Episode 176: HR in 2024: The Impact of People Analytics, AI & ML (Interview with Dawn Klinghoffer, Jeremy Shapiro, and Thomas Rasmussen)
2024 has arrived, and for those who have been following the Digital HR Leaders podcast since its inception in 2019, you know it's our tradition to start the year with a deep dive into the top HR trends and opportunities based on David Green's LinkedIn annual HR trends and predictions.
This year, however, we are going to kick-thing off with a spin.
Joined by Dawn Klinghoffer, Global Head of People Analytics at Microsoft and a Board Advisor at Insight 222, Jeremy Shapiro, Global Head of Workforce analytics at Merck & Co, and Thomas Rasmussen, VP of Organisational Development and Learning at Shell, David and his esteemed guests will be shining the spotlight on the future of people analytics (rather than solely on HR) in 2024 and beyond.
Listeners can expect to gain valuable insight on:
The integration of human intuition and data-driven decisions in people analytics;
Strategies for leveraging data to influence organisational decisions;
Balancing quantitative data with qualitative human insights;
Real-world people analytics examples and case studies from leading companies;
Prioritising and strategising analytics projects in HR;
The evolving role of AI and machine learning in people analytics;
Ethical considerations and transparency in handling HR data;
The criticality of HR and finance collaboration for measurable outcomes.
So if you are looking for some inspiration on how to seize the window of opportunity in 2024, then this episode is for you.
Support from this podcast comes from ScreenCloud–the digital signage platform that helps HR around the globe elevate their digital employee experience, with 'screens that communicate'.
To learn how ScreenCloud can enable your organisation to increase employee engagement, drive productivity, and improve compliance, visit screencloud.com
[0:00:00] David Green: Happy New Year and welcome to a very special episode of the Digital HR Leaders podcast, as we look forward to HR and people analytics in 2024. For those of you who have been following the show since we started in 2019, you'll know that every year we like to kick things off with a discussion on the top HR trends and opportunities to look out for in the year ahead. These episodes are traditionally based on the annual set of HR trends and predictions I publish on LinkedIn. There is a 2024 edition of 12 opportunities for HR in 2024 on LinkedIn, and though we will draw on some of the themes from the article, this year we're going to switch it up a little and instead focus on the year ahead for people analytics. I'm joined by three guests who have all personally inspired me, as well as many others in our field, and who between them have over 40 years' experience in people analytics.
Joining me are Dawn Klinghoffer, Global Head of People Analytics at Microsoft and a Board Advisor at Insight 222, who has spent the last 20 years building and leading the people analytics function at Microsoft. I'm also joined by Jeremy Shapiro, now Global Head of Workforce analytics at Merck & Co, who previously held a similar role at Morgan Stanley, and who co-authored the seminal Competing on Talent Analytics Harvard Business Review article in 2010. My third guest is Thomas Rasmussen, now VP of Organisational Development and Learning at Shell, and who has previously built and led people analytics functions at National Australia Bank, Shell and Maersk. Thomas has recently published a brilliant paper on Moving People Analytics from Insight to Impact with Mike Ulrich and Dave Ulrich.
In our conversation, we discuss how to drive business value with people analytics, exploring some of the findings in Thomas's paper, as well as the eight characteristics of leading companies identified in Insight222's recently published People Analytics Trends Report. We also discuss how AI and machine learning are already impacting organisations, HR and people analytics, and how these technologies will transform the world of work in the next 12 months and years ahead. So without further ado, let's get started with a discussion on the outlook for people analytics in 2024. Enjoy.
So, let's start by discussing the market outlook for people analytics in 2024. Our research at Insight222 finds that people analytics continues to grow despite a challenging global economy. Dawn, as someone who's been in the field for over 20 years and who regularly speaks with peers, what are your thoughts on the outlook for people analytics this year in 2024, and what are the priorities that you're focused on at Microsoft?
[0:03:00] Dawn Klinghoffer: So, nice to be here. Thanks, David. And I am usually pretty bullish on the outlook of people analytics, but I would say in particular in the next couple of years, I think what I've seen in the last couple of years is that the reliance on people analytics has never been stronger. I think that that really started during the pandemic because obviously everything was about employees, but it hasn't really dissipated. I would say that one of the things that we are very, very focused on, unlike many of our competitors, we have not had a mandate for everyone to come back to the office five days a week. We have stayed with our premise that flexibility is here to stay, and we want to try to give people as much flexibility as possible. We do have a company policy that says you should be working from the office three days a week unless you have your manager's permission, but your manager really is empowered to be able to approve that.
So with that in mind, we have been very focused on understanding when are the right times to prioritise people coming back in person. We call those the moments that matter. And while we have our three key moments that matter, just like everything we do, once you get under, once you start peeling that onion, you start understanding that those moments that matter might be a little bit different for folks that are in the engineering teams versus in the corp groups versus the sales organisation. And the sales organisation, you think about it, they've been flexible forever because what do we need from our sales organisation? We need them to be meeting with customers, that's what we need. We don't really want them to be sitting in their offices day in and day out. So, that is definitely one of the top priorities that we're working on.
[0:04:53] David Green: Thanks, Dawn. And, Jeremy, same question to you. You've been in the field for nearly 15 years and as well as your role leading people analytics at Merck and Co, you also co-convened the New York Strategic HR Analytics Meetup Group as well. What are your thoughts on the outlook for people analytics in 2024, and what are the priorities that you're focused on at Merck?
[0:05:15] Jeremy Shapiro: So, David, I do feel like 2023 was not a typical year in a series of not typical years. And as we move into 2024, I believe that people analytics is facing a pivot point. And that pivot point really is focused in on AI ML and how that's both creating advantage and great things for organisations, and also a great deal of activity and work inside of HR that the data science analytic teams inside of HR have a unique position in looking in, and so I'd love to talk more about that as we go on today. So, that's kind of the major pivot point. But so Merck & Co, we're a pharmaceutical company, and with a mission of saving and improving lives, when we talk with both our stakeholders and then our team internally, we've got some guiding principles that we work through, and that will continue in 2024.
The first is empowering the value chain. So, as we're discovering new medicines, as we're manufacturing, as we're going to market, we look to see where the opportunities are to insert people analytics thinking, quantitative thinking in decision-making to help enable leaders to enable employees is really important to us. Democratising data is certainly critical in order to make that happen as well. And there's a final part of our internal value stack as well, which is to create the light too, so where we can actually do something and then almost do that and get one more thing and do something that can just be influential, but a little bit unexpected. That's kind of the cherry on top of our goals for 2024.
[0:07:13] David Green: Thomas, like Dawn and Jeremy, you've been a guest on the Digital HR Leaders podcast before, so we're grateful for that. And while your present role at Shell as the VP for Organisational Development and Learning means you're not currently responsible for people analytics, I know you are still passionate about the topic and indeed you've recently co-authored a paper with Mike Ulrich and Dave Ulrich on moving people analytics from insights to impact. We will put a link to that in the show notes as well. As you reflect on what you've heard from Dawn and Jeremy, can you provide an outline of the paper and elaborate on ways that we can increase business impact with people analytics; I know that was one of the big topics that emerged from the paper.
[0:07:58] Thomas Rasmussen: So, I guess I'm now a consumer of people analytics, so I do play a role in terms of going from insights to impact. And so, I'm still part of the analytics value chain, even though I don't do analytics, I lead it, and I'm thoroughly enjoying it. And this paper with Dave and Mike Ulrich is really about how we go from the abundance of data and insight possibilities we have, to really boosting value generation. We have an idea we call guidance for impact, and we're trying to look at the evolution of people analytics. So, we all started with reporting and benchmarking. A lot of that's activity tracking or comparing yourself to peers, figuring out that you are indeed average on most things, looking at best practice, so these cool ideas about, "This company did that and this company did this", and just being wary that you can learn from that, but it might not be the biggest opportunity in your company. In fact, it might not even work in your company or in your industry. And then, of course, predictive analytics, which is all about the methodology and the tooling and the cool geeky stuff.
Basically, we're saying you absolutely need all of that, but you need to ground it in stakeholder understanding, so really understand what are the things that your key stakeholders care most about, starting with the external ones, so your shareholders, your customers, and your communities, but then of course also your internal stakeholders, so your board, your senior management, your employees, very important, and also HR. So, if you start with that, that can really guide you. And then from that, we draw some implications and thoughts on, "Okay, what does that mean for how you set up analytics, what you measure, and so on?" But that's the idea in brief.
[0:09:43] David Green: Jeremy, Dawn, I'd like to hear a little bit more, expand a little bit on what Thomas has said there. How much of this resonates with your work and your experience, both at Microsoft and Merck, and in interactions with peers? Jeremy, I'll come to you first.
[0:09:59] Jeremy Shapiro: Thomas, I loved the paper, it was really great. Thank you for writing it. What I really appreciated, just even in the initial read through when it came out, was he used two very important "I" words: Insight and then and then Impact. And I didn't, not on purpose, but I added another word in the middle, and it was another "I" word, which is Influence. So, as we think about our day-to-day work, of what do people analytics teams do, what do data science teams do inside of HR, to we're doing one of two things. We're either building full stack systems that are creating either automation, where there's very little human, but a lot of it, even some of the newest work that we've done, there's a human in the middle. And so, what's the unlock code for our organisation, and as I talk to peers too is, as we convert insight into influence, that influence is the thing that creates differential, at least impact the things where you're not quite sure how to move that component one step over.
I don't know if the rest of you feel like this too, but I feel like one of the hardest parts of influencing is context-setting. No matter how much context you provide, there is a level at which I can make a decision, I'm comfortable making the decision based on the insights that we provide. Sometimes it's at scale, sometimes it's bespoke. But as we move through to, what are my true concerns as a decision-maker, that requires (1) influencing skills that's faithful to the insight, faithful to the data, but then also really understanding the context in which the decision is being made and the impact it can have; (2) the more impact it could have, the more the insight, conviction, context become more critical. I don't know, Dawn, what do you think?
[0:11:59] Dawn Klinghoffer: Well, absolutely. It reminds me, last week we had Katarina Berg come and speak to our HR leaders, directors and above at Microsoft. And one of the things that she's kind of known for saying is that she's data-informed, not data-driven, and you know that data-driven is one of our taglines that we like to use. And I think that that's right, data-informed. What she said is, "You can't throw away the experiences that people have and that gut feeling". But really what I was thinking about is, Jeremy, it's about context, okay? It's not just about that gut feeling. That's where I think context is so important. And if you don't have that context, then agree, it's just data, okay, and it doesn't really tell you what you should do.
One of the things that we like to put in the back of every single presentation is the so-what slide. And so we'll go through our data and talk about the insights that we got, but the most important slide is that so-what slide at the end, "So, what are we going to do about this?" And that is where we have the influence, which can drive to impact.
[0:13:18] Jeremy Shapiro: Yeah, Dawn, one funny note on that too is that we have a common customer of the so-what slide; sometimes we'll answer the question, and sometimes we won't. And it really depends on where we need the conversation to go, as well. So, what's the degree of commitment we need from whatever group that we're that we're working through as well. So, like if you try to fill in every answer, colour in every line, the result usually isn't as good, depending on the conversation, as if you leave some colouring for the rest of us.
[0:13:50] Dawn Klinghoffer: Well, and that's why we like to say, "We co-create with our customers". So, someone like Thomas, we would bring Thomas in, we'd share the information, and then we'd co-create the so-what together, because sometimes the most powerful actions are when you're taking multiple groups together and you are all coming at this in a united front. If I think back to many years ago, it was like the people analytics team having to kind of go on stage to get everyone else rallied around. And I think we've made so much progress in that it's not the people analytics team influencing everyone else, it's like you come with your partners together.
[0:14:31] David Green: Thomas, I know listening to that, obviously now as a customer of analytics, as a people analytics leader or people analytics team, how do you get that context?
[0:14:40] Thomas Rasmussen: I think it's about relentless pursuit of objectives for your key stakeholders. So, you also get some of that context by truly understanding where is your business at, where are your customers at, where your investors at, and also where your internal stakeholders at? Because it sort of guides you a little bit in terms of, where should I be looking, which challenges are we trying to pursue, which problems are we trying to resolve? And you can only get that by being in that partnership Dawn talked about and talking to people. You can't get it from a book or from a webcast, you have to be in the business to basically get that, right, including all the stuff that's basically sometimes just somebody's got feel or experience or intuition. There can often be a lot of gold in that as well.
[0:15:30] Jeremy Shapiro: Yeah, if I may, Thomas, on that, you bring up understanding your stakeholders, and there's one practice that we engage in that we've spoken about in the meetup as well, and that's delving into the world of sustainability, and not just for the intuitive reason that one of the sections of most, if you happen to be in corporate life, the sustainability report is going to be within employees, but sustainability trains people to think about multi-stakeholders, and sustainability trains you to think about different angles in which your stakeholder might be interested on the same topic. So, it has some really healthy habits for people analytics teams. You can look at one thing in five different ways. We can ask ourselves, who is this for? We can then target and customise a work product based on a better intuition and a sharper notion of stakeholder management too. So, to the extent to which folks have done that before, I hope that it's been useful too and for us, it's invaluable.
[0:16:35] David Green: And maybe in terms of bringing it to life for listeners, it would be great to hear from each of you an example or a case study of how people analytics is delivering or has delivered business value at your company?
[0:17:42] Dawn Klinghoffer: You know, it's very timely for me. We're working on a big project with one of our engineering groups. And it's the first time that we've really, really partnered unbelievably closely with the engineers specifically. And we went and we presented to the senior leadership team last week, so Satya and his direct reports, and one of the things that one of the engineers said is he said, "I feel like this partnership is the beginning of something that is unbelievably beautiful. There is so much gold in the data that we all can collectively bring together". And while intuitively we've always known this, I mean, I've always known that for us to be able to work directly with business data and influence business decisions is what you all aim for, but I loved hearing the engineers specifically say that to the senior leadership team. And it's exciting to think about the possibilities.
I also know that many people struggle with having that relationship with the business. Jeremy, it's not completely data democratisation, because obviously the data that we're bringing forward is extremely sensitive. So, how do we do it in such a way that we keep the data extremely confidential, but we are able to partner with the folks that understand the data the best to come up with those insights? And so, I feel like we're on to something and we're really excited about what the future holds.
[0:19:30] David Green: Thomas, obviously you've built people analytics functions, I think, at Maersk, at Shell, at National Australia Bank. Have you got a case study or example that you can share of people analytics delivering value at one of those organisations?
[0:19:46] Thomas Rasmussen: Yeah, sure, there's so many and also many published by Dawn and Jeremy, I might add. I think the one that that is most powerful is really from Shell, in terms of that groundwork in terms of safety. So again, you start with safety as an outcome, which is one that every one of our stakeholders really cares about, whether outside or inside. So, this is really about sending our colleagues safely home to their family and figuring out how people analytics contributes, and identifying that if you increase the quality of leadership with 1%, the amount of accidents or near misses goes down with 4%. So, it is the best business case for investing in leadership I've ever seen. And even though most of the HR community said, "Yeah, we already knew that and we've been saying that for years", then just quantifying it, finding causality with seven years of data from all over the world really helped us spark that conversation where, "Okay, hey, why are we actually doing it?"
So, great leaders drive a community where we care about each other and we look out for each other. We don't just follow process to follow process. We do it because we care about each other and truly it's like cultivating that environment. That's the most powerful thing I've seen, also because it really resonates with the culture at Shell where safety is always number one. So, I think where you find stuff that really goes to the core of the company's culture, then at least the adoption of it is significantly easier than if you take in other places, I'd say.
[0:21:21] David Green: And, Jeremy, same to you again, obviously prior to Merck, you were at Morgan Stanley for a long time with people analytics. So, what's one of the most powerful examples that you can share of the business value that people analytics has?
[0:21:33] Jeremy Shapiro: Well, it's funny, as I was reflecting on this question, the most fun I have during the day is when I hear a story about, we've democratised some type of analytical work product where a leader has gone and done the work themselves and then we'll see it. We tape a lot of our town halls, we're a large organisation, so we do record a lot of things, and we'll watch live a leader using -- so, one good example is we use Glint, which is a Microsoft product -- Dawn and I did not coordinate this -- but it will automatically topic model comments, right? And sometimes, there are tens of thousands of comments, it's hard to look at it. So just last week, I just happened to be listening to one of the leaders' town halls for no reason, I just actually missed her, I wanted to see what she was talking about. And she starts with an analysis of our work product to that. We had no idea, our fingerprints were not involved beyond the democratisation of the work itself. Those are the best days when you know that the biggest lever is one that the direct team didn't touch.
There's also, of course, project work too that really is critically important, and we think about how we work on those projects based on need, criticality, and really, does it work in the value chain? So probably, if you're listening to this, you've got some stake in the organisation and there are probably some groups, like Dawn talked about the engineering group, that are critical junction points in a value chain. For us, it's this function that's called a PDT. If you're not in pharmaceuticals, you would have never heard of it, and nor should you, right? It's called the Product Development Team. And these product development teams are like boards of directors for a pharmaceutical, too. This is a really important decision-making body. They think about clinical trials, they evaluate evidence, talk about pivot points that touch all parts of commercial and manufacturing and multiple areas within research. The better those teams do, the better patients do; it really has that stakes to it.
We've engaged in a multiyear partnership with those teams to help them optimise their team and optimise their onboarding so that their decision-making is accelerated in very, very technical and thoughtful conversations that are critical for our progress as a company too. Those are some of the most interesting ways that you can kind of look at that. And I almost flip it to, how did we get there, and then what opportunities did we get from that? From doing that, those bits of work, that's where success can breed success. And so, we're able to then work on a novel robotics arm line as well that we would have never gotten involved in, or working with our commercial partner to try to figure out what are the next skills we need for the future pipeline we have available to us. So, I kind of think about powerful and influencing in those ways, right? It has a direct impact on the value chain, it has a direct impact on the ability to create even more influence in other areas too, so it has a couple of nice hallmarks of things you want to do again and again.
[0:24:57] David Green: Thomas, in your experience and again now maybe as a customer of analytics, how can people analytics leaders and HR professionals frankly influence senior business stakeholders; and what's your guidance to listeners, maybe people analytics leaders that are listening, around prioritisation?
[0:25:15] Thomas Rasmussen: It's a great question and, Jeremy, loved your point about the third "I", Influence. So, if I was to rewrite the paper I'd add that in the title, in hindsight! I think it's a two-way street. So, it's about influencing, and we can learn from lots of our colleagues that do that really well as part of their jobs, HR, finance people; well, influencing is a very broad skillset. But then I think it's also about being influenced and being open-minded and being willing to learn in people analytics, or whatever role you're in, in particular by what is it your key stakeholders care most about, because then that gets you to, "… and how do I help deliver that?" I think that's really it. I do think it has some implications for where people analytics should sit, because it's easier to be influenced by the strategic conversations in a business if you have access to them. And it's also easier to prioritise if you have the backing of the senior leaders to pursue those relatively few high-value projects at the expense of trying to do too many things at once.
[0:26:29] David Green: How do each of you, Dawn I'll come to you first, how do each of you prioritise and how does your team prioritise?
[0:26:35] Dawn Klinghoffer: So, really, really good question and something that I kind of think about it daily actually, because if you can imagine, I'm almost reprioritising on a daily basis based on where the company is going. You know, for a long time I used to say our biggest prioritisation lever was, "Are you ready to take action?" If people are not ready to take action right then and there, that tells me, you know what, I'm not sure that this is the top priority. Because again, if you can't see the value of the work that you're doing, then why did we do the work to begin with? But I would say that things have become a little bit trickier, because some of the work that we're doing is so complex and doesn't have such a short runway, that we might know exactly whether we're ready to take action. And sometimes we have to prioritise work that we think can have the biggest impact, and sometimes we have to play the dance where we're going to do a little bit of this at the same time we're prioritising other things that people are ready to take action on. And so it's almost like, "Okay, look over here while we work over here. Don't focus on the stuff that we are still investigating because it's going to take my team months to be able to get to the point where we're even going to be ready to share". And so a lot of this is just this constant dance of, "I know we're going to be asked about this. They're not ready to take action yet, but we better start working on this now so that when they ask about it, we are ready".
I would say I'm not always successful. For a long time, I was pretty successful at it. I felt like I was nailing it. I kept getting to the work before they would ask. Well, you know what? In the past few months, that was not the case. And there was one piece of work that we were working really, really hard on, it was very, very, very important, and all of a sudden one of the senior leaders said, "I need this now". And I kind of thought to myself, "Oh, my God, we're still a month out". I remember saying that, "I will get it to you in a month", and he looked at me and he said, "A month?" At that point, you don't want to share all the dirty laundry and how much time it takes to prepare a dataset to analyse, but that's literally where we were. We were still in the preparation. That 80% -- I always look to this article that I read many years ago about how 80% of the time is making sure that the dataset is accurate, clean, has the right attributes in it, all this stuff is able to be joined with other attributes. 80% of your time is taken up with that. The 20% is the actual analytics, the hypothesis-building and testing things.
[0:29:28] David Green: Jeremy, similar to you, I know you kindly shared a case study in the book with me and Jonathan around how you prioritise at Merck, which was very much talking about some of the stuff you did in the pandemic. Would love to hear a little bit from you around prioritisation as well.
[0:29:42] Jeremy Shapiro: Well, I can appreciate this conversation both as a podcast and this therapy. So, I really enjoyed hearing what Dawn was talking about there, because we also try to get ahead of the curve as well, and where are things going, so we could put data engineers on the needed work prior, because we know that the appetite is always going to be faster than the ability to engineer the data and set the way. So, Dawn, thank you for that gift as well to know that we're not the only folks thinking about that. And we don't get it right either. The past few months also has not been that easy.
So, two points on prioritisation. One is, people analytics teams, if you're running a people analytics team, have a Jira board or priority list that's yours. And so, treat this like you are in a production house, if you haven't already; do you have a backlog; do you have a list of work that are candidates for being worked on so that you can have those good common conversations, and talk to people about, "Here's where we think our trajectory is going. Do you agree? what other components are there?" The second part of this is for our team, and so we talk internally as an analytics team about a value stack that influences how we think about discretionary time. So as an example, if there is an employee population at risk for a safety purpose, everyone drops what they're doing and we work on that. That is, there is no question and anyone's empowered at 24/7 to stop and just do that, and we will always say, "I've delayed this project because we're working on a hurricane safety situation", and there's no question. And then we work through a series of considerations, is it part of the value chain; does it support our goals in diversity, equity, and inclusion; how have we improved a new skill for either HR or for ourselves? So, it literally has a six-component list and that's our checklist. So, as we look at things together, the entire team is enabled to use that type of tool.
[0:31:54] David Green: Dawn, the two characteristics I'd quite like you to expand on are around the skills of the team itself and the data-driven culture, which I know is something we've spoken about in the past, I think I've spoken to all of you about that in the past, actually, so essentially the skills of the people analytics team. So on skills, we found in leading companies that the people analytics leader is investing in three key skills: people analytics consultants, data scientists, and behavioural scientists or IO psychologists. And in the data-driven culture, we found that the CHRO is making it clear that data analytics are a central part of the HR strategy. And HR business partners, particularly HR business partners, are developing their data literacy. So two questions on this. First, can you expand on the skills of the people analytics team itself, particularly those three roles and how important they are in your team at Microsoft; and then secondly, maybe explain to some of the HR leaders that are thinking of investing some money in upskilling their HR professionals this year, why developing skills in data literacy is important to them, but also to the function and the individuals within it?
[0:33:50] Dawn Klinghoffer: So, I think that we're on a precipice where I think the skills might be changing in the future with the move to AI. And so, I have the right to say that those might not be the skills that we are looking for in the future. Certainly, we will need IO psychologists and behavioural scientists and we will need consultants. But if I think about one of the skills that's really, really important for us today, it's data visualisation, it's telling the story, okay, it's taking the analysis that you've done and building the right influencing materials to have the conversation. And I think about, how will that change in the future with AI? And then I think about even our analytics, you know, I think about what we are working on with our Insights HR platform, which is our own homegrown platform that we use with HR; it's self-service for them. We're making it so that you can have a conversation with Insights HR, and you can tell Insights HR what you're looking for, and it's going to serve it up for you instead of you having to go and find the report and click on the right fields and all that kind of stuff.
So, if I think about building the capability for HR in the future, it's how do we ensure that HR knows the right questions to ask, and they ask the questions in such a way that the data that is coming back answers the question and makes sense for the outcomes. And so, I do think we're in this really exciting time where I think that will change. It's always going to be necessary, data literacy, always going to be necessary, but how we teach that data literacy I think is going to be very different in the future.
[0:35:56] David Green: Jeremy, the two characteristics I'd like you to comment on, and actually you mentioned one right at the start, so that's great: democratisation and personalisation. So again, what we mean by democratisation for listeners is that in leading companies, the people analytics function is democratising data out to managers and executives across the enterprise frankly, and employees for that matter; and then on the personalisation side, the people analytics function s a strong focus on personalising people analytics products or for employees.
[0:36:27] Jeremy Shapiro: Absolutely, and look, it's a journey, it's a journey like everything else. And just to cheat for a second, so as Dawn was talking about skills and so forth too, if we date timestamp ourselves as December 2023, that'll protect us in skill base. So, inside of HR, we just hired one of our first ML engineers inside of HR, we have them at the company. But we're beginning to see the entry of very different skills. I'm dusting off skills from full stack development that I haven't used in ten years, in today's age. That's interesting, I feel like that's a very interesting component. And I think it does relate to democratisation and into personalisation too, because software does democratisation, software does personalisation, research projects don't. And so, if you think about the work you're doing in terms of research projects and in terms of application stack, so our application stack, we're a Workday shop; we use Visier; we use Glint for employee listing; and all three are integrated together as well.
So, from a democratisation standpoint, one of our key learnings from this year was deploying out what we've nicknamed the leader dashboard. So, the leader dashboard, our managers have had access to their talent data in Workday since launch, so that's not an issue, and a decade before that, we gave them access. That was reporting access. Now we're giving them analytical dashboards to the most senior leadership so that they can make decisions on their own. That's enabled by Visier, has Glint data integrated into it as well, so they can all see this together. The most interesting learning from this year is, providing the data is 5%, or the dashboarding and the tooling is 5% of the work, 95% of the work is context-setting, making sure that the use cases are clean on how do I use this; why do I use this; and that is a huge opportunity for both people analytics teams, for HR teams as well to have great access to the data.
So, in a perfect world, what the democratisation word means is that there is, instead of a conversation of, "Here's what I think and here's what you think", as an HR professional, as an HR VP, I've got a view, the leader has a view, but we're both looking at this analytical product together, where the leader, they're calling up and saying, "Hey, I'm noticing this, what do you see here? I'll kind of work it in that way". That to me is, I think, one of the greatest components.
One of the other things that I'm really excited about is the embedding of analytical work product at scale. So, I mentioned kind of text analysis for comments that are automatically topic-modelled. One of the things I'm on a personal push for is zero PowerPoint for a lot of this work as well. So, a leader shouldn't have to be creating a novel deck to any extent. So, I guess the net of it is, I'm adding to work for one of Dawn's groups in Glint and decreasing their PowerPoint team.
[0:39:43] David Green: One of the other two characteristics that we found was related to measurement, and it was particularly related in leading companies measure and deliver financial impact from people analytics. And deeper analysis found that the relationship between the people analytics team and their finance counterparts was important. There were 65 teams out of 271 who told us they had a strong partnership with finance, and 99% of those said they delivered measurable outcomes from people analytics work in the last 12 months. So, this is a quick-fire round really. So, can each of you provide one or two tips on how to successfully partner with finance? And Thomas, that can be as an HR leader, HR professional, as well as people analytics. So, Thomas, I'll come to you first on that one.
[0:40:31] Thomas Rasmussen: Find a friend in finance, would be my first tip. They're great people. They have experts in measuring stuff. They call them econometricians and they don't just measure money, they can measure anything and they do this for a living, right? So, if you find a friend and build a coalition of the willing, they can influence you and you can influence them and it will help you. If you want to measure value, you need to partner with finance, right? So, if you don't have a friend already, you need to get one.
[0:40:58] David Green: And, Dawn, you actually used to work in finance, admittedly a long time ago, so you'd probably have a reasonably good idea on some good tips on how to partner with finance. What would you offer?
[0:41:08] Dawn Klinghoffer: Learn how to speak finance language, okay? If you can speak the same language as your finance partners, then you have a common ground to start with. And then, you can teach them how the data might look a little bit different when you're thinking about it from an HR lens. But yes, you have to start with a common taxonomy. So, go and study up in terms of finance and how they call different things. I mean, attrition is one of those attributes that is just as important to finance as it is to HR, for different reasons. And so, understand that and you can start from a common ground.
[0:41:55] David Green: And, Jeremy, do you have tips of working with finance?
[0:42:00] Jeremy Shapiro: I guess my short, quick one is, just be helpful. There are teams that need help too, right? So, where is it that we can work together and just help each other in some ways? That's where the relationships are born, and I've had great privilege working particularly with FP&A functions. If you want a good starting point, start with FP&A. They're wonderful to partner with, they get run rates, they understand exactly what we're trying to do in so many different ways. And so, that's the quick answer, and I just want to append one notion, because even as, David, you talk about it, and it's in the paper, is delivering financial value. Absolutely, it's a habit that we all have. But if you're listening to this, or you've read the paper, don't select a project because you can't find its financial value upfront. We talked about multistakeholder analysis before, so I would actually look at it on two dimensions: what is the stakeholder, because it could actually be that it's an interim step to value in that way, if financial value is the final outcome for it; it's also the time horizon of by when.
There are things that all three of us do as companies that take a really long time to realise. You know, Thomas, I heard it takes a couple minutes to put up an oil rig, right? Planning takes a long time for that to happen. So if I'm demonstrating financial value, I was wearing very different fashionable styles when that project first started. As well, the pharmaceutical pipeline averages ten years from inception to realisation for things as well. So, I just try to be very conscious on the duration and the dimension; who is this stakeholder and how it is, is valuable, of course it's valuable. But just don't select your projects on it because it'll get you in a rut.
[0:44:03] David Green: So, the last two questions are both related to AI. The first one, specifically AI in people analytics, with obviously the impact of people analysis; and then, the second one is the question of the series, which is around AI in HR. So firstly, from a people analysis perspective, how is AI already impacting on your work? What does this mean for your processes around ethics? And what excites you most about how AI will shape people analytics moving forward? Dawn, I'm going to come to you first, just because I know, obviously, there's a huge impact at Microsoft around using AI machine learning.
[0:44:37] Dawn Klinghoffer: So, the first thing I would say is, it's not that this is brand-new for us, and even in our people analytics team, we have been doing AI for a long time. And I think about one of the first ways that we used AI is in our text analytics portal. And so, Jeremy talked a little bit about Glint, but before we acquired Glint, there was a need to analyse large, large numbers of comments in a really easy-to-consume way. And so, that was our first foray into natural language processing machine learning models to really understand all of the different sentiment that we were getting. And even today, largely we're on Glint, but we still use one other technology. And so, when you're wanting to combine different sentiment from different technologies together, then we do that outside of Glint.
Ethics has always been extremely important to us. We have an ethics charter. We like to say just because we can doesn't mean we should. We are very clear with our employees what we do with employee data, what we do not do with employee data, and transparency is obviously extremely key to all of this. And I think that's going to become even more important as we move forward with AI, but glad that we have this base at least to build on.
[0:46:15] David Green: Jeremy, I'll come to you. Maybe you can think about, you talked a little bit around, you've hired in a specific machine-learning engineer into HR. How do you see AI shaping people analytics moving forwards maybe? And again, we won't hold you to it, we'll timestamp it at December 2023!
[0:46:37] Jeremy Shapiro: Yeah, that's always helpful. So, there's two things that come to mind. One is the pace of change, particularly this year where AI went mainstream. And so, the generative part of AI certainly became popularised and so forth too, and that lit a fire, but the pacing was already there. What we're now seeing is energy around some of the critical areas in which AI is truly an enabler. Within pharmaceuticals, AI and ML work is a critical component now of our discovery work, of our development work inside of drug discovery. I mean, we saw it happen during the pandemic. A lot of vaccine research was conducted first. Some of you may be familiar with the terms in vitro and in vivo when it comes to when it comes to things. Well now, we actually have a phrase of in silica as well, that it happened inside of modelling. That's how important, and the value and the upside potential for the for the future. This happens in so many different areas of the organisation that HR needs to be prepared for a few things. One, we need to be prepared to help; help employees change their work from kind of, "I did it this way, now I'm going to it this way". That's all efforts in the same direction, is how are leaders thinking; how are the HR team coaching; how are team leaders making sure that they are working in that in a new style? Very important.
HR itself can be transformed. One of the things from a project standpoint is you're selecting projects. Well, all employees do stand to benefit from an HR, AI, and ML type of project, depending on your project scope, right? And so, if you already have a tight relationship with whomever is working on AI ML, continue that and make that closer; and if not, that's your next phone call right after this podcast, is make sure that you're working with the team internally that can really help change the way that we provide services to end users, that we can find information. We all would like better portals, right, to find, "Now, what should I do when this happens?" A lot of the new technology is going to solve this practically for HR. That's very exciting.
[0:49:15] David Green: And, Thomas, same question to you, and maybe with a slightly broader hat, and obviously thinking with your OD and learning hats on as well, how do you see AI shaping those disciplines moving forward?
[0:49:28] Thomas Rasmussen: So, I think AI will have a profound effect on all fields everywhere. And it's equivalent to when we started using computers and then the internet, and so on. I'm hopeful that if we do it wisely, it will be for the good, and also it will really be augmenting a lot of the things that we do. So I am quite hopeful. And then I also note that the more technology helps us, the more we crave for human connection. And so, I think that's where it's called people analytics. So I think for us, for Dawn and Jeremy and the people leading this space, finding that balance, right? Because yeah, it's great, AI can do a lot of stuff and computers and phones, and whatnot, but we'll still create the human side. So, how we find that balance becomes increasingly important. But I'm hopefully optimistic about what AI can do for us in the future. Yet to be seen though, yeah, let's see how we go.
[0:50:30] David Green: And, Thomas, I'll stay with you for the last question to start off with, and this is the question we're asking everyone in this series. And I guess it's a little bit of an extension of what all of you have said, and interestingly you mentioned the people in people analytics. I suppose you could argue the human in human resources applies here. If we think about HR as a whole, how will AI transform the role of HR and HR professionals?
[0:50:55] Thomas Rasmussen: So hopefully, it will make it easier for people to work with HR, it will make it easier for people to work in HR, and it will help us deliver more value to all the different stakeholders. That's what I'm hopeful about.
[0:51:13] David Green: That's good, nice and succinct. And, Jeremy, same question to you, how will AI transform the role of HR? You talked a little bit in your previous answer, of course.
[0:51:21] Jeremy Shapiro: Yeah. So, my hope is that constant hope that we have whenever a new technology comes out, that this will make HR roles better, more enriching and enlivened. I think it's here in so many different ways. The one new and interesting component of where some of this work can take us is, it also moves subject matter expertise into a common space as well, and that is really interesting to me. So, I don't know where that goes yet. But it then leaves for the HR leaders, judgment and creativity and new ideas. I'm very optimistic about where that's going to go.
[0:52:08] David Green: And, Dawn, you get the opportunity to bring us home, and obviously a company at the forefront of this. How will AI transform the role of HR?
[0:52:19] Dawn Klinghoffer: So, I'm going to build on the notion of thriving, and what I'm really hoping is that AI really helps HR be energised and empowered to do meaningful work. So, by removing the drudgery of so many of the tasks that happen today in HR, hopefully AI will really help remove that so that, like I say, people are really energised and empowered to do what they consider to be meaningful work.
[0:52:51] David Green: Fantastic, and that's a perfect way to end this conversation. Thanks to all of you for sharing your time and expertise with listeners. And again, a really great episode I think to start the year off on the Digital HR Leaders podcast. Can you let listeners know how they can follow you on social media and find out more about your work? Dawn, I'll come to you first.
[0:53:13] Dawn Klinghoffer: So, please follow me on LinkedIn. I post all of the articles that we share externally within my LinkedIn profile. And so, go there and hopefully we have lots more to share in the coming months.
[0:53:26] David Green: Jeremy, I'll head across to the East Coast so you can offer the same.
[0:53:31] Jeremy Shapiro: LinkedIn is a wonderful, wonderful way. There's probably a link there as well if you want to join the New York Analytics Meetup group. You do not need to be in New York for that as well. There's a Slack group somewhere in there too. Everything's moving so fast. Make sure that you've got a community. That's anywhere in the world, just please phone a friend.
[0:53:53] David Green: And, Thomas, finally to the Netherlands, how can people stay in touch with you?
[0:53:58] Thomas Rasmussen: Yeah, so I'd say follow David at Insight222 and Dawn and Jeremy first, but I am also on LinkedIn and, yeah, happy to connect.
[0:54:09] David Green: That's very kind of you, Thomas. Thank you. Thanks to all of you. We're recording this before Christmas, so I will wish everyone a happy holidays. For those of you listening, we're in the new year, so I wish everyone a Happy New Year as well. So, thank you all for joining.