30 November 2021

Workplace Stories Season 3, Skills Progression: The Soup Cube Skills Methodology

Dani Johnson
Co-Founder & Principal Analyst
Stacia Garr
Co-Founder & Principal Analyst

TL;DR

  • This is the fifth episode of our podcast: The Skills Odyssey, Season 3 of Workplace Stories.
  • In this episode, Stacia Garr and Dani Johnson of RedThread Research and Chris Pirie from The Learning Futures Group talk to Patrick Coolen, global head of people analytics, HR intelligence, and organizational design at the Dutch bank ABN AMRO.
  • Patrick explains how he and his team are working on understanding Skills and using bubbles, heat maps, Escape Rooms, and partially homemade soup to create personalized opportunities for their employees.
  • “It’s a lot about at scale, personalize, and make sure it has value not only for the organization but also for the employees … if we need to go on180 degrees somewhere else in another direction, we need to be able to do that.”
  • How to have employee-centric mindsets, use the data that you already have, keep it business-centric, and not be afraid to ask for help.
  • Patrick answers the most important question of all: How do I get started?
  • A special thanks to our sponsors, Visier and Degreed, for their support of this season!

Listen

Guest

Patrick Coolen, Global Head of People Analytics, HR intelligence, and Organizational Design at the Dutch bank ABN AMRO

DETAILS

“How you are able, as an organization, to reconfigure resources like Skills and have the ability to allocate the right talents in your organization at the right time– I think is also a competitive advantage.” So says our guest this week, Patrick Coolen, Global Head of People Analytics, HR Intelligence & Organizational Design at ABN AMRO, a large Dutch-headquartered bank, and we don’t think many people would disagree with him. But how to allocate? Based in Amsterdam, Patrick is leading the charge on Skills at ABN AMRO to do just that—make Skills a competitive resource for his enterprise—so he has some ideas and experience to share on what he and his team see as the answer. The result is one of our most interesting traveller’s tales so far on the Skills Odyssey, encompassing everything from pragmatics on how to start with people analytics, the usefulness of Emsi data, a good deal of Dutch common sense, and a rather beguiling metaphor on, er, soup. Trust us: you’re going to go with it!

Resources

  • In the episode, Patrick says he is happy to make connections and drive the conversation through LinkedIn (as his commitments allow, obviously).
  • The Escape Room his organization uses to train people on analytical skills comes from this Netherlands-based company, Fresh Forces.
  • Patrick has written two guides for people trying to get their heads round people analytics that you might find useful, his ‘10 Golden Rules for hr analytics’ and ‘8 Big Tickets for People Analytics.’
  • And finally: see how much you can do with Maggi!
  • We do recommend, if you haven’t had a chance yet, to catch up with the first Workplace Stories season on Skills, which we released February thru June 2021, entitled ‘The Skills Obsession’: find it, along with relevant Show Notes and links, here—where you can also check out our intervening season on all things DEIB, too.

Webinar

As with all our seasons, there will be a culminating final live webinar where we will share our conclusions about the show’s findings: we will share details of that event soon as it is scheduled.

Partner

We're also thrilled to be partnering with Chris Pirie, CEO of Learning Futures Group and voice of the Learning Is the New Working podcast. Check them both out.

Season Sponsors

 

 

We are very grateful to our season sponsors for ‘The Skills Odyssey,’ Visier and Degreed. Visier is a recognized leader in people analytics and workforce planning; with Visier, organizations can answer questions that shape business strategy, provide the impetus for taking action, and drive better business outcomes through workforce optimization. Visier has 11,000 customers in 75 countries, including enterprises like Adobe, BASF, Electronic Arts, McKesson, and more; you can learn more about Visier at visier.com. Degreed is the upscaling platform that connects Learning to opportunities; they integrate everything people use to learn and build their careers, Skills, insights, LMSs, courses, videos, articles, and projects, and match everyone to growth opportunities that fit their unique Skills, roles and goals. Learn more about the degree platform at degreed.com, and thanks to both of our season sponsors.

Finally, if you like what you hear, please follow Workplace Stories by RedThread Research on your podcast hub of choice—and it wouldn't hurt to give us a 5-star review and share a favorite episode with a friend, as we start to tell more and more of the Workplace Stories that we think matter.

TRANSCRIPT

Chris Pirie:

Welcome, or welcome back, to Workplace Stories brought to you by RedThread Research, where we look for the ‘red thread’ that connects humans, ideas, stories, and data, helping define the near future of people in work practices. The podcast is hosted by RedThread co-founders Stacia Garr and Dani Johnson, with a little bit of help from myself, Chris Pirie of The Learning Futures Group. We’re excited to welcome you to our third podcast season, which we call The Skills Odyssey.

Our first podcast season focused on what we call The Skills Obsession, where we asked ourselves why so many organizations and leaders are currently focused on all things Skills. We learned that the shift to Skills-based practices was something of a journey—an Odyssey, if you like—and we decided in this season to go deeper and find more examples of programs, strategies, and experiments. So, we’ll be talking to leaders who are starting to run experiments and programs using the Skills concept to rework how we think about all aspects of talent management; we hope to learn why they've embarked on the journey, how they're progressing, and what they hope to accomplish. We'll seek to find out the approaches they're taking, the challenges they're encountering, and the successes or potential successes that they're having—and we'll definitely meet some amazing talent leaders along the way. So, listen in: It might just help you think through your own Skill strategy, and it will certainly be fun.

We are very grateful to our Season sponsors, Visier and Degreed. Visier is a recognized leader in people analytics and workforce planning; with Visier, organizations can answer questions that shape business strategy, provide the impetus for taking action, and drive better business outcomes through workforce optimization. Visier has 11,000 customers in 75 countries, including enterprises like Adobe, BASF, Electronic Arts, McKesson, and Uber—you can learn more about Visier at visier.com. Degreed is the upskilling platform that connects learning to opportunities; they integrate everything people use to learn and build their careers—Skills, insights, LMSs, courses, videos, articles, and projects—and match everyone to growth opportunities that fit their unique Skills, roles, and goals. Learn more about the Degreed platform at Degreed.com, and thanks to both of our season sponsors.

Patrick Coolen:

So, it's a lot about at scale, personalize, and make sure it has value not only for the organization but also for the employees. And five years ahead, I don't have a clue on this. Welcome to the world of people analytics—maybe next year or in two years, we have different priorities. And that's why I emphasized the ability to reconfigure earlier in this conversation. That's also true for our own team; if we need to go on180 degrees somewhere else in another direction, we need to be able to do that.

Chris Pirie:

Our guest on this episode is Patrick Coolen: he’s the global head of people analytics, HR intelligence, and organizational design at the Dutch bank ABN AMRO. It's a global personal bank that's very much subject to the digital transformation of businesses everywhere. Patrick calls himself a true believer in making HR more evidence-based and data-savvy in order to improve people-related decision-making, and we had a fun open and very candid conversation about his and his team's efforts to apply a broad set of data and machine learning techniques to the concept of Skills in the quest to build what he calls Personalized Skills Analytics at Scale.

We covered a really broad range of topics in our conversation, during which Patrick codified many of the lessons that he's drawn from his experiments, including the critical importance of employee-centric mindsets, the value of getting started today with the data that you already have, the need to keep it business-centric, and importantly, not being afraid to ask for help. We talked about Skills gap analysis, the use of machine learning models to take on the structured data and gain insights on Skills on both the demand and supply side, how to teach data fluency using an Escape Room, and how a vintage Dutch soup commercial is an appropriate metaphor for the best approach to blending open-source data with home-cooked and analytical models and algorithms. Let's hear Stacia open this great conversation with Patrick Coolen.

Stacia Garr:

Patrick, welcome to Workplace Stories: thanks so much for your time today and for sharing your insights with our audience.

Patrick Coolen:

Thank you for having me—it’s a pleasure.

Stacia Garr:

We're so excited to have you! I think we mentioned in our pre-call that you are our first Dutch member, so we are excited to have you join us and bring your perspective to the conversation today. We're going to start with some quick questions to introduce you and your work practice to our listeners, and then Dani and Chris will go into deeper into some areas that we'd really like your perspective on. Let's just start with where you work; can you give us an overview of ABN AMRO?

Patrick Coolen:

Sure; I work for ABN AMRO, already for a fairly long time. ABN AMRO is a bank in the Netherlands, predominantly a European bank, but we follow our customers throughout the world as much as other organizations we’re impacted by digitalization, digitalization of work—that’s why we want to be a personal bank in a digital age. And of course, the specific Skills are very scarce today, for instance, digital and data Skills, analytical Skills, and well, we are struggling to recruit them purely outside the organization; so we're thinking more and more about re-skilling and making sure that we can sweet spot those areas in our organizations where we have a surplus of specific Skills so we can move them to another part of the organization. And it's also about care, I guess—not letting people go and making sure you did the utmost to relocate within the company if that's possible. And around 25,000 people work at ABN AMRO.

Stacia Garr:

Well, tell us a bit about yourself. You, as you just alluded to, have been an ABN AMRO for an impressively long time. So, what have you done there and how did you end up working in people analytics?

Patrick Coolen:

Yeah, I started working in IT, and I actually programmed in COBOL on our mainframe, so it’s that long that I've been with ABN AMRO. I started in '95, right after graduation at ABN AMRO. And I worked for four or five years in IT, and then moved into recruitment where I was responsible for hiring IT management trainees. But that's where I also started to learn ABN AMRO in a much broader perspective—I saw what divisions we have, what products, what clients. And in the end, I decided to stay in HR; I thought and I think I am making more impact by working from within human resources instead of implementing infrastructure or software, which is also very important of course, but it makes me more energetic and more fulfilling to work in human resources. And I did a lot of stuff within human resources: strategic projects, I set up a recruitment department and, in the end, I also joined the management team of HR. And that is where I started to get more curious, I guess, about a lot of things—for instance, why do we invest in leadership programs, which are expensive? How do we know what Skills we actually need, or what competencies we need? Do we recruit the right people? How can we help our business and our clients? And I always joke a little bit, I guess I asked too many questions about that, so I was kicked out—and I'm just kidding—but I started in 2014 in my current role, which is HR analytics, manager of HR analytics, dealing with those questions, trying to really understand what drives our workforce in being effective and successful. That's kind of an overview of 25 years, I guess.

Stacia Garr:

Well then, let's focus more specifically on your current role: can you tell us a bit about how you would describe the current role, the work you do, and what types of problems you're trying to solve right now?

Patrick Coolen:

Well, first of all, we started, really, with people analytics. We define people analytics as advanced analytics, so using statistics, machine learning, to understand our workforce and how it can benefit our organization. But it's not the only thing we do within our team; we also are involved with strategic workforce management, so doing a gap analysis on your workforce and articulating, together with the rest of HR, interventions to close the gap if there's one. We are very much involved in a survey management, dashboarding, and of course also a little bit of capability building; I think the overarching goal of my current role and our team is to make sure that HR is working in a more evidence-based or fact-based manner. So, the culture, the data-driven culture, in ABN AMRO is one of our targets, I guess.

Stacia Garr:

You've mentioned Skills or competencies a couple of times. Can you tell us how you came to start focusing on Skills?

Patrick Coolen:

Well, Skills have always been important. I mean, you need them for recruitment, you need them for learning for career development—not only from an organizational perspective, but also from an individual perspective. Me as a person, I want to know what I'm good at, and how can I improve my specific Skills, so it's beneficial in many perspectives, I guess. What is maybe a little bit new, although I think you have waves in the economy, I guess, but because of the digitalization I spoke about in the beginning, we need more specific Skills that are very scarce. We are having trouble recruiting all those high demand Skills that we need for banking in a digital age, so that is a problem. We cannot attract those Skills only from outside our company, so we're also thinking about re-skilling our people where possible. And the third thing we did and not my team, but another team in ABN AMRO, was identified together with the business what are the critical Skills? So, what are these critical skill clusters that we need to focus on? And I mentioned a few—digital and data, which is probably true for every company—and from there, we started to think about, okay, how can we capture these, and how can we make sure that people who are in a certain area of our organization who have specific Skills don't leave the company but are reskilled and brought into an area where we need those Skills. If that makes sense.

Stacia Garr:

What would you say is the most challenging aspect of the work you do right now?

Patrick Coolen:

A few things, I think. Well, besides technical stuff in analytics, working with fragmented data, incomplete data—that’s always the case, but that's doable—I think articulating the real problem specifically for larger questions we get, or larger research programs we have, what is really the thing people want to know? So, asking the ‘why’ question a few times is very important. And that’s challenging, that’s tricky, because sometimes people think they know what the problem is, and they know what the hypothesis is or the resource question they have. And it's very important to challenge that and to make sure it's articulated in the right way. Otherwise, we investigate stuff that is not important in the end. And so that's in the beginning of what we do; at the end of what we do, it’s important that people actually use our insights—and I'm, by the way, very happy with our internal customers using our insights, but it's not always a walk in the park. Sometimes you need to push a little bit, specifically on the long-term, mid-term and long-term, to make sure that they use their insights. And not only those that they like, but all the insights we provide. So that's another one. I already mentioned making HR more evidence-based, I think those are the most important challenges I have, and maybe the last one, and we will talk about it, I guess a little bit more later on, is creating analytics at scale, people analytics at scale, for our organization—bringing our insights, not only to PowerPoints but also to our employees, by for instance, providing recommendations for learning and vacancies. Those are just a few things.

Dani Johnson:

You mentioned earlier your focus on Skills, and how and why your company is focusing on them. One question that we're asking everybody within the season is, what does Skills mean to you? Because we get varying answers. So, I'd love to, I'd love to ask you that same question. What is a Skill?

Patrick Coolen:

Well, a Skill, for us, is related to knowledge competencies and the abilities of our employees. I guess it's just maybe new buzzwords. We always like to go back in history and find out where the other constructs are related to what they're talking about right now, but Skills is for us competencies, it’s knowledge, it’s abilities. And you also hear the division sometimes between hard Skills and soft Skills—hard Skills, being knowledge, soft Skills being the competencies and ability part of the story. That's just basically what it is. So, the talents of an employee, so to say, and those are very important to understand.

Dani Johnson:

It doesn't seem like your organization is really hung up on which term you use? You have a general idea of what it is?

Patrick Coolen:

Well in communication, yes, we’re using the term ‘Skills’ as well, but in the analytical part of it, we need to define it. So, that’s why I break it down in knowledge, competencies, and abilities, because we pretty much know how to measure them.

Dani Johnson:

That makes sense. You've mentioned the digitalization of work, and how your organization is moving forward, sort of thinking about the future. How do you think codifying Skills, or this focus on Skills, is going to help us in general kind of rethink the fundamentals of work?

Patrick Coolen:

That's a good question. I think that like I said, maybe we were a little bit Dutch or maybe down to Earth, so I'm not sure if it's really shaking the fundamentals of what we do, because like I said, we've been working with Skills or knowledge or competencies already for decades; the problem is though that it's really impacting the digitalization, our organization and organizational level as well. And because of the lack of data and insights, it's very difficult to find the sweet spots of where we have a surplus of talent, Skills, knowledge, whatever, or we have a lack/shortage of those. That's very difficult, so the operationalisation of how you work with Skills is more the problem and how you work with them, like we do in a machine learning way is, I think, a fundamental change compared to a few years ago.

Dani Johnson:

That makes a lot of sense. You mentioned earlier that you need Skills in order to hire the right people into the organization to determine what they need to know, to determine where they can go and all that kind of stuff, and that you, like the rest of the world, sees a talent shortage for some of those Skills. Are you rethinking maybe some fundamental things we've been dealing with for a century, like roles, for example, maybe we shouldn't hire for roles, maybe we should hire for Skills, you know, those types of things?

Patrick Coolen:

Yeah, we're making that shift as well, although we're not saying goodbye to job roles for a hundred percent. We rationalized our job descriptions—we had almost one per person that 10 years ago, I'm exaggerating a little bit, and recently we implemented a new job model, which is more like job families, which is across business lines, so that's very useful. That's already something where you can see where in your organization your Skills are appropriate to use, where you can go to. So that's already very useful in itself, and the Skills, I'm not going to say it's like a gig economy, so we're only looking for Skills, but it's more and more of a combination of role or a job family Skills in a project which determines what people are suitable for a job. And I personally believe that it's very important to, to make it scalable and transparent—so just bring it to the employee what your opportunities are, and then movement will happen. If you need a lot of communication around it, it's maybe too complex already.

Chris Pirie:

What's the role of people analytics in helping you understand the Skills that you have in your organization today? Do you, for example, work with other organizations and how do you collaborate?

Patrick Coolen:

Well, people analytics offers the opportunity, I guess, to use Skill data for all the models we have. That's maybe a little cryptical, let me explain; we can use skill data, in pretty much every research we do, and that can emphasize or debunk the importance of specific Skills in specific areas. I'm not sure if I'm completely clear here, but by using skill data, we help our organization understand what Skills are important in a specific situation. So that's just the analytics we do on a weekly or monthly basis. Another thing where people analytics can help is because skill data is fragmented in our organization—typically, we get our skill data from assessments or self-rating, or 360 instruments, and things like that—but it's not a full coverage for the organization. So not all employees have used such an instrument, and definitely not at the same time, so it's scattered over time as well. So that's a problem if you want to use that data for analyzing, for instance, what is the distribution of talents today for a whole company? And I think people analytics and machine learning can help, because machine learning offers the opportunity to use unstructured data, for instance vacancy data, job profile data, or data about what learning interventions people have and it can capture that and structurize it as a skill profile, which is immediately useful to create a distribution for your organization, and which is a good basis for vacancy recommendations for instance. I want to be clear, this is not in production for us yet, we’re currently working on it before people start asking.

Chris Pirie:

Can you send us the code? 😉

Patrick Coolen:

Exactly, how did we do it? To be honest, we just embarked on this mission, but it looks very promising to use machine learning to create structured data from unstructured skill data in this case.

Chris Pirie:

What are the sources—typical, and maybe not so typical?—of the data that you're using to feed your machine learning algorithms?

Patrick Coolen:

Well, it's structured data on one hand; although fragmented, you can still use it. And the things I mentioned, like job profile data—if somebody is hired for the vacancy, we can use vacancy text as well. We can use the learning interventions a person did. We can look at real-time career movements in our organization as well, to boost, for instance, recommendations; we just look for pretty much all the relevant information per employee, and what is there. We will use it, of course, definitely within the boundaries of GDPR and our privacy and legal regulations.

Dani Johnson:

I was just going to ask about that data that you just mentioned; that’s awesome, and we know from experience that most of that lives in different systems. How are you getting that information? Are you, do you have a data lake that you're consolidating that information in, or are you going after it one by one, depending on the question you have?

Patrick Coolen:

Well, we are working towards a, you can call it a data lake, where our commercial data, our HR data, and other data is consolidated in one environment. The technique is there, but the onboarding of all the data sets is not finished yet. So, we have some of our HR data in there, and a lot of it is still not in that environment; until that is the case, we still need to do the data management and pull the data out by ourselves, so to say, by using data management scripts, so it's all available for us—we can collect it—but it's not all consolidated yet in one data lake. So, we need to put some effort in that, but it's doable.

Chris Pirie:

In terms of this machine learning experiment that you're running, can I use that language, or this sort of effort? Where would you say you are in the journey?

Patrick Coolen:

Well, we're validating the structured data we get back from the machine learning models at this point, just to see if it's valid and if it's really usable. It doesn't need to be perfect by the way; I mean, it's already very nice if we can have a full coverage skill profile for the whole organization, but we're testing it, we're testing the validity of it. That’s where we currently are. What I didn't mention yet is that we're using the Emsi API for this as well, so we used them a little bit and we do a little bit of ourselves within the modelling. And if we are happy with the models and the outcomes, then we need to talk—we are already doing that—but then we need to talk with our HR and IT people to make sure that the intelligence is brought to production either in our intranet environment or within our learning system when I'm talking about recommendation of vacancies, for instance. So, we want to provide the top five vacancies, best match for vacancies for every employee, and maybe the top five best match of rescaling vacancies. And maybe in the future, the top five vacancies outside of our company that fits you most—that would also be very nice if we can do that, then help our employees with that.

Chris Pirie:

This is a really personalized scenario, isn't it, where people are actually getting, if I understand you correctly, recommendations for jobs that they might want to prepare for or apply for elsewhere in the company?

Patrick Coolen:

Yes, it's true. It is definitely personalization—that’s the only way it can work, otherwise we will provide them with the same five vacancies every month, and they will not use it anymore. So, it needs to be personalized, and the good thing with machine learning—the word ‘learning’ is already in there—you can improve the model and the accuracy if you will, of the model. So yeah, well, hopefully that will help our employees to find those jobs within our company or outside our company that really fit their talents or their Skills.

Stacia Garr:

Let's maybe transition to making this a little bit more concrete for some folks. In our pre-call, we talked about a few examples, and one was around managing Skills supply and demand. I know you've kind of touched on that at a high level, and obviously the Emsi data is labor market data. So, I imagine that's a part of this as well, but can you talk about why Skills supply and demand was an issue, especially for you all? And can you talk to us a bit about what you're doing about it?

Patrick Coolen:

Yeah, it's an issue for our organization, because of the fragmented data I talked about to get that organization-wide view of Skills. That's just a tricky thing. We cannot wait until everybody has done a self-skill rating in our learning system, which was implemented two months ago, which is beautiful by the way, but it takes some time for people to onboard, right? So it's a technical challenge we have, and our organization is really interested to see where our people are with specific Skills that we can move to another area. And if we can identify those people, we can communicate with them, do some marketing internally, or like I already mentioned, provide recommendations to them—but it starts with maybe creating a sort of a heat map of your organization or the sort of a heat map of a business line, where you can see red/amber/green specific Skills. Is it increasing? Is it decreasing over time?

That is already very helpful, so our first use case is exactly that to create heat match, which is a distribution of your Skills based on a machine learning model, to make sure that our HR experts have those heat maps with them if they talk to the business and talk about what is next and what is important in the future, and how do we look at this data and these heat maps—what does it tell us, and how can it help communicating through specific pockets in your organization to create a movement within our organization? So that's one, and the other use case, I think I already elaborated on that a little bit, is using the same skill profile, which is the basis for your heat maps, for learning recommendations, for vacancy recommendations, and bring that intelligence straight through the individual where he or she can use it. So that's the second use case.

Chris Pirie:

I'm interested in this heat map concept—really, really interesting! Does it have the demand side as well as the supply side? So, if I'm a line of business leader, am I seeing both the Skills that I currently have in my organization, and the Skills that I need right now?

Patrick Coolen:

That's a good question. Well, the heat maps I'm talking about is the actual data, that is the supply of talent in your organization, right? And on a continuous basis, our HR managers, other experts, talk to the business to find out what the situation should be. So, the targets on specific Skills or jobs within a certain area of the organization, that's a conversation that my HR colleagues have within the management teams—which is of course, very difficult or impossible to automate. But the outcome of that is something you compare with the heat maps, right? So, then you see if you put them on top of each other, you see a gap which you can color code again. So, you could cool code heat maps with increase/decrease of Skills, but you can also create heat maps with the gap compared to the target and organization part sets. Does that answer your question?

Chris Pirie:

Yeah—very, very cool. And I think what you said was future Skills requirements is a business planning function, and that's a motion that's already going on, and then using that against this data that you've gathered from individuals and a number of sources gives you that opportunity to do the gap.

Patrick Coolen:

Right, correct.

Stacia Garr:

My question was pretty similar, which was what other planning functions is this?

Patrick Coolen:

Then my answer is also similar. 😉

Stacia Garr:

What you guys were talking about to some extent is a little bit tactical, right? Like what are the gaps right now, and you can color code those: is this information being fed back into your strategic workforce planning function so you're looking at not, you know, 12 months down the road, but three years or five years, et cetera?

Patrick Coolen:

Good question and the answer is yes, but what I'm talking about now is the analysis—is trying to understand what is happening. And that is the basis to feed the different interventions you need to, whether in recruitment, whether in learning whether in the reward area or a succession planning. So, the normal HR services use these types of analysis where relevant.

Stacia Garr:

Can you talk to us a little bit about how you were helping translate that information for those different audiences, because we all know that there are different levels of data sophistication in different parts of HR? So how are you helping those folks understand what to do with this data as they're then helping the business?

Patrick Coolen:

From an analytical point of view, again, we are kind of in the middle of this exercise. But it's very important to think about how we visualize the insights we will get. So, I'm talking about heat maps, but maybe they're already too complex, I don't know. We'll have to see how we are going to visualize our insights on these specific-to-use cases in general, but it is less so in my department, we're thinking about PM experience that employees have related to Skills— they enter the organization and then they do an assessment, maybe because they are recruited and they're assessed; if they're hired, they encounter a learning environment, and they're asked to do some personal development. And in all those activities I just mentioned, there is a skill element. So, Skills are everywhere in the employee cycle, so we're trying to make it very intuitive for our employees to know where they should be if they want to work on Skills, or engagement, or vitality. But that is not something particularly where my department is in the lead. But the experience, the employee experience of our employee cycle is, of course, a very important view.

Stacia Garr:

You mentioned a few minutes ago that you all are working with Emsi as one technology. Can you talk to us in general about the technology that's underlying this effort? So how much of this is tech that you built, you and your team built? Are there external vendors you're relying on to help you with this?

Patrick Coolen:

No, it's us and the free-to-use Emsi API who are doing the work. So, in our pre-discussion I called it a little bit of us and a little bit of Maggi, which is a Dutch brand. It's used in soup. It's an old commercial. I forgot to mention that earlier, but so you can now bring up a picture of Maggi.

Chris Pirie:

Don't worry. We'll definitely link to that in the Show Notes. 😉

Patrick Coolen:

Excellent. I think it's three decades ago that I used Maggi by the way, but it's still an interesting metaphor, but we think it's important, where you can, to do it yourself. I'm not saying it's the only way. Definitely not; not everyone has the opportunity to do it yourself, but we're a little bit against what we call black-box analytics—so give something to your vendor, and they give something back and not fully understanding, maybe not on a code level, but understanding how things are created because there's, of course, I don't have to tell you, a lot of risk of biases as well in using algorithms. So, we prefer to create our models ourselves as much as possible, but well, and then intelligently, use some of the APIs that are out there, like Emsi.

Stacia Garr:

One of the concerns we've heard about is kind of the ability to scale, and you mentioned a few minutes ago about scaling analytics. Can you talk to us about, given that foundation or that approach, how you're thinking about scaling these insights across the organization?

Patrick Coolen:

Well, for every use case— let’s use learning interventions or sorry, learning recommendations or vacancy recommendations, or for instance, recommendations to managers, how they can work on the engagement of their teams, those are just a few use case examples related to recommendations—the modelling part, that's us. So, we think that is doable; if the outcomes are valid enough, right? If those are valid enough, we can, we are very confident to use them, but we are less experienced yet to bring it to what we call to production. So again, we have to talk to our HR/IT colleagues. And what system are we going to use? How are we going to show those insights? Is that–where in the employee cycle is that happening by the way, and which are our underlying systems that we do with, and I guess it can be your intranet page where we have a sort of a personalized environment already: that does make a lot of sense, but maybe it is the learning platform in some cases, or maybe your dashboard, maybe a power BI dashboard can be the case as well. So, depending on the use case and the audience you're trying to reach, well, you have to choose, so to say, an IT platform or software, your intelligence should land on. That's how you approach it.

Chris Pirie:

I was just thinking about your soup analogy— ‘a little bit, you a little bit Maggi.’ Do you think there's any element of where Skills would be competitive advantage for you, particularly your skill profile for the future might be something that you would want to keep in-house because it's such a competitive advantage? Have you thought that through at all?

Patrick Coolen:

Yes, that’s why you hear me only talk about digital and data, which is pretty much the same for all organizations, not so much about other areas. But I think it's also a competitive advantage, how you are able as an organization to reconfigure resources like Skills, not only Skills, but so the resource-based few, so to say, within organizations to you, the ability to allocate the right talents in our conversation at the right time, at the right place—that is crucial, and that's hard to copy for other organizations as well. So, I think it's not only determining the critical Skills, it is—the competitive advantage is also how agile and how quick you are in reconfiguring if needed. I think that's underestimated in many cases, I guess.

Chris Pirie:

Yeah, and a potential, massive value for valuable return for this kind of work.

Dani Johnson:

That also goes back to why you want to do it in house instead of cheating off your neighbor, so to speak, or using something that's commercially available, as far as those Skills profiles go.

Patrick Coolen:

That's definitely related. You have to create an organizational capability, so to say, that is hard to imitate for other organizations, and then you have competitive advantage. That's not only the case within people analytics, but in other organizational capabilities as well.

Chris Pirie:

I was going to go to what I think is kind of the next question in line, which is a little bit around the other organizations inside of the bank that are involved in this kind of work and what kind of collaboration do you do, which functions are important, legal workforce planning, learning functions, and what does the collaboration around this project look like?

Patrick Coolen:

Well, you mentioned one stakeholder that is always part of every project we do, and that is compliance and legal. I mean, regardless of the use case, it is extremely important that whatever we do meets the standards of regulations and maybe even more, ethics, as well. I mean, so that's a stakeholder that is very important. What we created in the past is a sort of a framework where we asked ourselves a few questions: what is the purpose of our research, and does that purpose allow us to capture specific sets of data? Is there a link between the purpose and the data we collect? Is it anonymous, and what is the security of the data where it's hosted and stuff like that—that’s all very important. If we ask those questions and the outcome is green, then we can go ahead. But if it's red or amber, we talk to our compliance officer or a legal officer and well, have a discussion if we should go along or not. That’s kind of how it works. And in case of doubts, we talk to them and we collaborate. With other stakeholders, I guess, it’s more use-case specific. For instance, vacancy recommendations are recruitment learning a little bit use cases that we haven't built yet. But if you want to build manager dashboards where you provide information and recommendation to how to improve your engagement, those are other stakeholders as well. So, I would say senior management, privacy compliance is always involved. And depending on the use case, you involve others where relevant.

Dani Johnson:

Early on you mentioned that one of the big challenges that you have is getting other parts of the organization to use the data and insights and not just the ones that they like, which I thought was an interesting framing of that, because we see that as well—a lot of organizations are a little bit afraid of, of the data, of figuring out what's actually going on. As you think about how you're working with these organizations that you just mentioned—recruitment and learning and other talent functions—how are you encouraging them to look at the data, to use that data, when maybe it's not the norm?

Patrick Coolen:

We do a lot of things because it is human behavior, right? It's very common when you see five insights and you like two of them, maybe it's not even deliberately that you say those three, I'm not going to put some action on it. It's not necessarily a deliberate action, it’s just human behavior, let's go with these two! But maybe in general, more broadly, we are trying to ensure a data-driven culture in our organization, by doing our job, reaching out to our process owners, reaching out to the management of HR or to management teams; we are building a dashboard, which includes some of our insights, which are available for the whole organization or specific groups. For instance, we created a dashboard with a lot of bubbles or spheres so to say, which represent topics people talk about. So, we have surveys with open questions, and we do a lot of topic detection on that. And the outcome of those models is represented in a bubble chart, a very easily readable bubble chart, which is available for the whole organization. And we also do capability building—once in a while, we do workshops with interactive cases, we have a fact-based Escape Room to teach critical thinking and how to handle and use data. So, we do some extra things on the top of it.

Dani Johnson:

We’re going to have to go back to the Escape Room! So talk to us about that.

Patrick Coolen:

Everybody wants to talk about the Escape Room. I want to talk about modelling. No, what do you want to know about the Escape Room?

Dani Johnson:

It is like a physical space?

Patrick Coolen:

Yeah, in the beginning it was. We developed it with Fresh Forces, a Dutch company. And if you're interested, they created a wide label version of it and it's online. So, it's digital. Sorry. It's digital. So, it's an e-facts based Escape Room, I think it's called Analysis Paralysis.

Dani Johnson:
And it just walks people through the Skills that they need to know in order to look at, like data?

Patrick Coolen:

Well, it's a little bit about critical thinking when you're using data—so how should you look at data, and when you shouldn't trust it and when should you use it? It's so it's definitely not a technical, data-science Escape Room; it’s much more for all the other people in HR who are working with data to give them some tips and tricks. And it's fun!

Stacia Garr:

It sounds like it. So, you have been working on this for a bit of time, and in this whole season, as we called The Skills Odyssey, with the implication that people are beginning this Odyssey, and we're talking to those who have at least made some progress on it. So, if you were to look across the work that you've done so far and give advice to others, what would be some of the critical lessons that you think you've learned to date?

Patrick Coolen:

Well, if I focus on the people analytics part of my team, I think it's already a few years ago, but I wrote like the 10 golden rules of people analytics, I'm not sure if you know it, but there are a few, a few lessons learned, I guess. First of all, it sounds all very complicated, but if you have people who know how to do this—I’m not a data scientist, I can’t create models, my people do, so it's my team who does the intelligent work—but you can start today: you can start small, you don't need perfect data. That also debunks, in my opinion. I can easily use thousands of records; if I still have 8,000 records, I can create models. The ironic part is that data quality is much more an issue for reporting than it is for analytics.

So, start today. If you have a survey, you can use it to do some analytics. You can have your first steps into the advanced analytics world and make it very business relevant, as soon as possible, so don't be bothered about HR topics like engagement, absenteeism, retention if they are not relevant for the business; if they are, of course, then you should. But it's much more in our case about client satisfaction, sales, quality of work. So make it business-relevant, make it small, and don't be afraid to use advanced analytics. And ask for help if you need help, PhDs universities, venders, they can all help you out if you don't have to data scientists from the beginning within your company. These are just a few, I'm sure I miss a lot of the best practices.

Chris Pirie:

That's really great advice, and we'll definitely link to your golden rules in the show notes for sure —as well as the soup commercial!

Patrick Coolen:

Thanks! And I just recently posted another one, The 8 Big Tickets for People Analytics. Maybe that's also useful to include it's a little bit about not specifically Skills, but in a broader perspective, what are we looking for in the next year or two related to people analytics.

Stacia Garr:

Okay. So, I know we're coming up on the end of our time here, so let's maybe wrap up with a few closing questions. You are where you are right now in the Odyssey, but if you were to look forward to, let's say five years from now, what do you see is the future? What do you think that we'll be talking about five years from now that we're not talking about today?

Patrick Coolen:

Well, I'm not sure if there's something new, but maybe I want to emphasize some of the things. What we will be really working on is personalized analytics at scale, and what we like to call analytics for value—so really understand on which projects we're going to allocate our resources for. So those are things that are getting more and more important, and transparency in what we do and make sure that the intelligence we have is brought to employees and the organization and not only to management teams. So, it's a lot about at scale, personalize and make sure it has value not only for the organization, but also for the employees. Five years ahead? I don't have a clue, honestly, welcome to the world of people analytics. Maybe next year or in two years, we have different priorities. And that's why I emphasized the ability to reconfigure earlier in this conversation. That's also true for our own team. If we need to go 180 degrees somewhere else in another direction, we need to be able to do that; if we need to step up in psychometrics and survey management, we need to be able to do that if that's the question within our organization. So, five years ahead, is uh … uh … I couldn't tell!

Chris Pirie:

Point well taken.

Stacia Garr:

We’d like to give you a chance to share your preferred way for people to connect with you and your work?

Patrick Coolen:

Yeah, LinkedIn is definitely the best option: feel free to contact me on LinkedIn, if you need any additional information for your listeners. That's perfectly fine.

Stacia Garr:

The final question, which we ask pretty much every guest that comes on, is what we call the purpose question, which is why do you do the work that you do? What is it that motivates you?

Patrick Coolen:

Well, I do the work I do because on a daily basis I work with talented people in my team who are even more curious than I am, and that's why I keep learning every day. So that keeps me motivated.

Chris Pirie:

Back to that agility as well, personally, and organizationally, and at the team level, I heard that sort of idea that agility is a real asset.

Stacia Garr:

Well, Patrick, thank you so much for your time today: we have really enjoyed this discussion, and appreciate your generosity.

Patrick Coolen:

Thank you for having me.

Chris Pirie:

And good luck!

Patrick Coolen:

Thank you. I need it. 😉

Chris Pirie:

We all do!

Chris Pirie:

Thanks for listening to Workplace Stories; it’s a podcast brought to you by RedThread research. If you'd like to stay updated on our research and insights into people, practices, including our latest studies on the Skills and analytics that organizations need to foster a more inclusive workplace, then simply sign up for our weekly newsletter at RedThreadResearch.com. You'll hear about our latest research and find all the ways that you can participate in our round table discussions, Q&A calls, and surveys right from your inbox—it’s a great way to share your opinions about everything from DEIB to people analytics, from learning and Skills to performance management and leadership, and you can also meet and exchange ideas with your peers in the industry.

As always, thanks so much to our guests, to our sponsors—and to you, our listeners.

Dani Johnson

Dani is Co-founder and Principal Analyst for RedThread Research. She has spent the majority of her career writing about, conducting research in, and consulting on human capital practices and technology. Her ideas can be found in publications such as Wall Street Journal, CLO Magazine, HR Magazine, and Employment Relations. Dani holds an MBA and an MS and BS in Mechanical Engineering from BYU.

Stacia Garr
Co-Founder & Principal Analyst

Stacia is a Co-founder and Principal Analyst for RedThread Research and focuses on employee engagement/experience, leadership, DE&I, people analytics, and HR technology. A frequent speaker and writer, her work has been featured in Fortune, Forbes, The New York Times, and The Wall Street Journal as well as in numerous HR trade publications. She has been listed as a Top 100 influencer in HR Technology and in D&I. Stacia has an MBA from the University of California, Berkeley, and a master’s degree from the London School of Economics.

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