Episode 219: What People Analytics Leaders Need to Know About Scaling Their Function (Interview Henrik Hakansson)
Scaling a people analytics function isn’t just about growing a team—it’s about building the right foundations, securing stakeholder buy-in, and investing in capabilities that drive real business transformation.
To discuss this topic, in this episode of the Digital HR Leaders podcast, host David Green sits down with Henrik Hakansson, Head of People Analytics at Volvo Cars. With a track record of building and scaling people analytics teams at Sony, Delivery Hero, and now Volvo, Henrik shares practical insights from his journey—what worked, what didn’t, and the lessons he’s learned along the way. Join them as they explore:
How to establish and grow a people analytics team from scratch
Key differences in building people analytics functions across diverse industries
The skills, capabilities, and partnerships needed to drive real business transformation
Securing stakeholder buy-in for advanced analytics
The role of people analytics in strategic workforce planning
Whether you're just starting out or looking to take your people analytics function to the next level, this episode, sponsored by Talent Neuron, is packed with actionable insights.
Hit play to learn how to scale people analytics and unlock its full potential for your organisation.
About TalentNeuron:
TalentNeuron is shaping the future of workforce transformation. From Strategic Workforce Planning to skill gap analysis, TalentNeuron seamlessly combines external talent intelligence with internal data into one powerful platform. Join leading global enterprises already using actionable insights to boost organisational readiness and performance.
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[0:00:00] David Green: How do you scale a people analytics function to deliver real business value? That is a question we hear regularly in the work we do with clients at Insight222. It is also a central theme guiding our annual People Analytics Trends Research. My name is David Green, and in this episode of the Digital HR Leaders podcast, I'll be talking about how to scale people analytics and deliver business value, with my guest, Henrik Håkansson, the Head of People Analytics at Volvo Cars. Henrik is well placed to shed light on this topic, as he will draw from his extensive experience on building and scaling people analytics teams at first Sony, then Delivery Hero and now Volvo Cars. In this episode, Henrik shares his journey of establishing and then growing people analytics teams, how to get stakeholder buy-in, and how to invest in the right capabilities and partnerships to drive real business transformation. So, with that, let's hear from Henrik and learn a little bit about his background and what enticed him into the world of people analytics.
Henrik, it's fantastic to have you on the show. To kick things off, I'd love to start with your journey. What led you into the world of people analytics?
[0:01:27] Henrik Håkansson: Well, thank you, David, thanks for having me. So, if we look back maybe ten years ago, give or take, that's when I started my journey. I would say started my journey into people analytics, even though it didn't happen until maybe five years ago, because I joined Sony and into a sort of a generalist position very early on. And I was asked to do some work on a performance-related topic. And I decided to use Excel spreadsheets and do a bit of VLOOKUPs, conditional formatting, and all of a sudden, people saw me as sort of the guru in tech in HR, which is kind of telling of where HR was ten years ago in terms of technology. A month or two after that, we had an opening in the HRS IS organisation, so I moved on to that role. And at the time, we were implementing a new global HCM system that was put in charge of rolling out absence globally for Sony. And a couple of months into that, I started realising we have quite a lot of reporting needs, and basically went to the project lead, "What are we doing about reporting?" And he said, "Great, you asked, now you're in charge".
So, all of a sudden, I was in charge of implementing a reporting strategy. And with that came also data governance, data quality, of course, pieces that were really critical for getting reporting up and running, and this was mainly the focus back then. So, we talked about reporting rather than talking about analytics at this time. And a couple of years later, working with the data quality, getting it up to an acceptable standard, you, as many people know, there's no point in doing analytics if the data quality is bad because you just come to the wrong conclusions. So, we got that in place, and then around 2018, 2019, I was asking if we can do something more with reporting and had that vision for a while, and we started to look into tools, and eventually got to build a team in 2019. So, I brought a couple of people together, and we started officially on the people analytics journey.
[0:03:35] David Green: And we're going to talk a little bit more about building a team from scratch, as you did at Sony, and then obviously, we're going to talk into the subsequent roles that you've had at Delivery Hero, and more so obviously now at Volvo Cars, where you've been for a year. But being in the space, in or around the space, for around ten years, by the sounds of it, and you've built multiple people analytics teams, or three people analytics teams during your career, I'd be interested to hear from you, from a practitioner view, how have you seen the function evolve over that time?
[0:04:05] Henrik Håkansson: I think as I alluded to in my introduction there, that originally was very much reporting focused, it was focused on numbers, it wasn't focused very much on insights at all. It was figuring out headcount, it was figuring out maybe attrition, and even that was something like, how do we really calculate that in the best possible way? But it was just numbers and metrics that we needed to report so that the company management was basically happy. And I think it's still a journey. In some parts, I still experience people who focus much more on the numbers rather than what the numbers actually are telling us. Now, I do see a lot more focus on impact and reasoning around the data, however. And of course, also I think maybe this is more what's coming now and in the next coming years, is how we connect the people data to all other kinds of data across the organisation. Because as I said, data was quite new to HR back then and it's now more focused on how do we use the data in a smart way.
We collect tons of data, we have all these HCM systems that store tremendous amounts of data, but one thing is data quality of course, but also, why do we capture all of this? What's in it for the employee to try to understand? Why should they spend time filling out their personal profile? What benefit does that give them? And I think it's slowly impacting other areas too. And as I said, it was very HR-focused in the past, which is also why I tend to say, and I've said this ever since I started the journey, I want to call it 'people' analytics. It's not HR analytics, because that insinuates we only do things for HR, and that's not really where the value is to me.
[0:05:43] David Green: So, let's go back to Sony again, Henrik. So, this is where you built your first people analytics team, obviously as effectively a founder, people analytics leader. Can you walk us through that process? What was it like starting from scratch?
[0:05:57] Henrik Håkansson: I think it was very interesting. In a sense, as you said, back then, maybe if you look at US, some of the really big companies, Google, Microsoft, they had established people analytics, but most other companies hadn't, and we were very early in that journey. So, there was little to no best practice, or, "This is how you do people analytics successfully", so it was very much of a discovery journey. Originally, we didn't have any dedicated tools, per se. We had a team that we sort of put together of people who had some experience working with reporting and understanding, but no one came from an analytical background.
I think one of the challenges I saw was that analytics was often an afterthought, because it wasn't naturally part of the process, it was something that came afterwards. Now we've delivered this solution; how do we make any use of it, or how do we see if it's an effective process, or something? But it was always afterwards. It wasn't part of the design process. And I think this was one of the biggest challenges coming into, or trying to build a new function that no one had any experience in working with in the past. So, it's very reactive and quite limited impact as well because again, it wasn't something that came maybe from the very top that now we need to establish people analytics. It's more, we start seeing this trend within HR tech that maybe analytics should be a focus, so it grew maybe from bottom-up rather than top-down perspective.
I think also, one of the challenges that we didn't have that demand, since people didn't necessarily know we existed, it was much more, we had to try building things based on ideas rather than a demand or a request from someone. But over time, people started realising the more things we built, the more questions came. And we've reached this tipping point where the solutions sell themselves, essentially. But it is a bit of a journey to get to that point and convincing people that we need to spend a couple of weeks building this, and you won't see any progress until we're done. So, having that investment is also, people tend to question what you're doing if they don't see any results of what you expected. And in the end, I think one of the other in a way fun challenges was that we used a tool that was sort of an early access kind of tool with no one else really using it, maybe compared to some other more common analytics tools, that there was no way of googling things. They had a bespoke coding language, which made us try to figure things out on our own.
We had great support from the vendor, but it's still one of these things that looking back, maybe would have been better going with something that was more established or something that we could learn from. But again, it helped us build our bespoke solutions as well.
[0:08:44] David Green: If you could go back and you could do one thing differently, what would that one thing be? And then if there's one thing that you did that really helped you, what would that be as well?
[0:08:57] Henrik Håkansson: I think one of the biggest differences was, when I was at Sony, I went to the US for an assignment and that was based on me showcasing some of the dashboards we were building. But even back then, dashboards were a new thing. It was very cool. I know the discussion a lot surrounds now basically talking, do we still need dashboards or should everything go into Gen AI? But at the time, we got to present this to the right people. And I think this is one of the biggest things, being able to get the audience. When I was in the US, I worked with the leadership team, the executive team of North America, and that was completely different questions, completely different buy-in. And it also helped the function understand. I know you're a big proponent of talking to the right people, but I think this is the best thing that we could do. It was very much focused on more of a delivery support function. But if you want to be a proactive strategic partner, which is how I see people analytics, then you need to have the right dialogue with the right people in the organisation, which tends to be the more senior people.
Then, one thing to be mindful of there is also, maybe not trying to shove it down someone's throat if they're not interested, because there were some people who were more interested than others, and we'll get to that in a minute, perhaps, when we talk about my journey. But finding the people who are interested and working with them, that's the biggest benefit, and then they can help build the interest and the relevance of what you're doing, rather than trying to convince people who are against what you're trying to do.
[0:10:30] David Green: Yeah, as you said, identify the right stakeholders, find the ones that care, meet them where they are, and then obviously as you start to have success, they will be your advocates to help you grow within the organisation. I think it's good. And maybe one thing you'd do differently?
[0:10:50] Henrik Håkansson: One thing I'd do differently, I think being mindful of how much work it is. I think what I've learned over doing it in my other companies is that things take time. And not over-promising. My new motto nowadays is, "Under-promise, over-deliver", because you always think that things are going to be simpler than they are, and they're not, especially when it comes to things that you don't control. I mean, I can be in charge of data quality, but that doesn't mean I'm going and fixing 50,000 records that might be incorrect somewhere. So, building the partnership is important but also, being mindful that things will take time, and explaining that to people as well.
[0:10:32] David Green: So, yeah, as I said earlier, you've also scaled people analytics teams. Now, you're in your third company, So, Sony Delivery Hero and now Volvo Cars, and in different industries as well. What were the key differences in building those functions across such diverse organisations?
[0:12:40] Henrik Håkansson: I think as we've already talked a bit about Sony, but there everything was new. It was new in that people analytics on its own was new, so that was one of the bigger challenges. It was a relatively small team, as I said, a bit of a limited visibility of how we were organised, because maybe we hadn't realised what people analytics could do at the time. And it was very much focused on just getting started and seeing what can we do with this function in the long term. I think moving on to Delivery Hero, that is a tech company, so it's born out of data, which is quite different. Only been around for now, I think it's 12 years, but when I joined, it was around not even 10 years old. So, there wasn't all the legacy, there were custom solutions. It's more you throw a bunch of things at the wall and see what sticks, that's the mentality, so you build tons of different things. But I needed to take a step back there as well, and that was what I learned from Sony, to really build a solid foundation, which again took time. But once we had that foundation, we could scale and basically run with our solutions very quickly.
I think also, at Deliver Hero, was a big change when we had the money, and then in 2022, the whole market changed. And most of these startups where the investors wanted to see actual value, and all of a sudden, we needed to go from very much of a growth company hiring hundreds of people in a single location every month, to focus more on a sustainable long-term organisation and cost savings, I think that was also very different, especially when it's such a fast-paced market, things are changing very, very rapidly.
Then, going on to Volvo Cars, I think here the organisation existed when I came, but they hadn't had a leader for about 18 months when I joined. So, there was a bit of a gap between the former strategy and where people analytics was today, or rather one year ago when I joined. So, it was a bit of a lack of strategy and direction, which was then up to me trying to rethink how we wanted to be organised. Very large organisation, of course, but there is investment and willingness. But as I said, the difference between perhaps Deliver Hero and Volvo Cars is there is a lot of legacy. Volvo Cars is an almost 100-year-old company with the tools and the systems and the culture that comes with that; for better, for worse, but there is a lot of complexity when it comes to an organisation with this kind of legacy. Just moving on from an old HCM system to a new one, for example, takes years; whereas in a new company, maybe it takes a couple of months, because you don't have anything, you don't need to migrate, you don't need to do that.
I think here, it's also a lot of focus on the cross-functional topics, which is really interesting. We've established the data and analytics community, which is really trying to build this data mesh across the organisation, so there's huge potential in my opinion, and also means a lot of people are involved, a lot of opinions. And we have a saying that the HiPPO, the Highest Paid Person's Opinion, we wanted to try to steer away from that and build proper processes to make sure we deliver what's actually the company value rather than the person shouting the loudest. But it takes a bit of time to find your place. But at the same time, a very appreciative audience when you do things right.
[0:16:08] David Green: And a couple of things about maybe now, focusing on Volvo Cars. You've been there a year. What would you say your key achievements have been in those 12 months? What does the team look like? And I love that, and again, maybe developing a bit on that data and analytics community, you talked about connecting people data with business data to solve business problems. I presume that that's part of that plan to do that?
[0:16:36] Henrik Håkansson: Absolutely. I think on the achievement side for me, it was establishing the right organisation, a bit based on my experience, but also now, we do start to see trends in best practices, So, it's a little bit easier to rely on things that you can also explain that this works. I know from experience what works. But we had a very scattered organisation, we had resources that were borrowed, we had consultants part-time working for us, it was very lacking in control and a very silent way of working when I came in. We were delivering things, but at the same time, it wasn't as efficient, or there was no North Star to aim for. It was more of a delivery support function. So, one of the biggest achievements to me is establishing this organisation now with a backend of data engineers, data governance specialists, front-end developers or business intelligence engineers, and what I like to call analytics partners. I know you refer to them more as consultants or translators, but essentially the same kind of role. Since we have a lot of consultants in this company, I try to avoid using that terminology.
Also, position them more as partners, because I don't want us to just be on the receiving end, I want us to be involved in the discussions. And I think establishing the stakeholder management model, which is also partly based on what you've put together in your book around identifying the key people, we had hundreds of people reaching out to us, asking for different things, and how do we know which one to prioritise? So, we've tried to establish key people to work with and putting a bit of the discipline and responsibility back in the functions to give their priorities to us. Otherwise, we end up having multiple different requests, maybe from the same function, let's say engineering, having different requests depending on who you ask in that function.
This then also, lends its hand to the DNA community. So, people analytics was one of the hubs. We have nine hubs, as we call them. We use a federated hub-and-spoke model, which is a centre of data and analytics core and platform functionality. Centrally, it gives us the frameworks, the policies, sort of the frames of which we can operate in, and then we can decide within those frames how we want to run things in each hub. And then, we have specialised perhaps analysts working more as spokes. So, for example, if we want to have someone specifically working with our Chinese organisation, then that could be a spoke dedicated to that area. And I think this works quite well. We work with the other hubs, and unfortunately many of them were also established about a year ago. So, this has been definitely a big journey for Volvo Cars and a big investment as well. And I think this, what we discuss now, is how do we share data in the best possible way between these hubs.
We, for example, have a potential to do things very closely with finance, as people cost is very relevant, and a lot of the metrics that finance looks at are related to people. Then, of course, we also have engineering, manufacturing, supply chain, these other functions that maybe might be more interesting if you look at things like engagement, how does that impact our productivity in the factories, and these kind of things. So, I think that's more for the future, but we're not quite there yet, as we're still establishing a lot of the foundations across these hubs on making sure we still adhere to GDPR. I mean, just as much as we have people data, we also have customer data that is very sensitive. So, we need to be mindful of how we do this in the best possible way.
[0:20:18] David Green: Again, I think interesting when we look at the background that you've got in those three companies, working with both white-collar and blue-collar workforces. I'd be interested, and again thinking about the people listening to this that maybe are responsible for different groups of employees in people analytics, and in HR for that matter, did the differences in managing these two types of workforce reflect the challenges that you faced in people analytics?
[0:21:44] Henrik Håkansson: I think definitely. At Sony, we didn't have so much to do with the blue-collar population, even though there was definitely that group as well, but I think this has been most prevalent at Volvo Cars. I think one of the biggest differences is demographics, in terms of who's a white-collar and who's a blue-collar worker at Volvo Cars. And it's also that the office environment is very different to plant. If someone's sick, for example, for a day, if you're in the office, that's fine. You'll probably show up the next day or the next week and you can catch up on all your emails and things that you missed out on. If someone doesn't show up to the factory, then the production line essentially stops. So, we need to have a much more continuous way of working with our blue collars. It's quite a different environment.
You brought up people listening as well, which I think is interesting because we've had quite a challenge. As I said, if someone leaves their position on the production line, that means someone else needs to cover for them. And most of the blue-collar workers, they don't have their own laptops, they don't need it necessarily. So, if they are to answer people listening, like an engagement survey, for example, they actually need to leave and someone needs to take their spot while they're away. And how do we ensure that we give people enough time to actually respond in a good way? Or is it just, "Go there, do that quickly, so you can come back"? So, how do we rely on the results? And this has been a very big discussion with our data protection and cyber security organisation as well, to make sure that what we collect, we give them the right sort of conditions to actually be able to provide us with the right input.
As I said, it's a continuous way of working in the factories. It doesn't stop. We deliver or build cars 24/7, whereas in the office it's maybe the usual 8.00 to 5.00 or 9.00 to 5.00 kind of work. So, I think those are quite interesting challenges. And I think then there are very specific questions that we might ask, but I think that's also a journey. We have dedicated analysts who sit with the plants just to have a close feeling, so that it's not corporate coming in and telling you, "This is how you should do things", but we actually need to have the context of what's going on in the plants to make sure we deliver the right kind of analytics.
[0:23:06] David Green: How did you build the internal relationships and get stakeholder buy-in for these initiatives, particularly, I guess, as some of the stakeholders won't necessarily understand these analytical methods?
[0:24:07] Henrik Håkansson: I was going to say, I think we're quite early stages at Volvo Cars on this journey. I think everyone's very much into gen AI. I read an article, I know you've read it, about how people just want AI now. "AI will solve everything", but maybe we need to stop and ask what is the actual problem we're trying to solve? Does AI make sense? I think this is maybe more where we went a little bit further at Deliver Hero. At the time, no one was really asking so much for it. I mean, we were a tech company, but people weren't so much into Gen AI. ChatGPT didn't exist when we started this journey. It was sort of pre-Gen AI; and post-Gen AI is quite different, I think. We built an attrition predictor. We had one person spending Fridays or a couple hours every week. No one asked for this. I knew we had a bit of a challenge with attrition in the tech market on the startups, so a lot of people moving around. So, we wanted to understand if we could somehow see any benefits from that. So, it took about six months, since we didn't have any dedicated efforts on working with this. But over those six months, we developed a product and we showed a concept and all of a sudden people start realising, "Oh, this is what we can do with people analytics, or more advanced things", because people again were thinking, "People analytics, it's just dashboards, it's metrics, it's headcount, it's turnover, and that's it". But this really helped us get buy-in.
We also, showed it to some of the people outside of HR to sort of help leverage their opinions as well and drive the conversation. And in this case, the product didn't eventually hit the market because we needed to rebuild it. We had some data privacy challenges in how we predict without consent, and then we couldn't use that data for anything. So, in the end, we decided to rebuild that into a team identifier instead. So, it was not personally identifiable, and then it was fine to use. But this also helped us build a very close relationship with our CTO at the time, who was, of course, when you're in meetings with him, he was asking, how did we build an integration? What's the logic behind this and this? Questions that we perhaps wouldn't get from the HR community in the same way. And I think this was really helpful because when he was interested, he would come to us. And then, when he starts using things, that sort of forces the rest of the C-level and the other senior managers to also really want to use this kind of technology.
So, I think for us, it was trying to experiment with something, because we knew no one was realistically going to ask for it until then Gen AI showed up. Now, everyone's asking for it, but then we need to take a step back and figure out what makes sense. And I think, again, what we talked about data quality before, I think this is such a big part that we need to solve first, the whole data governance, the ownership, the accountability of the data, before we start building super-advanced technology. Because, again, we can do it on subsets of data that might be easier to do just as a concept, but then we really need to figure out what makes sense and what is irrelevant, because as you know, building advanced models are generally a little bit more time-consuming than just putting together a dashboard or some quick analysis.
[0:27:26] David Green: What skills and capabilities did you invest in to support the growth and delivery of the people analytics function? And maybe that's for Volvo cars, I guess, given that it's the current one.
[0:27:36] Henrik Håkansson: Yeah, and as I mentioned before, we have data engineers, we have business intelligence engineers, we have data governance specialists. Under my umbrella, we also, have talent intelligence and then what I call analytics partners. I think the talent intelligence is quite interesting as well. When we talk about, "People analytics is very young", then talent intelligence is sort of branching out from that even younger area and lots of potential, but of course much more difficult because you're working primarily with external data. So, there's a completely different set of skills of more data mining, scraping and modelling of external data, and also being aware that you will most likely never have 100% of the data that you need. Within people analytics at least, we can assume that we own the data we can try to collect as much as possible.
The reason I have this setup is also because we have sort of a build mentality, and this is how we build. When I came in, there was always that the data warehouse was almost complete. So, for me, it didn't make sense to go with buying a solution. I think if we bought a solution, then maybe focus would be on different kinds of skills. But for me, I need the data engineers. But we also work closely with our IT organisation, and as I said, the data and analytics community, to be able to leverage resources and capacity from others, so we don't have to build the same team over and over in every hub, but we can actually utilise the competence across. Some of the hubs have gone further, for example they have ML and AI specialists. We're not quite there yet, in my opinion. Some of the people on my team are very strong in statistics and modelling, so we have some of that competence, but we don't have any dedicated resources for it quite yet.
But as I said, if we look back at the skills I'm looking for, people analytics is still quite hard to find people with years of experience working in any kind of role within people analytics. So, I tend to hire people with passion, if I'm being honest. I think if you have the passion, you're interested, you want to learn, then these are the people that will be really good at their jobs. Two of my best hires have been a learning partner who had basically no technical background. In three months, she started learning SQL and visualisations and inquiring data in a really good way, and all of a sudden she was one of my strongest analytics partners at the time. So, I think this has really a lot of potential if you're interested, rather than hiring for skills, as I said, mainly because it's so hard to find people with years of skills or experience in people analytics.
[0:30:19] David Green: So, let's look a little bit to the future, and I appreciate this isn't always easy to do because it's changing so quickly. What are the sort of areas that you're looking to invest in as you continue to build the people analytics function at Volvo Cars, particularly in line with your objective to obviously create business value?
[0:30:37] Henrik Håkansson: Yeah, I think as I mentioned, was mostly talent intelligence. I think people analytics is an area that people nowadays start to understand more and more. People want to invest in it, but we still have an organisation that very frequently asks, what is everyone else doing? What is our competition doing? And you might have seen recently that some of the automotive companies are starting to struggle. We are facing a lot of headwinds in the coming months and probably next few years as well with demand declining, and just a challenging environment. So, we need to be ahead of the competition. So, how can we leverage external data? And that's where talent intelligence comes in, whether it's regarding strategic workforce planning or site strategy, you know, where do we want to build a new tech hub, for example? Should we invest when everyone else is laying off, so we can sort of vacuum the top talent from other companies? So, what's our strategy and what makes most sense? And I think this is where we can really impact the bottom line as well of the company, to really be proactive rather than reactive in the way that we think about our talent or potential future talent. So, for me, talent intelligence is a big part.
The other thing that we do internally has always been my focus building this really strong foundation, so that we can walk before we run, so to speak. If we try to build things without a solid foundation, it's going to be a house of cards, and we're going to have to rebuild solutions in six months that we developed today, because all the data's changed or we don't have a proper integration to our data warehouse, all of these things. So, I think investing in a solid backend is also one of my key focus areas, and then products to support this. So, we're working now with a model to build a few key products that will act as a baseline that we can then work on personalising and democratising to the business, and also potentially local analysts who want to maybe do their flavour of the products that we develop, but rather still having, "This is what headcount is, this is what turnover is", having these basic metrics, "This is how we look at engagement", and then allowing people to take it from there.
[0:32:52] David Green: So, again, maybe a couple of things really just reflecting on your journeys at the three organisations. What would you say are the key enablers that have helped you to scale your people analytics functions and deliver more value?
[0:33:10] Henrik Håkansson: I think we've touched upon it already to some degree, making sure you have the right relationships and visibility of the team. I think this was also something that really came to me when I was with Volvo Cars. We had a lot of talented people, but there was no representative of people analytics leading them to do work maybe on a lower level than what we really needed to. So, me coming in, being able to have the conversations with the right people was a big step up for us. I think single source of truth is also something that is crucial, especially a large company like ours. We have a lot of people wanting to report numbers. Even when I talk across not just the HR community, but there are a lot of managers who have their master spreadsheet, so to speak, of, "Well, this is my headcount, this is how I work with things". But what if we can help people and sort of market and socialise the products that we build, so that our audience is not just HR, but it's actually the whole business?
One of our biggest, I would say, audiences would be our people managers. It doesn't mean you have to be C-level, but just generally democratising, so everyone in the organisation has a good understanding of the demographics, diversity of the organisation, even basic things like understanding tenure and performance, but enabling people to see that on a managerial level. So, again, it's one thing, yes, we want to focus on the C-level, of course, because that's where a lot of the bottom-line value is. But I think to really get buy-in and understanding why people analytics is important, it is to socialise also, with the rest of the organisation and, as I said, not just focus on HR.
I think also, maybe more longer-term is thinking about how we consume data and how people want to consume data. Gen AI might be the solution that we all need to go for eventually. As I said, I don't think we're quite there. Maybe it's like Iron Man with holographic projections that you can spin around using your hands, or maybe it's just a dashboard or report. I think the future is quite uncertain in that sense, but making sure that we can allow us to give people insights at the right time, yeah, I think that's the key drivers for succeeding in this role.
[0:35:31] David Green: What are some of your strategies for enabling HR professionals and maybe people managers as well with analytics? What are some of the practices that you've done to help enable your colleagues in HR and people managers in your various companies, but maybe I know you've done something recently at Volvo Cars?
[0:35:50] Henrik Håkansson: Yeah, I think what we really want to focus on is data literacy, enabling people to understand. And this is something we do both with our team, we try to focus on, of course, our community, which is the HR community initially; but also, we had a data and analytics conference, an internal conference for all of Volvo, it was open to all of Volvo Cars back in the spring. And that was a big success, I mean obviously, mostly focused on the DNA community. They found it very interesting to see, "Oh, I'm not alone in doing analytics in this company. So, it's nice to see colleagues from other places". But I think building up the data literacy is constantly, when I go to conferences, when I talk to peers, this is one of the biggest challenges we have, understanding how data can be used. I talk about data-driven versus data-informed and making sure people are not scared of the data, but rather use it as sort of augmenting decision. Just because you have data, that doesn't mean it tells you everything. It doesn't, it's not the entire truth. But having a solid backbone, as I said, a source of truth that, I mean, I can trust this data, maybe it doesn't give me everything I need, but at least I can use this to inform my decisions.
Then, going out to the organisation, doing marketing and socialisation, I think is very important, because we can spend time building amazing products, but if no one knows about them, that makes it very difficult for people to actually consume it. And then people will still stick around with their spreadsheets or local solutions, and I think part of that is also, that becomes time-consuming. That also costs money, because someone needs to pull a report somewhere when we maybe have a solution ready that could save a lot of people a lot of time. I think, yeah, data literacy for me is one of the key drivers to democratise data as a whole.
[0:37:45] David Green: Well, that gets us to the question of the series. It's the last question. I can't believe we've got there already, Henrik. And you'll be able to talk a bit more about talent, intelligence and workforce planning; we can have a good conversation around this. So, how do you leverage people analytics to inform strategic workforce planning initiatives?
[0:38:02] Henrik Håkansson: Well, I think people analytics should be the baseline before you get started with strategic workforce planning. I already talked about the source of truth. But if we don't know today, how are we going to know tomorrow? That's essentially my point of view with why people analytics and strategic workforce planning is very closely connected. I do think the third leg of that is talent intelligence as well, because we also need the external perspective. If we blindly look at what we have internally, then we might make decisions that do not sync at all with what's available on the market, in terms of especially in the world we live in today, with very fast changes in terms of political situations. We have wars going on in places we perhaps didn't expect, which impacts talent availability. And maybe if we have sites in countries that are affected, all of this, sort of the geopolitical risk is very high.
I think strategic workforce planning has a potential to be a very strong influence on the bottom line of the company, if you do it right. And if you look at people analytics, you can highlight the internal trends, you can start seeing what's going on in the company, sort of a health check and tendencies that happen within the company. Talent intelligence then brings that external point of view with what's going on on the market. As I said, it's rarely comprehensive data, especially if you go to some countries. Maybe you can say, "Oh, well, let's just look at LinkedIn, what's going on", but many countries don't use LinkedIn. They use other tools, or maybe it's just government data you have to rely on, and then you have to rely on if that government data is reliable or not. But I think bringing that internal perspective, the external perspective from these different functions, I think, is key to help you develop a solid strategic workforce planning.
With the world moving fast also comes, especially now we see with the chat GPT and AI and skills, even that moves very quickly. So, even if we decide today to establish a tech hub somewhere, maybe all of a sudden all of those skills are much more readily available somewhere else. We can't just focus on costs and convenience anymore, we need to be mindful. I've had stakeholders who say, "Let's just ask ChatGPT what to do". Well, what if everyone does that, then everyone will build in the same places. So, I think investing in in talent intelligence, for me, is why that's so important, to get ahead of the curve, and start thinking, "What's going on in Africa?" for example. There's universities betting on skills that we need in the coming years. So, perhaps that should be some focus for us. That's not something you can google or figure out, based on looking just at people analytics' internal data. So, for me, that's the holy trinity of developing solid strategic workforce planning with people analytics informing, and also getting the business input from strategic workforce planning, talent intelligence, bringing the external perspective, and also, SWP essentially guiding both people analytics and talent intelligence what to focus on, so that we can improve our bottom line in the end.
[0:41:17] David Green: Henrik, it always is a pleasure speaking to you, it's been a particular pleasure speaking to you today on the Digital HR Leaders podcast. Before we wrap up, where can our listeners find out more about you and connect with you perhaps? I'm guessing LinkedIn is probably the best answer.
[0:41:33] Henrik Håkansson: I would say LinkedIn is always the answer, you can find me there. As you know, I write articles and post videos of conferences and stuff I attend. Of course, I'd be happy to meet some of the listeners as well at conferences. I do plan to attend a few in the in the coming year as well, but LinkedIn is definitely the best place.
[0:41:53] David Green: Henrik, thanks very much and I look forward to seeing you probably at some point in the New Year, and I wish you a great holiday in Florida.
[0:42:00] Henrik Håkansson: Thank you so much, David, it's been a pleasure.