In this episode of the Digital HR Leaders podcast, David Green sits down with Angela Le Mathon, Vice President of People Data and Analytics at GSK, to explore how GSK is utilising data-driven strategies and AI integration to future-proof their HR initiatives. As GSK embarks on an ambitious digital and data-driven HR transformation, Angela shares her insights on how a comprehensive people data strategy can not only support but also drive significant organisational change.

Tune in as they discuss:

• The importance of integrating AI into people analytics and how it enhances HR decision-making.

• The challenges faced during the implementation of a robust data strategy, including aligning it with AI and technology.

• How GSK is leveraging a data-driven approach to manage and optimise talent decisions.

• The role of upskilling and reskilling in building a data-literate HR team.

• Practical strategies for overcoming the hurdles of adopting AI in HR, focusing on ethical considerations and governance.

Whether you're an HR leader navigating the complexities of digital transformation or someone interested in the intersection of AI and people analytics, this episode is packed with valuable insights.

Support for this podcast comes from Visier, the people analytics platform that empowers HR teams to bridge the gap between productivity and business performance with data-driven insights.

Learn more at visier.com.

Learn more about Insight222 and myHRfuture

• Insight222: Insight222 

• MyHRFuture Academy: MyHRFuture 

[0:00:00] David Green: Here is a question for you: does your people data strategy support the HR transformation strategy required to drive the organisation forward?  A bold question, but it's a question that People Analytics leaders and Chief People Officers should perhaps reflect on.  In our research on leading companies, at Insight222, People Analytics has become the heart and soul of effective business decision-making.  If the foundations that underpin your data strategy are strong, they can drive significant organisational change and positive business outcomes.  On the other hand, if they are not, they can hinder progress and limit the potential of your analytics activities, especially when looking to scale your insights across the organisation.   

So, joining me today as a tremendous example of a company that is making significant strides in this area, is Angela Le Mathon, Vice President of People Data and Analytics at GSK.  GSK is currently undergoing an ambitious journey towards a digital and data-driven HR transformation, and what is supporting and driving this success is its comprehensive data strategy.  I'm particularly interested in what this looks like for GSK, how Angela has developed and implemented the people data strategy, and the challenges she and her team have encountered along the way.  Together, Angela and I will both explore how this heightened the visibility, credibility and impact of the people analytics function.  We also discuss how a comprehensive data strategy has helped with the implementation and integration of AI into the people analytics function's activities and practices.  With that, let's get started as Angela introduces herself and her impressive background in people analytics. 

[0:02:06] Angela Le Mathon: My name is Angela.  I've been in data and analytics more broadly for over 20 years, but specifically people analytics for 10, which is where we met many years ago.  And I would say, gosh, looking across my career, there's probably three big buckets that I've been privileged to participate in.  One is the data and analytics component, which is the sort of foundation; I would say the second piece of that is very much around AI risk and strategy, and that's just constantly emerged as the industry has progressed; and then I think the third bucket, more recently, has been around skill data and the expertise around skill data.  And so, my career over the ten years in HR has largely framed around those areas, and I think it's been exciting.  I'm really fascinated by the future of where AI is taking HR, and I'm just delighted to be on this journey. 

[0:03:01] David Green: When you reflect on the space, you talked about the three areas that you think sees people analytics develop, but maybe as a people analytics leader as well, how have you seen people analytics go up the people agenda over the last decade? 

[0:03:14] Angela Le Mathon: I think as more and more companies realise the importance of making really intelligent talent decisions and realising the value of kind of people in the organisation.  So I think intellectually, we know that a company is a collection of people, but the ability to have the insights required to manage a large workforce has been quite difficult.  And so, I think the importance of people analytics over the years has been a reflection of companies trying to make sense of all our people and trying to make the best decisions to help them thrive.  And I think it's been tricky, because you need a lot of data structured in a very particular way, it needs to be relevant, it needs to be timely.  And so there's so many components of the way we have to think about people insights that I think companies have started to recognise it needs to be a more formal structure.  We can't just rely on managers to do this, we can't make assumptions about people from a cost perspective, which is where I know some companies focus heavily on the financial aspects or award constructs.   

You start to realise that you need a lot more nuance when you look at the workforce, and so I think people analytics is that space to offer that, because it embodies scientific approaches, it embodies technology, but most importantly, it embodies people and behaviour, and I think that's kind of those three building blocks that are really important for companies today. 

[0:04:3] David Green: And it's interesting, isn't it?  I mean, again, like an increasing number of your peers, I think when we did the research at Insight222 last year, it was just over 20% now, people analytics leaders, part of the HR leadership team reporting directly into the CHRO as you do at GSK.  How does that help you as a people analytics leader, being part of the HR leadership team rather than being one or two steps removed?   

[0:05:00] Angela Le Mathon: I think it's the ability to influence the agenda by showing and demonstrating how important people analytics really can be to decision-making.  I think traditional HR models and traditional mindsets about how to make talent decisions limits your ability to really be effective, right?  And so I think by sitting at the table, you start to show all the ways in which you can drive better synergy, you can drive better integration by leveraging data.  I think the ability to look at talent programmes that we're launching and be able to say, "Look, here's how data analytics is going to transform the way we think about making talent decisions and how we think about even delivering talent"; I think sitting at the table and looking at our learning programmes and being able to say, "Here's the best way to upscale or reskill our workforce", because you're now talking about skill data in a very tangible way, in a very concrete way, rather than a very theoretical way.   

So, I think sitting at the table, you transform the conversation from not just data and insights, but data as a service, how do we actually provide a service through our data models?  How do we think about data as a product?  And so how do you productise a lot of the insights that we're generating into the business in a meaningful way?  And so, you really start to supercharge the function for the future.  And you're doing it in a very cost efficient way, because you're leveraging technology and so you're not necessarily labour-intensive.  You're not exactly increasing headcount, but you're getting more value, and I think that combination is extremely attractive to businesses. 

[0:06:32] David Green: Now, at GSK, I know you're in the midst of a digital and data-driven HR transformation journey at the moment.  What does that actually mean for GSK, and what does that mean for you and your role? 

[0:06:44] Angela Le Mathon: I think for GSK, I mean we've been on quite a transformation.  We had one of the largest demergers in our history back in 2022.  HR as a function also transformed, so we decentralised many parts of our business.  And I think people data and analytics, as a function, took a top seat at the table.  And so I think specifically what that meant is, if I look at our tagline or our purpose, it's uniting science, technology, and talent to get ahead of disease together.  And so what that means specifically for us is that AI and skills becomes a really big opportunity for us to understand how we actually support talent decisions.  And so I think, when I look at my AI programme and how I think about leveraging AI for HR, there's a few things that come to mind.   

When you look at AI, there's no data, no AI, so that relationship needs to be intelligent.  So, we need to come together and partner to create a very effective AI data and tech strategy that allows us to look at four things.  I think one is the master data management over our critical data assets, and that's really where you start to gain competitive advantage.  So, we've always known that you can't make intelligent decisions just looking at people data, you need to look at people data, business data, and multiple other data sets in a really smart way, so that's one.  The second pillar is, you then need an inventory of AI models that are both custom-built or acquired.  Many of the solutions that we're looking at today, different companies are adding on AI models and various components.  We also have then those that we're building, and it's not always clear how they integrate with each other, and so therefore we're not always seeing the benefit.  Having a bit of a structured inventory around what that looks like becomes really important.  So, that's the second component.   

I think the third component is an investment in the right AI platforms and integrations, and so this is how we drive scale.  I think many of the tech stacks that you're looking at and many of the integrations aren't always done with that sort of end goal in mind of driving value.  And so, we're really looking at how we actually rationalise our tech stack and how we make sure that we get the right integrations and the data flowing in the right place.  So that's, I think, the third component.  And then I think lastly, it's the creation of an AI layer in the appropriate way that gives you risk and control, right, that gives you the right governance, and I think this is where you start to realise the value.  Because I think what I have seen from some of my peers is that a lot of wonderful AI solutions come on the table, but then there's all these derailers, there's all this regulation, you can't actually leverage it, and so you never really get the benefit, because there's an ethical component that isn't easily explained, right?  It's the black-box syndrome, and you want to make sure that if you're putting AI models into HR and supporting decision-making, that you're more of a white box, that there's more transparency, there's more fairness. 

[0:09:29] David Green: How does that help you through the transformation journey?  Because I think you talked a little bit about having that master data management in place, and obviously if you're going to be solving business challenges with people analytics, it's not just about the people data, it's about bringing the other data together as well.  Just be great to talk a little bit, maybe it's a two-pronged question, so maybe how is this helping you through that transformation journey?  And then, how did you set it up? 

[0:09:56] Angela Le Mathon: So, I think quite early on we recognised, because GSK more broadly is looking at AI, we knew that that's one of the opportunities we wanted to explore, so we know that AI became the space we really wanted to drive value.  I think the second thing we then realised is skills became another opportunity for us, so how do we build skills for the future, because we wanted an agile workforce.  And so with those two components, I understood quite quickly that having a really strong data foundation would be critical for our ability to test and learn.  So, the beauty and the challenge of both those areas is that everyone's talking about AI, everyone's talking about skills.  Getting the ROI is the tricky part, right, being able to evidence the value.  And so, the foundation has now been established for us to test and learn, for us to really explore how AI and skills are really going to drive value for GSK.  I don't think it has to be every process, and I don't think it has to be every part of our business, but there are critical areas where we know these are going to drive value. 

So, the foundation that we've now established helps us to test and learn really cheaply, cost-effectively, and I think it sets us up in such a competitive way.  And this is kind of what my focus has been, I would say, for the first year in my role, was getting up just a strong foundation for us to build off of. 

[0:11:14] David Green: In terms of actually getting the support both within HR and probably from other parts of the business, how did being part of the HR leadership team help you to secure this investment?  And secondly, who were some of the other stakeholders that you had to involve in the business to first of all, diagnose the problem, and then come up with the solution that you needed to do to answer those challenges, I guess?  

[0:12:23] Angela Le Mathon: So, I think there's a couple parts.  I think, sitting at the table means that I'm always in a position to bring that sort of data lens to a conversation.  And so, what my boss loves to say is that I'm a delightful irritant, because I am always the person that sort of has a different approach or a different lens to how we can tackle something.  And so I think that's step one, right, is if you're going to sit at the table and you are embodying a skill set that is new, that is different, but also that is a disruptor in many ways, because let's face it, AI, Gen AI, these are disruptors, you have to be comfortable leaning in, you have to be comfortable almost challenging the status quo in a different mindset, so I think step one is being able to do that.  I think then step two is rapid prototyping.  So, it's great to sort of be an agitator in the room; but then second, it's being able to quickly demonstrate this other way of thinking, this other approach.  And so, I think what I've done successfully is be able to translate that in a concrete way that says, "Here's another way we can use data analytics to solve a particular challenge". 

So, for example, when we were having discussions around career opportunities, the discussion was very much around employee experience, and the discussion was very much around, "What's the platform for employees to experience?"  And then I came and said, "I think it's deeper than experience.  I think what we really need to understand is skills.  We need to understand what are the skills people are looking for through the experience?  What are the skills that people are engaging with through those experiences?"  So, it was really trying to understand, there's a data component to that.  If we can isolate for that, then there's a much bigger story and opportunity that presents itself.  And so, being willing to challenge, even when the room is looking at you kind of saying, "What does she mean and what is she referring to?"  It's not easy.  You don't always want to be the change agent at the table, it's not a safe way of working.  But I think if you do it in the right way, then you do help bring the sort of leadership team along and you bring them with you.  And I think that's the journey that I've been able to be successful at so far.  And so, yeah, let's see where it goes. 

[0:14:32] David Green: No, I mean I know you're doing some excellent work there, Angela.  And thinking back to the data strategy piece as well, what were some of the challenges that you faced during the implementation, and how did you overcome them? 

[0:14:47] Angela Le Mathon: I think, so when I really started the journey, I think it was a combination of getting people to understand the opportunity and the size of the opportunity.  So, there was an education around AI, data, and technology as sort of an integrated subject, and the idea that the strategies have to align; I think that was a big challenge.  So, it was getting our partnership with technology into a good place, because I think there was a different way we managed our relationship with tech, there was a very disparate way in which we thought about our tech stack.  And so, I think step one was getting people to see that, so it was partnering heavily with technology.  I think it was also my engagement with risk, because I think it was making sure that the risk teams understood that this is a smart way to integrate data.  And so, when we think about some of the data solutions that we built and how we leveraged it, it was very much getting them comfortable that all these different data sets coming together was sort of being integrated in an intelligent way. 

I think thirdly, it was getting the business comfortable with these different scientific methods and methodologies.  So, when you're taking people data, and you're doing more than analysing it in very basic ways, you're actually applying various scientific methods and approaches to the data, the business can be sometimes a bit like, "Wait a second, what is this methodology?  What are you showing me?  This looks too complicated".  But when they start to realise the value on the other side of the conversation, and that these methodologies are proven, tested, that there's a logic to it, it builds enormous trust.  And so, I think the challenge has really been bringing people along the journey, because it sounds really complicated when you describe it, it sounds like it's a big undertaking, and it gets intimidating, and you think, "Oh my gosh, I don't know if we have time for this, just get me my report".   

But when you then evidence the value of the questions you're trying to answer, that can't be done in the traditional methods, you do have to step your game up a little bit, and apply a bit more rigor and structure.  When people start to see the value, hence the rapid prototyping, there's like a de-click that happens and people suddenly realise like, "Oh, this is the way forward".  And so that's kind of the challenge of it, is many moving parts, many moving components, not always well understood, but once you can evidence that in a very meaningful way, then you get the buy-in and then you get the excitement. 

[0:17:06] David Green: Earlier, Angela, you alluded to the importance of scaling.  So, we've been conducting our Insight222 People Analytics Trends Research now for four years, we're actually in the fifth year at the moment, and last year we identified eight characteristics of companies that are leading with people analytics.  These are the companies that are creating value on a sustainable basis.  And it's about two areas really of four characteristics.  Each one is around investment, and you've talked a little bit around that, business prioritisation, getting the right skills in the team.  And then the second one is around scaling, and you talked around that.  And one of the areas that we see companies scaling is by democratising insights out to managers in the business.  And you talked about one of the ways that companies typically do that was with their technology stack.   

So, I know you're partnering with Visier at GSK, as your people analytics technology partner.  How has that collaboration, first of all, allowed you to scale; but also, how has it influenced your HR transformation efforts as well? 

[0:18:12] Angela Le Mathon: Yeah, I mean I think Visier is a very powerful insights tool, and so it's allowed us to look at our people data in a very different way.  And I think this has been really helpful to get people comfortable with people insights.  I think when you consider that a lot of the function was, and sometimes is, in a very sort of manual spreadsheet type of hell, where they're trying to do all these things themselves because they're trying to make sense of a lot of data, it gets difficult, right?  When you're trying to stitch together yourself a lot of different data sources, it's time consuming, it's labour-intensive, and it tends to get almost outdated by the time you get to an answer.  And so, I think what Visier's allowed us to do is in a very timely way, consolidate a lot of different sources of data and visualise it in a really intelligent way for us to kind of, in a timely fashion, get to the heart of the matter, see a trend, see movements in the data, and be able to react.  And so we spend more time investigating as opposed to curating, and I think that step changes where the value comes in.   

So, Visier has been critical on our change journey when it comes to how we become more data-driven and how we make more data-driven decisions about talent within our function. 

[0:19:29] David Green: And I know you talked about AI earlier, and obviously when we go, like me, go along to one of these technology conferences, every vendor's talking about how they've built AI into their tools.  But actually, in the area of the people data platforms, I think some of those companies, and Visier is one of those, has actually embedded AI into what they're doing.  So, I think with Visier, their AI tool is Vee.  I don't know how that's supported as well, because you talked about getting AI out into the field.  Has that helped at all? 

[0:20:10] Angela Le Mathon: So, we're in the early days of testing Vee.  I think there's pluses and minuses.  I think Vee, because it's relying on some of the technology from Microsoft, I think you get into that black-box territory a little bit.  So, though it's through the platform, there's some things that we're still trying to figure out.  So, I think Vee is one of those test-and-learn scenarios.  But I think separately from Visier, my team has also built our own custom LLM in HR.  And so, I think because we've built it and we've designed it, it's helped us to make sense of our data.  And so, what we've done is, within our annual surveys, we get a lot of verbatim comments from our workforce.  And so, the traditional methods that we've applied to try to make sense of that was quite time-consuming and difficult.  And so, we then leveraged the LLM model to help us rationalise in an overly smart way, and so we built this tool called Spotlight.  And Spotlight enables us to actually make sense of our verbatim, to actually uncover insights that people are trying to share, in a way that we haven't before.  And I think that's been a massive unlock for us, because it's helped us understand that there's different ways that employees express challenges or concerns.  And so, we've been able to actually get into a bit more detail.   

So I think though the tool that Visier Vee is offering us is good and it's interesting, because we've acquired that solution, we're still very much testing and learning it.  I think the difference is, our custom, in-house solution has been where we've seen a bit more value, because we understood it, we built it, and so therefore from a decision-making perspective, we're slightly more comfortable leveraging that, there's more of an ethical component.  I think what's still difficult sometimes with some of the solutions that we acquire is we're still trying to understand where it's coming from, how it gets into those conclusions, there's still quite a lot of fact-checking that goes with it.  So, that's kind of the dynamic that we're in a little bit with some of those technologies. 

[0:22:13] David Green: One of the other eight characteristics in the Insight222 leading companies, in our People Analytics Trends, is this piece around creating a data-driven culture.  So, that's partly through role modelling by the CHRO and the HR leadership team, but it's also by inspiring and upskilling HR professionals.  And that's definitely part of digitising HR, as it were.  At GSK, how are you approaching upskilling and reskilling your HR function?   

[0:23:36] Angela Le Mathon: So, I think we take a very hands-on approach to upskilling, reskilling, specifically when it comes to people insights.  So, we do quite a lot of trainings.  I have a training with my leadership team next week for two hours, where I'm taking them through quite a lot of our insight tools and kind of evidencing and demonstrating how they leverage it.  They're very hands-on in those kinds of sessions.  We run sprints and we have different quizzes and games.  So, we actively sort of bring it to life.  My team individually does a lot of kind of lunch-and-learn sessions.  They'll join various leadership teams and they cover various components of our product catalogue.  So, within the HR function, we have the privilege of being quite hands-on.  And we focus heavily on almost upskilling champions, if you will, or change champions that are then in a better position to go out into the businesses and then diffuse that.   

So, my team being about only 27 people, we can't sort of train the whole organisation.  But by focusing our energy on the key HR professionals that are most positioned to then drive that through to the business is really where we drive the value.  So, we take a very, very, very hands-on approach, and we make sure everyone's comfortable, that they understand what the products are, what the insights are.  We make it very clear, certain solutions offer certain things.  So, we make it easier to say, "Tool X helps you solve problem Y".  And so, we explain that relationship, and we explain the limitations, so we actively are communicating and explaining.  Because I think some of the noise with AI is the assumption that all the tools can do everything, right?  So, if someone's in Copilot, "Well, can I just do it in Copilot?  Do I need to go to Visier?"  And so, it's almost educating and explaining the differences of how those two solutions work together.  And that's a lot of how we support HR, is a lot of education piece. 

[0:25:32] David Green: Again, one of the other areas in the eight characteristics is around ethics, and I think this is particularly important when we're talking about AI.  It's critical to build that trust through the responsible use of these technologies, not just from an ethical, but also from a regulatory perspective as well.  We've got increasing regulation coming in around governing the use of AI tools, both for consumers, but also for employees as well.  We're in the implementation phase of the European Union's AI Act at the moment.  What sort of governance have you put in place at GSK to ensure the ethical use of AI with regards to people data?   

[0:26:14] Angela Le Mathon: I would say, luckily for us, because AI is a business sort of imperative, there's almost an enterprise accountability for how we think about AI, and so we have a central governance body that sets out guidelines for the ethical use of AI in each part of the business.  So, there's that kind of broader wrapper.  Specifically in my team, I've just actually hired an actual AI Risk Manager, and their primary accountability is to ensure the responsible use of AI models of our data products.  So, there's an individual who is going to be looking at that accountability.  And then I think also, supporting us when we think about the AI solutions that might be available within different parts of our tech stack, really being methodical about what data is required to make the solutions work, how are those models actually built; and so that's where you get into the bias testing, you conduct risk reviews, you establish metrics and controls around all of those components, to just make sure that before we turn something on, that we understand it. 

[0:27:14] David Green: I know that one of the questions our listeners always want me to ask is, how is that team structured?  What are some of the main teams within sub-teams within your team, and what are some of the key roles within there as well? 

[0:27:28] Angela Le Mathon: Sure.  So, my function is I've got four verticals.  And so, the first vertical is data governance and platforms.  And so, that's really where the metadata management comes in, how we think about managing our data.  And so, they oversee our data lake, and they oversee the foundational components of our data solutions.  The second vertical I have is digital products.  And that's effectively where we then visualise a lot of our data, so the management of Visier, the management of our Spotlight, our in-house tool, the management of our Power BI platforms.  Any visualisation tooling or digital product is managed by that second vertical.  The third vertical is data science, so that's the AI ML part of that.  They're very much looking at trying to make sense of data insights.  That's the third bucket.   

Then my fourth bucket is my employee listening and behaviour change, and so that's where we get into more of the qualitative aspects of how do we make sense of people data, but from a behavioural component; because at the end of the day, we're talking about making decisions about people, about talent.  And so, how those decisions will play out in the organisation becomes really, really important.  And so those are the four areas that I've kind of established. 

[0:28:44] David Green: How can workforce analytics enhance HR decision-making and drive business success? 

[0:28:51] Angela Le Mathon: I think workforce analytics can enhance HR decision-making and drive businesses success.  I think if you build trust in the data first, I think that's critical.  I think, you won't get the business to even look at the data, let alone make a decision off of it, if they don't trust it.  So, I think building trust in the data becomes critical.  And I think, for me, that's translated into the sort of scientific approach that we apply, right?  So, it's all about the science.  I work at a life sciences company, so that was a natural sort of synergy.  And I think science is the best way to provide the objective frame for data analysis and insights.  And so, that's where decision science, data science, behaviour science, or actuarial science comes in, is that we always strive to provide the latest thinking, the latest methodologies for how we make sense of our data, with a lens that the goal is to support how our people thrive.  And so, I believe that this approach has been the thing that's been the most effective to supporting the businesses trust in what we deliver and helping them make the best talent decisions for the organisation. 

[0:29:59] David Green: What are some of the successes that you've had with this?  Because again, it's a bit like AI in some respects, there's a lot of hype around it and I think a lot of organisations are struggling if they're making the shift to the skills-based organisation.  Maybe one or two things that you've done that have really landed well with the business. 

[0:30:17] Angela Le Mathon: I think one, we built out a skills data infrastructure and we did that in about six months.  And I know a lot of companies, this takes close to five years to build, right?  And so, I think we did it in a very cost-effective manner by leveraging AI technologies, by leveraging some of our internal critical data assets.  So, that's created a bit of a competitive advantage for us in that we have a solid foundation.  And I think in doing that, we're now in a position to test and learn in different components.  And so, I think we're not making the bold claim of being skill-based.  We want to really understand, now that we've got a richness of a bit of a skills engine at play, how do we now start to test different use cases to drive value?   

So, I think that's the part that we're sort of -- that's the journey that we're on now, is that we're being very pragmatic.  So, we've got the data, it's given us some initial insights, and so there's been a couple of aha moments just with that foundation in place, and now we want to look at testing and learning.  And so I think, for me, that's probably been the area of competitive advantage, because we didn't go all out with various solutions of over-promising things.  We're partnering with the business to say, "Look, we've done some foundational work, and so we've got a good way in which we can look at skills, we've got a strong engine.  Let's partner together and actually identify use cases and solve for that in a very intelligent way".  And I think that partnership of it not being HR trying to do everything on its own and then pushing it on the business, the fact that we've said, "Well, we've gotten this foundation, come in and work with us", I think that partnership is what's making it quite exciting, because we're holding hands together while we solve for specific skills challenges in the company.   

I think there's also recognition that skills building isn't an option, right?  So, everyone may do it differently, but we all have to agree that that is an area of focus.  And so, I think that's the pragmatic way in which we've been able to approach that. 

[0:32:12] David Green: And lastly, how do people stay in touch with you and find out more about what you and the team are doing at GSK? 

[0:32:19] Angela Le Mathon: Well, David, I'd say thanks to you and the wonderful community that you created on LinkedIn, I'd say people can find me there as well.  I am known to comment on a few of your posts, and I'm quite active when I can be in various forums.  So, I'd say anyone interested in getting in contact with me, understanding a bit more of what we're doing at GSK, and just having a discussion more broadly about people analytics, AI, or skills, reach out to me on LinkedIn.  I'm happy to have a chat. 

[0:32:48] David Green: Well, Angela, thank you very much, and thank you for everything you do for the field as well.  So, yeah, thank you. 

[0:32:53] Angela Le Mathon: Thank you so much.  I'm happy to be here.  Thanks, David.