Ian O'Keefe of Ikona on People Data & Analytics

Speaker A: This is a fantastic episode with Ian o'. Keefe. I, uh, use the term longtime friend and colleague. Often I get called out for it. Uh, Ian, however, is certainly one of those people who is a longtime friend and colleague and he's helped me in my career and I'd loved to learn new things about his story, uh, his affinity for music and growing up in New Jersey, and how the idea of creating has led him to productize People analytics solutions so they can be sustainable and lasting. A lot of other great anecdotes in his story, uh, of his not only young life, but what he hopes and believes about the future. So thank you for being here. I hope you enjoy the episode and if you would please click subscribe, as we have new episodes coming out weekly, if not more frequently. So again, thank you for being here and enjoy. Hi. Uh, welcome back. I'm here with Ian o'. Keefe. Ian, how you doing?

Speaker B: Great. Great to see you, man. Thanks for having me.

Speaker A: Of course. We've been wanting to do this for a while, and now we have the opportunity. So if you would please introduce yourself and then we'll talk a bit about your past, what's happening now, and where we think the world of People analytics and working.

Speaker B: Yeah, sure, no problem. Uh, Ian o'. Keefe. Uh, I am an analytics leader. I've been in people analytics for, you know, 15 years, give or take. Uh, in industry for about 20. Uh, I've led teams at Google, JP Morgan, Amazon. I'm also now working with venture capital startup funds through semperverance and doing a little bit of nonprofit advisory work as well. It's, uh, it's been a very cool, wandering journey, uh, through orgs, very large, uh, to very small. And People analytics is, I'm very happy to say, alive and awake and thriving in all those different settings that I've, uh, been a part of.

Speaker A: Yeah, I'm smiling and I know if you're listening, you can't see me smiling, but Ian has been a contributor to the People analytics and Future of Work conference over the years, and you have a great narrative around a lot of things and you've got research in terms of how the People analytics space has grown over the years. And you have put up this unicorn image of, uh, People analytics professional. And I laugh because you are one of those unicorns. I, I think, and, uh, partially, maybe, I don't know. But the reason I, I share that is we all have entered the space from different educational backgrounds, different experiences, and I'm really curious to learn about your upbringing what your inspirations were to get into this space, if you would have started at the beginning, I mean, where are you from? What was your young life like there? And believe you want to go way back.

Speaker B: Yeah, yeah, we can go back there. Uh, yeah. I grew up in, in New Jersey in a, in a small kind of seasonal shore town called Manasquan. It was the, the birthplace of a lot of bands, including Bruce. My parents were musicians. So a lot of music in the house growing up, and we try to keep that alive in our house today with our kids. But yeah, growing up there within commuting, spitting distance of New York City, uh, that definitely had an influence on the kinds of jobs and professional tracks that people had around me growing up. And it was always a place I wanted to go and be a part of. And out of high school I went down to Virginia to UVA for undergrad and was a psych major there. I focused on the corporate psychology. Not quite IO psych, but psychology in the workplace. And that always fascinated me. What made people tick and then what they brought to different settings, including work. And so, uh, my background is really, uh, in psych and then through different roles and orgs that we'll talk through here today. I combine that with more quantitative skills and went to Northwestern and got my master's in predictive analytics there years after my undergrad. And blended psychology with quantitative model building, with HR expertise with management consulting, which is what I started my career in out of undergrad, uh, at places like Deloitte and others. And those skill sets and experiences are I think, all useful for people analytics. Having the quant chops, an appreciation for psychology, decision making and motivations and teams leadership, having expertise there, knowing how to frame and solve problems as a management consultant, and understanding, uh, technology throughout all that is, among other things, really important, I think, for people analytics professionals. And I couldn't have told you at the time, uh, in each of those different experiences, that it was leading towards a career in people analytics. But I think a lot of my peers couldn't have told you that either because none of us knew what it was up until not too long ago. And our backgrounds just led us there. And I think that's a big part of the appeal of the space, is that so many different backgrounds and paths lead, uh, to this space and it's evolving still. And I think that's really exciting to

Speaker A: your point, given what you shared. Again, I have this smile because when you talk about psychology, we talk about quantitative analysis, we talk about technology, and obviously AI is prominent in the news and it deservedly is disrupting not only people analytics and how we gather and analyze data, but the very nature of work itself. I'll just go straight to a pointed question that if I'm listening to you I want to know. What do you think the future of people analytics is? And if you want to answer the second question first, first by all means, what do you think the future of work is going to look like? Given the proliferation of data analytics and

Speaker B: AI the past few years, uh, and the past, well few decades and especially in the past few years there's been a massive reduction in the amount of cost uh, associated with large scale computation infrastructure, storage, compute, that's all become a lot more available, a lot more accessible and a lot less costly. So I think those were barriers historically that were relegated price wise and expertise wise to fewer uh, than they are now. I think that all opens up the aperture for many to get into the space and to think more creatively about the kinds of products and applications of analytics given the infrastructure that this kind of work tends to ride upon data and the proliferation of it in the workplace by people is enormous and you've heard me say it before Al, I think that the data that people analytics professionals have historically used and done amazing work with teams that I've led included tends to come from data producing systems that are within the scope of HR, let's say and increasingly outside of the scope of hr. When you think about different collaboration, uh, systems slack and teams and other uh, types of collab systems. But with AI, let's just start there. AI and ML, like we've had AI and ML in the workplace and in our lives for a long time. I think HR has started to in uh, the past 10, 15 years get really professionalized maybe is the right word and has moved along the maturity curve when it comes to the productizing machine learning models and uh, putting, putting models into production systems with product integrations, generative AI, the ability for, for a machine to create new content that hasn't been created before by being trained on massive amounts of data preceding it. Like in the case of large language models just in the past couple of years and really in large part the past 18 months. ChatGPT kind of stole the show. Uh, but there's 150, there's hundreds of LLMs out there now, uh, and they've been with us for a long time. The appreciation for what they can do to generate new content in pretty much any contextual setting is it's a game changer and I think from least of all from me. But like you hear Yann Lecun and you hear Swami at Amazon talk about it and you hear Sundar and Google talk about it and it's really one of those uh, seminal moments I think in the history of tech, not to overstate it, where we'll look back in 10, 15, 20 years and look at this moment where generative AI really received massive investments, massive infusions of creativity and innovation and risk taking by lots of different professionals and firms in all different sectors and, and that will change our lives really. But also the workplace in uh, ways that I don't think we've yet to fully imagine yet. But the use cases in the HR space are, there's many of them I think hr, operations management, leadership training, er, recruiting. There really isn't a space that couldn't benefit from minimally, uh, the automation and co piloting by generative AI of transactional tasks in each of those spaces. So I'll pause there because we could just go.

Speaker A: Your perspective on this is uniquely valuable. You've had impactful leadership roles at Ah, Google, JPMorgan Chase recently Amazon and not to go to specifics about any of those experiences, but the very nature of people analytics I see shifting and also the adoption of AI as it continues, it's going to, and this is echoing what Sam Altman has said repeatedly and there's an article just today that reinforced his position on this, is that this is going to not only disrupt jobs, it's going to eliminate certain jobs and those people who know how to work with AI are going to have a competitive advantage over those who are not embracing AI and specifically generative AI and um, the value it can bring. So given your lens, given the way you view the world as a historical people analytics leader, which it's an essential premise is to bring insight to the fore at different levels in an organization so leaders and managers can make better decisions. Where do you think we're heading? And I'll be specific. If I'm a chief talent Officer or m, I'm sorry a chro or a Head of Talent. Should I be prioritizing the adoption of generative AI to capture and disseminate insight across the organization? Or am I uh, in a wait and see moment?

Speaker B: What are your thoughts? I think HR orgs and chief people officers, if they haven't already, definitely should start to ask that question of what is our GAI strategy? And uh, if that's a sort uh, of a piggyback question on perhaps a larger question that's been asked at the organization overall then I think that you've got the benefit of the org having breaking that ice for you. But I think that this question, it's not going away. I don't think it's a fad. Look at the data, look at the research, look at the, the amount of companies investments and frankly progress that's being made on these models that are getting better header at uh, doing lots of different types of tasks that we previously thought were relegated to humans. So I think having a strategy is really important. I think not knowing how to operationalize that strategy in every single part of say your HR operation is where I would say most of us are uh, frankly today those use cases that are uh, germane to your HR operation, whether they're highly repetitive, potentially automatable tasks that ride on in uh, the case of like LLMs, let's just talk about that. That ride on lots and lots of content language material. These use cases should be considered. Right? I wouldn't put a function, a certain function, uh, under the spotlight at the expense of another right now but, but I think there are uh, in any org use cases where generative AI can really benefit and take uh, the. I'd say that maybe some of the pain and some of the tediousness is the word I'm looking for out of transactional manual activities and create more room for kind of more productive, more innovative things. And I would also say it's not an all or nothing. Will GAI replace all recruiters or all er, professionals or all HR service operations and service professionals or all trainers? I think the answer is no. If you want to just talk in absolute terms. But I think the answer is definitely yes to some activities that I would envision most professionals would perhaps be delighted to give up. I think more in terms of like it, co piloting rather than replacing jobs outright, but augmenting what humans do and take a lot of time to do to unlock more capacity to unlock the creative endeavors and innovation and productivity of people is how I like to think about it. I'm not one of those doomsdayers that the robots are coming for your jobs and that's that. I think differently about that.

Speaker A: Yeah, likewise. And based on what you shared, I'm curious and uh, your thoughts about how people analytics plays a role. At the outset you said you have to have a generative AI strategy. Then it invites the question if I'm a chro, I haven't grown up having a data analytics or AI background and I'm going To have to entrust people on my team, whether they be in HR or outside of hr to help formulate that strategy. Many are identifying the people analytics leader to say okay, that is the individual and um, group who's going to take charge of this. Others aren't even thinking about the people analytics team because they've largely been the data aggregators and report generators and they haven't been thought within, thought of within the AI conversation. What are your thoughts? If you're coaching a chro head of talent, head of HR operations, what would you suggest? And granted I imagine it depends, but it depends on the internal capability, the systems and technologies that they have in place. But in general what would you advise?

Speaker B: Yeah, I think generative AI isn't going in my opinion to pre die people analytics or HR agenda. It should be accounted for as a uh, technology wave of innovation that we need to understand its implications on different aspects of HR and HR operations and how we manage and run the workforce of an organization. Just X years ago you would have been left out in the cold if you didn't have an opinion on how the uh, uh this emerging thing called you know, cloud computing and cloud storage is changing the way that you think about your IT infrastructure and your data handling and provisioning. Like that's not something that's emerging now, that's something that's known and in place. And so I think this is definitely emerging. It's early days and we have to understand uh, in its most basic forms like if you think about the application of something, when we think generative AI, we think ChatGPT and large language models for instance. So the applications of that within in kind of small testable use cases within an organization, just as you would if you're early uh, or not yet, uh, up to speed on model building uh, and using uh, ML, um methods uh, and building models and deploying models and integrating that with different uh, products and dashboards and kind of uh, customer facing data artifacts in your org, uh what are the use cases that you want to prioritize, your first instance of doing that and by extension what are the areas of focus that are most pressing and the biggest problems that you have to solve that might be a good application for which to solve for it. I think generative AI is no different in that respect. And what is different is understanding what it is good at doing and what it's not good at doing. As far as we know right now, ChatGPT makes some really silly mistakes. I was just on a, a webcast today that was Done by a hired score. Ernest, uh, Dang and his teammate were on there talking through ChatGPT and just their impressions of large language model evolution. And one example of where ChatGPT makes is good and bad. It was in talent acquisition as a use case. And they put forward a very simple like three bullet point resume of a data scientist whose name was I think David. And then they asked ChatGPT to give me a summary of David's qualifications for the role. And chatgpt did that beautifully. Then they put forward another resume which was identical except the name was changed to something else, Daniel or something identical. And chatgpt summarized that resume slightly different, but more or less the same. And it's okay, just a different flavor of the same. And then the question was who do you think we should hire? Which one should we choose? And that's when ChatGPT started making things up about both of them that weren't on the resume. If you want someone with more systems integration experience, I'd go with David. Whereas someone that has actually this experience in a financial services setting, you go with Daniel wasn't on the resume. Not at all. It was just complete hallucination. And you might say maybe whoever was first in whoever, whoever applied first, we, maybe we should talk to first or just be random about it. But it was funny how it clearly just totally made it up on the spot, had no basis for that. And, and two things occurred to me there. It's one that tell me about a hallucination or just something that's completely made up generatively, that doesn't make any sense and would need a human to sniff that out. That, that so you need humans in the loop. And then secondly, it's not that good at reasoning yet. And maybe asking you a clarifying question and having a discussion like a human being would. It knows more that's been published and can synthesize more than humans probably can. But it, if you get pushy with it and you start to. I want you to tell me something that maybe you don't know the answer to. Maybe not unlike a human, it'll make it up. But if you take it as absolute truth, you're, you're obviously doing yourself a disservice. At any rate, taking in massive amounts of information, reading an entire manual or policy guide or operations guide and understanding down to the letter and the page. What exactly is our procedure for doing X, Y and Z in this circumstance? How would you advise our application of policy? Xyz, take it with a grain of salt and have a Human in the loops, just like ML years ago. It's not a panacea, I guess, is the moral of that story, I think, just understanding what it doesn't do. Let's get real about it. It's early days. Let's pick a use case that's small and go fast on that and scale it and learn and see what we can do. Is where my orientation points would be.

Speaker A: My key takeaways from that is that, uh, Chat GBT are taking psychedelics.

Speaker B: Yeah, they do and we don't know why. And the other example that, that I saw earlier today, just incidentally, which made me laugh, was chatgpt. Pick, pick a random number for me. And the number that it shows more than any other number was the number 42, which isn't a random number. Right? Like that's the answer to everything. 42, if you read that book. So anyway, it's technology and it's not omniscience. And you might not know the difference right now because of all the glitz and the banter that's about, but, but I'm trying to just, uh, do my best as a professional to go deep and upskill as this field of HOFs, because it is evolving. That particular part of it's evolving really quickly.

Speaker A: Uh, absolutely. And of course we can talk about the legal risks of applying AI in certain selection processes as you just highlighted with Hired Score. It's also the case going back, not going down that that path, at least not yet, is who within HR is going to be able to stay up to date on all the innovations that are happening, that are leveraging Chat GBT and large language models in, in general? And so it could be the people analytics professional as we just talked about. Then it invites the question, do they have the capacity to remain not only educated buyers of technology, but to implement, maintain and enhance over time? So there might be an irony here, is that we might actually have to grow that capability in terms of number of hires to ensure that we are selecting technologies wisely, that we're implementing and realizing the core value propositions that we're not compromising, you know, legally or ethically or otherwise. So going back to coaching and HR leader, would you say, hey, you either need to hire somebody to ensure all these things happen or potentially even partner with a consultancy, but to not do it, I don't see that as an option. What are your thoughts?

Speaker B: I agree with the last thing you said. More than anything, to do it, to not do it is not an option. So I think let's start There and uh, who should do that? The bad answer is it will probably depend on uh, the org that you're in and the capabilities of the HR function and the capabilities of the broader kind of uh, enterprise analytic function or enterprise technology team. Uh, so I think this conversation is happening around the enterprise, not just within uh, HR as a domain. Uh, and I think that given uh, the newness of it, the speed of progress and evolution in the space, it would be unwise to tap person X as the person. You're just going to know all that and just inform all of us. I think understanding in organizational approaches to this, uh, and leaning on partners throughout the org to do that is going to be important. I think people analytics leaders are very skilled and proficient in different domains such as technology, such as analytics, such as understanding like where legal and compliance would come into play and identifying when and where and who to call in to what conversations. I think is a skill that good people analytics leaders uh, have and how to use that skill to help play, call and collaborate and coordinate around the responsible use of this new generative AI technology in the people space is one that I don't think any one person is going to make alone that call. They're not.

Speaker A: I want to build off a couple things that you said in there. Number one, just not being an HR challenge, there's this overlap now that has been gaining momentum over the last five years. In particular, somewhat a random duration, but certainly during the pandemic where we have HR tech evolving into work tech, we have operational work tech evolving into HR tech collecting data. So you know, that then screams, you know, what is the governance model? The idea that we have these functional silos. I do not imagine we're going to wave a wand and everything's going to get broken down or even if it should get broken down because there's certain reasons why these have existed in the first place. So my point of question are just twofold. Number one, people analytics or HR in general, being in the work tech decisions to understand how that data can and should be used or not used. And also related to that, the governance models on technology selection and adoption and prioritization and budgeting. Uh, HR historically has been a laggard insofar as that, okay, this decision was made and people analytics and HR have to deal with it. And I would think at least this is what I'm seeing in certain organizations that HR and people analytics more specifically are actually in the selection discussions. So they can then have a voice and influence how that data are used downstream. So Again people, analytics and HR in the work tech discussion and the surrounding governance.

Speaker B: What are your thoughts there? I think HR and representatives of HR people, analytics professionals being often being those representatives are a necessary and b helpful for orgs as they go through technology selection processes and they think about the enterprise wide data model and how this technology or this stack or this infrastructure design is going to help us all be better at harvesting insights for our customers and for our employees. Once you enter into the employee space and you look internally as employees, uh, and managers being your customers, HR's gotta be there. And I think to your earlier point al, HR hasn't always been there because it's a technology discussion. Oh and there's a people oriented use case. You know like we're going to bring our technologists and our analysts and our scientists and our researchers and, and we're going to uh, be at that conversation as well. I don't think we, I wouldn't take that for granted but I don't think we have to. We've climbed that hill. I think as a function HR has not to be taken for granted but I think we've gotten there. The asterisk next to that statement would definitely be in kind of new emerging technologies. It's not historically HR's job to be on top of new emerging technologies that have enterprise wide application LLMs do. Understanding what the people implications are of say adopting the use of LLMs in a workplace environment is important and uh, increasingly these technologies are available just with an API call. You have them at your fingertips and you can incorporate them into your technology infrastructure design. From a product development standpoint, uh, from an infrastructure inclusion perspective there's not a lot in the traditional sense that's going to stand in between an organization and using ChatGPT unless it's like totally firewalled and we're just not going to allow access to that by a day to day end user. But the point is I think like vendor selection, quote unquote, that mental model, it applies and it doesn't to these types of technologies that are just an API call away. And you're not looking at vendor strengths as a strategic partner as the only criterion. But there are vendors that are putting forward really interesting compelling solutions using for a long time machine learning and AI and now uh, generative AI. I think having a strategic partner in your vendor and one that is going to not pretend that they have cracked the code and figure out uh, the answers and the limits and maybe constraints to this technology, I think that has been and will continue to be important, but extra so, uh, within this conversation.

Speaker A: Yeah, and I want to focus on people analytics and I want to focus on it within the context of talent strategy and workforce force planning. And I have a few follow on questions related to that. And then I want to ask you some lightning questions, uh, which will shed some light on who you are and your interests and so forth. But I just want to tee that up now. But what you just shared triggered a thought. And you and I participated in Talreos, a Talent Analytics Leadership Roundtable and Economic Opportunity Summit at Northwestern a few months ago. I really have to be proud of

Speaker B: myself, but it was a wonderful conference.

Speaker A: It's not only you and I were talking about it, but the whole group of nearly 100 people analytics and workforce planning leaders were there. And we talked about the role, uh, of people analytics leaders helping formulate, measure and manage people strategy, uh, because we've been a, uh, discipline, as in under this banner for 15, nearly 20 years, uh, of people analytics. So it's a recognized discipline. Yet a lot of the insight that we have generated historically has not been adopted at scale and it hasn't. It's been arguably underutilized. The pointed question, as we evolve as a people analytics discipline, it's way more than just dashboards and reports and doing analysis AKA or research project on a certain topic at a certain point in time. It is hopefully helping inform talent or people strategy. So what's your hope for the profession? Do you see people strategy and analytics being under the same umbrella? Or do you see, uh, people analytics as a discipline informing another individual or group around talent strategy and workforce planning? What do you see that?

Speaker B: Yeah, it's an awesome question. Generally speaking, I think that, and I'm not speaking about like org design or anything here, I'm just in terms of the way that analytic insights turn into something other than a research paper or uh, a PowerPoint presentation or a report is by at a minimum of the people analytics team M in partnership with their, their HR centers of expertise and their confederates. And I'm thinking about program and product owners and policy owners. If you are answering a question out of interest, that is the bare minimum that you can do. If you answer the question and you're doing so with intentionality of what will you currently, if you know the answer to this question, you've heard me say this so many times, will you do we think the answer to this question really could inform a material change in this program or that the way we run that product or process or policy or procedure like Something material needs to change to encase all the goodness that we just learned in this insight and benefit from it. And then you can measure the uptake uh, of that new process or policy or so that's where I think you start to create that flywheel effect in a closed loop. You have to make a change based on the insight. And the change that you've heard me talk about more recently is on the product side. So embedding insights and recommendations and nudges in production with production integrations in your products, whether they're in house, uh, designed and maintained products or third party hosted products, that's where you uh, catch people in the flow of their work and you embed that suggestion, pause, moment, nudge, recommendation in the right point of the user experience and you work with your design team to make sure that that's intuitive and helpful to, to scale the effect of your insights. I think back to your question like where this goes like minimally. If as a people analytics professional you're not pressing your partner teams and your leadership to consider what we're going to materially do differently to a policy, process, procedure, program like you've got to do that, that often prioritizes up or down certain initiatives right away taking things further and, and building out those, those embedments, uh, whether it's product integrations or mechanism design or new process re engineered processes, that's extra lifting beyond the analytics team. But that would bleed into like your HR operations and effectiveness agenda. And if your overall strategy to your other question is to making it up, improve the, the efficiency of our HR organization, the productivity of it and the customer satisfaction of our internal workforce, you would obviously index your projects and your studies and your insights generation down those alleyways and you know what programs and processes and policies that in turn you would affect to and measure to be more efficient. Hopefully that answers your question.

Speaker A: But it answers what I take away. There is there needs to be a systematic, thoughtful uh, approach to utilizing the insight that's generated. In other words, if you commission somebody to generate insight for the sake of what, who's the audience for it, how do you take appropriate action? If that is done, then the business case is there for doing more. For me as this, you know you mentioned earlier and this has been your experience, you've worked with large enterprises and you mentioned the access to these tools is an API key away in many cases. And so for mid sized companies for uh example, which might not have people analytics teams yet they can still generate insight to help formulate their people or talent Strategies, what's your coaching to them?

Speaker B: Standalone independent people analytics teams uh, are relatively speaking not a cheap endeavor compared to other backgrounds and skill sets that you find in the HR function. I would personally say I think the upside uh, of investing in that you're going to get multi x return on that investment. But just apples to apples like a standalone team, uh, it is a commitment, right? And so the mid size orgs and smaller orgs that are looking to commit down that path, but maybe not so much so that they have a standalone team with all the component parts which I'll talk about in a minute. I see in the market teams combining analytics with HR tech or analytics and HR operations. And so there's leadership uh, roles that are bundled in that way because the org has thought deeply uh, about it and decided that we want to commit to the analytics agenda. It's really tied into the changes that are happening within our kind of technology space and that has to sync up with how we think about our HR operations or all of our programs that might be bundled in as well. So people like analytics plus and strategy or ops and tech, different combinations depending on the circumstance of the org. I think you can, you can think about your org design that way. And if there are a couple different components, domains of HR that need to be comprehensively tied together with an analytic and technology thesis, so to speak, that's where you can think about making that type of analytic investment. Because you're already making a uh, technology and an operations investment or you don't have an HR function to begin with. I think that's where you can incubate and grow that analytic capability and have uh, a very kind of short and direct line into the material implications to your tech stack or your operational workflows or your programs. The other thing too, the borrowing and sort of the knowledge sharing and collaboration that orgs that are smaller can have hr, uh, teams can have with their corresponding enterprise analytic teams or there's a team of data scientists that are enterprise wide. A lot of them like that I see in large orgs like that are on people analytics teams, data scientists or applied scientists or researchers. Not all of them, but quite a few have come to HR people, analytics teams from inside the org and they want to apply what they know to people related problems and opportunities because those are exciting and first of kind and noble maybe even. And that's an opportunity that I think you'll find partner teams that are outside of HR but skill wise complementary what you're trying to do, an opportunity that they'd be willing to bite on and to go along for the ride with you on partnerships and domain and capability bundling perhaps uh is the way to describe it. Those are I think viable strategies for orgs that are getting started and don't quite have the size and scale and size of checkbook perhaps to invest full on right away.

Speaker A: Yeah and I would just want to build off what you're sharing because these individuals that are coming internally either on a full time basis or maybe on a project basis have the relationship equity. They have the context of what's happening in the business.

Speaker B: So right, like oh we're borrowing a data scientist from the consumer banking side or from our AWS team or from take your pick at any org. It's a great way to move fast and to learn.

Speaker A: And again correct me if you think differently, the ease of use of many of the technologies now enable not only those types of individuals but others to focus on the action that's appropriate given the insight that's being generated as opposed to just building the insight itself. So one thing I want to do in the interest of time you mentioned component parts. Uh, can you just provide an overview of those component parts within the context of what you believe is going to be the case in the future? In other words, we mentioned tech companies. What is the role of consultancies or consultants to augment the internal capability in organizations both large and small?

Speaker B: Good question. I think that in house practitioners are always going to win out over consultants when it comes to the depth of knowledge and understanding of that organization's data and the data producing systems and like what we can count on what's clean, what's not, what's centralized, what's not Consultants outside perspectives I think will almost always win out on in house practitioners when it comes to use case application benchmarking use case applications of new types of technologies. What if organizations of a similar size and scale tried and succeeded at or tried and maybe stumbled on how can we learn together and raise all boats? I think you know consultants are are great for that Also there are I'll broaden your definition of consultants to include just generally speaking like third party vendors that you might look to. There are a lot of really new exciting innovative seed round series a funded workforce tech startups that are using state of the art like ML and that are going deep on like generative AI in specific thin slices of kind of the HR domain and the employee life cycle that odds are your people analytics team or your HR organization just hasn't the prior the priority the resourcing the capability, the time to just go really deep on. An easy example would be generative AI for resume parsing and review by recruiters uh, and plugging that into your operational workflows. You can build that internally. We built something similar JP Morgan years ago. That's not an uncommon uh, commodity now amongst many different startups. But when you think about um, ML and AI applied to identification and the scaffolding and architecture of skills and skills taxonomies and how that fits into your current job architecture, there's startups that do that quite well. How you think about training, leadership development, take your pick. So I think where you want to buy and build is uh, it's an important conversation and it's a relative one compared to what your in house teams are in a position to deliver on or not. I think niche use cases, understanding broad industry patterns and trends and where firms that are similar to yours, competitively speaking uh, or M from maturity and size and scale standpoint what we're learning about organizational adoption of these technologies to get more out of your HR org and your workforce. Recruiters or vendors and third parties are a great source of knowledge for that.

Speaker A: Uh, I'd be remiss if I didn't ask this question because we and HR have sometimes gotten in our own way. In other words, people analytics and talent intelligence, workforce planning are naming conventions. They're disciplines that are out there. Those people like who are in the weeds so to speak, know some worthwhile distinctions. Others outside of that community have no idea what the differences are and it gets confusing and sometimes it gets paralyzing. No decision is made or certain technology is adopted and it underwhelms and thus it penalizes all adjacent processes. So my question to you is how do you draw a distinction between talent intelligence, people analytics, workforce planning or is it really one in the same uh, with underlying nuances that the people generating the insight should know about and it's only really valuable to them. Yeah,

Speaker B: I think there's some common threads. The way I would think about common threads across all three of those brands I guess you might say is roughly speaking I think you need four core capabilities to bring a full stack team to bear in any of those spaces. You need data engineers as well, in no particular order data engineering. You need analysts that can, that can SQL in, write reports, build dashboards, visualize data. Uh, I think it's pretty well tracked. Space data scientists, applied data scientists, applied sciences to model, build, predict, um, to productionize models for product integration in partnership with product and tech teams would Be third researchers, IO psychologists that handle your qualitative research, your listening efforts, your interface with HR line organizations and leaders, content validation, interview focus groups, the anecdotal evidence to marry up against your quantitative findings. And there's other uh, obviously disciplines as well. But those four I've seen, everywhere I've been, I've built around those four. And I think whether uh, you call yourself people analytics or talent analytics or HR analytics, workforce analytics, you need to either have those capabilities on your team or near your team from a dotted line, partnership, collaboration standpoint. The differences I see this is how I think about it. People analytics in my mind means everything I just said across the entire HR data model which includes every transactional bit of information from all data producing systems under the banner of the human resources domain. Talent analytics including talent acquisition? Yeah, absolutely, absolutely. From acquisition to onboarding to comp. A lot of times some of these get carved out independently for reasons related to the organ. That's totally cool too. But people analytics in my experience usually is responsible for analytics insights across the entire HR organization and data model, whereas talent analytics is focused more on talent management, town evaluation, the kind of the talent stack promotion, succession planning orgs might have different uh, activities clubbed into that, but you may or may not have talent acquisition be a part of that just because it's such, that's such a monster unto itself. And it's high volume, it's high velocity. Whereas like the rate of promotions and succession planning refreshes, like how often does that happen compared to the number of recs we hang out and candidates we're reviewing, we're talking like minute by minute, hour by hour over here and maybe quarter by quarter over here. You have to take some of that into account. But and then workforce, I think there are corollaries with workforce planning, really close corollaries, Workforce planning and financial planning and the partnership with finance there, uh, understanding ins and outs and, and what those forecasts look like, counterbalance by hiring outflows and unregarded attrition against your projected headcount targets that you're reporting to the street. Like you can't get that wrong and you want to do your best to land your actuals with your, with your estimates. With finance, I think capacity management and planning, those are more of the applications on like the workforce, uh, planning side of it. And yeah, those are how I think of some of those variations.

Speaker A: Super valuable distinctions there. So thank you for that. I want to give you a chance to wrap up and have some closing comments, but before I'D like to ask you these rapid fire questions.

Speaker B: Yeah, uh, I'm ready, man. Let's do it.

Speaker A: All right. Favorite genre of music lately.

Speaker B: It's, oddly enough, bluegrass. And Billy Strings is, like, completely lighting me up with his guitar. But I. I like 80s rock, but. But bluegrass is on my screen now, but I would have to go with, like, 80s rock. Let's just say that.

Speaker A: All right. Hey, it's summer. Weather's good, so bluegrass.

Speaker B: I just love the guitar. I love his versatility on the guitar. It's amazing. 80s rock, though. That's my answer. That's my answer.

Speaker A: All right. Sounds good.

Speaker B: Who do you.

Speaker A: Who do you learn from? Who are your inspirations? It could be for years ago or current.

Speaker B: I learn every day from my wife, Michelle, about myself and about my relationships with everyone around me through her honesty, uh, and coaching with me. If I could use those words. I learned from my peers as well, of, uh, which al, I would proudly call you one. But I like to think that I'm, uh, constantly in learning mode and where I get my different learnings from. I don't try to draw too hard a boundary around that. I just want to be open to anything.

Speaker A: Outstanding. Thank you for sharing that, and thanks for the kind words. And it's certainly shared. What do you do for fun?

Speaker B: I try to play the guitar as much as I can. And the piano, I try to. I do that to decompress. I cook. I've got a wannabe garden out back right now that my kids and I are attending to, and I get out and spend as much time as I can with. With. With my family and with the kids. Try to be physically active to the extent that my body will allow me without breaking down. And, uh, yeah, that's pretty much it.

Speaker A: Fantastic. Your hopes for the future. You mentioned your kids and what type of work environment do you hope they walk into? And specifically, what's your hope to influence that type of.

Speaker B: Yeah, m. My hope would be that they. They walk into a work environment that's highly curious, that's highly innovative, that's respectful, that over indexes on integrity and respect. Uh, and humility, I think, is key to all that. To be a learner and not presume to know the answer to what's right in front of you or what might be coming around the corner. So I think exposing them to as much as I can to what's happening now and talking to them about that and what's coming around the corner and watching them process that with the magic and the wonder that they bring, uh, as they view the world is something. And yeah, I just hope we all collectively think ahead a little bit about what the next generation is walking into and we get it right when it comes to like new emerging technologies and the responsible use of that and we don't mess it up for them. I think every generation says it about the next one. I just don't want to mess it up for them. But uh, I'm optimistic and uh, I look forward to it.

Speaker A: Likewise. We're on that journey together, my friend. So as we start to wrap Closing Comments ideas I just want to say

Speaker B: a uh, big thank you to you Al, for everything that you have and currently do and will do for the community. Your, your place in the community is second to none and you provide us with uh, amazing place to learn and to listen and to uh, share ideas. So thank you for that. And if, if others want to just listen in on this, they know where to find all of us through your platform. And LinkedIn is usually the best place to, to find what's new and current with me. I would just leave it with that and leave it with a big thank you and an expression of gratitude to you my friend.

Speaker A: It's a joy to share this journey with you. Thank you for your kind words, thanks for being your awesome self and look forward to seeing you in person. Uh, back at you man.

Speaker B: Thank you.

Speaker A: Thank you for listening to this episode of the People Data for Good podcast with me, Al Adamson. Please click subscribe to become aware of future episodes and support our community. And again, thank you for supporting People Data for Good, promoting the ethical and responsible use of people data analytics and AI for the benefit of individuals, teams, groups, organizations and society at large. Again, thank you.