Episode 12: Building a Successful People Analytics Team (Interview with Ian O'Keefe, Global Head of Workforce Analytics at JPMorgan Chase & Co.)

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Building a people analytics function in a global Fortune 50 company is as wonderful an opportunity as it is a significant undertaking. This was the challenge facing my guest today, Ian O'Keefe, when he took the role as Global Head of Workforce Analytics at JPMorgan Chase in June 2016. Ian and I walk through his journey in building the function at the bank as well as reflecting on his 10 years in the space, which has included people analytics roles at Google, Sears, and American Express.

You can listen below or by visiting the podcast website here.

Ian is one of the most knowledgeable, experienced, and insightful leaders in the space so I know that listeners will enjoy this episode of the podcast.

In this episode, Ian and I talk about:

  • The three-year build of workforce analytics at JPMorgan Chase, which he built from the ground up

  • The skills and capabilities you need in a people or workforce analytics team

  • How to balance demand for analytics from the business with prioritising the work

  • We also reflect on the people analytics space, in general, based on Ian's unique perspective of having worked in several sectors like tech, banking, and retail

  • Like we do with all our guests we look into the crystal ball and ponder what the role of HR will be in 2025

This episode is a must-listen for anyone in a workforce or people analytics role. HR and business professionals interested in how people data can drive business outcomes. And CHROs looking to build or scale their people analytics headquarters.

Support for this podcast is brought to you by pymetrics, to learn more, visit pymetrics.com.

Interview Transcript

David Green: Today I'm delighted to welcome Ian O'Keefe, Global Head of Workforce Analytics at JPMorgan Chase to the Digital HR Leaders podcast and video series. Ian, it's great to see you. Great to have you.

Ian O'Keefe: Nice to see you, David. Good to be here.

David Green: Thank you for joining us. Can you give listeners a quick introduction to you? And also, your background and your vision for workforce or people analytics.

Ian O'Keefe: Sure. I'm born and bred in the U.S. in New Jersey not far from here. I spent much of my career here in the east coast in New York. Psychology, predictive analytics by way of education. And I guess my vision is really rooted around how we can use data to help people make decisions to affect outcomes. You hear that from everybody. I think more and more that's becoming rooted in a lot of end-user design and creating products that are embedded into day to day workflow of the org. And we try our best to do that as much as we can at the bank and I've seen that done a lot of different ways throughout my career. It's fun to be on the journey.

David Green: Well, great. We're certainly going to touch on some of the work you're doing at the bank a little bit later. You've been in this space for over 10 years and haven't gotten any grey hairs so that's pretty impressive.

Ian O'Keefe: I hide them well. Hide them well.

David Green: And I think we come across each other when you were at Sears doing some work there. You've worked in financial services at American Express and then onto Sears in retail I think up in Chicago. And then down to Silicon Valley to work at Google and then back home effectively to work at JPMorgan Chase.

Ian O'Keefe: A wandering journey.

David Green: A wandering journey. And what I'd be interested to... Having been in a space for that length of time, what are the main changes that you've seen over those 10 years? And then actually then looking at different sectors that you've worked in different organisations. Some of the similarities or differences around the challenges and the scope of work?

Ian O'Keefe: That's a great question. I think a few things. One, probably the biggest change is the amount of attention, awareness, and investment that has gone into the space increasingly almost exponentially over the last five years. You see heads of jobs. You see different types of teams being stood up. You see a ton of capital being pumped into startups. VC's are paying attention. I think it's a real thing. It's not a flash-in-the-pan. I would sum it up to that. Advances in technology. Different ways of listening to the workforce to platform your data, social, mobile, cloud.

That goes to the third point which is around the employee experience and how you can use data and tech to curate better experiences to people. Similarly, to how companies might do that on the consumer side from a marketing perspective. Productising and creating useful products and services internal to the org has been central to a lot of the way we think about it. And I think some of the best orgs in the world that are doing people analytics take that to heart.

And the fourth thing I'd point is this has always been there but I think it's been amped up through a number of milestones lately is the focus and attention on privacy laws and regs and that takes on different formations in different industries. But how we treat, handle, land data, use it don't use it, ethics, that has taken on real meaning and dominated I'd say in a good way a lot of the agendas that I see on our team at the bank and in the industry, in general.

David Green: And thinking briefly about your time at Google. Obviously, Google didn't invent people analytics. Although, perhaps maybe they invented the term. They certainly popularised it. When you were at Sears, my understanding the analytics team there was quite small. And then when you went to Google, there's this very large team doing a number of different things. What were your reflections on the time at Google?

Ian O'Keefe: I think the phrase people analytics, HR analytics, talent analytics it's ... The royal we in the industry space have been I'd say maybe a little bit unsure about what we mean by it. And that's taken on different team formations as a byproduct. I'd say one of the differences that I've seen in my journey has been the brand of people analytics that an organisation might centre around.

I think the through-line there is the data that's generated by people when they're at work can be harnessed and brought back to the surface for awareness, for action. And how you do that is really a reflection of what your org centers around from a business perspective, the culture that predominates the org and we go from there.

David Green: And I think Google the work that they did in some of the stuff that they put out there about project Oxygen and project Aristotle and some of the other stuff that they did. I think that probably did inspire a lot of other organisations to actually invest in this space.

Ian O'Keefe: Undoubtedly. I mean, it's groundbreaking work. The combination of methods, the ambition to study patterns throughout the workforce over time is really difficult to do. And to do that with discipline and to do that in a way that that tells a story and that motivates an entire organisation to do something differently that's to be celebrated regardless of the function it's coming out of. And I think Google did that really well around leaders, around teams throughout the entire recruiting cycle. And there are fortunately over the last number of years, lots of use cases from many different orgs to be celebrated. And I think we're in a pretty exciting space in that regard.

David Green: Well, the wanderer returned back to the east coast about three years ago I think now.

Ian O'Keefe: Three years.

David Green: And you took on the role as Global Head of Workforce Analytics at JPMorgan Chase. And a quarter of a million employees, operating in 60 countries. What is actually involved in that role, the Global Head of Workforce Analytics? What are the key responsibilities?

Ian O'Keefe: It's changed over time. When I first took the role on the way that we did data in HR was pretty fragmented across lines of businesses and functions, that's not uncommon as a starting point. It involved really understanding what type of analytical activity is happening where and whether that's more on the tech side platforming database managing information to straight-up reporting, Adhoc, turnaround. Interactive MI dashboard building to more interpretation, storytelling, consultative work to data science. Those capabilities were capabilities that I had a meta monologue coming in that I was looking for. And some of them were already present in the org. Some of them were happening, reporting, for instances often where most people analytics teams got their start and that's where a lot of the incumbents come out of.

That, for instance, was happening in many places according to different processes and tools. Understanding what's happening where. Understanding the level of sophistication and maturity of those activities. Understanding where you have complete gaps where you might have to go and buy or borrow some of that skilled team across the org. That was what I did for my first probably six months at the org and created a roadmap out of that.

And built the first incarnation of people analytics, listen to me, workforce analytics at JPMorgan in early 2017 was when we established what was then and is still the current capability set of workforce analytics. It really involves envisioning what we could do with data translating that to capabilities. Looking to see how much or little we had of that across the org. And then building and investing accordingly. And putting points on a board as we went.

Data engineering, reporting, consulting and data science was where we started. And that has since been broadened to include more on the data management, governance, privacy side into a data office that workforce analytics now sits in. And I view that as a milestone in how we think about workforce analytics at the bank, that we created a data office on par with the product services and orientation points that the other CDO's have across the bank which is pretty cool.

David Green: And actually, I should ask, why workforce analytics?

Ian O'Keefe: That's a really, really good question. I think I early on I came from Google and I have a little bit of an affinity to the word people analytics. I think it's pretty simple for us. It was a reflection of where we were and the work we had in front of us to get the foundation right. And doing analytics across the entire array of data that comes into work every day when people enter the doors, definitely not where we were. We're moving in that direction. And I think we have orientation points in finance, workforce planning and that's how we branded it. And I think it's evolved to include a lot more of that's more akin to talent and people analytics now. That's not a promise we'll change anything. But I think it's a pattern that I'm not unconscious of.

David Green: I suppose there's an argument isn't there that people analytics isn't always going to be about people. Especially if you're involved in the workforce planning side of things and actually sometimes you're not going to be using a person to do some of the work that you might be looking at. Maybe you're actually a bit more future-thinking than some of our colleagues out there.

Ian O'Keefe: Let's agree to that, David. That sounds good.

David Green: There's been about a three-year journey at the moment, and I'm sure there's many more years to come. But what have been some of the key milestones on the journey so far in bulding that capability within the bank?

Ian O'Keefe: At the bank, I'd say one of the first milestones we hit was really expressing the viewpoint of what workforce analytics is. What it can be? What we need to build to get there? And then moving from what I'll call just ideation into execution. And that happened around 2017. And from there we went from a small team to a larger team to a pretty sizable team as we built products and expanded and scaled on our capabilities. We've had a number of the product milestones throughout the journey.

Last year we rolled out our first machine learning product to the recruiting organisation which is being scaled up now. We have a number of tools and products in place that lean on natural language processing, classifier modeling. We've done a lot to clean up the foundation internally. And I'd say recently, we established our analytics office which was an organisational change, a good one.

I'd say that was an evolution in how workforce analytics is embedded within the broader data ecosystem in HR, in tech, and increasingly outside of HR and corporate functions. And maybe a soft milestone but more of a trend really is in the last 12 months, being a part of enterprise-wide analytic conversations across the corporate sector and with other data science teams has been I'd say a marker for us that we can move the claims to be true maybe 24 months ago which is a great trajectory for us.

David Green: And I guess that looking outside HR, looking at working with colleagues in enterprise analytics that helps you really focus on what the right business problems that you're tackling.

Ian O'Keefe: I mean, a quarter-million people that work at the bank there's different data on them depending on how you ... The lens you put on it. If you put a retail or consumer side lens on it versus a privacy lens corporate lens investment banking ... There's bespoke systems and applications that people use when they get to their desk as a trader or as a teller. And the through-line on all of that is they were hired once they're still here. They've moved around. They were put onto a team. They had to leave. There's a common call for the worker, team, or demographic data that will complement studies. And we've been involved in some of that.

And increasingly we're starting to think about concepts like productivity, collaboration, that everyone cares about and no one owns. And how you put data requirements and methodology and ultimately insights and action to get better at those things is really interesting to think about. HR is at the table and our team is at the table along with other teams thinking about that from their own perspective. Very exciting stuff.

David Green: I'm sure. What are some of the big challenges you've had to overcome on the journey so far?

Ian O'Keefe: I think there's never enough time. You probably hear that from everyone. As you create the team you create anticipation demands and that creates something you can't quite keep up with. And so I think managing expectations and just putting realism into what is able to be accomplished by when. And oftentimes, that also comes with the more you do the more you realise what needs to be done especially when it comes to the cleanliness or relative cleanliness of the data ecosystem that you're working within.

It's probably not so satisfying for a client or an end-user to hear that you have a great idea about maybe how to measure throughput or productivity but it's going to take us weeks or months to clean the data up and prep it. And that conversation can get very weedy very technical and ultimately from a business perspective seemingly unnecessary. Great. I hear all that just figure out a way to get it done I need it next week.

And so, I think moving the conversation away from that and to more of a planful, I get it, it's going to take time. That's been a challenge and I think the analytic literacy that comes along with that amongst people who are asking for your help is one that is pretty common across every org I've seen. And one that I'm sure you guys have heard from others as well.

David Green: I've not met an organisation yet that hasn't got a problem around the data literacy side. Really understanding of what it is and what it can do.

Ian O'Keefe: That's right.

David Green: I think it's something we'll be fighting for a few years yet. Interest is what's really coming out there is expectation setting, good stakeholder management really. Both  in HR but also in the business I guess as well. And managing that demand and being able to prioritise.

Ian O'Keefe: A friend of mine Stella Lupushor, who I know you've spoken with recently she once said very pointedly in a conference that, "The advice she'd give to herself five years ago is that people analytics, or workforce analytics, talent analytics, we're not really in the analytics business we're in the change management business." And when you're building new capability, and you're doing something different with data, and you're putting it into a format, or a form function that is cool and insightful. It's going to delight and surprise some and maybe create a little bit more questioning than you asked for or expected in others. And so how you push the org forward to do something differently with the data.

My favorite question to ask in all of this when we're asked to do something is, what would you do differently if you knew the answer to your question. And would you change a process, a policy, a procedure, a program? What do you think? Sometimes the data, you need a little bit of a hint from the data to take your hunch seriously. But if you're not already thinking in that direction it's probably a pretty good indicator that this just might be interesting and not so actionable. That's interesting and actionable and differentiating that has been a challenge, an ongoing opportunity for us and probably for the space, in general.

David Green: I guess that could really help around prioritisation. If you're not convinced that a business leader or someone asking for the work is actually going to do something with it. I guess that could really help.

Ian O'Keefe: It helps a lot. I mean, the willingness to act on the insight you think you might see is a huge driver of frankly mobilising resources to dive in. I think sometimes you need to explore the data a little bit to see if there's a there there. And that increases a little bit of confidence and maybe a willingness amongst the end-users to do something. But if there's a clear line between getting the question answered and a changing or maybe accelerating something that's already written into the strategy agenda for a function or for a team that's really compelling for us. That says readiness. Complexity mostly becomes an afterthought because the resourcing to get it done regardless of complexity is probably going to be there if the upside is already written into the I statements of the asking party.

David Green: And I guess it's that ongoing dialog, that you said you maybe do some additional analysis to get a couple of insights. This is what the data is telling us at the moment. We need to do more work.

Ian O'Keefe: Exactly. You get that problem say written down. You scope the study. You kick it off and you explore. And you're prepping and you're just understanding the very basic nature of the data. And sometimes you learn enough to kill the project which is hard to do. And sometimes it's really necessary. Something that often is a starting point for a lot of teams just to get going in people analytics is predicting turnover or doing some correlation studies with engagement survey data. And if you don't have an attrition problem you probably shouldn't build an attrition model.

And so sometimes that's not so intuitive to know just based on backward-looking data. But usually, it is pretty knowable pretty quickly. We've entertained and deprioritised requests when it comes to putting industrial-strength data science to problems that aren't really there. And that helps us to create capacity for other things.

David Green: And like you said, that lends itself onto the next question which is around building the team. Basically, it's acumen and understanding the business problems are a key part of that.

Ian O'Keefe: Yes.

David Green: Given that you've built the function from the ground up at the bank, it would be really interesting to understand your thinking around skills and capabilities that you needed in the team. And almost the order in which you hired those in. Because you can't just hire 30 people straight away you've got to build it over time. It'd be really interesting to understand especially the mix of skills and capabilities that you've got. And then the sequencing in which you brought those in.

Ian O'Keefe: I love that question. The skills and capabilities was probably day one of what I started talking about and putting forward ... There's basically four skill areas that we did for and do look for in no particular order. The first is your quant skill. Knowing how to interpret data. Knowing math, knowing statistics, you can find that from a variety of disciplines but that's skill bucket number one. Number two is being technically proficient in database management, querying for data, navigating the stack as it were. That we'll talk about how we built and bought each of these in combination.

Your third is your business acumen. Knowing the business. Knowing how to identify issues. How to scope, prioritise, frame problems. Think like a consultant as it were. And your fourth is your subject matter expertise and research orientation on issues that are germane to workforce and people analytics. Incentives, rewards, teams, leadership, engagement. The list goes on. Nobody that I've ever seen has an advanced degree in a mathematical discipline. Has spent time as a CTO of a tech stack. Spent time at a top tier strategy consultancy, and has an advanced degree in IO psychology. That's very unicorn.

David Green: It'd be a pretty special person.

Ian O'Keefe: If they're out there please come in and see me. Aside from that, we look for breadth and depth. We look for depth in one or more of those and breadth across more than one. Your traditional t-shaped practitioner, that's good enough to be dangerous in more than one area. And how we hired and built the team off of that framework was we found quite a bit of people have spent a lot of time building and customising and curating the database environments that we often store and pull data from across the tech team and across our different reporting teams at the time. We didn't buy that. We organised around that already being there.

We did buy quite bit of our mathematical data science capabilities. We had a starting point on that in tech, but we really bought more around it. The business acumen and the subject matter expertise of different HR domain areas, by and large we already had at the bank. We did go to market for some of that to get more of an analytic spin on your maybe traditional HR centre of expertise from a talent standpoint.

And I'd say we really started around the foundation and reporting and dashboarding. Bought and upskilled and trained our way into an increasingly advanced data science. And the data engineering and the platform work that really turbocharges your data science, we by in large borrowed and have since contracted with our technology org on a dedicated basis to make sure that we have the right operating model in place to have them work on our platform environment to land data as a matter of HR-wide strategy going forward. That centerpiece to how we're evolving and maturing the entire technology backbone when it comes to HR data. That's how we did it.

David Green: Great. And I think I've heard you answer a question similar to this before from Al Adamsen in actually a PAFOW conference. If a CHRO came up to you and said, "Look, Ian, I'm going to build a people analytics team or workforce analytics team in my organisation, what is the skill set I should hire for first?" Assuming that he didn't have it or she didn't have any of those skills that you had within the bank already in place? Where would you go do you think?

Ian O'Keefe: Well, the place I would not go first is the data science side. I think it's hard to attract and retain a full-stack especially data scientist. Someone that can not only get in prep and build pipelines for data but also analyse be statistically proficient in doing so. And build products and applications that are deployable back... That's a pretty rare breed. We were fortunate to have a number of them on our team. You hire a person like that in too early and that's going to be a pretty bored person pretty quickly who-

David Green: Probably a high flight risk.

Ian O'Keefe: Whose day to day probably slips into activities that are just one piece of that broader string and that probably doesn't feel too good for that person. The first place I go is probably on your foundation. Make sure that you have a strategy and a view on how to get all of your data into one place. If it's all in one place already, great. Let's look at the levels of quality and governance and security controls around that. I'd call that broadly speaking data engineering and infrastructure. You might have that already resident in your technology organisation. And that's probably the first place to look. Having a good foundation. It's not entirely linear but I think without that it'll catch up pretty quickly and bite you if you don't focus on that sooner than later.

David Green: That's great advice. That's great advice. You highlighted at really high levels a couple of the projects that are being done. It would be great if you could share in a bit more detail a couple of the project or products maybe that you developed in the workforce analytics team.

Ian O'Keefe: Sure. One big push that we've had over the last year or so and we've turned into a product is a problem that I think ... It's an opportunity, and a problem that many orgs face is finding the right people for the right roles at the right time as fast as you can. And the speed and the way that you cut through perhaps a really big pile of applications is difficult. And often that's manual, and the processes by which you do that are often almost working in opposite of quality of candidate.

And we partnered pretty early on with our recruiting leads, and our global head of recruiting to attack that problem. It's how do you sort the stack and how do you just understand where there might be hidden value in the stack that is just really hard to examine and interrogate by human beings when you're talking thousands, tens of thousands, hundreds of thousands? The bank receives millions of applications a year across as many job families as you can count.

We built a few machine learning models to understand basic decisions across the applicant flow. Is a candidate likely to be reviewed and passed along to a manager and then hired? And then stay in his or her seat longer than 180 days, which is roughly a productivity breakpoint on a lot of the high volume high churn, in some cases, roles that we have that are in our call center environment, that are in our operations environment. That's been a really successful one for us. We see that our models they don't make decisions for people, but the way that we've designed it is to give our recruiting organisation another signal to work with. A predictive signal.

And we've trained our org to understand what they're looking at in the tools that they work in. We embed a signal in the tools they use every day. It's not a manual offline clunky cross-reference. It's embedded in the tools they use every day. And we've built I think a pretty well-thought experimental design construct to understand how fast and how much quality candidates are passing through the system by recruiters who see a signal versus those who don't see a signal.

And so I think scoping the problem really sharply upfront down to the job family, in our case, was really important. And then understanding how we'll be able to tell if it's working is important. Having that be part of implementation and deployment on the backend is something that we've paid a lot of attention to. And I think we've seen the results that we'd hoped for. We're being objective and honest throughout that whole process. We don't want to find a signal we hope to see. But we're seeing really positive results, and we're scaling that product across more and more jobs this year.

David Green: And I guess that's the sort of thing that creates demand for the business. And the benefit is being felt by the recruiters but also by the business as well the hiring results.

Ian O'Keefe: It is. That's a good example of where we plugged into a number of different strategy items that have already been expressed by the recruiting org, by business leaders who are looking to grow and scale and do different things in their respective organisations. Getting good people is always going to be key to that. And to getting them quickly without doing it in more manual cost heavy ways is something that we thought we could help with and I think we are.

David Green: And then I guess helping that process but also making sure you still get the right people.

Ian O'Keefe: That's right.

David Green:  Which is I guess saving time and money? But actually getting the right people because, obviously, generates the business outcomes.

Ian O'Keefe: And a model can only take you so far with that. We don't make decisions for people, we give them data to make decisions. And so we're nudging people to look at these candidates before those. And we are I think allowing candidates that are good for the role to be seen sooner by recruiters and then by hiring managers who make the decisions. Are they the right fit? Will they get along with the team? The model doesn't tell you that. The model tells you if they have some basic requirements, and a few other things that are probabilistic of their success in the role. And we do this in a way that is ... You probably touched on a point here is fair.

And we've done a lot of work to make sure that adverse impact's being built into the model, that that doesn't happen. That we've remediated against that during the model build and that we have a model adverse impact identification or remediation procedures that are in place to make sure that the model while that may be powerful and accurate doesn't inadvertently disenfranchise a class of applicants that was unintentional.

That's a really, really important part of it. And the more that you balance the decision-making or decision nudging that the model might suggest versus what people will do, I think that also serves as a counter check too of maybe building unintentional bias in the decision process based on how it's always been done versus how it should be done going forward.

David Green: That's a great example I think of how workforce analytics can really support the business. Where are you looking to go in the next 12 to 18 months?

Ian O'Keefe: The next 12 to 18 months. We will continue to scale our machine learning products to more and more decision points across the employee life cycle. Recruiting is a garden of opportunity that we look to with some excitement. We look at internal mobility. We've got a very robust diversity inclusion agenda in front of us that bridges between products and projects. What if scenario modeling, how to help promote certain diversity agenda items across the bank. That's a big one for us. And I think also just increasing the maturity of our platform and moving to cloud across the entire HR function. And the underlying infrastructure to do that is very central to our agenda. That's where we're going.

David Green: Well, we look forward to talking to you in 18 months, when we have you back. If we look more generally at the people analytics space now. This is a space that you know very well. You've been in for quite a long time now. What excites you most about people analytics?

Ian O'Keefe: I think I'd say besides the analytics. Besides the rolling up your sleeves and just getting into the work, there's always new opportunities to learn. I think what we're asked to do is sometimes it's right in our wheelhouse sometimes it's on the edges of our ignorance. And we methodologically seek and seek to find insights and datasets that sometimes have been in front of us all along. Sometimes there's new data streams coming about that we're being asked to check out or create.

We build products we buy them. There is a ton of I'd say really exciting developments in the startup community. We build and buy I'd say in equal measure. And so just seeing how the space continues to evolve I think we're probably at the front couple years of a 10-year cycle that will produce some really exciting headlines and results over the next couple of years.

I think learning, driving value, and helping wake I'd say organisations up to what I think is the most prolific yet underappreciated data asset in the entire company. And it's the data on people. How you think? How you act? How you behave? How the place ticks? A quarter of a million people, how many interactions, decisions happen every single day that could be nudged, influenced, perhaps looked into a little bit more? It's mind-boggling. And I think it gives us all an opportunity and responsibility really to make sure that we do that in the proper way that continues to innovate and drive value.

David Green: And that might lead onto the next question probably. What's your biggest concern-

Ian O'Keefe: That we do that wrong. That we do that the wrong way. That we get ahead of ourselves. We being not just people that run teams like this but customers of ours, clients of ours. I think the ability to look into data and glean insights creates the classic could we should we dilemma. And that begs the question of ethics and really getting a lot more intentional around how we treat questions of could we should we as a matter of discipline and rigor in practice and not just a conversation. It, obviously, needs to be that, but I think it needs to be a lot more than that. I'm afraid we see a headline about ... And there have been quite a few headlines. But we see a zinger that creates a tipping point event perhaps legislatively or from a reg standpoint that makes things a lot more difficult than they otherwise could've been. That's a big concern of mine.

David Green: And I guess in actually working in the people analytics field, we have an even bigger responsibility because we're really the custodians in many respects of the data that we need to be helping to educate our leaders, our colleagues in HR, the business around not just what they can do but what they should do. I think we have a big role to play.

Ian O'Keefe: A huge role. Analysing which of 40 shades of blue gets the most clicks on our website is not as I'd say personal as understanding how many different formations of a team or variance of a compensation scheme might get certain types of results inside a company. And aside from that, super-sensitive data about people. How much money they make? Diversity related information. Behavioural. How they think? What they're afraid of in surveys?

I mean, this is really personal stuff, and we've got to not only do the basics of governing the data appropriately but entertaining and helping and guide to think through the questions that probably shouldn't be asked and answered with data and the questions that should. Because if you're going to do something differently you might be able to decipher between the two and avoid putting perhaps super sensitive datasets next to each other. Seeing some correlation and attributing that to something that otherwise would be perhaps disastrous in the worst case for people, lives, careers. We have to really be responsible with that.

David Green: It's about trust at the end of the day. I mean, if you look at what Accenture published at Davos. I mean, we're looking at workplace trust in people. And organisations generating the value they can out of workforce data. And there was a finding that I think 92% of employees are actually happy for their organisations to process data about them as long as they get an individual benefit from it. But I think nearly 2/3 were more nervous now than they had been because of some of the scandals we seen last year in the consumer, and the consumer space. It's a tightrope I think that we're walking on.

Ian O'Keefe: It is. I mean, there's generally speaking, there's a power imbalance between organisations and people. Trust is going to be centred to ... A centrepiece anecdote to that. I think certain orgs certain industries might be ahead or behind. And unfortunately, when you read headlines nowadays like you do, it just only illustrates the importance of your point. I think trust if you're asking for data you've got to play it back. There should ideally be some kind of symbiotic exchange.

If we give the permissions that we do as the individual consumers to banks, to different companies, you can watch what I watch on television or track what I respond to on email because you're going to presumably give me something in return that's valuable. I think orgs need to think that way and have analytic programs that put that type of reciprocity into the ecosystem in a way that people will feel comfortable. And frankly, feel like it's adding value to their day and helping them get better at work and be happier at work. And that's where I'd love to see the two.

We could probably talk all day but we do need to wrap it up. And we'll move to the last question which is a question we ask every guest on the show. What do you think the role of HR will be in 2025?

 I love it. I love it. My mind is filled with possibilities. I think HR used to be called personnel at one point. And finance used to be called accounting at some point. I think there's probably there's a step forward in just how we think and frankly, label who is in this space tending to and promoting the employee experience which I think is going to be a theme that starts to really shape how HR today thinks about the roles and a set of responsibilities tomorrow.

The analytic practitioners in this space are going to be I think central to that understanding and to that end-user experience design. I think products. I think trust in building products that are helpful and useful that promote a better experience for individuals, for teams, for orgs is going to shape the agenda going forward. I think that from another standpoint a lot of what HR does today is built around processes that we think that we seek to standardise as much as we can globally and puts security controls around for the right reasons.

A lot of the data that I think people analytics orgs are going to be harnessing and looking at going forward and outside of HR for that matter too, are around behaviours. Are around how people how they think how they feel. What they do behaviourally? And people probably will have the opportunity to opt into having that data played back to them in ways that are insightful.

We see this in different products today. I think that is only going to perpetuate on itself hopefully in a good way, and an ethical way, and a controlled way. And I hope we see a different type of appreciation for the data that the people generate when they walk in the door every day. And what it means to not just the org for planning and top side reasons but for people every day. I look forward to seeing that evolve.

David Green: Well, that's a vision that I'd quite like to see come to fruition. Ian, thank you very much for being a guest. How can listeners stay in touch with you?

Ian O'Keefe: LinkedIn is great. Feel free to ping me. I'm on Twitter a bit. LinkedIn a little bit more so. And I pop up now and then at conferences such as yours and others, David. Keep a lookout.

David Green: We will do. Thank you, Ian.

Ian O'Keefe: Thank you.

David GreenComment