Episode 236: How People Analytics is Powering Business Strategy at Mastercard (with Anshul Sheopuri)

 
 

What does it take to move people analytics from the margins to the core of business strategy?

In this episode of the Digital HR Leaders podcast, host David Green is joined by Anshul Sheopuri, Executive Vice President of People Operations & Insights at Mastercard, to explore how people analytics is transforming from a support function into a business-critical capability.

More than three years since his last appearance on the show, Anshul returns with a new role, a broader remit, and fresh insights into what it takes to embed analytics into enterprise-wide decision-making at scale.

What you’ll discover in this episode:

  • How the role of the people analytics leader is evolving into a portfolio leadership model

  • The key catalysts behind the shift from traditional HR metrics to integrated, business-first insights

  • How Mastercard is leveraging AI to drive employee success and business transformation

  • A comparative look at building analytics and AI capabilities at IBM vs. Mastercard

  • The importance of governance, ethics, and responsible AI in people analytics

  • Practical steps to scale analytics and embed it into enterprise decision-making

  • The future of people analytics—and what leaders need to prepare for next

If you’re looking to understand how people analytics can become a true strategic partner across your organisation, this conversation offers a practical and inspiring roadmap.

This episode is sponsored by Mercer.

To thrive in an AI-augmented world, organisations must rethink how work gets done. Mercer’s Work Design solution uses AI to deconstruct jobs, redeploy tasks, and redesign work for greater agility, productivity, and impact.

Unlock your team’s full potential. Learn more at mercer.com/wfdemo.

[0:00:00] David Green: There's a quiet revolution happening inside some of the world's most forward-thinking organisations.  It's a shift that's changing the very fabric of how business decisions get made, not just in HR, but across the enterprise; and at the centre of it all, People Analytics, but not as we once knew it.  No, the People Analytics function and its leaders have gone through a tremendous evolution, from serving up HR metrics in isolation to driving insight-rich strategies that shape how entire organisations operate, grow, and evolve.  I'm David Green, and today on the Digital HR Leaders podcast, I'm joined by someone whose own journey reflects this transformation in action.  It's been more than three years since today's guest last joined me on the show, and since then, a lot has changed, not just in his role, but in the scope, influence, and ambition of People Analytics itself. 

So, today, I'm delighted to welcome Anshul Sheopuri, Executive Vice President, People Operations and Insights at Mastercard, back to the show to share how his role has evolved from building analytics capabilities to leading an enterprise-wide people operations portfolio.  Together, we explore the catalysts that made this evolution possible, from the rise of AI to the growing demand for integrated business-first insights.  And we also dive into how Mastercard is embedding analytics into decision-making at scale, what it takes to govern AI responsibly, and why many organisations are still struggling to move beyond the dashboard.  I won't give too much away.  So, with that, let's begin.

Anshul, welcome back to the show.  I can't believe it's been more than three years since you last joined me here.  The last time you were here, you were still at IBM.  Now, you're, as I said, Executive Vice President for People Operations and Insight at Mastercard.  Can you share a little bit about the journey that brought you to Mastercard and how your role has evolved since then, and maybe for people who haven't listened to the first episode, a little bit about your background as well? 

[0:02:11] Anshul Sheopuri: Sure, absolutely.  Honestly, I was a little surprised myself, David.  It's been three years and time does fly by, doesn't it?  Excited to be here with you and I'm looking forward to this conversation.  So, as you said, I've been at Mastercard now for a couple of years.  I head up what's called People Operations, and Insights.  And the way I think about the role is essentially around creating delightful employee experiences at scale, all the way from hiring to alumni, the entire employee lifecycle, and making sure that those experiences deliver what we want them to deliver for the employee, and not just strategically, but operationally, so the last mile consumption of the experience works as intended, all the way from the tactics of the experience to what the purpose of the experience is, that entire gamut of it works as intended.  And of course, there's no way to do that without data and AI and analytics to power those experiences, and that's no different than consumer-grade experiences that we might all experience while we watch our favourite movie or hail a cab on our favourite ride-sharing app.  So, that's a little bit about how I think about the North Star of it and how tools, processes, and data come together to make that happen. 

In terms of my journey, David, as you mentioned, prior to Mastercard, I was at IBM.  I'm not a career HR professional.  Before my time in the HR function at IBM, where I headed up analytics and technology, I worked in the general space of operations, data, tech, working with financial services, retail, digital marketing, healthcare, the gamut, and had a lot of fun through those journeys, but then figured I want to try something different, and that brought me to Mastercard, and it's been a fantastic two years here.

[0:04:16] David Green: Great, and we're going to learn much more about what you've been doing at Mastercard, what your team's doing at Mastercard as well throughout the course of this episode.  And it's interesting, actually, people's different journeys into people analytics type leader roles.  Obviously you brought that outside-in experience in from other business areas as well.  And obviously, you've been involved in the people analytics field I think it's, if I get it right, around ten years now, I think, actually it might even be a little bit more.  How have you seen the role of the people analytics leader evolve over that time? 

[0:04:52] Anshul Sheopuri: Yeah, I think that's a fantastic question, David, and I do believe it has changed dramatically over the past decade.  I think at its very core, the purpose of the role I don't think has changed.  The purpose being a strategic force multiplier for the entire HR function.  If you think about a business partner in Poland or an employee or a people leader in Japan, their ability to focus on the right sort of areas of focus for the team development or addressing the opportunity they did after more efficient recruitment, better employee experience, better engagement, whatever it might be, data and insights enables you to sort of zoom in on the right areas to focus on and really be a force multiplier.  So, at the core, the purpose hasn't changed, but I think two things have changed.  One is the expectations, if you look at consumer grade experiences, what good looks like.  Other functions, you take marketing, you take finance, in all of those functions, there is an expectation more and more of more just-in-time nudges, more intelligent insights, more relevant and contextual insights.  So, I think that pervades into the HR space as well.  Obviously, there are external trends that also inform the space.  So, that's from a standpoint of the expectations. 

Then there's another piece of it in terms of readiness.  And I think that also has changed, with the tech landscape having transformed so much, and you know this better than anybody, David, the number of HR tech vendors that are there.  And more and more, the technology is so much more consumable.  When we, ten years ago, were trying to do these things, there were very few vendors that did that.  So, your ability to build consumable solutions at scale was at a different maturity point than it is now.  So, both the demand and the supply, both has changed quite dramatically.  And that, I think, really has changed the expectations and the potential of the people analytics leader role.  So, I think that's one piece of it, with the force multiplier not having changed, but the supply and demand on dynamics having changed. 

The other thing, and tied to my point around the vendor landscape and just the maturity of the ecosystem, this then also means that the data and analytics in the people space is just so important to make the entire ecosystem work, the right digital platforms, process optimisation, service delivery.  It's sort of like the glue that binds people, process, and technology together.  And I think that's another reason why this function has become so strategic and so necessary for modern HR functions.

[0:07:53] David Green: Yeah, that's really interesting because obviously, I know we'll talk about this as well, everyone's talking about AI in HR at the moment and personalising experiences, as you said, getting consumer-like experiences as employees at work.  You can't do that if you don't have good quality data to actually provide the right recommendations for people.  So, yeah, I like the way you described that as it's almost the glue that kind of links everything together.  It's interesting, in the research that we've done at Insight222, and we published last summer around the ecosystem or the operating model in people analytics, we've definitely seen quite a step change over the last three to five years when we last looked at the operating model.  One of those is that people in your position are increasingly reporting directly to the Chief People Officer, like you do, and that's around 22% I think based on our last survey, and that was 13% five years ago, so that's quite a significant increase. 

We've also seen the responsibilities of the person leading people analytics change as well.  Yes, you still have many people analytics leaders that are focused on data and analytics, but we're seeing others like you emerge at that, we call the portfolio leader, where yes, you've got people analytics, but you've got another strategically important area as well, people operations in your case.  We've seen others with people technology, which I think is close to what you're doing as well; we've seen others with workforce planning; we've seen others with people strategy.  And I guess it just shows that in many respects, maybe people analytics has moved.  It's certainly moved from the periphery to the centre, I think of a modern HR function, and I think it's good.  And certainly, I saw a piece from Gartner the other day that's saying that people analytics is the fastest growing area of HR.  So, hopefully that augurs well for the future. 

So, let's just focus a little bit on some of the stuff that you're doing at Mastercard, Anshul.  So, as the people analytics function continues to evolve, maybe from a more narrow focus on people insights for HR, to a much broader and more impactful focus on insights at the intersection of people and business, what would you say have been the key catalysts that enable you to make this shift in direction at Mastercard?

[0:10:25] Anshul Sheopuri: Yeah, I think that's a really important question, David, and something that I continue to always reflect on, because that, I think, ensures that we're creating the ecosystem and the context for our insights to thrive.  Because in the end, it's all about adoption of the capability.  And I think of two or three different things when I think about what are the catalysts for success.  The first and probably the most underappreciated of the catalysts, I think, is being really clear and operating at the intersection of business need and user/employee need.  And I intentionally didn't pick data as a number one catalyst, because it's so important to be clear what your users want and what the business wants, and really operate at the intersection of the two, because operating in only one space doesn't get you the value proposition.  So, that's the first. 

The second, if you're trying to drive greater insights, but also more business impact, you've got to operate with data that is sufficiently high quality for the people space, but also in a way that is meaningfully connected with business data, whether it's in Salesforce and sales data or other data, so you can connect the dots across the ecosystem and can answer questions that help ensure we have the right people policies, right people focus areas in service of people outcomes, but also business outcomes, right?  And then, that ties back to the third catalyst, which is really thinking about scale up front, because what is important is to make sure that the actions are embedded in workflows, they're not one-offs, and that really brings a scalable sort of mindset to how you design and implement programmes.  So, if I was to net it out, it would be number one, business and user needs; number two, a cohesive data foundation; and third, actions and workflows.

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I mean, one of the things that we've identified recently, we've been talking about it for ages in people analytics, one of the things that we do is we democratise insights and we get those insights out to people managers, to HR business partners, to leaders.  And what we identified at Insight222 was a gap, quite a big gap between that democratisation and the adoption.  It was 71% of companies in our last research had actually democratised insights.  I'm not quite sure what the other 30% are doing, but anyway; only 47% had achieved good adoption in HR; and less than 30% in the business.  So, I think exactly, if you're operating at the intersection of what users need, then you're more likely to get that adoption that you need.  And with the adoption, you then get the scale, I guess.

[0:14:38] Anshul Sheopuri: That's right.  And I think also, to the point you made, David, in really interesting stats, I think it also boils down to, how do you do that at scale?  And if you're in a commercial organisation, like a commercial product business or software business, the product management discipline is a really mature way of thinking about how do you create capabilities that have high adoption in a scalable manner.  You think about market scan, you think about adoption, you think about your user needs, you bring a design-thinking mindset up front, you co-create your work in Agile ways.  Those practices and doing that in a more disciplined way, I think, more and more we're seeing that across the HR discipline too.  And I think that will augur well to operate at the intersection of business and user needs.

[0:15:35] David Green: Yeah, we definitely, I think certainly the more progressive-thinking HR leaders, Chief People Officers and then the functions, they do seem to be thinking about how do we put the user at the centre, rather than maybe what we've traditionally done in HR in the past, which is we design things for employees.  I think now we're doing things with employees and maybe that's quite a big difference.  And of course, one of the catalysts for that and for change is AI.  So, we recently had your boss, your current boss, Michael Fraccaro, the Chief People Officer at Mastercard, on the show back, I think his episode went out last October, and he talked about some of the fascinating ways that you're using AI to drive employee success and leadership growth and development at Mastercard.  I'd love to hear your views on not only how AI is transforming people analytics, but is also a driver for business transformation as well.

[0:16:32] Anshul Sheopuri: Yeah, no, I think that's a great, great question.  You know, this is something we're all very passionate about, it's something, David, you and I have chatted about quite a bit.  Maybe I can share a couple of examples.  The one that perhaps is most canonical, and in fact even comes way before my time at Mastercard, is our internal talent marketplace.  I think it's a great example of operating at the intersection of employee needs.  Employees in any organisation are always looking for access to clear career paths, stretch projects.  It's something you see pervasively.  Businesses always have a need in terms of moving quickly, opening up stretch opportunities for people, the ability to attract the best talent to the right roles, right?  Those two needs come together in our internal talent marketplace, which gives us opportunities for people to apply for projects, not just people with roles that might be the right fit for them, short-term projects, etc. 

What is fascinating about our internal talent marketplace is adoption levels, David, that I see, which is that we're at 90%-plus, which is pretty high when I speak to peers at other organisations.  So, it's something we feel really proud about, and it's an exciting sort of platform for our people to achieve their career aspirations.  And I think it's one of those great examples of coming together at the intersection of user and business needs.  Another one, which is more recent, is AI for workforce planning, and it's something that we've really spent quite a bit of time on.  And it's one of those things which I think ties more closely together, connecting people and business data.  Because when you think about making workforce planning decisions around locations and where you grow and grow capacity, you're thinking about things like talent availability, cost, skilled profiles, but you're also thinking about other types of data, real estate data, office space utilisation, you're thinking about client proximity to your workforce, so where are the clients located and how does your workforce tie to that? 

So, I think that's a good example of coming together of different data sources to answer questions around where we should think about our capacity adds, etc.  So, I think that's another good example of how I think we're moving the needle more and more from just people insights to holistic people and business insights.

[0:19:16] David Green: I know from the previous episode we recorded together, so when you were at IBM, that IBM was very much focused on building their own AI and people analytics products.  You obviously mentioned earlier that the HR tech market has really grown, particularly from people analytics technology products over the last few years.  It'd be great to understand the approach that you're taking at Mastercard around build versus buy, or maybe it's a bit of both perhaps?

[0:19:41] Anshul Sheopuri: It's one of those questions which I think every organisation needs to think about.  I think at its core, all built by hybrid choices are, at least the way I think about them, is at the intersection of cost, quality, and experience.  You've got to be mindful of cost; quality is the value that it delivers; and experience is, it needs to have the appropriate, thoughtful, delightful experience for your user group.  And that trade-off across those three vectors is the way I think about it.  But as you said, the HR tech landscape has changed dramatically.  And so, there may not need to be a need to build things when something is available off the shelf.  And I think a key dimension on the first-level filter I like to think about in the way I reflect on build-versus-buy decisions is, how unique is your need?  Because if your need is not very unique, if it's pretty common, markets have a way of responding to common needs and you're going to have a number of HR tech vendors in the ecosystem that service the need.  And so, where do you want to be differentiated; and where is their common need?  I think that's sort of an interesting exercise that we go through. 

Then you need to couple it with, how does that connect to the integrated employee experience?  Because you may make a choice.  It might be the right thing in its own part of the employee journey to either build or buy, but that can create friction in the integrated employee experience.  So, to think about it holistically is also another important consideration, in my view.  So, I think you'll see, and I think the entire marketplace has evolved a lot more towards buy in those capabilities that have more common needs.  But you do see a lot more build in areas that are tip of the arrow, and I think that's just a natural progression of the technology lifecycle.

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The next theme, kind of linking from technology to around governance and ethics, clearly they're at the heart of responsible AI and people analytics adoption.  What steps has Mastercard taken to ensure robust data management, ethical AI usage, and proper governance with your people analytics initiatives?

[0:23:17] Anshul Sheopuri: I'm going to share, David, what our perspective is, but I think this is also a call to action for any people analytics leaders listening in to this podcast.  I think the world is your oyster.  I think you have the ability now to be leading in this space and charting the direction of what responsible AI means, and really helping companies navigate.  So, obviously, when we think about responsible AI, having a framework, having a strong governance framework, and many companies now, as we do, have data and AI councils with membership from different functions across the company, and that provides a necessary oversight with responsible use guidelines, and so on and so forth, and just that forum for people to come together and have the conversations in a regular way, and I think that's super, super-important.  It also then means the ability to connect with a broader ecosystem, because this is a space which is very dynamic.  There's a lot of different legislation in different countries, and the ability to connect with different thought leaders, whether it's in universities or social organisations, to have those connects and understand what different best practices people are taking, it does take a village to make change in this sort of space.  So, I think that, to me, is another important element which we'd like to focus on, making those broader connects with civil society, academia, and regulators to learn and then constantly be sort of upskilling ourselves and evolving and shaping thought in this space. 

Second, so first is the governance framework and sort of senior leadership; second, security protocols.  And this goes all the way from third-party risk management, if you're working with vendors, to how you think about AI security and monitoring, and so the gamut of those things.  And the third, which is closest to my heart around responsible AI innovation, this goes from just transparency.  And, David, you and I have talked about that previously as well, being clear with users how data is going to be used, human oversight, so decisions.  AI is an enabler, not a decision-maker for decisions.  Bias mitigation and having the tools to do that, those are all in the realm of responsible AI innovation.  And then finally, and I think this is another one which I think is really important, continuous training.  How do you create space for training and training different persona groups, right, whether it's employees, people leaders, and even the HR function.  Are your PVPs on the front, your HR business partners on the front lines, sufficiently trained to address the first-level questions?  I think all those are important considerations.

[0:26:23] David Green: So, with everything that we've discussed today, how do you see the role of people analytics evolving, or how should it evolve over the next few years?

[0:26:34] Anshul Sheopuri: That's such an interesting question and it could go in so many different ways.  Maybe a good way to start is where the field was three to five years ago, and in the context of where it's headed.  If I think about where I spent my time five years ago, seven years ago, there was a lot around the foundations of data, governance, making the data self-serve, thinking about how you think about different walled gardens of data.  And I use that word around different parts of the employee journey could be in different walled gardens.  So, really thinking about data being more seamless, interconnected, we had sort of begun to experiment with AI.  Some of us were further along, some were in experiment mode.  That's sort of where the function was five years ago and a lot on creating dashboards for people data.  That's where the team spent most of their time. 

Now, as I think about now and where we're headed into over the next five years, I think there's going to be a lot of, be very clear with responsible AI.  There's going to be a need for even greater transparency, a need for all the different things that I said, coming together at scale across all the different use cases.  So, I think that's one big shift and it'll need to occur across the ecosystem.  The second is this point I spoke about earlier on people and business-centric analytics.  And that's a huge shift in mindset and operate model in the way we work, because it's not just about creating a dashboard that somebody is asking you for, it's really thinking about your user.  How does the business, the employee, the people leader want to use the data for what actions?  How is that transparent?  And then, the closed-loop feedback system around that, what's the adoption as well as where is it working and where is it not, in terms of those outcomes?  So, that's going to require different skillsets and muscles, like product management, thinking about adoption, campaign management, just very, very different skills and roles to make that happen. 

Then finally, I do feel, along the lines of what your study said and what you shared, David, I do feel like these different pieces of the ecosystem support that exists in HR, I broadly call it digital, whether it's tools, process, technology, service delivery, those worlds will come together more and more.  And they need to come together more and more in the service of the employee to make that experience more seamless.  And so, the notion of the portfolio leader, I think, will also evolve and get more interconnected.

[0:29:28] David Green: So, you talked about, and I think the thing that I'm really hearing from you is that people in business, bringing them together maybe more than we were doing at scale three to five years ago, but bringing together at scale now.  So, what's maybe the mindset shift or the tip that you'd recommend to people analytics leaders that are maybe a bit earlier in this transformation and looking to affect this within their organisation? 

[0:29:58] Anshul Sheopuri: Yeah, I would suggest two very tactical pieces of advice.  One is speak to your users, actually have a conversation, ask them what their needs are, how do they feel about the product that you're conceiving of, not after you've built it, but that you're trying to test, and carry them, co-create with them on the journey.  So, that's one.  And then, second, understand the business, like deeply understand how the business operates, how we work with customers and how what we do in HR enables not just the employee experience to be great, but the customer experience to be great.  So, deeply understand the business and speak to your users.

[0:30:45] David Green: With that, I think that leads nicely, Anshul, to the penultimate question, which is the question we're asking everyone on this series.  You've touched on this, I think, in some of your answers, so please feel free to summarise, but also add anything new that you want to add here as well.  How can HR use AI to improve employee experience and wellbeing?

[0:31:08] Anshul Sheopuri: Again, another topic so passionate, David.  I think a lot of organisations do focus on helping the employee experience on areas like we've talked about.  But to me, it's also important to focus on the small things, the everyday moments that matter to employees.  If you're a candidate or a hiring manager, and you're trying to just schedule an interview to talk to each other, it's a small everyday moment.  But when you make a reservation at a restaurant today, or you go to your favourite app and you just go down and you pick a time and a date, and that's the end of it, that should be what the experience is every day for candidates and hiring managers.  And that's one example of tools that we're deploying, an interview scheduling tool, to really simplify the experience. 

Or I'll give you another example, around when we ask for vacation, when we request vacation, or we approve vacation, it shouldn't require five clicks to get to that.  You should be able to interact with a bot to do it in one click.  Or you want a letter because you are applying for a mortgage, or you want to apply for a visa application.  You should be able to get to that faster than calling somebody, having a conversation with them and that being turned around manually.  So, for me, what's really important also is these everyday moments driving.  And I think there's a difference between thinking of them as productivity, which could be one way to think about it.  But this is also transforming the employee experience, because what is taking several days to back and forth via email to schedule an interview becomes an instantaneous sort of schedule; or you get a letter in two minutes versus a couple of days.  So, I think those everyday moments and really building AI into them can really transform the employee experience.  So, in addition to some of the things like talent marketplace we talked about, I think this is super-important and critical. 

From a wellbeing standpoint, I would say a couple of things.  One is, most companies are doing some form of employee experience surveys.  So, having AI assistants to summarise teams and identify focus areas, that's certainly one area that, from a wellbeing standpoint, can be helpful.  But I'll even share with you, David, an experiment I did earlier in the day.  So, we have an external vendor-provided network analytics tool that we use.  And I was just reflecting on my own interaction habits around organisation networks, how many emails that I send outside work hours, the time I interact externally outside Mastercard versus internally, how much time I spend multitasking in meetings or not.  These are all reflection moments that, yes, improve the productivity and effectiveness of the team, but are also responsible and are so critical to the wellbeing of everyone, including myself and my team.  So, we have these sort of reflection moments built in into our analytics that we offer to employees and people leaders that they can reflect on, and as something I was just doing in the morning and reflecting on myself as well.  So, that's something I think is really important for wellbeing, especially in this moment where a lot of information does get thrown at different people.  And just being mindful of that is, I think, is a great starting point.

[0:35:07] David Green: That's really good.  As I was listening to you then, Anshul, particularly the first part, when you were connecting it to some of the people operations work that you're responsible for as well, it made me think a little bit of an episode we did a few weeks ago with Prasad Sethi, formerly obviously as you know of Google People Analytics, I think for 14 years.  And he was saying that one of the things that really helped him as a people analytics leader was when he took on responsibility for compensation as well.  So, he wasn't just responsible for the team producing the insights, he had to implement them as well.  And I guess that's what you're doing as well, you're implementing some of those insights into the work that your team is doing around people operations.  And I just wonder if that also helps you to even make the analytics better by the fact that you're also implementing the insights into the other part of the responsibilities you have.

[0:36:0] Anshul Sheopuri: 100%.  What better way to know your employees than to really be responsible for the implementation and adoption and the experience around it.  I think it really is a really good forcing function, which is why I think you see more and more of those three pieces coming together.

[0:36:19] David Green: Well, Anshul, once again, it's been wonderful to speak to you.  Thank you so much for being a guest on the podcast.  Always learn so much having a conversation with you.  Before we go, could you share with listeners how they can follow all the great work that you're doing, contact you on social media and find out more about the work you're doing as well at Mastercard?

[0:36:41] Anshul Sheopuri: Yeah, very happy to do that, David.  But I also want to take this moment before I share how that comes about, I want to just take a moment to thank you.  I think it was a few years ago when we were at a conference together.  We were having this conversation around sharing work, and you had encouraged me, "Hey, Anshan, why don't you go and write a blog? 

[0:37:02] David Green: I did, I remember that, yeah. 

[0:37:50] Anshul Sheopuri: And prior to that, I would never write, and you got me going on that journey.  So, now I write a few blogs a year, and so certainly, either on LinkedIn or I write for the Forbes Technology Council.  Those are really good ways to follow me.  But, David, I want to just take the moment to recognise you for that impact that you had on me.

[0:37:27] David Green: Well, that's very kind of you.  And I would encourage other people analytics leaders, professionals out there, do share what you can publicly because it helps the field.  And I think it also helps you, if you're a leader, to attract talent to your team as well as you grow your people analytics.  Anshul, thank you so much, as I said, for being on the show again.  I look forward to, I think we're seeing each other in a couple of weeks in New York as well.  I think that'll be before this episode goes out, so we won't be able to reveal any more than that at the moment.  But yeah, I'm really looking forward to seeing you in person again and thanks for being on the show. 

[0:38:03] Anshul Sheopuri: Well, thank you for having me, it was wonderful. 

[0:38:06] David Green: What we heard today isn't just a story of transformation at Mastercard.  It's a glimpse into where the whole field of people analytics is headed. So, a big thank you to Anshul for joining me today and sharing his journey and what is possible for the function of today and tomorrow.  And a big thank you to you, our listeners, who tune in each week to learn more about this exciting, evolving field.  If this episode inspired you, please consider subscribing and leaving us a five-star review on your favourite podcast platform.  Your support enables us to keep bringing you powerful insights and engaging conversations every week. 

To connect with us at Insight222, please follow us on LinkedIn and check out our website at insight222.com.  Also, if you're interested in the latest trends in HR and people analytics, don't forget to sign up for our bi-weekly newsletter at myHRfuture.com.  That's all for now.  Thank you for tuning in and we'll be back next week with another episode of the Digital HR Leaders podcast.  Until then, take care and stay well.

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