Leveraging People Analytics for Workforce Planning with Mariya Meshcheryakova

[00:00:00] Mariya Meshcheryakova: You know you have a stack of 5,000 resumes. Is each one going to get looked at? Well, by a human, perhaps that would be impossible but by an AI, not necessarily so. So at least that gives you hope that there's some sort of screening that can happen on everything that's sent in.

[00:00:35] Tanya Early: Welcome to Draup Dialogues, an exclusive podcast series of industry leaders sharing their views on workforce planning, people analytics, and the future of talent intelligence. Today we're talking to Mariya Meshcheryakova, People Analytics Leader at Equitable. Mariya, thanks for joining us, maybe you could give our listeners a little more information about yourself before we kick off the Q&A.

[00:01:00] Mariya Meshcheryakova: Yes, I'm happy to do so. So I have been doing people analytics, specifically statistical people analytics at Equitable for the past six years. I come from an organizational behavior background and I'm deeply passionate about the field. It's a field that has over the course of my career- probably got started really in it in 2013.

Really evolved and grown dramatically and so has talent intelligence. And it's been an exciting journey to see the evolution and see new technologies emerging, honestly on a almost daily basis these days.

[00:01:46] Tanya Early: I agree. I agree. I've been in the space for about five years now, and it's just- the changes have

been immense,

so.

Well, so first I thought maybe we could talk a little bit about location strategy and how you think about that. So, in an era of remote and hybrid work, how do you determine the location strategy to fulfill some of those difficult roles?

[00:02:20] Mariya Meshcheryakova: You know, it can be very challenging to figure out where a role should be based.

And it really comes down to first having that conversation with the hiring manager and figuring out whether that's a role where physical presence matters. So on one hand, imagine something like an event planner. You have to physically be where the events are. You can't be remote. On the other hand, we have very different roles like-

tech roles where you have a software developer, most of whose meetings are with folks who aren't even in the country, much less that same office. So even if you were to get them in an office, between the time they're spending on solitary work and the time they're spending in meetings with people abroad, there really is

no real value they're gaining from being present in the office. And so that's the number one consideration. And then, the second thing you really have to think about is, is the talent where your offices are. So for certain roles, the talent may just not be at those same locations, there are niche roles, there's certain areas that are great for certain sectors but terrible for others.

And while your organization may be well positioned in that for the majority of your roles you are in a great location, for some roles there may literally be no talent or the talent might be so expensive. Because just like you, the other organizations in your field also have a little bit of need for the role, but just enough to fight over the very small amount of talent in that area.

And so sometimes you will find that you could suddenly end up that you're paying New York City salaries in the middle of nowhere, because as it happens where you are, there's only so many people with that skillset. Just to get availability.

[00:04:30] Tanya Early: Availability,

yeah. And the growth I think also, you know, are you gonna be able to sustain that too?

[00:04:38] Mariya Meshcheryakova: Absolutely.

[00:04:39] Tanya Early: So how can people analytics help their organization discover and acquire those core and niche skills in non-traditional locations, really? So to help them gain a competitive edge.

[00:04:56] Mariya Meshcheryakova: Yeah, so you know, one, because if you are trying to do a kind of a first blush look, you often realize that, okay, we should just hire in these very, very gigantic major cities because the talent availability of just about everything is higher in like a New York City or in the Bay Area.

And, but you will also find your budget falling. You will be vastly over budget, if you try to do that. So this is where kind of some deeper analytics comes into play. And you can make some, and you can look into a specific role and where

availability for that role is. So you may find, for example, a smaller part of the country where that niche role has a lot of, there's a lot of talent availability for it because historically there may have been a company that did training programs. There might be universities that have programs that teach that specific thing. And so you may find places where there's a lot of talent availability, the cost is relatively reasonable and you can hire.

Or alternatively, sometimes what we find is that it makes sense to train. You will, you might find, hey, if I want it here and I want this skillset, you know what, maybe I want to be the company that starts that training program and develop that talent. And that may financially make a lot of sense, but this is where that talent intelligence comes in.

What are the relative costs? How much more will it cost me to hire a higher, or how much more will it cost me to train? Because both can be prohibitively expensive depending on exactly the niche we're talking about. And this is where talent intelligence really gives you an edge.

[00:07:01] Tanya Early: Yep. Determining whether you're gonna buy or build, borrow that talent. It's definitely, I agree. We talk about that a ton now with the large companies that I meet with every day, so. What do you think are some of the emerging trends that you're seeing in location analytics and how do you think that's going to change and sort of reshape

workforce planning in the next two to three years?

[00:07:29] Mariya Meshcheryakova: So I think that the sophistication of what we're able to do with the sort of analytics is really changing because the problem is not that there is data. The problem is, how do you get that data in a usable form? How do you actually become able to turn it into

a valuable insight, because there's a flood of data coming in from all sources. And traditionally the problem has been that it's so hard to transform. It's so hard to get into a usable form that by the time you get anything out of it, it's already outdated. So this is one of the things that I'm seeing is that now there's a lot more technological advancement that's allowing that speed to decision making to really increase and that makes the data more relevant and useful.

Certainly as we see these AI technologies coming into play, that ability to more broadly search more broadly summarize. That's very helpful as well. That's really speeding that process up because a lot of it really does come down to how fast can we get from raw data to an actionable recommendation and is it still going to

be relevant by the time we get to it?

[00:09:07] Tanya Early: Absolutely.

And we've talked about location strategy and thinking about that but then there's also the piece of competitive intelligence and understanding your peers. So, how do you think that organizations can use competitive intelligence? And with our people analytic analytics functions right to close the gap in order to impact those business outcomes.

[00:09:39] Mariya Meshcheryakova: So this is actually something that is, I would say, an underrated benefit of talent intelligence. A company doesn't just change its strategic direction and take no action, right? If a company is massively changing a strategic direction, it is hiring people to support that change. So when you see somebody hiring a certain type of professional, for example, professionals that are in a niche that is just emerging, you know a lot about which way their strategy is headed.

So yes, they may be years away from announcing this new product. However, if they've hired the talent, then when that announcement is coming several years down the line, you are not just finding out that your competitor's outpacing you. You are already aware that they were going to be doing this. And that also is important because it helps you differentiate between what I call media trends and real market trends. So often you'll see in the media something suddenly get traction. A good example a few years back was the metaverse. Everybody was very excited about the metaverse but for if you were looking at the talent intelligence landscape, you could possibly see whether the hiring was in just a few organizations or whether it was widespread throughout the market.

Whether it did seem like a lot of companies were working on it or a few and just how much devotion they were giving to it. Because if you hire one or two roles, that's very different from hiring 30 or 40. So that really gives you an idea of the scale of a trend as well and sort of intelligence can really give you a competitive edge and help you anticipate

future changes in your competitor's strategic directions.

[00:11:46] Tanya Early: Yep. And I think, so we were just talking about benchmarking and gap analysis against those competitors and looking at those skills and, I guess I'm wondering also, how does knowing those skills and that compensation data help improve internal talent mobility as well and retention strategies also?

[00:12:11] Mariya Meshcheryakova: Yeah, that's a great question. And there's really, I was once told by someone that you are paid in two ways in an organization. You're paid with your compensation and you're paid with lines on your resume. So when you want to retain talent, it is important that you have two things.

One is that your compensation is not so out of line. And I- everybody knows that a person that stays in an organization for a long time, typically underperforms one who moves around. But at the same time, happy people are rarely keen to change their jobs tomorrow. And, that's true to an extent.

If you see- if you suddenly start seeing that you're massively undercompensated to the market, then suddenly you're willing to move. And that can be really problematic for an organization because as somebody works for you for a long time, they only become more valuable. They gain institutional knowledge and if they've kept up with that skill growth, they're just-

they're better than anything you can hire in the market at a certain point. So losing that talent to a different organization because you didn't keep up with comp, is really, really unfortunate. Similarly though, and this comes back to what I just said, that they kept up with scale growth. Now you don't want your workforce to be falling out of line with the market in terms of skills.

So when you think about your learning and development strategies. You really want to be looking at, what are the skill sets that are in the market and does our workforce have those skill sets and should we be actively developing certain skills? Because, let's be real, most people learn most of their skills on the job and there's no reason why you can't develop a lot of your skills.

And, modern tools make it so much easier than it used to be. No longer do you really have to, for most skills, send somebody off to a graduate degree program. You can just offer them online courses in-house workshops. There's so many opportunities. Both in person and virtual to help them develop those skills.

And that keeps your workforce up to date.

[00:14:42] Tanya Early: Yep. And that keeps people from being sort of stagnant and getting bored in their jobs. I feel a lot of times moving, it used to be you'd move around in the

organization,

now we have a lot more opportunity to just upskill, right?

Because the skills are changing so fast. Well, interesting. So as far as,

AI and automation, when we think about that and like the growing concerns around AI ethics and bias, do you have best practices that you recommend that organizations really kind of adopt to ensure that there's fairness in the- in AI talent intelligence. AI driven.

[00:15:30] Mariya Meshcheryakova: Yes.

So, you know, the number one most important thing is that AI is never used without human oversight. Now, I am a person who gets very excited about new technology, so I am normally the pusher and driver of adopting technology. However, while AI has advanced incredibly in the last few years, there's huge limitations.

You should- one thing you wanna always make sure is that you're not copy pasting AI content or letting AI make decisions for humans. Because these models, they are limited. They have biases based on the data they were trained on. They are imperfect. For example, let's say like I've heard of AI assisted job descriptions, great, but you can't not have a human recruiter looking at this job description.

A manager looking at it. Honestly, the AI's tone might be odd and inappropriate for your organization's culture. You might have that they- you might, it might be wordy, it might be, there might be so many things that you should never have an AI acting on its own. And even more dangerously in the recruiting sphere where you, in the initial phases of recruiting, where you are having AI screen candidates, well you wanna,

one thing you need to do is you need to analyze for adverse impact. Adverse impact is not just for humans, and making sure that they do a good job of screening candidates. It's also for AIs and making sure that the AI is not getting obsessed with certain wording structures and now suddenly deciding that candidates of a certain race or gender are worse than- are somehow worse in its eyes.

Because that is- that would be a true disaster. But I think, unfortunately, we do sometimes see in the eagerness to adopt new technology people get a little inattentive about this sort of thing and create a lot of risks for an organization. Also, I think you really want, when you're using AI, to make steps as auditable as possible.

So you do wanna be- you do wanna, for example, always know why, for example, it's not just AI made a recommendation. It's why did AI make the recommendation? What justification is it using for that recommendation? And similarly, if you can break a task into steps that are more easily auditable, you should.

So if you, for example, we're having an AI categorize employee comments. You'd want to make sure that- you'd want to make sure that the AI first categorize them into categories and then you could review what it categorized and then you would wanna summarizing. You would do- you wouldn't wanna do that all at once.

Because you wanna create as much of a, you wanna create as much confidence in the result as possible. And you know, but at the end of the day, AI is always gonna be to some extent inaccurate which is why human is so essential to the process. Yeah, but. Yeah, there's a lot that goes into this.

[00:19:09] Tanya Early: Totally, I totally agree with that. And, you know, I think it's also a challenge when you think about integrating that data then with your systems that you already have and how do you do that? If you're bringing in talent intelligence here and you've got a ton of other systems, a lot of the clients that I speak to are moving to an environment where, you know, they're collecting data in like a repository somewhere and then they're developing AI agents on top of it which is what I hear a lot as well.

But if the data on the backend is not good, that's where the trouble tends to lie. But how do you think about that and how to manage that data piece?

[00:19:54] Mariya Meshcheryakova: I kind of, I almost wanna challenge the question here because it's often the problem- yes, the data integrations are very very complicated and it is hard bringing data sources together. And there is obviously

often in talent intelligence, a good data warehouse, a good source- a unified source of data is the number one most important thing. But I would say that your biggest challenge with integrating talent intelligence, talent strategy, is really in the cultural aspect of things. Meaning that most people, most managers, due to years of expertise, decades

often, of expertise like to use their gut in making decisions. And that's natural that is how we make all our decisions in real life. So it's not surprising that's the go-to for business decisions and our gut is not useless. So that's not necessarily always a terrible way to make decisions.

However, with talent intelligence, and when we're talking about these massive amounts of data, your gut is not going to be good enough. This is where it's all about building a data culture and a trust that data is helpful to your decisions, that it does help you reach a better outcome. And working on that aspect of the change and this- and I think this comes out of

helping, leaders solve problems. Because as data continuously helps them solve problems, maybe small ones in the beginning, then larger ones as we move along. Or rather not, I'd say not so much small and large but less and more complex, would probably be the general progression. That helps them build that confidence and

learn to realize that they beat talent intelligence, frankly, to address a certain challenge. Now they get in the spirit of, oh, okay, I'm thinking about where to base this role. And instead of just going with what I've heard, from the people around me. I am gonna have that research done and I'm gonna make sure that we benchmark that appropriately.

Because I know that last time we did that, that probably ended up saving us like 27% on that role. And so this time I'm gonna make sure I do that because now we're hiring even more people and now it matters even more.

[00:22:33] Tanya Early: Absolutely. And when you think about emerging AI capabilities, so how do you think that's gonna impact workforce planning, talent acquisition?

[00:22:49] Mariya Meshcheryakova: Yeah, I mean I think the big change that's going on is Agentic AI. Is the idea that an AI agent can be acting for you as an organization. And that's huge because they can act as first pass recruiters. So they can be reaching out to candidates, they can be scanning resumes, it can be going through

those basic screening questions. Certainly, anybody who's ever done recruiting has had the experience of calling up candidates and asking basically about your minimum requirements. AI could help you do a lot of that, like, a lot of that task which is, which amounts to a recruiter basically writing down an employee's answers to the basic, bare minimum questions.

This is where AI can really improve the number of candidates you can realistically screen. And can really help you- help you avoid certain pitfalls that have traditionally affected recruiting. You have a stack of 5,000 resumes. Is each one going to get looked at? Well, by a human, perhaps that would be impossible but by an AI, not necessarily so.

So at least that gives you hope that there's some sort of screening that can happen on every- everything that's sent in. And I think a lot of- but I think a lot of the change will really be in what jobs look like. I think there's very few jobs that are not going to be affected by AI and I think, often people are thinking about jobs being replaced by AI.

That I think, that is relatively a fraction. Most jobs I think, are just going to be influenced by AI, where AI is gonna change how you do the job. Where, yes, for example, there might be tasks that you can absolutely do as a qualified professional, but as it happens, if you do the task, it takes you three hours.

If you do it with AI assisting you, it takes you now 30 minutes. That's a two and a half hour savings, that's increased productivity. Not because you couldn't do it without AI, but because AI just helped your speed to execution increased so much. And I think that's where we'll really see a lot of the benefits of AI coming in and, boosting that speed to execution.

[00:25:25] Tanya Early: You mentioned the interviewing, and I actually have a friend who was interviewing at my age. I'm middle aged, so, and it's, I wonder what the difference is gonna be with how different generations sort of accept that. Like my friend was very upset that she was gonna have a virtual interview with an AI agent

for a job. She's, she's fairly high up in marketing and has been for in legal companies before, but, she was like very put off by it. And it'll be interesting to see if the older generation too is able to adopt that better and we're all going to have to, right? But, anyway, I digress, but I thought that was interesting, so.

[00:26:12] Mariya Meshcheryakova: No, I agree, it is interesting and I think an important thing to also be aware of is not- I think there's often a tendency to assume that like older folks are slower to adopt these technologies than younger ones. But really it is very individualistic, at the end of the day. And honestly, sometimes I find that the area, the business area they're in can influence that even more than their age group.

Because, let's say you are a person who works in tech or data science or something like that, you adopt new technologies on an annual basis anyway.

[00:26:56] Tanya Early: That's true.

[00:26:56] Mariya Meshcheryakova: So for you, yet another new technology is nothing. On the other hand, if you're in a part- if you're in a business area that has changed very little even in the last 20 years? That then even if you're- even

if you're relatively young, it can be very shocking and jarring to see AI changing something like that and to see that becoming part of your daily life.

So yes. So yeah, I do think that there's a generational component but I also think there's just certain types of people who are just going to be more likely to adopt it easily and some folks will struggle a lot more. And I think, and also I think a part that will play a role is how comfortable you are with technology as a whole.

Because some of us that are very much in the tech space are very comfortable. But then you have a lot of folks that frankly are somewhat scared of new technology that, that are intimidated by it. And so that's going to play a big role as well.

[00:28:06] Tanya Early: You're right. You make a great point and I definitely think that the function that you're in definitely has something to do with that.

So I agree. Well, let's see, so we've got our, rapid fire here, rapid fire section. Where am I gonna ask you a question or make a statement? You tell me first thoughts that come to mind with that.

[00:28:27] Mariya Meshcheryakova: Okay.

[00:28:27] Tanya Early: So one emerging skill for folks in HR analytics, what would you say?

[00:28:34] Mariya Meshcheryakova: Honestly, I'd have to say probably using generative AI, because certainly as a code assistant, and it's not, let's, be real like I.

we can write code all day long and we do, but sometimes it does speed up, it does speed up a process. It, for, example, like I've used this as a debugging tool before where I've wrote something and I'm like, I probably ha I have an apostrophe somewhere, or something like that. That's wrong.

And sometimes an AI can help you find that, a lot faster than staring at code and trying to figure out. Where exactly did you put that darn apostrophe that you weren't supposed to?

[00:29:22] Tanya Early: So true, okay. Hybrid, remote, or on site? What would it be if you had to choose one for building a high performing team?

[00:29:35] Mariya Meshcheryakova: Yeah, I would say in most situations, probably hybrid, just because i

you're looking at remote. There- there can be losses in team cohesion, there could be losses in a work culture. I will say like you can absolutely, with many businesses have people be fully remote and it'd be just fine.

Similarly, on site you have a lot of those benefits of those casual interactions, but you also lose a lot of your diverse talent because now suddenly it's just harder to hire somebody that has family obligations and honestly some jobs do not require, most jobs, I will say, do not require being

present five days a week. Especially in the corporate space, that's my own bias that I've mostly worked in this kind of environment where you don't see quite the full benefit of that everyday presence. So hybrid often gives you a nice balance, it helps you recruit the most talent while still, while still giving you the benefits of in-person presence.

[00:31:03] Tanya Early: Also expands your radius for where you can hire people if they're only coming in, yeah. A couple days too.

[00:31:11] Mariya Meshcheryakova: Yeah, very important cities like Atlanta and Charlotte where there's a lot of sprawl.

[00:31:16] Tanya Early: Yeah.

[00:31:17] Mariya Meshcheryakova: People are very spread out.

People are not always willing to commute an hour and a half driving, five days a week.

[00:31:25] Tanya Early: Yep, yep. Okay, the most underrated KPI for workforce planning.

[00:31:35] Mariya Meshcheryakova: I'd say talent availability. Talent availability because there's, I'll say like this, just because you want there to be talent in a certain area doesn't mean that talent exists.

And that's, often what it comes down to. You can't, even if I pay more money, if the talent is not there, they're just not there.

[00:32:01] Tanya Early: True, okay. A leadership principle that has guided your career decisions.

[00:32:11] Mariya Meshcheryakova: I would say I- the phrase is, don't judge a fish by how well it can ride a bicycle. And it's about, when you think about

people, you really wanna be thinking about the unique contribution that person brings and how you can best leverage it.

You don't wanna put people in a box because you have a certain expectation. With every person I meet, I like to identify what I admire about them, and I can even people like- even in, I will say, even people in my life that I've disliked, I could tell you probably a list of things I admired about them.

And I think that's important because when you interact with people, it helps you get the best of them and the best of their expertise because you see them for their best characteristics.

[00:33:12] Tanya Early: That's great, that was a good way to put that. I like that. Well, it has been a pleasure talking to you today, Mariya.

I wanted to thank you for your time and, everyone stay tuned to Draup Dialogues for more such content from industry leaders on what's trending and how to stay ahead of the curve.