How strategy leaders can achieve their moonshot goals

Summary

Lydia Wu, an expert in people analytics and AI, discusses the importance of people analytics at Panasonic and how it shaped the company’s HR strategy. She shares examples of using data and people insights to achieve strategic business objectives, such as connecting production line data to people data and understanding informal feedback discrepancies by gender. Lydia also addresses the challenges of data quality and accessibility and provides advice on building a business case and where to start with people analytics. Lydia Wu discusses the importance of financial survival in business and the need to tie people dollars to business dollars. She shares her experience of starting with basic tools and data, such as Microsoft Office Suite, and the value of curiosity in starting conversations. Lydia also discusses the rent vs buy debate in HR technology and the need for HR leaders to try out tools before committing to them. She highlights the power of people analytics in providing real-time insights to line leaders and driving conversations around high performance culture and employee well-being. Finally, she explores the future of HR technology and the shift towards targeting individual practitioners before licensing to enterprises.

Key Takeaways

• People analytics is crucial for understanding the impact of the human element on business outcomes.
• Connecting production line data to people data can provide valuable insights into improving productivity and reducing defects.
• Analyzing informal feedback can uncover gender disparities and inform initiatives to promote equality and inclusion.
• Addressing data quality and accessibility challenges requires a combination of manual cleanup, data architecture design, and building trust with employees.

https://www.youtube.com/watch?v=J6C5iJxu81E&list=PLdHHCpR_eGHcUnxG_eeJREMtnLw3XT6tO&index=2

Full Transcript

Abhinav (00:00)
you ever wondered how Fortune 100 companies with tens of thousands of employees do people analytics? Have you ever struggled with connecting people data to its business impact? Have you ever thought how AI is going to change the world of people insights? Are you just intrigued by what is the future of people analytics?

and how important role will it play in helping companies beat its competitors.

Hi Everyone I’m Abhinav and welcome to the Peoplebox Analytics Talk, where we invite incredible leaders to go deep into the fascinating intersection of data, AI and people.

And today, I’m delighted to welcome Lydia Wu. Lydia started her journey at Accenture and Deloitte, providing people analytics and HR transformation consulting.

to large enterprises. Later, she joined Panasonic to head their talent analytics. She’s an advisor to many startups and large companies in this space. And her knowledge and passion for people analytics and AI is second to none. Welcome to the show, Lydia.

Lydia Wu (01:04)
Thank you for having me. Happy to be here.

Abhinav (01:07)
Thank you. Lydia, let’s start with Panasonic. How important is people analytics to Panasonic and what role did it play in shaping companies overall HR strategy?

Lydia Wu (01:19)
Of course. So for Panasonic, I think like many other organizations who are growing, transforming and reshaping their strategy in the current macroeconomic environment, analytics is incredibly important. When we started on the journey back in 2018, we didn’t quite know exactly what we were getting ourselves into. It was more so the fact that understanding we had a lot of data in the cloud and we had to get all the data out of the cloud and make something of it.

But as we went on this journey, as we morphed and as we grew, what we realized was that analytics was really the key to unlock the value HR held in the company. Especially when you think about the manufacturing environment, which I’m working right now, 24 seven, 365 around the clock. It is incredibly important not to only understand what your production line outputs and inputs are, but also to understand how the human element really impacts the production lines.

Because For any leader out there who thinks that as long as you perfect a process, the people don’t quite matter as much, I will for sure tell you that the engagement of your line managers, the engagement of your QA person on that line is actually gonna impact the defect rates as well as the good throughputs that is gonna happen on the line. That is something that we’ve been able to statistically prove and I think that has really helped us have the conversation around how to better support our people because,

It’s not just a good people decision, but at the end of the day, it’s also a good business decision.

Abhinav (02:48)
What was the trigger for the company to build the best practices for people analytics and say, we need data. So we need Lydia.

Lydia Wu (02:48)
Yes.

Absolutely. Well, we need Lydia came through the interview process. Let’s be honest for a moment. We need data was really coming from the complexity of this organization because I think when you think about Panasonic or when you think about any larger brand names that are out there, not everyone necessarily thinks about the complexity of the legal holding structure, the subsidiary structure. And because of the organic nature of those organizational setups,

What ends up happening is that you also have disparate data sources because if your company grew through M &A or if it grew through acquisition of any sort, the data sources that you acquire, the historical data transfers that you acquire, all of that gets stored somewhere, but no one was really looking at it. And we were at that point in 2018 sitting on about 10 years at least worth of historical data.

that we thought we should make something out of. And if you think about the environment back in 2018, the war on talent was really starting to heat up back then. And what we realized was that across buy, build and rent, we don’t exactly have the top dollars in the industry for the engineers, for the top sales folks. And what we really had to do was get smart about what it meant to be a part of Panasonic and really what it meant to…

the talent that we needed and to grow and develop them internally and come up with the quote unquote business case. I have my sentiments about that word, but really the financial metrics behind why it makes sense to invest in your people and not just let it burn and churn and hire additional from the market.

Abhinav (04:34)
It’s so amazing you use the word be smart and that’s so right because not everybody has the money and the brand that many of these large companies like Google, Facebook, Amazon hold, And I think the only mantra for them, like you said, very rightly is be smart and be data driven. Now going even before the Panasonic, you started your career as a consultant with Accenture and Deloitte and I’m sure you must have worked with.

loads of large enterprises. And later you led the same role at Panasonic. So how different was it to be on the execution side than from the consulting one?

Lydia Wu (05:09)
Very. So I actually went to Panasonic because when I was in consulting, first of all, I got the bug to look into people analytics. But I also realized the challenge with working in consulting, especially when you’re a talent strategy, HR strategy consultant, is that your bill rates can only afford 20 % of the work on any transformation or implementation project. And usually for me, what that used to look like was,

You come up with a business case, maybe you’ll do a work breakdown analysis, maybe you’ll map some processes, maybe you’ll come up with a taxonomy. But when the rubber hits the road or really the 80 % of the work where it really, really matters to an organization executing on a transformation, nobody can afford you at that point anymore because they thought that the strategy consultants were supposed to deliver a deck and somehow magic will just happen and the implementation will happen on its own. So having seen that through one too many times and having.

deliver those strategy deck, which in a way is kind of like my brain children if I think about it. What I decided to do was like, you know what, I’m gonna see one through to fruition and let’s see what it’s actually like. Let’s see what people are actually stuck with. What they’re actually challenged with. What’s keeping the business partners up at night? What’s keeping talent management, talent acquisition up at night?

Abhinav (06:26)
And I agree if you don’t understand what’s the problem that they are facing in a day to day, it’s very hard to even understand the value of that data. And a lot of our audience are HR and business leaders and they always try to understand the impact. So Lydia, could you provide some examples of…

how you are able to use these data and people insights to achieve some strategic business objectives.

Lydia Wu (06:53)
Absolutely. So I think the tails are many because it’s been six years and on average, the way I looked at it was that we would deliver about three to four banner projects or like top high level hitting projects on an annual basis to one balance the business need, but also to balance the internal team need to do the research, get the data clean and really get the homework done.

I think one of the most fascinating projects I’ve undertaken in manufacturing was for the first time ever connecting production line data to people data.

For the first time ever, we were able to look at production leaders in the eye and say, hey, you know that line that you thought you need 55 people on? You only need 48 because by person number 49 in the last 12 months,

your defect rate has gone up. Regardless of why, regardless of how, instead of asking for bodies, let’s start looking at why is 48 the magic number for you. Line two, hey, let’s start looking at why 46 is a magic number for you, because every single line was so different. And that was actually how we were able to backtrack to the fact that engagement did matter, frontline manager effectiveness did matter. And it’s not just because satisfaction or employee sentiments.

It’s actually the outputs. And right now I’m in a battery manufacturing world. So for anyone out there who understand what battery manufacturing is like, you would know that when you scrap a battery at the end of the line, it’s really hard to recycle that raw material back into the process again. So scraps for us aren’t like, oh, just grind it into paper pulp and try it over again and you’re fine. It is actually dollars wasted natural resources that the earth has limited amounts of.

So for us, that was incredibly important. And for us, that was genuinely one of the biggest business objectives that we were able to convey that essentially also led us to be able to implement different HR systems, different ERPs, so we can really be smart about how we do the work at a manufacturing plant. So that’s sort of the manufacturing and throughput side of the house. I think the other part of the work that we did that was really fascinating for me was actually during the pandemic era.

This was back when I was with the regional head offices. And at that time, everyone was always talking about personas, personas in your employee base, design your journey according to personas. But it was like a very happy and fluffy concept and arguably to some organizations it still is today. So I went on this journey saying, okay, does personas actually matter? Should we really pay attention to them?

And what we ended up doing was that we ran a series of longitudinal engagement surveys. It was incredibly insightful because in the pandemic era, what it allowed us to understand was that.

hey, the traditional way we’ve been doing benefits with 401k, healthcare, eye care, dental, so on and so forth, it doesn’t actually work for everyone because if you’re a millennial or Gen Z coming into the workforce back then, having vision and dental didn’t really matter to you. Having somebody being able to tell you how to do taxes and financial plan for you and teach you about a 401k was actually what mattered. It sounds again, really, really intuitive, but.

in the face of limited investment dollars. It was also something that a lot of times when we were in boardrooms where the conversation went something to the effect of like, yeah, it’s nice to have, but we have all these other things, so why bother? So first of all, that piece of research led to us establishing a wellbeing credit across the organization to say, hey, in addition to everything that we think you need, here’s a bucket of money that we’re gonna give you for you to figure out what you need. And here’s a category of things that you can spend it on.

So that’s part one. Part two, as a subsidiary part of that research, we also looked into talent management because again, personas, what the heck does that even mean for talent management? It sounds very happy fluffy. Why can’t you just do bare bones talent management, get an annual performance review done and call it a day? Why are we spending money on this? It was all of these great questions that were coming up. And I think it’s questions a lot of HR departments are still facing today.

And what we did was that we connected the longitudinal satisfaction data to talent management to performance feedback data, hooked it all up to demographics, because the beauty of analytics is you’re allowed to wire data sets together that didn’t historically go together.

And it actually floored us to find that from a formal feedback perspective, so your annual performance reviews, everyone was about the same in terms of satisfaction. Nobody really loved it, but they understood it. And they were like, yeah, things are going well. We get it. Let’s move on. But what was amazing with us realizing the informal feedback, so the casual check -ins and the hey, how am I doings, those had such a discrepancy between gender.

in terms of satisfaction rates, that we were actually paused in the midst of a conversation and meeting to say, hang on, what’s going on here? And when we do these analysis, we actually run regressions against all demographics. So not just gender, but like ethnicity, generation, whatever region, so on and so forth, managerial population. And gender was the only one that stood out enough with a significant score that we’re like, wait, what’s going on? Ran a focus group. And that was actually when we uncovered that,

As women in the organization are going through the day to day experiences, they were actually finding it a lot harder to have these water cooler conversations with their most often are male. And what ended up happening is that because of that level of discomfort in terms of how we’re socialized and just how things work in general, they weren’t getting as much feedback. They weren’t getting as much insights as their male colleagues were in terms of like, hey, how was your weekend? Like the water cooler chats that eventually go into work.

Again, as I’m telling you this, it sounds so intuitive, but as HR, you always wonder, is this anecdotal or is this legit? And we actually statistically proved out the fact that it was legit and it is a genuine statistical problem in the organization. So coming straight out of that, what we did was that we actually instituted a formal mentorship program, even in playing fields across genders, across different parts of the population.

all because we had to wear without to say we’re gonna gather the data and we’re gonna let the data guide our investment and guide our decisions.

Abhinav (13:17)
This is fascinating, Lydia, because actually both the examples, the assembly line and the watercolors, are so insightful. And I’m wondering, it’s not just for the HR, but even for the leaders and the managers, just getting the data could help them so much with achieving their strategic objective, retaining their team, improving the whole employee experience.

In both of the examples, you talk about real large set of data. And whenever I’m talking to the HR leaders about, you know, starting people analytics or just using the whole aspect of people insights, one of the major challenges that they spoke about is the data quality and accessibility, For a large organization like Panasonic, I can imagine, or I can’t even imagine the magnitude of this problem.

How did you overcome that?

Lydia Wu (14:09)
Yes. So first of all, for whoever’s listening to this, we’re all going to virtually hold hands for a moment and just acknowledge the fact that unless you turn off your HCM system, you are never going to have a hundred percent clean data. It’s a pipe dream. And I think most of us who work in analytics have given up at this point. So a couple of different things in terms of how we dealt with this. When I first started in 2018, it was a one woman army, one person shot.

Abhinav (14:16)
Hahaha!

Lydia Wu (14:37)
and on a shoestring budget as well. So it was a lot of cleaning after the fact. It was a lot of dumping everything out into Excel, recognizing that different companies were using different data fields differently, because under the region column, I would have somebody use it to identify full -time, part -time employees. I would have someone identifying the home region. I would have somebody else identifying the office region. And it was a little crazy to see what all the values were within the broader ecosystem.

And on top of that, the way we had gender was like F, capital F for female, and then, or lowercase f female, or like FEM, and just all the variations. So it was a lot of manual cleanup to start, but I actually really appreciated that exercise because what that then allowed me to do was truly understand the power of data architecture and really the power of designing your data input processes and your data storage mechanisms. So, fast forward.

What do we do today? Step one, I do not skip the step of data architecturing when it comes to system implementation, when it comes to release management, when it comes to plugging in a new system. Sometimes when we go through system implementation, it sounds very easy to say, oh yeah, it’s a six week thing. Just plug it in, run a flat file, SFTP integration, and boom, there you go. But the problem is,

Unless you know the ROI that you’re trying to get out of that system and the broader picture of what you’re trying to achieve, plugging in a CRM on top of your ATS is easy. Getting that CRM to measure the funnel, measure the effectiveness of your programmatic advertising, measure if Indeed or JobCase or whatever the posting size that Procutor works better for you, that is a challenge. And unless you design that data in, unless you design that measurement step in a friend,

It’s really hard to build it in later in terms of retrofitting processes and kind of ripping a bandaid off people who just want to enjoy the fruits of their labor. So for me right now, step one is always understanding what am I trying to get to? What is my five to 10 year strategy? What do I need to convince the business to help me with my five to 10 year strategy? And therefore taking a step back, what do I need to capture and measure?

to deliver that message to the business accordingly. So that is the philosophy of the data architecture. The second part of it is then getting technology to really help control the data input. I think HR as an industry love opinions. We love to give people that open life space to say, open comments, other, tell us more. And I think it’s a phenomenal idea and it’s phenomenal value for the information we get. However, when you’re designing a system,

it becomes a little crazy when you let everyone freehand everything that you need to collect in the system. So what we then do is we basically gather all of the, what I call the 90%, the values that we expect 90 % of the time, turn them into multiple choice. So at least we know what our data catalog looks like. And then from there, the 10%, we give the other option. If needed, we’ll have somebody follow up on the other option. But most of the time, the 90 % catches everything.

Abhinav (17:46)
Lydia, when I talk with a lot of HR leaders, right, who are fascinated about data, who really want to be, not that they’re HR team, but the business leaders to be more data driven, they mainly speak about these two challenges. Okay, one is how do we go about building a business case to the leadership to invest more in people, data and insights? And second, and very like…

Quite obvious is where to start, what should be our first step, because even if the CEOs or the CXO approve, they say, okay, what’s going to be our first step? And a lot of times they don’t have clear idea. So a lot of these leaders must be in our audience. What would you advise them? One on building, how to build a business case, and second is where to start.

Lydia Wu (18:31)
Delete the fact that you’re an HR leader. Delete the fact that you’re looking at a people data. Let’s look at it from a home mortgage or a home loan perspective. When you go to a bank and say, I need money, give me money, what’s the first thing they ask you? Okay, well, what are you going to show for it?

And most of the time, it’s the evaluation of your house, the fact that your house is worth more than what they’re giving you. So push comes to shove, they can still make about 20 % in liquidating your home. And let’s hope nothing ever comes down to that. But it’s a very cut and dry mathematical equation. That equation doesn’t change in the corporate world. It doesn’t change just because we’re talking about people data. For some reason, a lot of leaders and a lot of practitioners I talk to, they think that people function and people data is different.

But at the end of the day, when you’re running a business, what is incredibly critical is the financial survival of that business. So you can pay everyone and make sure before you make sure that they’re happy working for you. Because if you can’t pay them, well, engagement isn’t necessarily top priority at that moment in time. So working from that logic backwards then, when you’re creating a business case, it’s not just about like, oh, we’re going to have a cost avoidance. We’re going to…

be able to make people happier, more satisfied, more engaged, all important, all incredibly valid. But at the end of the day, your CFO is gonna look you in the eye and say, what the heck am I getting out of it? Where is that extra penny for every dollar I put into this? And how are you gonna guarantee and prove to me that you’re gonna squeeze that extra penny out of that dollar? And it sounds incredibly crude to some. I’m sure some of our audiences are listening to me say this and going, oh my God, you cannot possibly.

But at the end of the day, when you’re trying to get money, when you’re trying to grow, that is the equation. And that is unfortunately the game rules that have been written and the game rules that as HR we have to play by. So how do I look at it? First of all, I never approached a question of how do we create a business case for people data? People data like technology. It’s a tool. It’s a mechanism. It’s not a be all end all. It’s not the end. So take a step back and figure out what is the business strategy and what are you trying to do with the HR function and people in general?

because then you have a case of, okay, let me tie people dollars to the business dollars. Once you figure that out, take another step back to say, okay, of that people dollar, let’s say I want half a million, but finance can only afford quarter million or 200 ,000. Then how do I efficiently squeeze out that $300 ,000? Because the answer honestly, 95 % of the time lies in technology, automation, data, intelligence, research, so on and so forth.

That is where that business case of HR analytics and data comes in. It’s not like, hey, leader, I need money for more data to build an HR specific data lake because most CEOs will look at you like you were a second head and tell you to go bugger off and go to IT and figure it out with what’s available today. It’s the angle of which you attack that conversation that I think builds the most successful business cases. And the angle should never be data necessity led. It should always wire itself back to the people problem and ultimately back to the business problem.

that the whole organization is trying to solve. I think related to that, one of the most critical questions any HR practitioner can ask their business first day on your job, if not during the interview process, is how do we make money? Because until you understand how your organization makes money, you always are going to feel like you’re running into a wall every time you’re trying to ask for funding and every time you’re trying to ask for money. And that’s really the balance of the equation. So that’s part one. Part two, where do you start?

I just had to get the conversation started. And in doing the incredibly painful dashboards and getting the conversation started, what I was able to do was generate a sense of curiosity in the organization. Because when somebody sees their turnover number, when somebody sees their demographic breakdown, the immediate next question was always like, OK, but how did it happen? How do you know? What do I do now?

The moment you get that hook, you can keep the conversation going. And once you keep the, once you grow from a one person team to HR having that whole conversation holistically together, and that was about a year and a half’s worth of journey, it’s a lot easier to then go to the business and say, hey, you know that data that you’ve been asking us for the last five years on? I actually have it. Let me show you what I’ve got. This is a month by month, incredibly painful process.

So can I get like $100 ,000 from you if you think that’s interesting so I can invest in something to make this a little less painful?

Abhinav (23:01)
Moving now to both of our favorite topic, which is AI. How did Panasonic leverage AI, and especially you there, leverage AI and machine learning to its people analytics and all the insights initiative?

Lydia Wu (23:18)
Yeah, absolutely. So right now I think we are still in the discovery and build phase of AI. So here’s how I look at the HR technology world. If you look at the last three to five years, I think the development of technology phases trends, especially in the world of HR has happened at a quicker pace than we’ve ever seen before in the industry. You start, initially it was like UX UI and then it was like, oh, HCM cloud. And then it was like, oh, employee experience. Those were slow. It was like,

two, three years apart, but in the last year alone, it was more so skill set than it was AI. And then it was sort of, how do you apply all of that to everything that you’re working on? Here’s a problem. I don’t think most of us out here are working on a solid technological foundation to be able to adapt to a quicker pace of technology evolution in our ecosystem.

We’ve been able to duct tape it. We’ve been able to sort of like wire together, hold it together with bubblegum. But my thinking has always been until you have a really solid technical foundation, you are always going to feel like you’re getting hit sideways with all of the innovation, with all of the technology. And your employees are never going to feel that you’re on top of it because you will always tell them, here’s why we cannot do something and not here is why we’re embracing something.

So I am actually in the midst of a HR system implementation right now because I have decided that we’re going to rip out and re -foundation and re -architecture and re -layer the sort of basement and foundation level of how we run HR from an infrastructure perspective. So we’re not really duct taping and trick and wiring everything. So we’re actually having the proper support beams and the concrete pours and things along those lines. So we’re not standing on stilts. And in doing that,

It’s really done with the future in mind. So how we architected the data, how we designed roles and responsibility, how we even designed a field of employee ID, which I’m happy to get to in a bit, was really the thinking of how are we going to now use all of this to propel us into the world of AI, into the future of HR technology, regardless of AI, regardless of the skill sets, regardless if it’s something else that’s going to hit us sideways in three months time at the current development cycle.

Abhinav (25:33)
which now brings us to this, I think, ever going debate of build versus buy. I’m curious to know, and I’m sure most of our audience would be curious to know, what side are you on?

Lydia Wu (25:48)
I am on the rent side of things, to be honest. So here’s my fundamental problem with build versus buy, and I’ll be very candid about it. Until somebody experiences the pain or joy of build, they’re never going to truly understand what build actually means. Because very similar to how solution consultants demo the most perfect version of a tech product,

Abhinav (25:51)
You

Lydia Wu (26:15)
When you are exploring the build phase internally, it’s always layered with assumptions of like, oh yeah, it’ll be easy because it’s easy because I’m assuming you only have five data fields. It’s easy because I’m assuming you only have so many historical data. It’s easy because I’m assuming there’s only one single organization. You’ve cleaned the data, you validated before you fed the data over to the data lake. So yeah, absolutely. We can build it for you because we are assuming humans are like widgets and things never change. That’s never the case in our world. Now, I also understand a

organizations with incredibly robust IT organizations and possibly HR leaders who just don’t want to touch data. That’s totally fine. It’s not for everyone who want to take that build approach. And I think definitely go for it. Try it out, but isolate yourself so that you’re not fully all in on it and have to peel back. Always build in sort of what I call the gate check periods in your build journey for you to say like, is this working? What are the indicators of it’s working? And if it’s not working, let’s just peel back and pivot another way.

So that’s my opinion on build. In terms of my opinion on buy, my God, they are expensive. I feel like I just share the sentiment of most HR buyers out there, right? Because if you look at a solution, it’s a beautiful solution, and somebody tells you like, oh, it’s $50 per employee per year, you pause. Because depending on the size of your organization, depending on the belief of your leaders that HR analytics is actually going to work, you pause because that’s a

hefty chunk of cash you’re about to shell out.

So here’s the reason why Lydia advocates for rent. Because in rent is what I call the pilot projects. It’s the easily accessible tool sets that you don’t need a lot of technological sophistication to be able to do.

It’s not a solution where you’re either all in or all out. It’s the dip your toe in the water, see how you feel. If you like it, let’s keep going. If you don’t like it, that’s fine. Let’s back out of it. Not enough people in the environment are doing this and not enough HR folks in the ecosystem are asking to say, hey, can I try it out before I buy it?

Abhinav (28:15)
Lydia, you have built so amazing systems, you have rented it, you have bought some of the systems at Panasonic as well. I’m personally very curious about what’s the most bad-ass thing that you have been…

able to see people analytics doing for you.

Lydia Wu (28:29)
It is genuinely being able to tell our line leaders in near real time how the people they have assigned to different parts of their production line are impacting the outputs of their production line. Because if you think about the sort of the office environment, it’s almost like everyday managerial training. I’m like, yeah, pay attention to your people. But if you think about the production line environment, especially when you’re running a giant facility that’s 24 seven around the clock,

Most line leaders think about output. They don’t think about the people and how that impacts output. Everything impacts output, but most importantly, you think about the output. And that’s fine, because that’s what we hired them to do. We need them to obsess themselves over the output so we can maximize our overall productivity. But the ability to tell them, hey, by the way, here’s how your people actually impacts your output. It’s not just your machine operating uptime, downtime, and maintenance time.

it’s the bodies that you’re assigning to those pieces of work as well. It sort of gave them the aha moment to say, oh, let me check in on how that person is doing. Let me be a little more human that if somebody needs to step out for 15 minutes and take a call or whatever, let’s do that.

Abhinav (29:42)
That’s so true. And Lydia, at the end of the day, when it comes to people analytics, the owner and the implementer is always going to be HR. You have been in this industry for 12 years now. Do you see that HR is becoming more and more data -driven with time

Lydia Wu (29:59)
It’s interesting. So I think the owners and implementers will always be HR, but I almost want to tell everyone, don’t ignore your IT department. It’s not HR or IT. It has to be HR with IT because whether you like it or not, your HR system, your data system, and everything else ultimately has to plug into the broader ecosystem. So find your best friend in IT, take them along for the ride because it’s going to serve you in the long run. That would be one part of it.

I think in terms of it has HR become more data driven. Yes, absolutely. HR has become more data driven. Is data literacy and data maturity still a challenge? Yes, absolutely. And this is where I will look at all of the educational institutions for future HR resources and ask them, what are you doing to teach people about data literacy, to teach the future generation of HR practitioners about data and the utilization of data?

Abhinav (30:50)
And the reason I asked this, and very rightly, you also said this is always this debate about balancing data -driven approaches to the human element of HR. I mean, you’ve obviously been to both sides. How do you advise HR to not just rely on one thing, but like use the power of both and balance it correctly?

Lydia Wu (31:09)
Yeah, absolutely. I think Data is actually what makes the human side things of HR more human. And the reason for that is a lot of times we talk about human side of things, we talk about the anecdotes, we talk about the qualitative things. But the problem with the anecdotes and the qualitative things is that you don’t always get the full picture. It’s the saying the squeakiest wheel always gets the oil essentially. And what data allows you to do is being able to look at the

qualitative side of HR, aka the human side of HR, with a lot more objectivity, with a much broader coverage, to be able to definitively and logically say, is this a problem in our organization? Do we need to act on it? How big of a priority is it for us? Because you’d be surprised at the number of organizations I talked to when I asked the question, like, so what did you implement last quarter? It’s like, well, what that one guy who put up his hand during our last quarterly said, that’s what we did.

Okay, well, is that representative of the whole few hundred that you have in the organization? Or is that just one person who was courageous enough to speak up and is actually the minority whom now you have forced to become the majority? So a lot of times I think in being people, people and being more people focused, HR is actually doing the organization and their employees a disservice without looking at the data side and without looking at the broader picture of what it is that they’re trying to do.

Abhinav (32:34)
That is so powerful, Lydia. I am certainly taking that note. And that brings us to the end of this talk. Lydia, thank you so much. So, so much for talking with me. I really enjoyed our conversation. And the work that you have done is inspiring for so many HR and business leaders. We definitely need more people like you in every company. I’ll just say keep up the great work, keep inspiring, and have a great day. Thank you so much again.

Lydia Wu (33:00)
Awesome. Thank you for having me.

 

Summary

Recruitment today is broken. Businesses are grappling with challenges like misalignment between hiring and business goals, overburdened recruiters, and poor candidate communication.

These inefficiencies are costly. According to HBR, the wrong hire can cost a company 5-7 times the employee’s annual salary when considering hiring, training, and lost productivity. Additionally, a study by Glassdoor found that a single job opening costs companies $4,129 on average, with costs increasing the longer a position remains unfilled.

In the first episode of The Peoplebox AI Talk, Abhinav Chugh, CEO and Co-founder at Peoplebox, sits down with Suzanne Salzberg, a veteran talent leader with over three decades of experience at leading tech companies like Highspot and TextNow.

Suzanne discusses how AI can streamline processes, from creating unbiased job descriptions to improving candidate communication and making data-driven hiring decisions.

Full Transcript

Abhinav (00:00)
If there’s one thing everyone is speaking about today, it’s AI. It’s going to disrupt every aspect of our professional lives. And some may say it has already started. So what will be its impact on the future of work, especially how we hire, develop, and retain our most valuable asset, People

and which part of the employee life cycle from hiring to retirement is going to see a biggest change What does it mean for the companies and HR? Hi everyone. I’m Abhinav, and welcome to the new season of our podcast, Peoplebox AI Talk, where we invite incredible leaders to go deep into the fascinating intersection of talent and AI. Today, I’m delighted to have Suzanne Salzberg on the show

Suzanne comes with over three decades of experience in the talent space. She has been the head of talent for big tech companies like Highspot and TextNow, and she’s now also a visiting lecturer at the University of

Welcome to the show, Suzanne.

Suzanne Salzberg (01:00)
Thank you, I’m happy to be here.

Abhinav (01:02)
Suzanne, you have been a talent leader for some of the most high tech companies. Let’s start with speaking about the disruption of AI in the talent space. Where do you see its biggest impact in the employee life cycle?

Suzanne Salzberg (01:16)
Well, I can, I can focus on the candidate’s life cycle in the talent acquisition you know context. I really think the, the biggest impact is going to become, honestly from the minute they read the job description because I think I see AI really helping to create complete, unbiased, easy to understand job descriptions. and it’ll also help hiring managers because one of the biggest pain points for talent teams is waiting for that job description.

The next big impact would be how fast a candidate hears back in the process, whether it means they applied and they get a response or if they’re in first, second, third, fourth interviews getting a response because I talk to a lot of candidates every day. And one of the biggest grievances from candidates is that they’re being ghosted.

And then I think the other major impact will be is how recruiting softwares are actually using AI on their backend and taking some of that load off of talent teams.

Abhinav (02:20)
That’s actually so well said. You’ve been in this industry for over three decades. Where do you believe that recruitment is most broken today and craving for AI to fix?

Suzanne Salzberg (02:24)
Right. Well, I feel like it’s changed, but I feel like right now where it is most broken is because recruiting teams have become so much leaner. The hiring is slowed. So full teams got laid off or there’s one recruiter left. And so those small teams have so, you know time management is a huge problem because now they’re doing four jobs. They’re the recruiting coordinator. They’re the travel coordinator. They’re looking at resumes. They’re doing the interviews. And so it’s almost impossible for them to go through a thousand resumes that they’re getting in a day because they’re also doing, I was talking to a previous recruiter that I worked with and she said that she was, you know, a CMO was coming in, took her eight hours to schedule their onsite, you know, with everybody’s schedules and I’m , and so that’s eight hours that she can’t be recruiting.

And so because AI can help with a lot of those administrative tasks, it’s great at doing travel planning, scheduling, you know, screening those initial resumes, like we said before it can really help with JDs. And so I think that that is a huge place where AI can help. I think the other place that it’s broken, is when you’re hiring for technical roles and engineers and they have to take a coding test, right? And that coding test is only as good as the person writing it. And so many times from a talent perspectiveyou know, someone will get a question wrong and it’s not even something that they’re measuring for that this person is going to do. And so I think if AI can write some amazing coding tests or tests that engineers or QA employees can do, like that will be a huge piece that is a big pain point for recruiting.

Abhinav (04:19)
Do you think that recruiters will be replaced because of AI?

Suzanne Salzberg (04:22)
Well, I think if you talk to anybody in talent acquisition, they’ll laugh thinking like, good luck. There’s no, because I mean, honestly, so many times recruitment and talent acquisition is siloed over here. And we just live in our own world and nobody really knows what we do, right? And so like they don’t, and it’s probably bad on us for saying all the work that we do and what we do, but I just don’t ever see, and anybody that does it, knows that they should not be replaced, right? But there is a fear because the people that don’t know really what we do say, oh we can replace the talent team with AI, right? So, you know, it’s that.

Abhinav (05:03)
So you really have had thousands of resumes in a day?

Suzanne Salzberg (05:07)
Oh, 100%. If you just go on LinkedIn and look at some jobs, it literally will say, a thousand people have applied to this job, for one job, right? So if you think that every job pretty much gets 500 to a thousand resumes, so it’s, and if you know, you have 20 recs open, 20 job openings, yeah, you’re getting a thousand a day for sure.

Abhinav (05:16)
Oh my God, and how do, how do you manage that? I don’t think it’s humanly possible to go through them one by one. And you know, it’s destined to have a

Suzanne Salzberg (05:38)
What I do is I help people navigate the job market and the hiring system. And, and so, and when the first thing I help them with is their resume. And so I teach them that a recruiter probably is going to spend… 10 to 20 seconds on your resume. And that is no lie. So that first half of your resume better be good. because we, there’s you know, fortunately The applicant tracking systems have this thing, it’s called quick review. And you can go into quick review and you’re literally just tapping. And, and that’s why people you know are frustrated that the recruiters aren’t getting back to them. But I said, if you knew what a recruiter was doing right now, all of these things, like that’s, like don’t blame the recruiter. Like it’s the situation that’s happening.

Suzanne Salzberg (06:26)
That’s, it’s real. It is a real thing.

Abhinav (06:29)
I had a very interesting talk, I was at, I was at a conference where there are a lot of job boards and they’re talking about what are the things that they are doing to attract candidates. And they say, we are giving them the ability to not only create their you know resume based on AI, we are giving them the capability to actually apply for say 35 different jobs with 35 different resume, all altered on the basis of the job description.

And we were just laughing that it’s not that AI is going to fight humans or, you know candidates are going to fight AI. It’s AI fighting AI. You know AI from the recruiter side is actually fighting AI on the candidate. And good luck to the Boolean searches and the keyword searches because now everyone’s resume will be built based on the job description. How do you see that world? What are the skills that both the recruiters as well as the candidate will now need to do a better matching.

Suzanne Salzberg (07:34)
I always teach candidates that the job description is the final before the exam.

So I will look at their resume and then I’ll say, look at this job description. And when a applicant tracking systems or LinkedIn or any of those companies are matching you to the Boolean searches, right? they’re literally taking keywords from that job description that recruiter put in. And if that’s not on your resume, guess what? You’re not gonna be one of the top 30 people that show. And so

You can do it without AI. I mean, it’s harder, but like I encourage people to have a couple of different resumes. I mean, sure, if you want to make them, you can tell an AI resume. It’s still at the point where it’s a little bit of a turnoff because like, is it real? Have you searched like what this company is looking for? So there’s a trust factor. There’s a trust factor because AI can make this beautiful resume that literally matches the job description. Then you have to question, do I trust this person? Are these, is this data real?

We’re at the point now where we can tell. We can tell if it’s an AI resume. And I’ve been to, when I’ve been to conferences and you have people that, you know AI still misspells things. You can always tell there’s certain little things. It’s like when you get like a scam email, there’s certain little things you can tell that make it a scam. And so I would encourage people still to do it the old fashioned way, but use that job description as your final, like, and you know, focus on what you’ve done to match the job description.

Abhinav (09:07)
And, you know, when I’m speaking with a lot of, you know, talent heads or, you know, experts in the talent acquisition space, one of the big fear that I hear is about the AI bias.

What do you think both the companies as well as the you know AI tools can do to mitigate this bias in the AI -driven, you know world.

Suzanne Salzberg (09:28)
Yeah, I totally agree that bias can cause AI to make decisions that are, you know, systematically unfair to particular groups of people. it can discriminate based on race, biological sex, national, you know everything.

And so because humans are choosing the data that the algorithms use, and even if like these humans are making a conscious effort to eliminate bias, it can still be baked into the data that they select, right? And so you can do extensive testing and diverse teams can act as effective, you know, safeguards. But even with these measures in place, I mean, you’ve heard the old saying garbage in, garbage out, right?

Suzanne Salzberg (10:11)
And so bias can still enter that machine learning process and AI systems can then automate and perpetuate bias models going forward and then you’re in big trouble, right? And so one of the ways that you can help to mitigate the problem, and I think this is where companies really need to focus on, is that businesses should look to engage their data science teams, all the other functions, like very early on in their organization as early as possible. And so then they can assure that the models accurately reflecting the decision -making process and that you know, the data is just weighed accurately. But engaging those people after the fact is not a smart decision. So just getting everybody in early and preventing that bias early is what’s gonna help because if we don’t then it’s just gonna keep perpetuating that bad bias going forward

Abhinav (11:04)
Yeah, and just about those checks, you know, there are a lot of laws coming up about using AI in recruitment. There are recent law in Europe. New York recently passed a law to build more checks when it comes to recruitment. What are your thoughts? Do you think these laws and compliances, will they help the technology or will they become a blocker in the technological advancement?

Suzanne Salzberg (11:28)
It’s probably a mix of both. I mean, it’s going to be a learning process. think it’s good that there, there are laws. I think there definitely needs to be regulation in AI. And it’s, it’s going to be a lot of trial and error, honestly. I mean, things are going to happen. Things are going to break. And I hope they just fail fast and fix them fast. But I definitely think there should be some regulations. just like when GDPR became a huge thing.

Other countries besides ours were like, you know you have to delete our data within three months and you have to put on there, like, do you want us to delete your data? So yes, it was a blocker, but it was also a good thing, right? And so, anytime something new is, that’s what happens.

Abhinav (12:11)
Absolutely. And I want to talk also about the data. You know data has been one of the most important things when it comes to you know making recruitment more effective, making it better. You know the more the enrichment you can do of the candidate profile, the more you know diverse data you can do that. You know in this world of AI, How, What role do you play that data will take in making better talent decisions? And how can AI further put a fuel to that.

Suzanne Salzberg (12:39)
Data plays a huge role in making, you know, better talent decisions. I mean, I talked to you about, you know, when CEOs want data, right? So where does this data come from? It used to be like, just my information, right? And so data can play a huge role in making better hiring decisions. We talked about the analytics in the process, huge, right? So just to give you an example, if you build a great dashboard, you can tell in every part of the process how long it’s taking how many diverse underrepresented groups are in the pipeline.

It’s a lot of pipeline analytics that are really important because if you want to do great diversity hiring, then it has to be intentional. And it starts at the beginning of the pipeline. It doesn’t just happen at the end, right? And so let’s say you have a hiring manager that you notice underrepresented groups are just never getting through.

Like, you know, then that’s when you talk with HR and you say, hey, this hiring manager’s never letting through a person from an underrepresented group, right? And so those, it helps you to make better decisions. It also really helps with, one of the biggest mistakes I think people are making is they’re thinking, oh, we need 30 QA engineers.

But AI can help you build these models based on your revenue goals and all these things. And then you can reverse engineer based on these models, how many people should we actually be hiring? And, and what skills should they actually be? Here’s our problem of company X. What skills should the people we hire actually possess.

Suzanne Salzberg (14:22)
The other piece that’s huge is keeping up with compensation. So companies like Radford, I don’t know if you’re familiar with Radford, but companies like Radford that most people use to like look at what we should be paying people, you know, they have 35 ,000 companies that feed input into their, their software.

They do it once every six months, right? And so like say in 2022, what was happening was because salaries were going so high, so fast in the US companies started going outside of the US to hire, right? Canada, South America, that was happening a lot. And the salaries were half, which isn’t great, but, but companies were like, let’s go to Canada cuz you know, but so what was happening, the salaries in Canada and other countries were going up so fast

Because every company got this great idea. And I would come back and say, hey, the senior engineer is now making this much. And they’re like, what? Like there’s no way, right? There’s people just didn’t believe you. And so if AI could, could, you know, take that data and analyze it and quickly and give me like actual data that I can say, no, look here, that would be amazing. And then because you wouldn’t lose candidates, you know, based on the fan companies were just offering 50 grand more per person just to get them, you know? So the smaller companies were like, what do we do? like you know, so, and then the employees that had brought on, before that from those countries were now like making way less. It was, it was crazy. I’ve never seen anything like it in all my years. So, so I think the data on compensation can really help create better models.

Abhinav (16:06)
I think it’s so interesting, they said that how AI or data can actually help align companies business objectives to their hiring goals. And this is one of the big problem that we see that when majority of the companies, this whole talent acquisition and talent managements are staying silos and completely disconnected. You know, I would love to hear your thoughts on how can companies better align both the talent acquisition and talent management to just build a more cohesive ecosystem.

Suzanne Salzberg (16:35)
This is a big problem that’s happening right now in the talent ecosystem, and it’s looming very large right now. And because of the crazy market that’s been, right now employees and candidates are feeling that, you know, going back three to five years, that was, we’re people first. We care about people. We’re people before profit

Like you heard all these things of like people are a number one goal and which is great but now it’s like it’s a bottom line and all we care about and which is you know as a CEO it’s important right? It’s important that you consider the bottom line, but now so what’s happening is Employees are not feeling like companies are loyal to them so guess what we’re not gonna be loyal to you so when Suzanne reaches out as a recruiter I’m getting people way easier to jump ship from a company because they think well we could have a layoff next month because we’ve already had three so I’m just gonna leave right.

The other thing that’s happening is candidates are seeing this or they’re just been burned because they’ve been laid off three times in the last two years so they’re interviewing, they’re getting job offers and then they’re continuing to interview.

And so I’ve had in the past probably six months where candidates literally called the day before and said, oh, I decided to take another job because they gave me 20 ,000 more dollars. so nobody’s loyal. So everybody’s pretty much just like, sorry, not sorry. You know like you haven’t been, you know, helping me at all.

Companies need to be, especially HR, needs to do a better job in hiring better leaders that are understanding hiring the right people and budget forecasting. So we’re trying to mitigate all of this and we just really need to have better succession planning and it’s just instead of everything always being on fire and oh the first, the first solution is to do a layoff, right? And so companies have to really get back to valuing the people, not just staying there, but actually valuing the people. I feel like that’s, that’s really bad right now.

Abhinav (18:43)
We have spoken about hiring Suzzane, but what about post -hiring? You know do you think that AI will make a difference in providing overall employee experience as well, especially say from the day one right from their onboarding

Suzanne Salzberg (18:58)
Yeah, yes, for sure. think it will. I also think that it really helps people. I mean I talk to people that companies every day that’s like, Oh I had to make this PowerPoint presentation and AI helped me make these beautiful slides like that used to take someone a long time to do. Right. So it helps again now that companies are leaner. It’s helping take a lot of those administrative tasks, even off of onboarding and all of those things.

As in recruiting, I don’t think AI should replace onboarding. I think you should have real people doing people’s onboarding because again, it’s the first impression from these companies. I think, this is a really interesting aspect that I think can help a ton, is

If you ask any employee at a tech company, especially right now, is they’re always having to fill out these engagement surveys? How are we doing? What can we do better? Like on and people are very honest because a lot of times they’re anonymous, right? So what happens is the company fills it out, HR is saying, oh make sure you get your engagement survey filled out. And then they have an all hands meeting and they report the findings and people can do you know anonymous questions.

And the biggest pet peeve of employees is they take the time fill these out, they give you the feedback, and then they never hear anything about what’s going to be done to fix what we just told you was wrong. And so I feel like, because it takes HR teams a long time to compile this data, look at where the problems are, and I think AI can do that really fast, and then also maybe recommend some solutions to helping, right? And so then HR is going to be the one that executes them, but they can get that stuff done faster because HR so many things on their plate, again, it just takes that administrative stuff off. So I think it helps everybody, everybody’s employee experience.

Abhinav (20:53)
Absolutely, being an employee engagement platform. I can 100 % you know relate to what you’re saying. It’s not about the effort to take a survey. It is not even about an effort to go and collect the feedback. It’s about what to do with that. And like they say that you know a feedback taken and not done anything is actually worse than no feedback taken, 100%

Because HR has so many different things to do and so many fire to douse, if you just tell them that this is what your number one thing to increase your ENPS or your retention, they would absolutely grab that. And I think that’s a great opportunity for AI. And my last question, you know, we’re speaking about it today, but I want, you know, I would love to hear your thoughts on that one thing, that biggest impact that you believe that AI will make in the talent life cycle in the next 10 years from now

Suzanne Salzberg (21:27)
For sure. You’re gonna see a lot of trial and error which I said well I think you’re gonna see a lot of trial and error which works, but I think the analytics and seeing where the process is working or not working. I think it’s really gonna help make data driven decisions as well as taking the time off the recruiting team on parsing those resumes that we get so many resumes I would say a lot of it depends on the industry for an example

I have a friend who’s a radiologist, and he literally said to me, there will not be radiologists. AI is going to completely take over, just the radiologists that are reading the scans, not the ones that are doing you know surgery. He said they can do it faster, they don’t make mistakes, and, and people can get that information way faster, right? So like you’re sitting there, you just got a scan, and you have to wait for the results. Painful, right? You can literally get it in 10 seconds.

Suzanne Salzberg (22:44)
And so he said, he literally told me, goes, my job is not going to exist. So a lot of it depends on the industry, right? So that is one job that yes, AI can replace that job.

Abhinav (22:44)
Wow.

 

Suzanne Salzberg (22:56)
Right? And so, yeah. And I think that what’s happening right now is we’re between generations. Right? And so even Gen Z, like they’ve had human interaction and AI right now, but like say a Gen A, like after Gen Z, they’re fine with no human interaction. Right? They’re like, I don’t want to talk to people because they can’t have a conversation. Right? I don’t want to talk to people. I’m fine if an avatar does my interview. So I’m not saying 10 years from now what’s gonna happen

Abhinav (22:56)
my god.

Suzanne Salzberg (23:27)
But I think right now there’s too many people that were in this flux between the generations where that’s why it’s not gonna happen but hey, I am not naive enough to say that like if you have future generations that have no problem never talking to a human it might not, you know replace it. That’s my biggest fear I would be very sad if that happened, but you know, some of it’s probably inevitable

Abhinav (23:49)
I want to say one thing to your radiologist friend that I am talking to, everybody. Like, I’m hearing this from a programmer. I’m hearing from an SDR. I’m hearing from a copywriter, from a marketer that our job will be replaced by AI. And to be honest with you, I honestly, I don’t think so. I think we humans have an incredible capability to adapt and learn and build new skills that an AI can’t do. So I’m

I’m sure there’s both of us to see that what the next 10 years we do, but I’m very optimistic that it’s going to be something good and not very negative. Well Suzanne, thank you so much for taking the time to speak with me. I just loved our conversation and this industry is moving with such a high speed. And I’m sure when we speak again, we’ll have a different you know AI landscape and new challenges.

Suzanne Salzberg (24:24)
Exactly.

Abhinav (24:42)
I’m sure we both and you know all our audience will be fully prepared for that. Have a wonderful day and thank you so much for your time.

Suzanne Salzberg (24:49)
Thank you. I enjoyed it. Have a great day. Bye.

Abhinav (24:52)
Thank you.

 

Summary

In this conversation, Abhinav and Dan Lapporte discuss the intersection of data, AI, and people in the field of People Analytics. Dan shares his journey in people analytics, starting at Charles Schwab, and emphasizes the importance of connecting HR data to business impact. They discuss the evolution of people analytics over the years and its increasing ubiquity in organizations. Dan highlights the need for HR to be more data-driven and the challenges in bridging the gap between HR and other departments. They also explore the role of AI in people analytics and the potential for democratizing data and information. Finally, they debunk myths about people analytics, such as the need to be an expert data analyst and the belief that people data and business data should work in silos.

Key Takeaways

• Connecting HR data to business impact is crucial in people analytics.
• People analytics has become more ubiquitous and is expected to deliver insights and value.
• HR needs to be more data-driven and bridge the gap with other departments.
• AI can democratize data and provide natural language processing for easier access to insights.
• People analytics can elevate top performers and help businesses succeed with all employees.
• People data is business data and should not work in silos.
• Data does not need to be perfect to start with people analytics.

https://www.youtube.com/watch?v=kkhVfMxfnxM&list=PLdHHCpR_eGHcUnxG_eeJREMtnLw3XT6tO&index=1

 

Full Transcript

Abhinav (00:02)
Often when we talk about People Analytics, it’s always about HR metrics, like headcount, attrition, or hiring. But how do these numbers actually connect to real business impact? What happens when we mix people and business analytics? And most importantly, how can we use this combo to help everyone in the company aim higher and reach bigger goals? Hi everyone, I’m Abhinav, and welcome to the Peoplebox Analytics Talk.

where we invite remarkable leaders to go deep into the fascinating intersection of data, AI and people. And today, I am thrilled to introduce Dan Lapporte. Dan has an impressive wealth of experience in people analytics, spending nearly two decades. His journey began at Charles Schwab, way back when people analytics went by the name of HR reporting. Over the years, he lent his experience to Fortune 500 companies like Chevron,

and Kaiser Permanente, and now heads people analytics at Lucid Motors, the luxury electric car giant based in Northern California. Welcome to the show, Dan.

Dan Lapporte (01:09)
Thank you, Abhinav Nice to be here.

Abhinav (01:11)
Dan, your journey in people analytics started by connecting HR data to business impact at Charles Schwab almost two decades ago. Could you share that story and why this piece is still so crucial?

Dan Lapporte (01:23)
Absolutely. When I was at Schwab, there was something of a war for talent among the brokerage companies, you know, Fidelity and Wells Fargo, there was something of a revolving door. And what I found is that when Charles Schwab lost top performers, we lost revenue, we lost income, we really struggled particularly if those employees went to a competitor. And so I began reporting on

top performer turnover, not just turnover, but top performer turnover. And I sent these reports out and they were just, you know, based in Excel. And one day out of the blue, I got a call from the CFO and the CFO said to me, you have my attention because it was so impactful when we lost those good people, those people who are bringing revenue or new assets to the organization.

And I just vividly remember it being a very powerful moment in my people analytics journey.

Abhinav (02:24)
Fantastic. And then that’s how a very, very impressive journey started.

And how is it different today, Dan? You know, it’s been almost 20 years with so many different companies, most of them Fortune 500. Today, when we look at the people analytics in the post-COVID world, is this any different?

Dan Lapporte (02:45)
It’s different because I think people analytics has become more of a thing. It’s ubiquitous. I think every organization either is thinking about or has a people analytics function and the expectation is that you are going to deliver insights and value. It’s no longer just about providing data. It’s about telling the story with data. So my example, a moment ago with

with Schwab is now table stakes. I think everybody should be reporting on attrition across performance levels.

Abhinav (03:24)
And in your experience, what’s really driving this shift? Why has it suddenly become so crucial and every company is actually looking at insights and not just tables and dashboards.

Dan Lapporte (03:35)
Yeah, Well, People are one of the most important or expensive items on the balance sheet. And businesses are typically in business to make money. I suppose if you’re a nonprofit, you’re not there to make money. But when you are able to convince people that, you know, our workforce is an incredibly expensive commodity or asset, and we’re not doing a very good job managing that asset.

people tend to sit up and take notice. And The analogy I like to use Abhinav is most companies have a better sense of how much money they’re spending on office furniture than they do on their people when you start to turn in, you know, include things like the cost of replacement or talent acquisition costs. Most businesses think of those as baked in costs, but they’re controllable and they’re manageable and they’re measurable.

Abhinav (04:33)
Could you walk us through a typical day or week in your role? As the head of people analytics at Lucid or in your previous companies

Dan Lapporte (04:42)
Sure, I would say my day is really mixed of three things. One is responding still to ad hoc requests. There’s always gonna be a need for a hiring rate or a turnover rate or maybe it’s around succession management. The second part is really strategically trying to engage with leaders, so taking the people function to the business and helping them understand what I mentioned before

about what cost people are and how they can perhaps run their business more efficiently with the right people and the right place, the right time at the right cost. And then the last piece of it is, you know, once you build a people analytics practice, it’s a matter of delivering on what you said you want to deliver. So that’s kind of the work of the work. If you’re going to be looking at attrition trends by, you know, gender.

it’s important to be able to provide kind of regular routine updates to people and what they might consider doing about things if they’re crossing some threshold that they’re concerned about. So those are the three areas I think people analytics plays in.

Abhinav (05:59)
And Dan, I’m sure in all these years that you have been into people analytics, some of the things might not have changed. For example, even two decades ago, the importance of attrition would be as high or at least would be critical as it is today. But walk us through, in these past 20 years, did you notice a shift in the type of people analytics projects that you tackled?

Dan Lapporte (06:24)
Absolutely. When I first started, it was miraculous to be able to calculate an attrition rate and provide some interpretation. Now there are AI chat bots or benchmarks that you can bring in and be able to give business leaders a sense of what does good look like? If our attrition rate is 15%, how does that compare to the industry? How does that compare to other functions within my business?

So it’s changed a lot and it’s become much more sophisticated and probably more valuable.

Abhinav (06:59)
It’s so interesting that you spoke about AI. You know, the world is like suddenly changing. It’s such a huge speed with the advent of AI. When it comes to people analytics, how do you see the best use of AI?

Dan Lapporte (07:15)
I think this idea of business leaders or HR business partners or any HR practitioner being able to ask natural language questions to a machine, right? This computer, what’s my current head count? How did my head count change year over year? Which organization is growing fastest within my business? Those sorts of basic business questions.

which typically come in over email and then you spin up an analysis and you send it off, can now be done automatically. And I would add, I think we’re kind of in the infancy there of bringing AI into people analytics. I think it’s gonna continue to grow and it’s a trend I’m actually looking forward to.

Abhinav (08:01)
Fantastic, we too, I think it’s a huge opportunity for companies like us to, you know, provide this power of analytics and insights in the hands of every manager, every leader, not just from people’s side, but also on the business side. And on that note, Dan, every time I speak with a leader or an HR head, everyone agrees that people inside should be aligned to business outcome. Yet, very few companies have been able to achieve that. You know?

And many people wonder that what are the specific challenges that companies face or especially you have faced in your journey to achieve this alignment.

Dan Lapporte (08:44)
People data is business data. The same way we need to account for people costs in our business, people data really needs to get up to the right hands. And a good specific example of that is when I worked at Schwab, it was right at the very beginning of the pandemic. And people were very concerned about frontline health care workers being unable to work.

And so one of the more interesting people analytics case studies was in concert with, you know, clinical practitioners and lots of different areas of the business look at how can we replace those people? You know, we’re not gonna be able to hire them because nobody’s gonna wanna be a respiratory therapist when you’re in the middle of a respiratory borne pandemic. But.

what we ended up doing was looking at different licensing requirements and then figure out if scenario A happens where we lose a lot of frontline employees, where can we find people who can still perform those roles within license and be able to meet patient needs? And again, I could talk for an entire podcast about what that effort involved, but that’s a good example of where data needed by the business.

was supported by employee information and people analytics.

Abhinav (10:08)
That’s actually a very interesting example. And I wonder that despite these amazing success stories, the first sort of destination for most of the business leaders to get any actionable insights is not people analytics. They go to their business analysts or the data analysts to try and find it. And most of the times that we have seen, the people data is not part of that. So I’m curious that…

There are many head of people analytics or head of people who always wonder that how do we bridge this gap between HR and other departments and how do we make business leaders excited about the people data and the actual impact of that on their business metrics as well.

Dan Lapporte (10:54)
Historically, when business KPIs became available, they were true business KPIs. They really were not related to people. And now you can’t find a business that isn’t looking at people metrics and people analytics. So it might be top performer attrition, it might be overall turnover rate, it might even be, you know, DEI metrics.

that are most important to a business. But that’s a way where the practice has matured, and as the practice has matured, it’s gotten me more visibility in regular business reporting.

Abhinav (11:27)
Interesting. And then in your experience, right, you have seen this in a large span of time. Do you see that HR is becoming more data driven today or is it almost always driven by the top, you know, who requires more people data and connected to the business data?

Dan Lapporte (11:46)
Ooh, that is a sticky question. So let me try to answer it with some political correctness. I think HR business partners are now expected by their leader or by their client groups to have some knowledge other than gut feel about talent. And I think HR business partners, hopefully I’m not offending my friends by saying we’ve sort of been dragged along as

data-driven decision-making has become more important across businesses, HR has been along through the journey. I will add that it’s not a natural talent for the HR function, and that’s why these tools, Peoplebox and others, are so important for organizations to invest in.

Because if you’re going to continue using technology from the 80s and 90s like Excel to try to generate decisions about people, you’re going to get into trouble very quickly. The questions will outpace the technology.

Abhinav (13:01)
Fantastic. And as the demand of both data as well as people analysts or the role of people analytics are becoming more and more in the companies, what skill and expertise do you believe, Dan, will be most valuable for the future people analytics professional in making sure that they are able to provide the right data and actionable insights to the business leader?

Dan Lapporte (13:29)
That’s a good question. And I think it’s a combination of sort of HR skills and knowing what matters about organizations and organizational design and business acumen. So I think the best people analytics practitioners probably come from other areas of the business. I didn’t start out like my career journey in human resources. In fact, 25 years ago, if you told me I was gonna be in HR, I would have chuckled. But.

I fell in love with the work because there’s such a power in being able to say, you know, all businesses talk about people as their most important asset. Well, then start treating it as an asset and effectively manage it and effectively understand it. And again, People Analytics is the pathway for that.

Abhinav (14:22)
Fantastic. And Any steps that you would recommend, Dan, for companies aiming to foster a really data-driven culture within their HR and the broader organization.

Dan Lapporte (14:38)
I have two or three that I think are vitally important. Number one, the messaging has to come from the top down. So if your Chief People Officer or VP of HR believes in data and being data driven, they need to reinforce that message so that the HR function follows their lead. I think the second one is, you know,

The only way to have a strong people analytics function is to guess what, invest in the people analytics function. So if you have an expectation of hiring someone and, you know, they’ll just jockey their way through Microsoft Excel or, or maybe Power BI even, and come up with their own formulas, that’s not a winning strategy. And then I think the third is being intensely focused on business problems. So when you’re coming up with.

people analytics metrics, focus on what it is the business is looking for. Is your business focused on growth? Is your business focused on controlling costs? Is your business focused on expansion? Maybe global expansion or something like that. And people analytics really does play a role in all of those things.

Abhinav (15:48)
Peace.

And it’s so interesting you said that I always start with a business objective. That’s exactly what we do when we help companies with OKRs. And one of the questions that I’m often asked is where to start?

Dan Lapporte (16:07)
I would say hiring a strong leader to make sure that you can sort of build a roadmap and a team. It’s not, you know, flipping a switch and you’ve got a people analytics function. You have to kind of grow from the ground up. If I was going to make a real recommendation, it would be look for someone who’s a star in finance or maybe a star in your business and help them.

to understand the importance of people and ask them to maybe take on as a stretch assignment, hey, we want you to develop this capability for us. And then, you know, that person doesn’t have to stay in that role forever, but getting a strong head start is vital.

Abhinav (16:54)
Very curious to know, Dan, in your long career and people analytics, what has been the most sort of mind-breaking or wow insight that you were able to provide

Dan Lapporte (17:10)
Yeah, that’s a really good question. And I’m going to go back to some of my work at Kaiser Permanente, which for people watching this internationally, or maybe outside of California for that matter, is one of the largest healthcare organizations in this country in the United States with more than 12 or 13 million members and a quarter of a million employees. And the question was,

Is there any correlation between what’s happening with our people and with Kaiser, it was specifically nurse managers. Is there any correlation with them leaving or their performance or maybe how they’re feeling about their job and patient outcomes? So we looked at business data from the quality side of the organization where we had hospital re-admittances or maybe even hospital

quality scores for patient satisfaction metrics, and then looked at nurse manager turnover. Because we know that when you have a lot of turnover in management, the employees under those managers tend to question, you know, well, why am I still here? My manager left or, you know, my manager moved on. Where’s my motivation? And we did that study and we… It wasn’t conclusive, I’ll be honest. But…

That’s the sort of thing where you can take very real business data, hospital quality scores, which lead to Medicare or Medicaid reimbursement rates, and people data, and marry the two together and see if you can find correlations. It’s fascinating work, actually.

Abhinav (18:57)
Fantastic. That’s fantastic.

Coming back to the wish on AI, Dan, you have seen now the introduction of AI and its capabilities.

How would you really like AI to transform the people analytics space?

Dan Lapporte (19:13)
I think bringing AI to the forefront of people analytics is really all about democratizing data and democratizing information. So if you build an AI model where security is in place, of course, because you’re dealing with sensitive data, but you give leaders the ability to simply ask a question or type a question and get an answer, that aspect of AI, that sort of natural language processing and…

machine learning and all those other components of AI, I think really will bring a sea change to how workforce data is democratized into business data.

Abhinav (19:54)
Fantastic. We now move to the last section of our podcast, which is my favorite, which is breaking the myths on people analytics. So I would share a myth, and I would love to hear your response on that. So the myth number one that we hear very often, you need to be an expert data analyst to use people analytics in a company.

Dan Lapporte (20:18)
I think what you really need is good cross-functional connections. You need connections with IT, you need connections with obviously HR, you need connections with the business. And if you know the right questions to ask, you can certainly find people or tools to build the answers for those questions.

Abhinav (20:39)
I couldn’t agree more. Myth number two, people analytics reduces people just to their data.

Dan Lapporte (20:47)
debunked. I think people analytics can elevate top performers. I think people analytics can help businesses succeed with all of their employees. So I think people analytics opens doors not close them.

Abhinav (21:05)
Fantastic. And the myth number three is that people data and business data need to be working silo. They don’t really need to be connected to bring an image.

Dan Lapporte (21:16)
Debunked. People data is business data. Again, I hate to sound like a broken record on this, Abhinav but most organizations spend a lot of money on their people between salaries and benefits and occupancy and provisioning. But we don’t do a very good job managing that resource. And I think the secret to…

for lack of a better word, selling a people analytics capability for a business is making that case and being able to present that business case. And there are lots of different ways to do it.

Abhinav (21:58)
And the last myth, which is we often hear, and it’s probably also one of the biggest blocker, is to start people analytics, data must be perfect.

Dan Lapporte (22:10)
No, you should never let the perfect get in the way of the good. So it would be nice for all HR data to be of the highest quality and the same as financial data, which requires reporting and monthly closes have to be exactly right. But it’s not. People are messy and therefore people data is messy. And to make a good decision, you don’t really need

The details, what you need is the trends and the interpretation.

Abhinav (22:45)
Thank you so much, Dan, for talking with me and for the incredible insights. You are one of the most experienced person I have spoken with when it comes to people analytics. The work that you have done in connecting people insights to the business impact is truly inspiring. Keep up the great work to make companies more data-driven and…

people-oriented. Have a great day and thank you so much, again

Dan Lapporte (23:09)
My pleasure