Mei Kim and Heidi Perloff – How to Make New HR Technologies Approachable for Everyone

Connect with Mei:

• 

Connect with Heidi:

• 

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:

• Estee Lauder Companies
• Compensation Consulting
• HR Data Labs Season 7 Episode 8
• HR Data Labs Season 7 Episode 6

• “HR VPs who embrace being digital and embrace the use of technology . . . tend to go farther ahead in their careers than others who don’t.”
• “Yes, we can get automation from AI, but it’s really the augmentation of what it enables us to do as human beings that is super exciting.”

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 for 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, co host, partner in crime Salary.com's own Dwight Brown. Hi, Dwight. How are you?

 

Dwight Brown  00:56

I'm good, David, how you doing?

 

David Turetsky  00:58

I'm doing fine. I'm doing well in the office for the first time ever recording the podcast.

 

Dwight Brown  01:03

New environment for you. That's good.

 

David Turetsky  01:05

Hopefully we'll be good. Because today we have two very special guests. Mei Kim and Heidi Perloff, and I'm going to ask each of you if you could give a little bit of background as to who you are.

 

Heidi Perloff  01:16

I'm Heidi Perloff, I'm the Senior Vice President of Global HR strategic initiatives and delivery solutions with the Estee Lauder companies. I've been in the HR field for many years in lots of different roles and just thrilled to be here.

 

David Turetsky  01:32

Awesome! Mei, how about you?

 

Mei Kim  01:33

All right, Mei Kim, also from Estee Lauder, Executive Director of global workforce analytics. I am a self proclaimed lover of data and anything digital

 

Dwight Brown  01:45

Yeah, fit right in.

 

David Turetsky  01:46

Yeah, You're one of our people, then. We are self proclaimed geeks. And that will make this conversation even more fun. But before we get to our conversation, we ask each of our guests, what's the one thing that no one knows about you? And we're going to start with you, Mei, this is your turn. What's one thing no one knows about you?

 

Mei Kim  02:08

Oh, gosh, I think my roots for the love of data comes from my extreme nerdiness and form filling. I love filling in data and little boxes on pieces of paper. So I do all my taxes. All my legal communications and documentation. I do it myself. Wow, form filling, which is totally....

 

David Turetsky  02:30

So the of past life for us census taker. Is that Is that why?

 

Mei Kim  02:36

Yes, I did have that job. Surprisingly.

 

Dwight Brown  02:42

I did. That's a perfect job. It

 

David Turetsky  02:44

1. Yes. That's perfect. Heidi, how about you? Well,

 

Heidi Perloff  02:48

I'm gonna go in as in a different route. I actually I love being out with nature. But we're a day, not multiple days. And last year, I was strongly pressured. I would prefer to use the word bullied. But my husband said no, call it strongly pressured by my husband and friends of ours to go on a seven day hiking trek through the Andes Mountains in Peru. I reluctantly said, yeah. But it was amazing. It was the best trip ever. And so sometimes eating into peer pressure can turn into a good thing. Which by the way, I do not, or did not share with my kids until now they'll probably hear this and probably Yeah.

 

Dwight Brown  03:33

Nothing like diving into camping headfirst yet.

 

David Turetsky  03:37

You're a thrill seeker just like Dwight is? Well,

 

Heidi Perloff  03:40

I think Dwight is is an extreme thrill seeker. I'm a little bit less on that spectrum. But yeah,

 

David Turetsky  03:47

that is one of the most accurate statements I've heard about Dwight in a long time.

 

Dwight Brown  03:51

Yeah the more adrenaline the better.

 

David Turetsky  03:53

At some point, we'll have to ask about the Sherpa and how all that worked. But this is one of those fun episodes because it gets us back to our roots with data and analytics around HR. Because today, our topic is about the technical skills in HR. How can we make analytics AI digital technology approachable, relevant and engaging for everyone? And so let's dive into our questions. So the first question, digital versus technology, what's the difference?

 

Heidi Perloff  04:32

For me? I do think they're different. But the funny thing is, I find a lot of people using them interchangeably. And someone I know who many of you may know as well often says, technology is a thing. Digital is a way of doing things. And I think that's super important. And it's something that I often use when I speak to people because especially my HR colleagues are business people who, you know, they hear technology, and those are for those people. And I don't do that. But when we start talking about digital as a way of doing things, and we talk about, you know, their what they're doing at work and how they're working, even how they live, their personal lives shopping online, right, got that they are being digital, and that it's a mindset. And so for me, I think that's an important distinction. And actually a first step in getting people comfortable with, you know, a more technical side of HR and business

 

David Turetsky  05:36

Mei, how about you,

 

Mei Kim  05:37

I chuckled, because I have a perfect example of me using technology and not being digital, I have a Tesla. And you know, in the Tesla, they've recently got some new upgrades on the software, and you can, when you click the turn by turn signal, you get a little camera that tells you, if the cars on the side on your blind spots, well, I don't use that, I just turn my head and look left and right or left or wherever I want to turn. And then my husband makes that observation in May you have all these technology at your fingertips, but you don't use it. So that's a perfect analogy of having technology, but not necessarily applying it in your life, or even embracing the technology to improve your life. So I think that is the difference that Heidi was alluding to technology versus digital, we have technology all around us, do we use it in our lives to embrace it, and to really improve it?

 

Heidi Perloff  06:35

And can I add when I think about digital right? Technology is a piece of it. But we were talking before about data, right? And how much, you know, may just add to that big data lover, right, right analytics and the algorithm, then, I mean, I work closely with Mei and she's using those things every day. That's all part of being digital. It's not just the technology piece of it enabled by but not?

 

David Turetsky  06:59

Well, I think a lot of us have a learning curve, to be able to bring these things into the way we do things, and may to kind of take your example a step further. You know, I've often especially when I'm giving presentations on HR and analytics, I talk about something very simple in a car, which is the fuel gauge. And the fuel gauge has evolved significantly since the early days, I have a 1954 Buick Century. And the fuel gauge is a fuel gauge, it doesn't even have that light that comes on if you're almost empty. But that light itself is training you that we're running out of gas, and you need to go get gas, that was an innovation. People had to learn that if they see that light on the dashboard, that triggers a behavior. Well, because they've never had that before. People who get these little innovations, whether it's the turn signal triggers the video camera to go on, or whether it's that light that comes on on your dashboard, those have to be learned in the process of driving. And the analogy back into HR. Using more analytics, to drive the things that we do has been something that we had to adopt. But people felt pressure because they didn't know how to adopt it. Right? From a practical perspective, technology versus digital, some people find it really hard to actually make those switches.

 

Mei Kim  08:27

Can I say that? It's okay, if you think it's hard, and it's okay, if it's hard in certain aspects and easy other aspects, right? I find it extremely easy to embrace technology and being digital at work. But at home, I am useless at my Roku, right? And I'm useless on my Tesla. But I think that it's okay. And I feel like we don't want to make our HR BPS and our communities in HR feel inadequate or like, Oh, I'm not good at it. I suck at it. It's a learned behavior. And whether you prioritize to learn it or not, is that question? Right? The the interesting part observation I've made over the years as a practitioner is that HRBPs who embrace being digital, and embraced the use of technology to change behaviors and develop insights, they tend to go farther ahead in their careers than others who don't. So I think it's a good observation where even when we look at high potential as succession planning, the HR leaders that have those tech tech savviness and the ability to use data and insights tend to float higher in that little nine box right.

 

Dwight Brown  09:42

And there's there definitely a learning curve that goes with it. You know, you talked about the about the Tesla and the side cam that goes with that I think about the backup cam and how long it took me to get to and those little white lines Yeah, exactly. And you see the same thing with with analytic So in HR, where you get the people who you try to help them understand the analytics, and they just don't want to do it. And then one day, all of a sudden, they'll ask about a particular analytic. And you're like, Oh, my God, they're getting it. It's finally starting to sink in and become ubiquitous for them. Yeah.

 

Mei Kim  10:17

And I've learned to, when I see that little nugget, I grab it right, both hands and say, go after that interest, go after that curiosity and encourage them to do more, right.

 

Heidi Perloff  10:29

And Mei I talk about this all the time, it's seizing that moment, I think the mistake we make many times is we just start throwing data analytics insights, and people take go use it, right? Like, oh, my gosh, right. When you find it's so interesting how, like, nobody cares, nobody notices until they have a specific need. And we're always listening for what's the business need, that someone has? And how can we bring them an insight, or, you know, a piece of information in a way that they might not have thought about? And all of a sudden, they're asking for more? Once it start, you know, it's not about the data itself. It's about the value that it's bringing. And it's funny how quick people suddenly adopt. And it's once they're in, right, Mei, they're in there. They're the ones that keep coming back for more, and it's super exciting.

 

David Turetsky  11:16

Well, Heidi To that end, you know, think about the the analogy from before. So may you had that thing probably beeping at you saying that there was a car next to you, and it distracted you from it. And you're like, No, I want to turn my head. And I want to see the car coming at me. And I trust that right? Well, you shouldn't, until you have that aha moment that says, I'm going to trust the beeping that says there's a car coming, I'm going to look at that little video screen. That's the aha moment. And in the same way, what we had what we used to do hiding does your point, what we used to do is send a dashboard that had 20 different analytics on it from HR that said, Hey, look at all the great stuff. But people didn't how to get the noise out of their metrics. They weren't looking at unmetric. They saw all of it. And they got scared because it was just noise to them. They didn't see the signal that mattered to them at that moment. Nowadays, the way I design especially with the way I design dashboards, I look at what's the five things that tell the someone and I make sure there's no more than five, so they don't get distracted. And it's about a topic to try and keep them on point. So I'm not sending noise.

 

Mei Kim  12:29

David, can I tell you my New Year's resolution?

 

David Turetsky  12:31

Absolutely

 

Mei Kim  12:32

is to stay as far away as possible from the word dashboard. Yeah. Because dashboards is everything and anything under the kitchen sink and what you think the your partners want, but it really may not be depending on the situation and the problem they're solving for. Dashboard may not be it right?

 

David Turetsky  12:54

Now talk about that technology versus digital. That's one of those things we say it's a dashboard. What's the technology? Oh, well, no dashboard brings up other computation. So yeah, no, I love your Yeah, that's that's a really good. So

 

Mei Kim  13:06

I, David, I dare you for the rest of this year. You can't say anything about dashboards, ideally.

 

David Turetsky  13:12

So it's February, in the next time I have a client

 

Mei Kim  13:15

paid me a buck, you have to pay me a buck for every time you say, Dad. I'll be I'll be listening to your podcast.

 

David Turetsky  13:25

I don't know if I earn that much money. So look at my W2 for last year to see if. Yeah. So what's really interesting about everything you just said is that what we're trying to find is a way for HR to adopt these things into how they do what they do, right. But yet, they have to do your point may, in order for their career to grow, in order for them to lead and grow. They need to kind of build them into what they do, right.

 

Announcer  13:57

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  14:07

Let's go to the second question, then. Artificial Intelligence, what do you guys think opportunity or threat?

 

Heidi Perloff  14:14

I see it as opportunity. I know that there are risks and things we need to be careful of. But I think we have to approach it from an opportunity side. And I've got so many great examples. I'll give one that I just love. And man, I'm gonna go right to kind of what you've been pushing on your team. So when they first joined us, she's got a small but mighty analytics team. And when we looked at how they were spending their time, these are highly trained people who are, you know, really skilled at the predictive and the prescriptive side of like advanced analytics. And yet they were spending probably 90% of their time on data and extracting data on cleaning data on engineering the data, right, right. And me and I were talking about AI and about what I like to call the low code, no code tools. And Mei said, I'm on it, let me see what I can do. And she gently cajoled her team. Maybe that's not quite maybe he's a little stronger than that. But I strongly encourage them to learn the tool and to use it. And there's one member of May's team, who was working on something she's like, Yeah, but I'm comfortable in Excel. And, you know, well, it's just I know how to do it. And yeah, it may take me you know, some time, but I know it's gonna be harder and longer, may forced her to learn the tool. She said, It took her two weeks to master the tool coming out the other side, she saved. I think, in that one example, she was saving, like 10 hours a week, let's say, wow. But she also said, I will never go back to Excel. Like she was converted from that one time. And the this tool has AI in it, because what it does is it every time we're extracting our data, we have to, you know, engineer it clean it the same way, every time that AI learns it, and just does it for you. She has now brought her team, I think where we are probably they were spending 90% of their time on data. Maybe we're at 50%. And you know, we're only scratching the surface right now.

 

Dwight Brown  16:16

The slowest adopters are always the biggest evangelist and the end.

 

Mei Kim  16:20

Yeah. All right, I'm gonna take the flaky way. I'm gonna say it's both an opportunity and threat. Okay. Let me talk about the threat first, and then the opportunity, because I think they all kind of fold into each other in terms of the threat, you know, what's really a threat is HR not knowing what to ask. Right? So let me give you an example of myself. I went to HR tech this year, or was it last year, and I was so blown away, every vendor out there was saying, we have AI, let me show you and everything. And at first I thought, wow, this is so impressive. And then I went to the best one. And I said, Let's do an early adopter. They happen to be all vendor anyway. So we said, Let's do an early adopter testing of your AI capability. Turns out it was not even like an infant who was like a newborn. Right? Yeah. Compared to a Chat GPT version. And so I learned that you don't know much about it, you tend to get hoodwinked by some of these businesses out there. And so the more the more you learn, the more you become good at what, how to use it, which is very easy to use. I mean, even grandmothers are using charging btw to develop birthday party games for their grandkids. Right, you can find practical ways of using it. But the more you know about it, the more you can actually assess some of the technologies that embrace AI in the HR business and the HR domain and be smart about your purchases. And your partnerships. That's the threat.

 

David Turetsky  17:58

Yeah, But I think I want to ask you a question about that. Because I was at HR tech too. And we definitely heard the same things from a lot of the same vendors. I think one of the things that bothers me the most is they're just taking a lot of buzzwords, and the buzzword is artificial intelligence. And they're trying to take a whole bunch of technologies that aren't really AI, and use that term to say, but we're doing it too. So I guess the question is, from your perspectives, we know how to cut through that noise, and be able to uncover whether or not it's really truly things like generative AI or

 

Mei Kim  18:35

yeah, I'm gonna I'm gonna continue the that response. Thank you for that little pivot there, because I want to talk about the opportunity. Oh, sure. Of course, sorry. So the opportunity now, if you listen to in Davos, Satya and Sam Altman, Satya Nadella and Sam Altman talked about the vision on generative AI in an interview, and one of the biggest learning I had was that you can use generative AI or AGI in the future for general purpose. So think of AI as your assistant, someone that helps you with basic skills. So the way you want to overcome the unknown. And the scariness of this is really to just use it to help your work daily. And that's how HR should be embracing it. We are in our team, we are taking image files with words in it and just saying, AI make me a table from the list of names in this picture. And it was able to write, but it's the simple stuff or even you know, it elevates the skills of HRBPs. When they say oh my gosh, I don't know how to do this formula with a nested IF and an F and a VLOOKUP in Excel. Well, let's ask AI to do it. So we happen to be one of our early adopters of the beta version of the Enterprise Strategy PDS, Estee Lauder so we have an opportunity to test this out with our own data. We found it tremendously helpful with many of the very mundane and boring tasks that we have to do as the practitioners in HR. Right? So, in order to overcome the learning curve, is to just think I always say have a phrase, there's gotta be a bat away, when that term comes into your head, then ad, can chat GPT help me? Right,

 

Heidi Perloff  20:27

can I want to answer your own go to your question as well, David, because, you know, we're on the practical side of things. So we're not selling the technology or the, you know, AI products or whatever, if it's gonna bring value to us, if they're taking something they're calling it AI, and if not, that's okay. For me, you know, if I need the value, and I'm gonna leverage it, great. We all know, AI has been around for a very long time, it only got sexy when open AI, like made it available to everyone. And suddenly, like even my 87 year old father, you're gonna kill me for anything and age, but is using it right and using it in really interesting ways. And it's, it's the power of the possibility. And all of these things that we're now trying to do with that, but I think, because people are because it's become this sexy word. I mean, it was all over HR tech. I wasn't there this year. But you know, they came back and said, Oh, my God, everyone was talking about AI. I went last year, he was talking about skills, like, Okay, but how could you forget, but the but the excitement of that is, now we're getting excited about all the AI, even if it's not generative, and people being curious that curiosity is super important to get people you were talking about this earlier, what gets people to want to try to, you know, test something to see if there's value in it. There's a curiosity. And I think that's terrific. And the question that we're now able to ask is, as people are starting to get more comfortable in any form, how do we then move them up the spectrum, and really take advantage of all the opportunities, including the generals, sure, to be able to bring bring value to individuals, as well as our organizations?

 

Dwight Brown  22:13

I think there's a there's a level of curiosity, that is gonna get people there. I mean, the ability to take AI, I've got a, I've got a colleague that loves playing with chat GPT, and trying different things, you know, write me a job description for such and such and seeing what gets spit out or being able to do kind of like the no code, low code, you were talking about being able to tell chat GPT hey, I want the R code to do a linear regression on such such data. And that it sort of builds on itself. And, you know, to that extent, I think people are going to start to be able to recognize when they're talking with these vendors and sort of cry BS on some of the vendors as they're looking at, like, Hey, I know enough about this, to know that. That's really not AI that you're doing. That's addition and subtraction that you're doing

 

Mei Kim  23:09

vaporware. Right, exactly.

 

Dwight Brown  23:10

But you got to build that acumen. And a lot of times it takes the curiosity to get there.

 

Heidi Perloff  23:15

Yeah. And I think where I'd like to see people going, as well as like, I hear a lot of people, you know, saying, oh, AI automation. Yes, we can get automation from Ai. But it's really the augmentation of what it enables us to do with human beings. That I think is super exciting. And that element of it, I think is is also what I'm hoping people will start asking as well, great that we're you know, you're doing this, but what is it enabling me to do differently, or better?

 

David Turetsky  23:43

I think one of the problems Heidi has been that the consumer versions of what AI has been over the last 12 years, has been Siri, Alexa, hey, Google. And they're not really that much AI there as much as it is IVR. But they've been disappointing at best. And when you look at people saying what's the difference between something like that, and the AGI that you mentioned, or generative, you know, however you want to call it, it's because they don't understand what questions to ask it may going back to your point before, you also need to be trained on how to actually be able to be responsive to generative AI in order to be able to get something out of it. And that's training us not the artificial intelligence. Artificial Intelligence is brilliant. We're not. And so until we come around to that we're still going to be infants in this right I mean, it those are skills we have to learn.

 

Mei Kim  24:45

You know, it's interesting that you ask that because you've you've taken some basic learning courses on how to prompt better you can actually ask the AI to teach you how to learn AI Exactly. So it's like telling me what questions I should be asking you. If I were to create a health plan for myself. Right, right, instead of prompting in thinking of the questions is how you ask the AI to tell you what questions you should be asking. So I think there's a, as a new depth to how you learn, because you don't have to think of the questions yourself.

 

David Turetsky  25:23

That's a great point. Yeah.

 

Heidi Perloff  25:26

But you know, it is about habit as well. I'm used to doing things a certain way. Right, exactly. Here's some interesting things I've seen. Turn signal. Exactly, exactly. But we, you know, I've seen some interesting things, you know, practices, you know, even with people that we know, who, you know, group of people who are going in and playing and testing and trying different things, and then they're coming together and sharing. And everyone says, they think they've used the, you know, if it's chat GPT, or you know, that they've used the AI to the full extent, and then they hear what someone else has done with it. And they're like, oh, wow, that's interesting. And then the conversation Wow, now that you said, that makes me think about this. And I'm watching people together, like Kohler and CO create, like, get smarter in a way that is, to me very, very exciting. Because it does take advantage of this opportunity. And it's a scaling, or the fast scaling of the opportunity, which is what we've seen with the speed with which people are going into chat GPT and the other AI.

 

David Turetsky  26:37

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 HRDLconsulting, to schedule your FREE 30 minute call today. Let's talk about data mindfulness. What do you mean by data mindfulness?

 

Mei Kim  27:09

I was thinking about this this morning. And I think people data is like gold. And it's like mining for gold. And when we mined for gold, we take care and how we produced a goal and refine it and you know, make it into the jewelry that we want it to be. But I think the idea here is to be very careful about how we think about data. And I think a very common problem that we have is when we do data and analytics is oh my gosh, did us wrong. The first thing people pick on is the data's wrong, right? But then you forget that mindfulness starts from the beginning of the journey. Did you enter your data, right? Did you practice good data, hygiene and discipline. If you want to harvest our data, in the end, you want to make sure you sow the right seeds in the beginning. And that means being mindful about how data flows through your entire ecosystem. Right? Also being mindful about how you protect the data, and making sure that you understand the responsibilities of handling the goal. So my as a practitioner, I tend to also help educate our HR community around data hygiene discipline, and the ability for us to have to deal with sometimes imperfection until we get to a closer point, right? Because sometimes the little minutia of imperfection drives us crazy and drives a lot of people crazy, but we have to kind of learn to deal with it. Newspapers get it wrong. Sometimes football statistics around, we tend to be more forgiving. There are when it comes to people data is not as forgiving.

 

Dwight Brown  28:56

perfect is the enemy of good sometimes.

 

Heidi Perloff  28:58

That's my favorite expression. As Mai was talking, it's making me think, you know, when you're saying it starts at the beginning, I'm thinking about how, like the damage that can happen when we think we've asked a question, but we're actually getting a different answer. And then I think about as information and data, it starts getting processed through algorithms, right. And new in the AI is learning and we thought it was going in one direction, but it lands us in a different place in this whole concept. You know, I was talking to someone when when AI the generative AI and chat GPT was first coming out and there was a whole movement of people were immediately talking about like, we need to be responsible as we think about AI and how we use this. And there were these two camps of people maybe there still are in ones that see it going in a good direction and ones who see it going in a very bad doomsday direction and the reality is depending on where we like the choices we make along the way who'd have an impact on that? But my fear is that we lose our critical thinking that we just become so dependent on the AI and machines and never question like, Well, wait a second, like, is it even the right data is, is the algorithm giving me what I want? And and I'm just, you know, as a technical person, I'm always asking that question. I'm afraid that some people will just say, well, give me the answer. That is the answer, and never question that. And so I'm thinking from that lens.

 

David Turetsky  30:28

Yeah, but that's never happened before. Like, Google's never wrong, right? The internet's full of fire. But that's, but that's I'm being I'm exaggerating, to some extent, Heidi, because that's what people rely on me to get their answers for things.

 

Heidi Perloff  30:43

As a self proclaimed hypochondria, I couldn't tell you how many diseases I had, because I've gone on the internet told me this is your problem. Exactly.

 

Dwight Brown  30:52

You're like, Oh, my God, I never knew. Right?

 

David Turetsky  30:54

Exactly. There was a time when we couldn't ask for your watches into the classroom, because they, the teachers wanted us to actually multiply and divide ourselves without actually having a device help. And now they have Chromebooks on every desk. So you're right. I remember that. But that doesn't necessarily mean that the machines are taking over and our AI overlords are going to, you know, you know, tell us how and what we can do. And I wanted to test one theory with you, when you were talking me about the about data and events, its origins, one of the first episodes that we did on the podcast was about how HR data is, is, is, is poor, especially poor quality. And we don't necessarily own you know, the people who run the systems, we don't own the data, the employees do. And they don't do a very good job of keeping it up. It's not their first thought in the morning when they wake up. And so a lot of times, I kind of have to talk to people about making sure that they practice good hygiene, you know, and making sure that the data is as up to date as possible. But that's a really big struggle for most corporations. in

 

Heidi Perloff  32:04

1. Isn't it interesting that it's difficult in corporations, and yet, almost everybody's LinkedIn profile is up to date. Right? Like we go in and we write we're really like, and, you know, we often asked like, why, why is that? Right? What's the motivation? And maybe that's not a fair example.

 

David Turetsky  32:23

But well I mean, that might be social media pressures, right? Because you don't want to be seen as being lazy and not updating your social media profiles. But yet, your your W four hasn't been updated since your divorce. So I was speaking about myself because I had to update it. And you know, that's important.

 

Heidi Perloff  32:41

Sometimes I think visibility has something to do it if you know when and how it's become visible. I remember being like, someone throwing me his huge, like, spreadsheet of information and saying, is this right? And I'm like, I need some context, in order for me to really be able to tell you otherwise, just like numbers and information, and it just feels messy. I need a reason to go in and check my W four, but I'm not gonna we're not gonna go. And you gotta give me that moment and do it at the right time. And not just, you know, right. Yeah. So

 

Mei Kim  33:15

David, it's interesting that you, you started with the individual data of how people are not thinking that it's a priority from an organizational standpoint to bring it into the the corporate systems. But if you think about how we want the data for analytics, it's more the organizational data that we care about, right? Like whether we have promotions, whether we've hired a number of new hires this year, and we have to think about our culture and onboarding effectiveness, or whether our leadership is effective in engaging and retaining workforce. Those are all data that is discipline in the organizational aspects of data. Now, the way to think about this is historically, HR systems have never been there to collect the data for analytics. It's always been just a transactional system. Exactly. Yeah. So we've been HR, sadly, it has been kind of a little bit behind because we're all talking about selling revenue. You know, HR systems are usually the last in the line for investments, right? So if you think about how we want and in the rare occasions that we do have to upgrade our data, our systems that start with the end in mind, right, versus Oh, we got to just lift and shift our processes the way we've done it, we've always done it that before this way, let's just move it to the new system. So coming back to being digital versus using technology, don't practice your old ways. Think about the technology and how it can take you to the end game and work backwards from there. Okay, well, we need to capture promotions. Okay, What reasons do we need, right? So have your analytics partner On at the table as you design those systems, so that you can think of the end in mind.

 

Heidi Perloff  35:05

And I agree with Mei, I'll also add to that, right, like a lot of people are even like in the AI space, people are just taking the AI and saying, What can that do for me, we also ask the question, what are the experiences that we're looking for? What are we looking to create? And then how do we bring in the AI the technology to enable that, right? There's, there's, like, I agree with me, like a lot of the systems have been built on the lens of transact,

 

Dwight Brown  35:29

you need some very visionary people to be able to, to draw what that picture is of the end that you have in mind. Because you do get so focused on one way of doing things, you can't even see your way out of the omnibox on it. And so, sometimes it's a draw process that goes with the two.

 

Mei Kim  35:47

That's right. That's totally right. Next year in HR Tech, I want to listen for vendors who can say, How are you doing today, David, in the morning, Oh, I feel shit, because I just, you know, just had a divorce yesterday. But then the day I said, Oh, I'm so sorry to hear that. Here's some, you know, assistance in terms of some listening tools that you have, or maybe some kind of therapy sessions that you can also also, let me help you with the paperwork. So you don't have to do it yourself. Right? What is when when did this happen? Blah, blah, blah, okay, Dawn, right. And that data goes into the system. So I mean, of course, I'm just like painting a dystopian future. But you know, what if what if that was the case where AI can help us sense things, that's one, and also help us collect the data as a natural part of that stage, and then bring it into the system.

 

David Turetsky  36:49

And I think that would be a really cool topic may to bring up on another episode of the HR data labs podcast, where we actually go into the practical applications and talk about how do we put them in so they do not become dystopian? Because we've all seen that movie. You all know that in spur for the humans in that. And if you've never seen Battlestar Galactica, believe me, it ends poorly for humans. So I think that's actually a really good place to end May. And maybe we can have you back on maybe even closer to HR tech, and we can talk about what we expect. I'll be at HR tech this year. I don't know if both of you are gonna say maybe you do it at HR. Exactly.

 

Heidi Perloff  37:38

Well, now I feel like we have to go.

 

Mei Kim  37:41

We shall!. Well, let's start driving now. Heidi?

 

David Turetsky  37:46

Yeah, that's, that would

 

Dwight Brown  37:48

be only if you use the side cam. Right. Exactly.

 

David Turetsky  37:51

Exactly.

 

Heidi Perloff  37:52

The road trip to HR tech. Yeah. And we can

 

David Turetsky  37:55

weeks as you're going and be the podcast. 50

 

Heidi Perloff  37:58

What's your car again? We got to get take your car

 

David Turetsky  38:01

1954 Buick Century? Yeah. That is the car to take on a road trip. Yes. And the technology is from 1954. No digital. So thank you both very much. You are both awesome. We feel a kindred spirit here because we're the same same type of people. We're both we're all four of us very geekish, especially when it comes to how all these things apply to the world of human resources. So thank you so much for being here. Well,

 

Heidi Perloff  38:29

thank you for having us and for letting us geek out with you guys. This has been a lot of fun, and we'd love to do it. Super fun lover.

 

David Turetsky  38:37

So, Dwight, thank you for being here.

 

Dwight Brown  38:39

Thank you, and thank you both for being here. This has been a fascinating conversation.

 

David Turetsky  38:43

And thank you all for listening. Take care and stay safe.

 

Announcer  38:46

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