eye on all the things that we're doing on People Managing People.

Subscribe to the newsletter and don't get an analysis paralysis. That's right. Stay focused on those KPIs. We'll see you next time.