Noelle London – HR Tech 2024 – How AI is Shaping the Future of People Analytics

Connect with Noelle:

• 

Connect with David:

• • 

• david.turetsky@salary.com

Podcast Manager, Karissa Harris:

• HRDataLabs@salary.com

 

Production by Affogato Media

Resources:

• Illoominus
• Salary.com Consulting practice
• HR Data Labs Season 8 Episode 20
• HR Data Labs Season 8 Episode 18

• “HR is not administrative. Organizations that look at HR as administrative are not truly seeing . . . that investing in your people is investing in your business.”

Announcer: 0:01

The world of business is more complex than ever. The world of human resources and compensation is also getting more complex. Welcome to the HR data labs podcast, your direct source for the latest trends from experts inside and outside the world of human resources. Listen as we explore the impact that compensation strategy, data and people analytics can have on your organization. This podcast is sponsored by Salary.com, your source for data technology and consulting for compensation and beyond. Now here are your hosts, David Turetsky and Dwight Brown.

 

David Turetsky: 0:38

Hello and welcome to the HR Data Labs podcast. I'm your host, David Turetsky, and we are recording live from the 2024, HR Technology show in the beautiful Mandalay Bay in Las Vegas, Nevada. I have with me today Noellle London. Noelle, hi. How are you?

 

Noelle London: 0:52

I'm great. Thanks for having me. This has been a long time coming, David!

 

David Turetsky: 0:56

I know we've been talking about this for a while! But so what does your company, illoominus do?

 

Noelle London: 1:01

So we are a turnkey people intelligence platform. Essentially what that means is that we bring together a disparate HR tech stack that doesn't talk to each other, we put it into one place so that we map out the entire employee journey, and a company gets both access to their entire employee journey in one place and cross tool learnings, but they also get the chance to learn about what their peers are doing in the market as well.

 

David Turetsky: 1:27

Oh, interesting!

 

Noelle London: 1:28

yes.

 

David Turetsky: 1:28

So you do some benchmarking along the way.

 

Noelle London: 1:29

Yes we do.

 

David Turetsky: 1:30

Very cool.

 

Noelle London: 1:31

Yeah, and very needed context. When you think of the number of things that are changing for these HR leaders every single day, how are they supposed to know what best practices are?

 

David Turetsky: 1:45

And what, what does even best practice mean? Because a lot of times now, when you say the word best practice, people go, best practice in what context?

 

Noelle London: 1:52

You know, it means calling your friend who's in HR on the phone and asking them, What the Bleep are you doing in this particular situation?

 

David Turetsky: 2:00

Right, which isn't necessarily legal from a monopolistic perspective. Sure

 

Noelle London: 2:04

Right? In some ways, yes, but there are ways, I How do you do that at scale, through a think that that's what's really cited on the product side, is that same thing that we see HR leaders do to share information with each other to help each other. product?

 

David Turetsky: 2:21

And when you're doing it through at scale through a product, it's not monopolistic anymore. It's actually called benchmarking.

 

Noelle London: 2:28

Right. And when you don't know the name of the company,

 

David Turetsky: 2:30

Exactly

 

Noelle London: 2:31

and it is aggregated and anonymized, then it levels the playing field.

 

David Turetsky: 2:35

And that's good, yeah. So Noelle, what do you do for illoominus?

 

Noelle London: 2:39

So I am our founder and CEO.

 

David Turetsky: 2:42

Okay, there you go. So we're gonna learn much more about you now, because now we're also gonna learn what's one fun thing that no one knows about Noelle London?

 

Noelle London: 2:52

One fun thing that nobody knows about me. I really enjoy a good treasure hunt. I enjoy a good treasure hunt. I would say, you know, if there is a, if you're driving down a rural road and there's a thrift shop on the right, I'm going to find a treasure within there somewhere. So that's a lot of fun for me.

 

David Turetsky: 3:16

We should never go on the road together then.

 

Noelle London: 3:20

I go pull over, pull over!

 

David Turetsky: 3:21

I love doing the same thing. And so yeah, that would be dangerous.

 

Noelle London: 3:25

What's the best thing you've ever found?

 

David Turetsky: 3:28

Actually, I found two portraits that were dated from the 18th century at a yard sale in Syracuse, New York, and I own a house from 1775 so those portraits fit in perfectly in my house. Now, I don't know if they're worth anything, but they're worth something to me!

 

Noelle London: 3:50

yeah.

 

David Turetsky: 3:51

And the context was I was there helping my sister move and it was just a really an emotional time in her life. So it just worked out perfectly. How about you? What's the one fun thing that you found?

 

Noelle London: 4:02

Well, mine is less maybe that one's this. One's less sentimental, but I do have a chocolate Labrador named Jefe, and we found a vintage suitcase that is like probably from the 50s, and it says on my way to visit grandma, like on the suitcase, and so it fits his like, food and everything perfectly, and it's become his suitcase.

 

David Turetsky: 4:28

That's awesome, that's brilliant! That's really cool.

 

Noelle London: 4:33

Yeah, it's a lot of fun, and it makes us laugh every time we look at it.

 

David Turetsky: 4:36

Aw that's really neat. I should do the same thing, although my dogs really don't travel with me, but that's really cool.

 

Noelle London: 4:42

Yeah, yeah.

 

David Turetsky: 4:52

All right, so now let's transition to get to the business end of this conversation. Which is let's talk a little bit about context of first of all, obviously, with Illoominus, you do a lot of people analytics, a lot of HR analytics.

 

Noelle London: 5:05

Yeah.

 

David Turetsky: 5:06

So what's going on in the world of people analytics and HR analytics today?

 

Noelle London: 5:10

It is. It's funny, because we're sitting here in the middle of what is this. Would you call this the largest HR technology show? Is this the largest one this?

 

David Turetsky: 5:20

Well, this certainly the largest HR technology show in the world,

 

Noelle London: 5:23

yeah,

 

David Turetsky: 5:23

but this might be the largest one I've ever been to, and I've been to 20 of them.

 

Noelle London: 5:26

Yeah, wow. That's a very interesting point. That's a very interesting point. What I was thinking as I'm looking around to all of these companies is that I think that the data piece is the piece that every single company in this show is trying to figure out, and I think it's a really exciting time. Sometimes I think about kind of the cable business and this idea of like bundling and unbundling, and it's like a constant cycle. I think what's really exciting right now about people analytics is that oftentimes, in the past, maybe somebody thought they had two options. The options were, I can go to an end to end platform that's going to do everything for me, that's really hard to make work. Or I am going to need to build something myself to make this work for me, and it's a really big project that's time intensive and expensive. And so to me, I think what's really exciting is that there are more options than there have ever been before, in some ways, so that organizations, and in particular, when I think about it, I think that a lot of these organizations have thought that I have to be a big company to have people analytics. I have to be a big company. I have to build out a large team. We're going to build something completely custom. Whereas now, I think what's really exciting is, you know, I'm here with Illoominus, and we go after those middle market customers, but that that customers now have options to be able to have the level of insights and data to make decisions that used to just be reserved for very large companies.

 

David Turetsky: 7:17

And to your point, there are lots of different vendors around in this show thact are pointing to different parts of the enterprise, whether it's small businesses, mid size to your point, or large. There's the Visiers of the world that are the 800 pound gorilla in the people analytics space.But you pay for an 800 pound gorilla in that in that solution. And then there are others in the in various places there that are trying to potentially solve different problems where it sounded like what you were talking about before was much more of a holistic people analytics approach for lots of different disciplines within people analytics.

 

Noelle London: 7:56

Yeah. And I think in particular, so that those companies don't necessarily have to. Most companies, frankly, can't pay for a Visier and aren't resourced to be able to use a solution like that. And so the ability to have and almost democratize those types of insights for companies that can't necessarily afford it. And I think the other thing that's exciting about that is, with these solutions, it means that, you know, sometimes there are it's hard to meet all use cases for a company, and every organization may have different use cases that one solution can't fit. Essentially, what we're saying now is use the solutions that help you to do the work best, and we can sit on top of them.

 

David Turetsky: 8:38

Sure. Well, and if you talk about people analytics, the thought process, yeah, there are lots of organizations that are ready for certain pieces of people analytics, like, for example, turnover rate and hires and

 

Announcer: 8:51

Like what you hear so far? Make sure you never miss a terms and things like that, more of the descriptive statistics that they used to get out of reporting. But they're not mature enough, they're not ready. They don't have the discipline like you mentioned. They don't have the people to do more sophisticated people analytics, and they have audiences, whether it's managers or business leaders or even functional leaders that need that data and need deeper insights because of new skills that are required, or pay gap analyses, for example, pay and pay equity. Yeah, that I mean, you know the compensation department, show by clicking subscribe. This podcast is made possible by Salary.com. Now back to the show.

 

David Turetsky: 9:44

The next question I want to ask is, kind of the intersection of compensation and people analytics, and where do you see those intersections, either from a data perspective or from a discipline perspective?

 

Noelle London: 9:55

Yeah, I think, I think, you know to tie this question back to the last question. It's almost like, when you think of an S curve of like an innovation curve, it's kind of like a data maturity curve. And the reality is, organizations are all in different places. They're starting from different places. They have resourced different places along that maturity curve. And so it's important that, you know, like, one size fits all people analytics and expecting somebody to be able to be at, you know, a super mature stage is not necessarily always where people are right now, at the same time, it's becoming table stakes, that every single

 

David Turetsky: 10:30

Right. HR leader needs to be data driven and needs to come to the table with this type of data to be able to contribute to conversations. So the reason why I say that is I think that people are at different places of that maturity curve. There

 

Noelle London: 10:44

Why I say that is, I think what's really are some of the companies that we work with that may be earlier, within their within their journeys, and they still may be at that place where they're still trying to understand what is happening, and not from that they you know, shame on you. You should know what's happening. But sometimes, in some of these industries where things are changing so fast, it's like 100 people leave every single week, it's really hard to keep your finger on the pulse, especially when that data has sat traditionally in an IT department that you have to knock on somebody's door and ask for every time that you want it. interesting about comp is comp is also something that starts to become a lever that some of these leaders have. So maybe they're starting with understanding head count. They're understanding they've got attrition happening in certain departments, being able to play with comp to help to look at, you know, are we seeing that people aren't accepting roles within the organization? Are we seeing that there's certain turnover that's happening with certain roles? Comp really becomes that lever to stabilize some of what's happening, which is really interesting. The other thing that I would say is that when we think about people analytics, we think about trying to bring together the whole story and the whole picture in one place. Oftentimes, again, as looking around the room and you see a lot of potentially point solutions that leaders are using to piece together to be able to do the role. There is important pieces of information, they may live in each of those different systems, but compensation and being able to look at the whole picture of the employee journey is really important. With compensation, sometimes we actually had a we had a group of Chief People officers that we brought together regularly, that we bring together regularly, and recently brought them together to talk about compensation. And something that they were talking about was the importance of bringing compensation data into the whole employee journey, so that as they're looking at things like retaining high performers as they're looking at engagement surveys, as they're looking at compensation, they're really wanting to bring all of those pieces together to understand the whole picture.

 

David Turetsky: 13:09

Right.

 

Noelle London: 13:10

And especially as a lot of them this year, with the broader macro economic environment, they don't necessarily have their, you know, their budget's increasing for compensation, and so thinking about ways that they can also be supporting their employees, thinking about things like flexible policies in addition to comp, that's a big part of the conversation that we're hearing from them.

 

David Turetsky: 13:35

So a lot of times when the analyses come back that we're losing people for whatever reason, and sometimes compensation might be the reason, although it's never the number one reason, it always could be the straw that breaks the camel's back. Managers actually typically are.

 

Noelle London: 13:51

Yeah definitely

 

David Turetsky: 13:52

Love my manager. But, but when

 

Noelle London: 13:55

I love mine. His name's Jefe. It's my dog.

 

David Turetsky: 13:57

There you go, the chocolate lab. But, but when you do the research and for comp, we know what the market position is. We know whether people are underpaid or overpaid. Comp has lived in a world of data analytics for years. We that's what, that's what most. I mean I've been in compensation for 35 years, and when I started in the people analytics world. The one interesting thing I found was that I was thinking about things in a way in which some of my colleagues had not thought yet because I was already in analytics. I mean, comp people kind of think about try creating models. We think about trying to describe difficult statistical algorithms and try and simplify them. And so when you kind of bring compensation into people analytics, one of the things that that I've always tried to do is try to explain more complex relationships of data in very simple ways so that the people that I'm talking about that comp situation, they kind of can get it much more easily. Because when I started in people analytics, one of the interesting thing, things I found was, was that we were talking over people.

 

Noelle London: 15:15

Yes,

 

David Turetsky: 15:15

They didn't grasp what we were saying,

 

Noelle London: 15:18

Yeah, yeah. And I think that's something, I think Yeah,

 

David Turetsky: 15:18

because they couldn't get beyond the fact that we were using the words, you know, analytics or statistics, and they kind of were like, Oh, I'm going to shut down now, because I hated that class in college. And, you know, we're really just describing how many people do we have. Or, you know, why do people leave? Or, you know, how many people do we expect to leave? Or what's the rate at which people are are joining or leaving, but, but we were trying to be so I guess the word is so smart that we were talking beyond them. that's very real! I think that oftentimes, and again, we're working with companies that, you know may not have very large, built out people analytics teams. They often have, you know, people analysts. They have, you know, HRIS systems analysts, that type of thing. But I think that more and more it's it's kind of expected that the data is coming from all of the people team, and it's not something that necessarily somebody in an IT department on a data team goes and does an analysis and comes and throws it over to the people team. It's like it's expected that all of HR start to leverage that data, use that data to make decisions, and so it's important that it still is accessible to HR leaders that maybe were not trained formally in data science, but they still need the data to be able to make these decisions, and that's something that I think really energizes us. To that point, that's a fascinating point, because when we went to school, and not many people went to school for HR, by the way. But when we went to school, or when we started growing up in the world of HR, there wasn't really this thing called analytics. It was really just reporting, you know, it was like, dump a table into Excel, and I'll add up the number of people, or I'll, you know, take an average on this, or I'll do something on that, and then, obviously, marketing adopted it. Supply chain adopted analytics. Finance went, you know, really deep in it. And so it started become something that these organizations found as a differentiator, that they could use statistics to describe things. And it became something of a differentiator for that company. Well, then somebody said the HR, hey, there's really cool thing called analytics. Why aren't you guys doing it? And we were like, We're administrators, we're.

 

Noelle London: 17:49

Well, and also, I mean, 100% I think that's, you know, when, when I think back on even our founding story, I was at a large management consulting firm, Accenture, and, you know, was a part of the ventures team. So we were bringing emerging technology to leaders within our clients. And every one of my colleagues, think about amplitude for a marketer. Think about supply chain technology. You looked at every other functional area, and they had the ability to bring everything together, help that leader tell a story. And the reality is, I was walking into the CHROs office with my like, cute startup logo map that was beautiful, but when you sat back and you looked at it, it was full of point solutions, and they weren't talking to each other, and so as. And this was during the time, I mean, thinking back on sometimes we throw up like a timeline map of the last four years of what it's looked like in HR. And you kind of watch people shudder, unfortunately, because it's been so intense of the level and rate of change, and yet. And I think, like on to your point on, you know,

 

David Turetsky: 19:05

Oh my gosh, yeah. Administrator, well, HR wasn't necessarily equipped with the tools and resources and you and you layer on top of that now, the really important role of playing such a strategic role within organizations as a differentiator. But HR has really, unfortunately, been the last to get that type of tooling to sit at the leadership table and say HR is the most expensive part of the entire organization and the largest line item, and we're we're not getting the resources to tell you how that's And that's totally disappointing.

 

Noelle London: 19:38

yeah!

 

David Turetsky: 19:38

And you know, just in the compensation world, we say to people, that is, as you say, the largest line item, being used! and yet it's kind of an afterthought.

 

Noelle London: 19:42

yeah

 

David Turetsky: 19:43

And we get crumbs.

 

Noelle London: 19:48

yeah

 

David Turetsky: 19:51

And we have to do a lot with crumbs. And so one of the things that we're seeing in every single booth here is going to be helping with those crumbs.

 

Noelle London: 20:05

Yeah

 

David Turetsky: 20:06

Automation and AI. 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/HRDLconsulting to schedule your free 30 minute call today. So the next question I have for you is probably going to take us a while, because this is a good one which is beyond the hype cycle, where is AI coming out for HR? Where do you see it going? What? What's the purpose of AI in the world of HR?

 

Noelle London: 20:52

The thing that you said in the last question around HR is viewed as administrative. I think that needs to be the first thing to go because HR is not administrative. Organizations that look at HR as administrative are not truly seeing the value that HR is bringing to the table, and that investing in your people is investing in your business. And so I think to me, just as we were just saying, HR has been back of the line to get the cool new things that are going to help them to do their jobs better. I think that should be the first thing to go is the administrative part of that through AI, so that, so that HR can focus on playing that strategic role, sitting at the ELT helping to create the strategy of the organization, of how you're going to invest in your people. And so I hope, I mean, I was even just talking to an HR leader today who was saying, you know, how what, what tools should I be using to start automating some of these processes? And there, there are so many tools that are out there that don't even require your budget as long as you're using them again in line with the way that you should be using them within your organization. That's the future I see.

 

David Turetsky: 22:10

And by the way, your IT department wants to know if you're using these things, how you're using these things. And I've had lots of conversations already where, you know, one of the underlying tenants is that we have to be very careful about how we're using that data, who we're telling the data to what, who we're giving the data to, how we're exposing what questions we may be asking the chat bots or the the chatGPT 4.0, or, you know, name it, Gemini or co pilot, or whomever.

 

Noelle London: 22:46

Yeah

 

David Turetsky: 22:47

But that's happening on a consumer basis right now within our organizations, not even just enterprise wise. So to me, there's a there's an entirety of, I know I'm going to say this and you're going to get mad at me, there's an administrative burden on either IT or HR to clamp down on behaviors, just like we did when it was byo for, you know, iPhone or a smartphone or whatever, there's a you've got to be clamping down, that people aren't exposing our organizations to the outside world by even just asking a question in a chat bot!

 

Noelle London: 23:25

Yeah. And I think that that you're you're right, so don't go and download, like all the things. Don't go and download all the tools just yet. Get some guidance from your IT team and lean on them. I think, like, back to this idea of a maturity curve right now, you know, when the guidance is there, there's a lot of potential administrative things to either automate process, like workflow processes through tools, to get a lot of that administrative work, you know, off your plate, if you will. But I think that also, I think that, to your point, there's a lot of this wait and see of what am I allowed to do, what am I not allowed to do? Where should I be putting my data? Where should I not be putting my data? I think similarly, when we start thinking about, like, okay, kind of the next step on, how do we start getting into a place where, you know, we're in a high turnover industry, and I really want to start being able to proactively anticipate turnover within my organization. I also think a lot of organizations are not quite there yet, but there are some things that you can be doing now to help you get ready for that. And that's kind of what we're seeing is best practice right now is that organizations, you may not be there yet, and that's okay. This is, again, like a maturity curve. And we're we're all going on this curve together. But getting that underlying data foundation right is important now. So the littlest things we see all the time, where it's like typos, marketing team has 3 m's instead of 1 m.

 

David Turetsky: 25:02

But go back to the earliest conversation about HR analytics without having that fundamental data in that I don't want to say standard, with a standard, HR analytics becomes much more difficult, too.

 

Noelle London: 25:15

What was that?

 

David Turetsky: 25:16

HR analytics becomes more difficult? Forget about even AI. But you know, having marketing with three M's, or having a s r dot for senior, and then have all the rest of your senior, say, spelled out with S, E, N, I, O, R. I mean that one of the problems is, is that that makes the interpretation either by the AI or the person who's helping the AI, they have to do extra work for that!

 

Noelle London: 25:42

100% and I think that you started the question with beyond the hype.

 

David Turetsky: 25:48

Right, right. Yeah

 

Noelle London: 25:50

Beyond the hype of AI, because that's some you know, every day we're being asked, you know, what about me, my ability to anticipate this, my ability to anticipate that? But a lot of, the majority of organizations are not ready, because the data is not in a place. You could buy a new, cool tool that's in this room that does AI, but if your underlying data foundation is not strong, you can't trust what's going to come out of there. And so that's a lot of what what we're talking about right now, and what we're actually working on with customers, is data health and integrity. So there are ways to also automate some of the data health and integrity work to make sure that that underlying data foundation is strong, so that as and when you're ready, you feel good, and that those results are accurate.

 

David Turetsky: 26:42

One of the podcast episodes we did must be a year and a half, two years ago, with Martha Curioni and Adam McKinnon, where we talked about actually training bots on being able to focus on data and find patterns in data that aren't consistent from an HR data perspective. So it's been a while ago, and actually I'm going to have Martha on again soon, but her point was, and their point, their thesis was, we can use artificial intelligence to look for patterns in data that don't make sense and help the bots actually fix our underlying data problems, instead of having to spend hundreds of hours of analyst or HRIT time downloading data. You know, good, downloading an underlying table, making sure it's still accurate and and obviously people make mistakes. I mean, AI will make mistakes too, but, but AI trained right, will make fewer inconsistencies than the people will. So my point is we could even have or we could use automation to make our lives easier when it comes to that question of whether or not the data itself is the underlying data is itself clean.

 

Noelle London: 28:05

Yeah, and that feels like that's the conversation to be having now, as most of your executives are probably asking you what you're doing to prepare for that and how you're approaching it, that seems like one of the best things that you can be doing right now is getting your overall data in a strong place so that you are ready.

 

David Turetsky: 28:27

And by the way, that's not a one time thing. That's an ongoing,

 

Noelle London: 28:31

yes,

 

David Turetsky: 28:33

let's call it hygiene. It's hygiene that needs to happen. Because whether we're doing a Benefits Enrollment, whether we're hiring someone, whether we're terminating them, whether we're M and A, and we're adding a new company into our organization, those are all places where,

 

Noelle London: 28:51

whether you're adding a new HRIS system, and you're transitioning from an old system of record to a new system of record.

 

David Turetsky: 28:58

I can't tell you how many times companies do that, and even within the same provider, don't do the underlying work to make sure that their

 

Noelle London: 29:08

schema

 

David Turetsky: 29:08

Oh, yeah, they're making sure everything works right. Or, Oh, you're going from a four character field now they have the ability to go to an eight character field for job code. Okay. Are you going to update your job titles, your job coach and your job? No, no.

 

Noelle London: 29:23

But also, if you want to get to proactive, you need historical data!

 

David Turetsky: 29:28

right

 

Noelle London: 29:29

You need that historical data for you to be able to get there.

 

David Turetsky: 29:40

And so, but by the way, we can have an entire podcast on data.

 

Noelle London: 29:43

Yeah, happy to come back.

 

David Turetsky: 29:45

So one of the things that I wake up in the morning and and love is thinking about how data can be better so that we can see better things in it and do better things. So I am a geek. Yeah. We should go on a road trip and actually

 

Noelle London: 29:58

Likewise. go do some shopping. Go thrifting, talk about some people analytics.

 

David Turetsky: 30:04

Yeah, exactly. Go on the road.

 

Noelle London: 30:06

Yeah. I love that!

 

David Turetsky: 30:06

That would be a podcasts, like a whole new podcast.

 

Noelle London: 30:09

Yep

 

David Turetsky: 30:09

Yeah. Well, you just heard Noelle and I are actually going to start a new podcast here. We're going to, we'll brand it something like chocolate lab on the road, or Jefe Speaks, or something like that.

 

Noelle London: 30:20

We'll keep working on the name.

 

David Turetsky: 30:21

Yeah, no, we have to workshop it. We're gonna workshop it here at HR Tech conference. Then let you know. Let you all know in the show notes, what happened. Noelle, this has been fascinating. Thank you very much.

 

Noelle London: 30:31

It has been a lot of fun. Thanks for having me!

 

David Turetsky: 30:32

We really appreciate it. My pleasure. Yeah, again, we're gonna have to have you back.

 

Noelle London: 30:35

Yeah, can't wait!

 

David Turetsky: 30:36

Thank you again. Take care. Stay safe.

 

Announcer: 30:41

That was the HR Data Labs podcast. If you liked the episode, please subscribe. And if you know anyone that might like to hear it, please send it their way. Thank you for joining us this week, and stay tuned for our next episode. Stay safe.