People Analytics Podcast

Episode 2: Using People Insights to Drive Business Impact at Panasonic

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.

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.

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