Amy Butchko – 4 Steps to Better Enterprise Talent Acquisition

Connect with Amy Butchko:

• 

Connect with Dwight:

• 
• dwight.brown@salary.com

Connect with David:

• 
• 
• david.turetsky@salary.com

Podcast Manager, Karissa Harris:

• HRDataLabs@salary.com

 

Production by Affogato Media

Resources:

• Compensation Consulting
• Maximus
• HR Data Labs Season 7 Episode 4
• HR Data Labs Season 7 Episode 2

• “The thing that’s really important is finding people who can help bridge the gap between all those seemingly irrelevant data points and turn them into something that looks like an executive could understand it.”
• “If you can home in on a couple of things that are really, strongly impacting your business, measure them effectively, and tell a story consistently, [you will be able to transfer data narratives].”

Announcer  00:02

Here's an experiment for you. Take passionate experts in human resource technology. Invite cross industry experts from inside and outside HR. Mix in what's happening in people analytics today. Give them the technology to connect, hit record, pour their discussions into a beaker, mix thoroughly. And voila, you get the HR Data Labs podcast, where we explore the impact of data and analytics to your business. We may get passionate and even irreverent, that count on each episode challenging and enhancing your understanding of the way people data can be used to solve real world problems. Now, here's your host, David Turetsky.

 

David Turetsky  00:46

Hello, and welcome to the HR Data Labs podcast. I'm your host David Turetsky alongside my friend and trusty co host from Salary.com. Dwight Brown, how are you sir?

 

Dwight Brown  00:55

I am great, David. How you doing?

 

David Turetsky  00:58

I'm freezing. Still, dead of winter here. 26 degrees outside. And I wish I was in Phoenix, Arizona with my friend Dwight.

 

Dwight Brown  01:08

Just so you know, it's 67 degrees here right now.

 

David Turetsky  01:11

That does not make me feel better. My toes are freezing. I love it.

 

Dwight Brown  01:16

I love you, brother.

 

Amy Butchko  01:17

Yeah. 32 here in Washington, DC,

 

David Turetsky  01:21

Washington, DC. And that is from our guest, Amy Butchko! Go Amy. How are you?

 

Amy Butchko  01:27

I'm good. I wanted to be under the weather conversation here because I'm watching the snow come down here as we speak.

 

David Turetsky  01:32

Oh, so it's snowing there in Washington, DC.

 

Dwight Brown  01:35

Wow. So you got cold temperatures and the snow to go with it. That's a treat.

 

David Turetsky  01:41

And we're just going to actually have an HR Data Labs fetch session about wet weather while we're at it. That's all this is going to be now. The devolved into this? Okay. I'm kidding. Amy. Welcome to the HR Data Labs podcast. How are you?

 

Amy Butchko  01:56

I am well, I am a little on the chilly side here. But it is actually it's a beautiful, picturesque snowy day here in I'm actually in Northern Virginia, which is close to where my employers headquarters is in Tyson's Corner, Virginia. So um, yeah, but it's absolutely beautiful. We've not had really appreciable snow in this area in over two years, though. So it's a novelty.

 

David Turetsky  02:20

So you might want to take a picture for next year's Christmas card is what you're saying?

 

Amy Butchko  02:23

I we've been doing that. Yeah. Now.

 

David Turetsky  02:28

Exactly. Why don't you give us a little bit about your background, and about who you are?

 

Amy Butchko  02:33

Well I am a talent acquisition vice president at a company called Maximus, which is headquartered in Tyson's Corner, Virginia, as I said, and in my role, I am focused on delivering IT hires at scale. Maximas mission is driven around delivering service at scale to citizens, through governments around the world. So it's a it is an exciting and heartfelt mission for all of us here. And my little slice of it is delivering the IT talent that helps enable the automation and some of the systems that those that those services use that yeah, so that's my, that's what I do right now.

 

David Turetsky  03:18

So Amy, Do I understand it correctly, that you're working on a book. Oh,

 

Amy Butchko  03:23

gosh, yeah. In my in my dreams. I I'm actually a frustrated potter and a brand new golfer. So I really don't have time for such things like writing. So that's why I do stuff like this. Oh, it's okay.

 

David Turetsky  03:37

We'd love having people on who use their creativity for vocal reasons. That's totally cool.

 

Amy Butchko  03:44

Exactly.

 

David Turetsky  03:44

So Amy, what is one fun thing that no one knows about you.

 

Amy Butchko  03:48

So the I typically like to take on challenges that I am not guaranteed to win, which is why I took a golf last summer. It was a very neat fit because I'm not good at it yet. And I may never be I mean, if you ask me. I don't know if you if you to play golf, but, boy, it's frustrating. So

 

David Turetsky  04:10

The reason I don't play golf is I have enough frustration in my life. That I need one more thing. So yeah,

 

Dwight Brown  04:16

Well I like to say that my pitching arm gets really good when I play golf. I believe the Caddyshack way.

 

Amy Butchko  04:22

Okay. Okay. That makes that makes sense. Yeah. Yeah, I'm still just trying to be a good golf citizen on the course. And truth be told I, I injured myself a couple of years ago, I flew off my bike. And so golf is a safer hobby, even if it is a little bit more frustrating than biking. But it has kind of helped me rehab my wrist. So so that's been fun. And so people don't always know why I came to golf. Just that's yeah, so that's a little bit about me often

 

David Turetsky  04:51

Say for them bicycling, that's for sure. Oh, yeah. Don't pick any of the things that Dwight would do. sickling but actually paragliding,

 

Dwight Brown  05:02

Jumping off mountains and cliffs and

 

David Turetsky  05:05

Jumping off of perfectly good airplanes, right?

 

Dwight Brown  05:08

No, I don't jump off of airplanes. I don't skydive.

 

David Turetsky  05:11

Thought, you did skydiving?  No, no, n, believe it or not? Yeah. No, please don't do that now.

 

Amy Butchko  05:20

Throw me off a whole

 

Dwight Brown  05:22

Whole new level of risk.

 

David Turetsky  05:24

Yeah, no, anyways, so wonderful to have you here. And so our topic for today, which is really fascinating is a four steps to get results with using data for enterprise talent acquisition. So Amy, what is the biggest challenge in talent acquisition today?

 

Amy Butchko  05:50

So, David, it kind of depends on on what you read. But right now, it's just kind of figuring out where you're going to find your next hire, and how quickly you can do it. So you know, in what I do with IT works, it's a notoriously small pool of talent relative to the number of openings. And that is still true, even the recent gyrations on in the labor market. But what we find is that you, it is difficult to find folks that have the right match of skills in the case of government contracting, which is my specialty over the years, it's been, you know, finding people with the right clearances, people who want to work on site on government facilities, that kind of stuff. So it produces a level of complexity, that becomes challenging, and, you know, and makes it even more fun to do than you could ever imagine.

 

David Turetsky  06:42

I could imagine, actually, because having tried to find qualified talent in the past, it just seemed maddening, especially trying to find as you say, there's a mix of skills and licensure and other things. How do we do it? Where do we go? What do we do?

 

Amy Butchko  07:02

Oh, gosh, well, that is beyond the scope of this for sure. But, you know? Well, I think it depends on, so one of the things that I have benefited from in my career in talent acquisition is really specializing, and getting good at at finding IT talent is, is what I do. And you know, and I typically have done it in the government contracting space, which is probably, you know, a slightly different flavor than what you would find in in private industry. But you'd be surprised, because the overlap and technologies is significant. And we do pull people out of all sorts of private industry into ours. Gov con was great, because of the resilience of our industry. You know, we don't typically struggle with the same types of ups and downs with funding. However, working you know, many of these companies, including Maximus, is a publicly traded company. So you know, you are on the hook for, you know, making sure that you've got the right people to do the jobs that we signed up to do for our clients. And that's something that every company can do better, probably.

 

David Turetsky  08:13

So if you take it from the perspective of okay, now we understand what the challenges are, how do you measure? Or how can you look at the talent acquisition world and figure out, Aae we measuring the right things? Are we is there? Is there a data problem here?

 

Amy Butchko  08:28

Short answer? Yes. And you know, and what's interesting about the figuring out what to measure question, big enterprises often have a surplus of data. And when you start getting into, you have so many different metrics that you could measure, but then which metrics actually are impacting the business that you're trying to improve? Right. So there's risk on focusing on too many things at once. So this is also kind of where my specialization brain has, has worked in my favor in the data space, is being able to kind of filter out what I view as noise or figuring out what is the noise, right. And, you know, smaller organizations, people, you know, folks that are working in organizations that maybe don't have as much data or collection mechanisms that are not as robust. Like, you know, we hate, we all hate our applicant tracking systems. That's not news to anyone, but they're really useful. They're really useful. One of the reasons we hate them is because they collect all those little bits of data. And you know, and then, you know, once you flip it over, and you start looking on the other side of things, and all that data starts to come out, then it really starts to get interesting. And then that applicant tracking system doesn't seem quite so onerous anymore. So you know, that's the kind of thing where a big enterprise can make a big difference and really start to look at what's going into their applicant tracking system and how their recruiters are using that applicant tracking system to figure out how to make hiring more effective.

 

Dwight Brown  09:59

Tht is Really interesting the, because so often we hear we don't have enough data. That's our biggest challenge. We don't have data. And what we're hearing from you is you, I think you said you have a surplus of data. And now it's just a matter of figuring out what to do with it.

 

Amy Butchko  10:16

Yeah, the, the thing that has that is really important is finding people who can help bridge the gap between all of those little seemingly irrelevant data points. Right. Right. And, and turning them into something that looks actionable, or looks like an executive could understand it, without knowing you're like David wants to understand how to do, you know, recruiting this afternoon? So yeah, so I think I think it could be both. And you know, and without knowing a particular organization's constraints, less data is probably not the pride to too little data is probably not the problem in modern times.

 

Dwight Brown  10:59

Which in a way is, ultimately, when you have too much data, you don't have enough data is what it comes down to. Right. You don't have the right data with it.

 

Amy Butchko  11:08

But you still have a way to aggregate it and look at it, or maybe you don't know what you're trying to measure. So you know, yep. So a nugget that would probably be useful to your listeners are some of the things that that I think are relevant. And you know, whether you're looking at recruiting, labor cost or time to fill, or time to start, those are two different things. Sometimes it takes a while to get the right person on board, even if you've identified that person. What are your tools costing? What is your cost per hire? What is your hiring volume? What is your net promoter score, right? Like these are all data points that can inform a talent process. But you know, without a way to really cogently look at it? And say, okay, these are the things that are impacting my business today. And which ones am I going to focus on to manage my team?

 

Announcer  12:06

Like what you hear so far, make sure you never miss a show by clicking subscribe. This podcast is made possible by Salary.com. Now, back to the show.

 

David Turetsky  12:17

I think there are a couple other metrics though, that kind of go to what you were talking about about acumen around not just understanding the data, but also understanding the process itself, which is things like my sources, my my best sources of hires, or the most diverse sources of hires, or what's the productivity gain from the latest sources. So, you know, my time to productivity with sources as well. So if there are a couple of really great schools that I'm getting new hires from, and they're in certain roles, if I'm getting better productivity out of certain schools than others, what we should be targeting them more. So I guess what I'm asking is, how do you build the acumen in your TA organization or an HR to be able to look at those metrics, figure out which ones are the right ones, for them to drive, those business outcomes you're talking about?

 

Amy Butchko  13:12

The first thing that I would do is say that, you know, the req type? What is the actual requirement that needs filling? And so I would be looking at it from the perspective of how much experience does a person need to be hired into a certain role, I'm going to break that into three chunks, just for simplicity for this conversation, you've got your entry level, early career span, you've got your experienced, but maybe not yet expert level person, you know, in it, that would be your your senior developer, your, your data analysts, to senior data analysts, those types of folks. And then at that, in that top level, from an individual contributor perspective, you would have your architect type level, and, arguably, you know, so so you've got that chunk. So we're going to, we're going to take those three levels. And then I'm going to also bring in a concept that is really important in today's market, and that you're probably starting to hear, which is skill based hiring. But what that really means is that we care more about the skill of the individual than the pedigree of the school that they came from, which means that your source of hire becomes much more difficult to say, well, all my best candidates come from MIT, or I get my best tech talent from Stanford. Right? Because what really ends up mattering is and the most important question that you can be asking is, what does a person need to know how to do to be effective in this job? And I would argue that at that entry level You have no one answer that question at that mid level, which is a really big level is where the vast majority of folks are going to be. And then at that architect level, I think, you know, it becomes even a little bit maybe simpler, because you're going to have a certain level of, of acumen at that point that is going to be more measurable than, you know, potentially the potential level that you might have in that early career area. So I think what you're trying to then do is figure out at each level of hiring, what are your strategies that go in and where are your most effective hires being made? And what we're finding is that with the advent of skill based hiring has basically a follow up because the bachelor's degree has become a an unreliable predictor of success in the workplace. Yep. Right. And it is also to your point about diversity, a hurdle that not everyone has the privilege of climbing, right? That's right. But it does not mean that someone does not have the the ability to do a role in my business, for sure. So there were two articles that came out this week that I actually pointed my business back to one can't one was about the federal government, removing degree requirements from cyber jobs, which is overdue.

 

Dwight Brown  16:21

Well overdue,

 

Amy Butchko  16:22

right? And then yeah, because you know, you'll get military veterans coming out, and they are far more qualified. And they've been doing the work, and maybe they have a degree, maybe they don't, maybe they're in the middle of it, probably that's the case. But but you know, they wouldn't qualify for that, you know, job on the basis of how it had been written in a legacy fashion, you know, and state governments are also starting to take a take a look at that. And once you start having that having those kinds of big movements in the market, that skill base becomes a lot more interesting. And, you know, when you're looking at source and how you go about targeting, attracting, retaining that talent, it becomes a little bit of a different conversation, and those levels do become important for how you do.

 

David Turetsky  17:10

Let me ask you a stupid question hearing me, when we look at those measurements of those particular groups that you're talking about? Are we actually taking people's resumes and looking at them? Or are we doing assessments on them? Either pre during the screening process or pre hire? Where do we find out that they actually have this knowledge? Because obviously, the resume has been in LinkedIn is not they're not really good, reliable sources of that.

 

Amy Butchko  17:40

Excpet for our profiles, which are completely right.

 

David Turetsky  17:43

Absolutely pristine, they're pristine.

 

Dwight Brown  17:47

They are spot on.

 

David Turetsky  17:49

But my LinkedIn profile does not have I mean, it has the skills that I say I have, but they're not tested. They're not the you know, there's no, I mean, they all did in the my, my certifications that I got from Johns Hopkins through Coursera. They're not there, you know, there's no stamp on them.

 

Amy Butchko  18:06

Right? No, there's not. And, you know, and even if the worst stamp, there's, there's no telling if, once you actually got into a role, if you would be effective at it, and you know, so I think that some of right, I mean, you know, some of what we do here can be very scientific, you know, and I know, this is a data podcast, so we want to make it as as clean and as as precise as we can. But in fact, we are still dealing with people. And you know, and I'm of a mind that, you know, to answer your question, you know, it is, you know, where I am, it's a mixture, and assessment can be used effectively, to, to screen in or out based on certain certain criteria. I mean, it can be simple, like, you know, how, how well does someone perform computational tasks. So that kind of thing, if that is, in fact, part of the job, although, you know, we have things that do computational tasks for us pretty effectively. Am I right?

 

Dwight Brown  19:03

Can they use the software? Yeah, exactly. Yeah. You don't have to know the equation. You just have to know to plug it into the software. Yeah.

 

Amy Butchko  19:12

Exactly. Dwight. So so maybe the challenge to your question, David, is, is that really the right are we asking the right questions?

 

David Turetsky  19:20

And that's a really good question. Because when the people are out, having you know, having gone through the process of putting in a resume to a job that I'm overly qualified for, in getting sent back a five second later note that says, although your qualifications are very impressive, we're going to move on other resumes. Yeah. Are you serious? Within five seconds, somebody read my resume and said, Yeah, he may be the best at this thing. But no, we have other people that are ahead of him.

 

Dwight Brown  19:52

Right. Right, really. So systems systems don't work the way that we hope that work for us. But that's the that's the you know, to your point earlier you were talking about the fact that this is a data podcast. But the Altman piece of this is that there's an there's both an art and a science to this exactly. You know, we can put the data to it all we want, we can build our algorithms and in the ATS, but at the end of the day, when it comes down to it, it's all about that one to one conversation that you have with the candidate. And David, to your point, being able to assess in some way or another, and hopefully, you've got some tools to be able to assess that. Alright, but nothing beats the you got to have the art to it. There's you got to have the conversations that go with it.

 

David Turetsky  20:45

Yeah, agree. And I think this was any what Amy was talking about, about creating a story around or narrative around your data, right?  Hey, are you listening to this and thinking to yourself, Man, I wish I could talk to David about this? Well, you're in luck, we have a special offer for listeners of the HR data labs podcast, a free half hour call with me about any of the topics we cover on the podcast, or whatever is on your mind. Go to Salary.com forward slash H R DL consulting, to schedule your FREE 30 minute call today. Why don't we talk a little bit about that? How do you create a narrative around the data that you use to recruit?

 

Amy Butchko  21:27

I think the first thing that I the first thing I do is figure out who my audience is. Right? So if I am speaking with an executive audience, I am going to most closely aligned with the things that they have told me are important to that, which are usually things like, how much revenue are we generating? How our net promoter scores things? Like how are we perceived in the market, by our job candidates? And by and how are our workers portraying our our EVP, or employee value proposition. So that's going to be the kind of stuff that I'm going to focus on at that level. If I'm managing somebody, I'm going to be looking really closely at what the volume of their activity is. So you know, how many people are we, you know, what, how many reps? are they holding? How many how quickly? Do they fill those requisitions? How many candidates does it take them to submit to a manager before we get through the, you know, enough interviews through on how many interviews need to be had, before we get a hire? Once we make that hire? How many of those offers are accepted? You know, so I'm going to get really, really granular and I already see your eyes glazing over. Because these are not interesting stats.

 

Dwight Brown  22:46

No, but they are interesting stats, it gives you a picture will take more data than less. Yeah. And just so you know, we're kind of weird with data. So it for us, it is actually interesting. We're already doing further computations. And are you talking about these, that's the eyes glazed.

 

David Turetsky  23:04

Thinking about data dictionary, we're thinking about the data governance, we're thinking about, you know, how we're going to collect all this data, you know, the benchmark we might get with you.

 

Amy Butchko  23:15

That's right. So so I wouldn't be you know, so if I'm going to be working with a recruiter, you know, I'm going to be working with a different data side, if I'm working with a recruiting leader, I'm going to be looking at that data in aggregate. And, you know, and I'm going to be assessing, you know, kind of a little bit like, going back to the first part of our conversation, you know, if I've got folks that are recruiting really high level architected engineers, you know, I'm going to have a different set of expectations than if I've got people recruiting, you know, entry level, or early career talent, where we may have a a larger pool, and be a different expectation set that can then be molded a little bit more based on who we get into that position.

 

David Turetsky  23:59

And what kind of narrative are they looking for? I mean, obviously, they're looking for, as you were saying, you know, tell me how this impacts the business. How does it impact our NPS scores? But what if no one's listening? You know, what do you do? Yeah,

 

Amy Butchko  24:12

Yeah, so definitely been there. And you know, and there are some groups that just are not that interested in it. It's just, they're not interested in the data. As much as sometimes like, I usually know that I'm in a conversation that's not going to go anywhere from a recruiting perspective, if we start talking about at the candidate level, why somebody didn't accept the job, or why this or why that, you know, it's just, it's, it's not actionable, because it is personal, right. So, finding things like that out through feedback. And ultimately, when I talk about data, I try to workshop it a little bit where I will talk to someone in advance Once and say, you know, does this make sense? You know, I had a boss who would tell me to explain it like he's a fifth grader. That was actually enormously helpful advice. And he's listening. He's probably smiling. He's totally she finally got it. Right. Oh, well, good. Good. Right. So you got so we have something we can practice on. Right? So yeah, so it's just figuring out like, what, how much complexity can you eliminate? And you know, and one of the other things that you know, so I was a journalism major in college, which was incredibly useful, except for the fact that I mostly learned to be an effective written communicator. So sometimes I will write my communications until I have my story together. And, you know, and then people will say, Gosh, Amy, you know, you're such a strong presenter. Hmm, okay. I'm, I'm a good reader. I'm a good reader. I'm actually not reading much today. But this is a topic that I'm pretty I'm pretty comfortable with and familiar with. But if I'm, you know, if I was new to this, my advice to myself and anyone else who wants to tell a better data story would be to simplify complexity, as much as possible, try to explain it first to someone who is not in your business. Right, when their eyes glaze over, it is time to go back to the drawing board?

 

David Turetsky  26:27

Well, yeah, I mean, the problem with trying to tell stories about data is that most of the time, the person who's explaining it is confused about what's the best methodology of being able to explain hard, hard concepts, who's able to grasp like, they start getting into statistics of, you know, well, the median of this as no one cares about that. And if you say, the average person, and they can kind of grasp what the average means. So what when I coach people about telling stories, I actually tell them to ignore those things completely. Don't talk about the median, don't talk about average, don't talk about how the range of acceptable values would be, no one gives a crap about that. Just say what you mean, just just tell people. And to your point about the fifth grader? Yeah, you have to be able to meet people where they are. And while there is also a range of people who can accept and understand things, trying to get to the lowest common denominator, most of the time will typically, most of the time will typically sometimes well hit home. Yeah.

 

Amy Butchko  27:31

Yeah. And I think, you know, the other thing that I think is important is repetition. And, you know, like, so I started in Maximus, a little over a year ago. And they didn't really have much of the data narrative around my practice. And my practice was relatively new. So I got to sort of come in and say, Okay, I'm going to look at what's going on here. And I'm going to pick out what I think I can impact. And that's what we're going to talk about,

 

David Turetsky  27:58

if you're coming from a position of strengthen it. Well, that's true,

 

Amy Butchko  28:02

too. Yeah. I mean, it doesn't mean that I wouldn't you know, that I won't go away and find the answers to more difficult questions or questions that I view is, you know, previously viewed as not relevant. Like, oh, wait. Yeah. So yeah. So I think that that's, I think you're right. But I think that, again, getting focused, and staying out of the temptation to overkill people with with so much, if the if you can hone in on a couple of things that are really strongly impacting your business, and then measure them effectively, and do it consistently and tell a story consistently, you I have been able to transform their data narratives that way,

 

David Turetsky  28:49

that can't be said any better. I've actually tried to teach people how to talk about telling stories or on data. And that's exactly it right there in a nutshell. It was just such a pleasure, Amy, thank you so much. I mean, this is a really great way for us to kind of get a little bit deeper into the recruiting world, especially with tech hires, and be able to understand the business problem, but also to be able to kind of communicate that business problem. So thank you so much for being here.

 

Amy Butchko  29:24

Yeah, anything I can do to move that cause forward?

 

David Turetsky  29:28

Well, you did a great job. Thank you so much for being here.

 

Amy Butchko  29:31

Thanks, David. Thanks, Jerry. Thank you.

 

Dwight Brown  29:33

Thank you. Thank you for being here.

 

David Turetsky  29:35

And thank you all for listening. Take care, and have a great day. And stay safe.

 

Announcer  29:41

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