Workplace Stories Season 3, Skills Odyssey: Bringing Skills to Life in the Workplace

Guest

Heather Whiteman, Assistant Teaching Professor in Data Design and Development for a More Just World, University of Washington

DETAILS

Is today’s guest the epitome of a people analytics scholar practitioner? Well, let’s do the math: relevant PhD? Check. Six years figuring out how to interest Silicon Valley engineers to come work for an industrial firm by drawing up a whole new company-wide Skills matrix that actually reflected what needed to be done? Check. Working in academic contexts persuading quants that while data and machine learning are great, it’s those human skills that will actually help them most? Check. And (how awesome is this!) working out how to use data to build a more just world? Check! We’re so happy to finally get self-styled People Data Enthusiast Heather Whiteman on the show, now that she’s at last fully unpacked in her new Seattle base (we tried for the first Skills season). It was for sure worth the wait. We get not just detail on practical ways to make Skills frameworks deliver but also the message that people analytics and data aren’t to predict the future—they’re to change it. Monkey psychology’s loss is definitely our gain.

Resources

• As mentioned in the episode, Heather has twin academic day-job roles: one, as Assistant Teaching Professor in Data, Design and Development for a More Just World at the University of Washington’s Information School (iSchool); the other as People Analytics Lecturer at UC Berkeley’s Haas School of Business. She also discusses her time building a Skills Curriculum in her previous six years at GE Digital.
• Heather is happy to make connections and drive the conversation though LinkedIn (as her commitments allow, obviously).
• We’d 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.
• Find out more about our Workplace Stories podcast helpmate and facilitator Chris Pirie and his work here.

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

Five Key Quotes:

I actually started out in monkey psychology and I ended up in people analytics, not just because the ability to train monkeys does wonders for your ability to train corporate executives, but because I came across this study where they were using an analysis that I was trying to figure out, and all I could find was this business psychology article that used statistics to show how people had been biased against certain individuals for hiring. And I just had this moment where everything clicked; I was like, wait, we can use data to prove that people suck and then fix it and then make them be better? Like, that's the coolest thing I've ever heard! Sorry, monkeys!

It wasn't until I was working at GE that I built this predictive model and I was so proud of it. I'm a little embarrassed to say, I was just like, look at me, I'm so special; I made this great model! And I had an amazing R-squared value, the predictability was great, and it performed very, very well. And after I was done patting myself on the back, I then realized how terrible it all was, because here I was bragging about how strong my model was: my model was perfectly predicting what was going to happen, and I didn't do a thing about it. And I just had this sinking feeling that this is not the point of people's analytics—it’s not to predict the future and then just watch it happen and pat ourselves on the back; it's to change the future, make it different, make it better, make it the way we want to be. So I realized that data on its own, even great models and algorithms and the like just don't matter, it's what you do with it. And so I was predicting attrition and I realized why weren't we doing knowledge transfer, why weren't we connecting different people, why weren't we putting it together? And those activities were happening, just not based on the data that I had been working. So that was the moment where I realized whoa, analytics by itself nothing really there—but analytics for a purpose, that’s where it connects.

I know it's hard—but it's also, hopefully for some people listening, it's kind of what makes it fun, makes it exciting. It's what makes it difficult. If it was easy, we'd just go do dollars and go join Finance, come on. Let's have a little bit of fun with it!

I’m teaching on MBA programs, I'm also teaching in information programs, so I'm typically spending a lot of my time with individuals who have very strong business Skills or very strong technical Skills. And I end up telling them that contrary to what you read, the hottest Skills are the uniquely human Skills, not how good of a data scientist you are.

Chris Pirie:

Welcome, or welcome back, to Workplace Stories, brought to you by RedThread Research, where we look for the ‘RedThread’ 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, and 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 program strategies and experiments.

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 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.

Stacia Garr:

In this episode we talk to Dr. Heather Whiteman, who is Assistant Teaching Professor in Data Design and Development for a More Just World at the University of Washington; she’s also a people analytics lecturer at the Haas School of Business at UC Berkeley (Go Bears!) and a Fulbright scholar. In addition to her academic background, Heather was Vice-President Global Head of People Strategy at GE Digital for six years, and has worked as a people analytics practitioner for approximately 15 years. In her corporate work, Heather had the opportunity to activate the insight she learned from her PhD, which was on the importance of Skills and capabilities in predicting performance; she is the epitome of a people analytics scholar practitioner. In this conversation, we talk to Heather about how she defines Skills, but also about why having a specific definition of Skills doesn't really matter. We discussed how codifying Skills can help us rethink the fundamentals of work ,and why that is so important to Diversity, Equity, inclusion, and Belonging.

Drawing on her practitioner days, Heather shares one of the most powerful, real-life examples we've heard yet of what Skills can enable in organizations; after listening to Heather, you will know how she and her team built their Skills taxonomy; you'll understand who was involved and in what ways; you’ll have real life examples of how Skills can solve hard business problems, such as how to create clearer career opportunities for employees, and how to understand what type of talent companies should build, buy, or borrow; and you'll also have a peek into the future of Skills.

Ultimately, we came away from this conversation with a deep appreciation for the work, but also the reward that can come from really bringing Skills to life in an organization. Let us begin with this first story of one person's Skills Odyssey.

Stacia Garr:

Hi Heather, and welcome to Workplace Stories; thanks so much for your time, and for sharing your insights with our audience today.

Heather Whiteman:

Thank you so much for having me; I’m excited to be here!

Stacia Garr:

Well, we are just thrilled: for our audience members, we had asked Heather to come to our last season, and we had some scheduling conflicts, so it didn't work, but we are so excited to have you with us today.

Heather Whiteman:

The show’s last seasons were fantastic, so I am stoked to be part of this next season.

Stacia Garr:

Well, we're going to start off with some quick questions, and this will allow you to introduce yourself and your work practice to our listeners, and then Dani and Chris are also going to get in deeper with some additional questions as we go through. So let's start off just right off the bat: can you tell us a bit about yourself, and how did you end up working on ‘Data, Design And Development For A More Just World’? I am so excited about this title, so tell us about it!

Heather Whiteman:

Yeah, so that is my new title. I am just now, in fact, literally this week, kicking off a new role as a teaching professor of Data, Design And Delivery For A More Just World at the University of Washington in their information school.

Chris Pirie:

Go Huskies!

Heather Whiteman:

Yeah, Go Huskies! I just relocated to the Seattle area for the position. This was sort of my dream position; so one thing about me, I was doing people analytics in some form or fashion for about 15 years, but I found people analytics through this idea of ‘we can use data to make the world a better place.’ And some people know about me, I actually started out in monkey psychology, and I ended up in people analytics, not just because the ability to train monkeys does wonders for your ability to train corporate executives, but because I came across this study where they were using an analysis that I was trying to figure out, and all I could find was this business psychology article that used statistics to show how people had been biased against certain individuals for hiring. And I just had this moment where everything clicked; I was like, wait, we can use data to prove that people suck and then fix it and then make them be better? Like, that's the coolest thing I've ever heard! Sorry, monkeys! I switched careers literally overnight. So I ended up going into industrial organizational psychology, I ended up consulting in the equal employment, affirmative action field, and that was where I got my first taste of people analytics. And then, you know, life happened: I took new roles; I did workforce planning; I did talent management; I did Learning and development, HR technology, all these things. And all the while going to school—I got my PhD in human capital management and teaching on the side—I had this moment where I was falling in love with teaching, and I wanted to get back to why I started doing people analytics in the first place, which was, I want us to be able to use data, technology, analytics, to make the world a better place. And I just fundamentally believe that the workplace has the opportunity to give economic opportunities, insights, it can have impacts for generations to come: somebody's ability to have a fair shot at a well-established career and position can make a difference for so many people. And so I just wanted to get back to the art of how do we teach people to build and to use data for a more just world.

Stacia Garr:

That's wonderful. Well, I don't think we've previously had anybody on the podcast who used to train monkeys and this is an amazing first, but I do not want to let that comment about U-DuB go without a response. You also teach at UC Berkeley Haas, correct?

Heather Whiteman:

I do!

Chris Pirie:

Go Bears!

Heather Whiteman:

No, I was going to say, leave it to the Haasi here to bring it up! So I know you, Stacia, are an alum. So I love the new position at University of Washington. I also teach people analytics at UC Berkeley Haas and that's because in addition to teaching people how to design and develop things for a more just world, I wanted to stay very true to my passion area, which is people data and how do we incorporate that into hopefully the skill set of all of our future leaders who are learning about ways to drive it. So people analytics at UC Berkeley, Data Design and Delivery for a More Just World at University of Washington. And then I am also a Fulbright Scholar and we'll be bringing analytics and people data to the Universidad Francisco Marroquín in Guatemala, and so I will be doing that over the next couple of years just while I'm at it. I've done some work with Hull University in the UK. I’ve done some work with some other California universities as well, but I'm kind of all over the place in a ' follow your passion’ good kind of way.

Stacia Garr:

That’s wonderful. I think we've spent a lot of time talking about people's data for good and think you're doing an amazing job of epitomizing that in how you're spending your days. So we talked before about how you had, as you said, spent 15 years focused on people analytics, and then you kind of had an ‘aha’ moment to focus on this topic of Skills. Can you tell us a little bit about that transition for you?

Heather Whiteman:

Yeah, my Skills journey didn't come automatically; I did fall into the idea of people analytics, I did fall into the idea of people analytics for good. And I spent, I don't know, a good while, 5, 6, 7 years, of really just looking at numbers as numbers and trying to apply them about people. And it wasn't until I was working at GE (General Electric) that I built this predictive model and I was so proud of it. Like I'm a little embarrassed to say, I was just like, look at me, I'm so special; I made this great model! And I had an amazing R-squared value, the predictability was great, and it performed very, very well. And after I was done patting myself on the back, I then realized how terrible it all was, because here I was bragging about how strong my model was, my model was perfectly predicting what was going to happen, and I didn't do a thing about it. And I just had this sinking feeling of 'this is not the point of people's analytics’—it’s not to predict the future and then just watch it happen and pat ourselves on the back; it's to change the future, make it different, make it better, make it the way we want to be. So I realized that data on its own, even great models and algorithms and the like, just don't matter, it's what you do with it. And so I was predicting attrition and I realized why weren't we doing knowledge transfer? Why weren't we connecting different people? Why weren't we putting it together? And those activities were happening, just not based on the data that I had been working. So that was the moment where I realized whoa, analytics by itself, nothing really there, but analytics for a purpose, that’s where it connects.

Up until that point, all of my data had been very much on whole people—how many humans, how many jobs, how many locations, what quantity, where. And I realized that wasn't getting the job done in terms of how we could effectively plan and we needed a more common denominator around what is it that we're really trying to do? We're not trying to place people; we’re trying to have the right Skills connected to the right jobs, in the right place. That was the moment that I realized that it needs to be people analytics for purpose, and that we needed a common denominator from which we could have rich discussions around where people could develop, where they brought strengths, where we could connect it. So that's really what kind of had the shift; I went overnight from counting humans to qualifying Skills, capabilities, and different aspects of what was needed to get the business results.

Stacia Garr:

I love that you pull out the common denominator component; we’ve talked a lot about that in the previous season, so that language has real resonance. Obviously, that is not easy to do. Otherwise, we all would've done this potentially a long time ago: when you think about Skills and also more broadly, this work, this, this purpose-driven work around a more just world, what do you see as being kind of the most challenging aspect of your work?

Heather Whiteman:

I think the most challenging is that, as you say, it's that difficultly to do it. So I know many people run up against the issue of ‘but there's just too much, there's too much to measure, there’s too much to follow, there's too much to keep up with.’ It changes. So our most important skill today is not the most important skill tomorrow. And also, Skills are both transferable and also they wear out over time; there are a lot of moving parts when it comes to something like Skills. So having an objective-criterion-based way to measure them, talk about them, but also approach change over time tends to be the most challenging, but, is in my opinion, one of the only ways to get at my own personal purpose, which is fair—having the opportunity to go after a well-designed, well-balanced workforce in a fair process. Because, when we introduce something like the Skills necessary to do the job, that's when we can start to really get at some of those root problems around bias and preference and ‘people who are like this’ have always been the ones that we've put into those type of roles, because they're the type of people who do those roles, right? And it's like, no, the people who have these Skills are the types of people who do it, let's look for the Skills. So it's one of the hardest things to measure. I also think it's why we haven't gotten as far as we might want in certain DEIJB areas is because we haven't really gone at it from a common denominator perspective; we’ve still been thinking of it as a person with these demographic characteristics as opposed to building our workforce based on Skills and really letting the Skills get at that.

So I know it's hard. I know, finding what are the Skills we need, how do we measure them? How do we take care of it as they change over time is the most difficult part—but it's also, hopefully for some people listening, it's kind of what makes it fun, makes it exciting. It's what makes it difficult. If it was easy, we'd just go do dollars and go join Finance, come on. Like, let's have a little bit of fun with it.

Stacia Garr:

I love that. I think that point about Skills and the different types; previous guests talked about them as durable and perishable Skills, and I think that's we've kind of adopted some of that language and I think that's good language. But you bring up an interesting point, which is that some of those durable Skills are transferable in some instances, but not in others, and so that adds to the overall complexity.

Heather Whiteman:

I like to borrow the term from chemistry, which is, I always think about the half-life of Skills. And there are certain things that can prolong or deteriorate the half-life of Skills. That has a lot to do with the kind of organization you're in, the ways you bolster them with other Skills. But we're definitely seeing that with digital transformation; certain Skills are getting shorter half-lives, more human Skills are actually getting longer half-lives. It Is really interesting as we watch transformations happen in organizations, because what's transferable and what's diminishing has a lot to do with context.

Chris Pirie:

Well, let's talk about context and the context—that’s a good transition there. We're calling this season The Skills Odyssey, because we love Greek poems, and the point is that this is a journey that we're all on around Skills, and that there'll be Sirens and Multiheaded Sea Monsters, and a lot of blood basically. But one thing that I think is making it particularly challenging right now to get on this journey is definitions and language and words and agreement on what pieces are what. So there's a lot of terms out there: Skills, capabilities, competencies, experiences, aptitudes. You, from our conversations before I know, are very thoughtful about the language that you use, so could you walk us through the terminology that you use and tell us what the term Skills means for you, and how you define it?

Heather Whiteman:

Skills is a really difficult topic to address, because there is so much contention over what the differences mean. Some of those differences are important, some aren't: when I think of something like Skills, I go back to the purposeful analytics, what is it that we're trying to accomplish? What are we trying to do? Especially in an organizational context, our goal isn't necessarily to get in a fight over the difference between the term Skills or competencies. And Chris, I bet you've seen otherwise.

Chris Pirie:

You know there are people who will die on that hill. But that's their loss, in my opinion.

Heather Whiteman:

And there's a good reason for it—meaning clarity is important, clarity of what we mean, what we're trying to accomplish, where we're trying to get. I personally have a preference towards words like ‘capabilities’ and even Skills, mostly for that because of what it implies. So while ‘competencies’ is the most commonly used academic term, and I will use it if I'm writing a very academic journal article, it doesn't connote what I think we all want, which is this opportunity for individuals to express their Skills and to show their abilities and the possibility of coming out with it. So I think that you cannot be capable without Skills; I think Skills are what make you capable. I will say that the reason I like capabilities is because I believe you can be skilled and not actually do anything with those Skills, and capability adds in that ability level, but depending on when you're starting from, it is impossible to achieve what you're trying to do without any Skills.

So, Skills to me are a fundamental aspect of being capable. And ‘capable’ implies more than competence; I would much rather be called ‘capable’ than ‘competent.’ Like there's just a kind of an emotional reaction to those words, and then how do you build them? How do you show it? So Skills are built by experience. They can also be built through training and knowledge and then applied through experience. Aptitude: I genuinely believe aptitude can be taught, can be built, that it's a muscle; then aptitude is where you're able to really express yourself from a capabilities perspective. Others will disagree or want to fight. Others will say there's a hierarchy, one is a subset of the other and things of that nature, but it all comes down to why we want to use it—what’s our purposeful reason for talking about it, for covering it, for getting into it? And I try to really direct the conversation back to that whenever I can.

Dani Johnson:

Just going to say, then you're going to love the next question, which is a question that we all hate, but I'm going to ask you anyway: what Skills are hot right now?

Heather Whiteman:

Whether or not they're hot, I don't know, but, as Stacia mentioned, I’m teaching on MBA programs, I'm also teaching in information programs, so I'm typically spending a lot of my time with individuals who have very strong business Skills or very strong technical Skills, and I end up telling them that right now, contrary to what you read and a bunch of articles and magazines, the hottest Skills are the uniquely human Skills. They are not how good of a data scientist you are. Don't get me wrong. I teach data science concepts and those are important, but as technology continues to advance, a handful of specialists can continue the work on technologically advanced wins. We need uniquely human individuals who can continue to problem solve, be creative, be connectors, be the innovative ideas who can pick up the technological advances and then implement them or apply them in new and unique ways, and the new unique ways is something that an algorithm cannot do for us. So it's those uniquely human Skills. And unfortunately, I meet a lot of students, even very advanced in their careers and lives, who always feel like their imposter syndrome is flaring up because they don't have these deep technical Skills. They're like, I don't know how to do machine learning, and I'm like, it's cool. Like, don't get me wrong. You can study, you should probably build your Skills, but if you had to pick between two things, uniquely human will get you further, because it'll let you have novel ways of applying the deep technological things along the way. So my idea of hot Skills are definitely more on the human side than the tech side, and the tech being something that you can add to your toolkit.

Chris Pirie:

You're talking a little bit about creativity and innovation there as being high value things that it's a little bit harder to expect a machine to do.

Heather Whiteman:

Things like systems thinking, which is how do these machines even connect, so could I pick up a platform that somebody else built for a different industry for different applications and apply it? We see it a lot with experts in our areas where we don't have to be the ones who created that matching algorithm to pick it up and apply it for employees looking for career development opportunities—that system, that's new approaches to things that already exist and applying them and I think that's where we can get a lot of value.

Dani Johnson:

You mentioned earlier this idea of context, and I keep on bumping up against that when I'm talking to leaders in this Skills research, actually, because what a project manager is in one area of the organization may not be a proto-project manager and another, which is another reason that we should not focus on roles, but on Skills themselves. But I would love for you to dive just a little bit deeper on that idea of context and what you meant by contextual.

Heather Whiteman:

It's a great question. When I think about something like Skills, first of all, the how we measure it is incredibly important; I am a believer that it means to be objective, it means to be behaviorally-based. We need to be able to identify and quantify, and that quantification can still be qualitative in nature, but we need that objective-criterion-based approach. And when defining the objective-criterion-based approach, that's where context comes into play. So that would be, this is what a project manager at this company at this time in our lives looks like and the Skills that it comprises. I am a huge baseball fan, and I know this analogy comes up a lot, but it is the Moneyball analogy. For anyone who hasn't seen, it's a movie (it’s also a better book), but it is this notion that if you design a team off of what winning means to you, you don't have to go after like star position players, meaning job role, job role, job role—you can go after the Skills that get you what you're really going for. In the baseball case, it was runs, it was looking to get people on base and moving them along. So we can take the context of what does success in project management, to use your example, looks like for our company: that becomes the objective descriptor, and then we define our Skills based off of that and measure them and really aim for it. That context can change. A project management role in a very small tech startup is going to look fundamentally different than project management in a large-scale manufacturing, multi-country type of establishment. And so if we were to just pick up a generic project management skill set, it's probably going to fit neither. And so we really do have to think about it in context.

Dani Johnson:

That sounds hard. It means we can't just take the Skills, taxonomies and ontologies that exist and plug them into our organizations.

Heather Whiteman:

It is hard! I laugh because these Skills matrices and models and all of these, I mean, they've been around longer than I have; they’ve been on Earth longer than I've been alive, so it's not that they don't exist. And if they all by themselves were all it took, we wouldn't even be having this conversation. So it's not that you just need a, let's pick up the project management skill set list, and we're ready to go. There's a reason that hasn't worked, and it’s because that doesn't work. You need to really think about what's success, and then fit it to that.

Dani Johnson:

So let me ask you a follow-up question there, because one of the complaints that I'm hearing from people trying to put one of these in place in their organizations is that by the time we get it built, it's already outdated. But what would you say to those people?

Heather Whiteman:

Oh, I don't know what I would say to them. I would say, but I could share what I would do. So I have lived through a couple of versions of trying to implement something along the lines: I have started from a framework of values for whomever I was working with saying, look, if we have to lay out the criteria for success on this, what would it be? And I don't mean success on Skills, I mean, of this activity and say, speed, validity, meaning are we actually doing what we think we're doing, and cultural appropriateness. What I mean by that is if you pick something up off of a shelf somewhere, it may or may not fit well with the words and the feeling and the culture in which you're trying to plug it in. And to me, that's usually what kills it, not how long it takes: it’s usually it took you a long time and when you rolled it out, it didn't click with anybody, so nobody wanted to use it. So you kind of balance. How quick do we need it? How strong does it need to be? But more importantly also, how do we get it absorbed into the culture in a way that it can be integrated? So I've built my own, I’ve definitely pulled from off the shelf, I’ve facilitated others building, and I think my personal preference right now, again, this is Heather's feelings only, people can take what they want from it. I'm now sort of a fan of grabbing from a pre-existing well-validated set of Skills, but to undertake lots of in-company assessments and checks to bring in the cultural, behavioral and well-fit. So I might, for example, get a skill set off of a shelf that's got hundreds, chop out all but the ones that look right, and then actually go through and modify them to fit cultures.

Chris Pirie:

And tune it up.

Heather Whiteman:

Yeah, and add in the cultural values and add in the pieces. That may not be what everybody wants to do, but I've found that it speeds things up while keeping it unique to the organization and where possible all of that work being done, not by somebody in HR—that’s usually my number one recommendation. If the HR person does it, it's not probably the best use of anyone's time. Anyway, nobody's going to look at it. But if you could get, in our example, the project managers to be the ones who pick them, the project managers to be the ones to edit them, the project managers to be the ones to test them out, you might get a little further along.

Chris Pirie:

That's the culture bit, right?

Heather Whiteman:

Yeah.

Dani Johnson:

So one more question from me for now: in organizations, we mostly see HR, it's usually HR, leaders trying to codify the Skills that are associated with a role, so what they want is a better job description. You're talking about this completely differently, so I'd love to understand, how do you think that codifying the Skills the way that you're talking about might actually change the way that we work?

Heather Whiteman:

I've had the luxury of starting out a new business where there were no job descriptions, and we were starting completely from scratch. We made a decision not to build any job descriptions until we were done defining what the most important Skills were. And it was hard, because I was working in a very established organization; how do you post a job without a job description? How do you meet all of your compliance regulations and other things along there? And it was one of those ‘look, we haven't been doing it perfectly up until now; we’ll keep kind of working as we need to, but let's really say what is the most critical skill set to do this work—let’s get that down,’ and that can also be the differentiator. So rather than posting a job for an engineer that looks exactly like every other engineer job posting ever, let's identify what Skills, capabilities, are most critical for an engineer at our company in this particular division, and then that becomes what we're actually sourcing for candidates on—that becomes what we're actually asking interview questions about instead of basing who joins our organization off of some cookie-cutter template that doesn't apply to what we're trying to do.

I think just by starting from Skills, we could change the way that we are asking people to do work. We're saying, look, you don't have to be a carbon copy of every engineer who ever came before: you can be the engineer who brings the most critical Skills to our organization, and really brings them front and center to who you are. And that critical skill-bringing piece too, I think, is what opens us up to more Diversity and applicants and employees and connections, because we aren't looking for copies and copies of what used to be, we’re talking about what we need in the future.

Dani Johnson:

You're rocking the boat, Heather! We have centuries and centuries of momentum behind us to do the things the way that we're doing them now. You’ve had that experience; how difficult was it to post a job for Skills versus posting a job for title or whatever else?

Heather Whiteman:

I guess one thing I should say is, it's not that you don't post the other things as well—like good luck trying to post a job to an external job board without a title, you can’t. And we do still need to have fair, equitable compensation practices, depending on your regulatory standing, you have to have a certain grading and set up, so it's not that you can skip all of those, not unless you’re looking forward to a little bit of fun with the legal team. But so it's not to say that you skip those, it's to say that if you take the time to define what success is, those can be your higher-order aspects, whereas the others become sort of the standard. So sure, in certain fields, there are legit certifications you need, it's not to say that we wouldn't still need those certifications, it’s to say that those certifications become entry and the Skills that our organization most needs becomes what puts you over and above.

And that does flip the model. It means that it's not just the people who've managed to amass all of these little letters after their signature; it’s the people who have the Skills we need, and have the required letters after their signature. So it's not to say that we throw out the rest, and we don't do that, it’s to say that that should be the, those are just the i’s that you dot and the t’s that you cross at the end. They aren't the starting point, and if you write your job descriptions starting from necessary Skills first, and then just fill that in, you'd be surprised how different they read.

Stacia Garr:

So let's dive in more on your experience, because this is one of the things that we are so excited to talk to you about. So you were a practitioner, as you mentioned, you were working at General Electric and you built a really robust Skills infrastructure platform and analytics capability. Let's start by understanding what problem you were trying to solve when you did that work.

Heather Whiteman:

Yeah, so we had a lot of fun. So I worked with great people, a great team really standing up the GE Digital business. The first problem was we were trying to become a software company in, at the time, 125-year-old industrial, and we plopped ourselves down in the Silicon Valley, and we had this funny situation where we were like, Hey, you ought to come do software, and some of the responses from potential candidates was don't you build refrigerators? Why are you calling me? And we're like, whoa, Hey God, there's a lot more going on than that. So we were trying to establish a brand in a place that we didn't really have a brand; we were competing against top tech firms who had, quite frankly, gobbled up most of the top talent in the technical space around there. We came at this problem with a different way of thinking, which is, ‘look, we don't need to be a handheld-electronics competitor, like with certain phones that start with a little letter.’ Like, that's not what we were trying to be anyway: we weren't trying to tell you what movie to watch next; we weren’t trying to connect you with your long-lost friend from high school that you didn't actually even talk to in high school anyway. And so we were like, ‘well, why would we try to copy that—why don't we decide what is digital for an industrial company?’ And that was the first approach really into Skills, which is let’s not carbon copy what a software engineer looks like for Apple, Facebook, Google; let's determine what Skills would a truly unique industrial digital person need and bring to the table, and let's lay those out. And that let us look at people in a very different way. We weren't just trying to find those senior software engineers who all worked somewhere else already; we were trying to find the people with that unique twist on the Skillset. And we really did take a Skills-based approach to basically do 10x hiring, from about 250 people to about 2000 within a year. That was all based on the idea of, we could use skill-based analytics to look for individuals who brought some very specific Skills—maybe they didn't have everything that a different company would look for in a software engineer, but they had our five most important things, and how do we look for those individuals and bring them in? And by doing that, we weren't hitting the same people who already had six offers from everyone else; we were hitting our own unique pool of diverse candidates, and people who came from different areas. We then kind of expanded strategies to different places, but it was having that Skills approach and being able to go back to it. So we could say, wow, we already have tons of that kind of talent, we don't have any of this kind of talent—how do we maybe attract more from there?

And because we kept tracking who we brought in, what Skills they contributed to the team, we could keep looking for gaps and then do some targeted filling there. Or in one case we weren't able to do hiring-based filling, so we built development programs that were specifically designed to the Skills that we were lacking and not able to hire, and it gave us this opportunity to say, that's a skill that's too hard to hire, so let's build our own. Or that's a skill that's too expensive to build on our own, so let's go higher. And we can look at the strategy at the same time. So that's kind of what Skills enabled us to do. If you take a very job posting-based approach, you may never get to.

Chris Pirie:

Can I just ask a little bit there: can you give us an example? I'm really interested in the level of granularity that you went to, because I think that's where we learned this in some other conversations it's where people can get really bogged down. I know at Microsoft, for example, we had job roles with hundreds of Skills and we really struggled to just find the sweet spot, especially when it came to recruiting, for example, in terms of which of the Skills that matter—back to your context comment again. Can you give us an example?

Heather Whiteman:

The model, meaning the list of Skills that was used was proprietary, so I can't give very, very specifics, but that being said, it was ballpark of about 200 across multiple functional areas, intentionally allowing them to cross, meaning certain Skills do exist across multiple job levels, and those became our key transferable Skills. Level of granularity was very much at both employee levels, but again, there was very strong sensitivity, and so nothing was mandated or pushed on employees, and it took a very big cultural movement actually to get us where we needed to be. But what we were able to do with that is actually have opportunities for looking at complementary gaps across teams. For example, I remember working with one leader who had two teams; they both had multiple open hiring positions. And when we looked at a graph, like an actual visual graph, of their Skills, strengths, and gaps, they were this weird, perfect mirror image of each other. And I'm like, do you know that everything that team A sucks at is everything to B is amazing at? They were like, really? I'm like, yeah, like could maybe A and B just talk more? So then they actually had these two groups, they worked in completely different countries, so it's kind of natural how differences might've occurred. When put together, they realized that really they no longer had tons of job postings, they may be at two or three between the two of them because they were such complimentary fits. And that would be the kind of thing that if we weren't able to go to granularity, you wouldn't be able to find.

Now I know that's an amazing place to get to, but you can get there and you can start to see, Hey, we have things—and it doesn't have to be a huge company to find that either—you can find those complimentary teams and be able to pair them up when you have it.

Chris Pirie:

Interesting.

Stacia Garr:

So let's talk about how you did that, because I am sure that everybody who's listening is like, Whoa, I want to do that, tell me more! So what specifically did you all build and how did you build it?

Heather Whiteman:

I have to be a little cautious because I don't work there anymore, so I will only share what I know to be publicly okay to share. But the system itself was built to be employee first: so just to start out that comment, everything that we had was actually built by employees for employees. So HR built none of the definitions for Skills; they were all built by actual employees and were facilitated for sure, but no actual content was created by an HR person. And then the participation in providing information was also completely at the discretion of employees. Do we explain to them, ‘Hey, you might want to go in here because you can do a Skills assessment and see where you have gaps, and then, oh, by the way, we'll build you a specific learning plan that directly shows you how to close them? You can go in and look at what it takes to do other jobs, like if you're thinking about trying to get promoted, well, why don't we show you what Skills the next job up has or, hey, did you know that you're actually a better fit for this other job over here?’ That would be kind of the stuff that would entice employees to go in and leverage the tools: everything was used aggregate, anonymous, and disconnected actually from other HR processes to ensure that we would not have any accidental implications that could be negative. So getting everyone to that level had mostly everything to do with building trust, ensuring employees knew that everything was their own, they could delete everything, they could ask for input from managers or colleagues if they wanted, they could wipe it out if they wanted, things like that. So that was a big piece of actually being able to pull it off was to start with the idea of employees first and what do they get out of it, then as sort of the, Hey, thanks. I hope you got something out of it. Could you give us something out of it, and tell us that you're okay with us using this data at an aggregate anonymized level? And in fact, we never use direct employee assessment information, because we wanted them to feel safe and confident in their own learning development opportunities. So we actually just, as principle, decided never to touch that because we wanted everyone to feel safe, so we only looked at things like how we might look at Skills in an interview process and things that are pretty natural.

Stacia Garr:

You mentioned that employees helped you write the actual descriptions of Skills. So was that the primary source of data for Skills? Where did you get this information from?

Heather Whiteman:

I would not necessarily recommend this to other people, but again, if I go back to the problem statement at the time, our problem statement at the time was we were becoming a digital company, and this was many, many years ago for the first time and so we didn't yet have our own definition of what that meant. So as more of a need from a common, consistent cultural thing, we built all of our own Skills definitions—not to say that I'm the absolute best, but this is where my background in IO psychology and assessment, like we ensured really strong validity, reliability; we did external studies to ensure that it was at the level of rigor that would require. I don't recommend other people just go out and build one of these, not if you're going to use it for business decisions. So we built it ourselves, but not because that's the right way to build one, but because we wanted the cultural benefit of having employees say, I wrote that, or I know the person who wrote that, she wrote that, she's the best at it, I trust her; therefore I trust the Skills model. That was sort of the real reason why we chose to build our own.

Stacia Garr:

So one of the questions we had was, well, who all was involved? So obviously employees were involved. HR was involved to some extent, obviously people analytics, who else was part of this effort?

Heather Whiteman:

Legal, Data privacy. We spent tons of times with works councils, employee representative unions. There was a lot of governance that was required to do this kind of work well and appropriately, but the biggest was in fact, the employees themselves, so it was the individuals who use the tool, it was the managers of people: Talent Acquisition was involved, for example, they might give feedback saying, ‘Hey, I know that the team listed those as the most important skill, but then every time we go to hire someone, they always tell me that that one doesn't matter, so are we sure that that actually belongs at the model?’ That would come up too, but content wise, all employees; strategy wise, employees plus HR; building wise, again, employees plus HR plus legal plus data and compliance.

Stacia Garr:

And then you mentioned employees had access, and I assume that includes their managers had access. Can you talk to us a little bit more about access and security?

Heather Whiteman:

This was probably the thing that took us the longest, because again, we did really want to ensure that everything was completely above-board appropriate, and so it did take a long time to get all of the right approvals and the setup—this is where we pulled in quite a bit on reliability and validity. But when it came to access and that it was a lot of partnering with works councils, labor representatives, and different connections, but access had most to do with the technology platform that we'd use upfront, and really making sure that individuals had privacy completely throughout the system. So no one, even myself or others on the team, could get to somebody's individual-level data. Like we had completely built a wall so that we could ensure complete privacy and anonymity throughout, and that went all the way through.

Stacia Garr:

So you put in all this effort, you have built this thing. What could you do with it?

Heather Whiteman:

A lot! We had really strong stories in general, and this is one reason why I wanted to come on the show, it wasn't just to talk about what we did there though. I know that's always what everybody wants to hear, but it was because I think that Skills lets you do things you couldn't do otherwise. And so examples of things that we could do—just forget my own specific experience with that company—Skills let you look at things from a different direction. So I mentioned the example of you can find complimentary teams and realize that maybe you don't have the gaps that you think you have, you just have a communication need and a connection; Skills let you help employees find what's going to make them the most fulfilled people or person in their career, so a lot of people end up in the job they ended up in honestly, half out of luck because it was the posting that was open that they accidentally found at the time that they were looking not because if they truly had the opportunity to craft all of their Skills and everything else. When you come at it from a Skills perspective, you can show employees what fulfilment in this type of role might have, what Skills, where you could grow, but you can also show other opportunities, and you can show how their transferable Skills apply across the board. You can do as we did sometimes, which was highlight when we as an organization had critical gaps, and actually sort of offer up to employees, ‘Hey, this is what we're lacking in—would you like to up your Skills in it, and be like our person?’ And the employees are like, ‘yeah, I would love to know that I was making a huge contribution to what the organization needs: if you just told me what the organization needs, I'd be game to help you fill it.’

I think as practitioners, we don't ever, or we don't always or often come at it from that direction. We go out there and say, I've got a posting, I need these things and you have a gap and we need to work on it, but you forget that employees genuinely want to connect and have purposeful work and make a contribution. With Skills you can show them where their opportunity is to do so—they will be more fulfilled, they will be better suited, you will be better off as an organization. Those are some of the things you can do when you take that approach. And I've been able to see employees really raise their hand when given that opportunity and say, oh, I didn't know. I couldn't do that job, but that sounds pretty cool. I know you need them, you need people doing that—I’ll give it a try! And those end up being your individuals who are so well-rounded and move into it, so it's a really cool opportunity.

Stacia Garr:

And passionate too, because it's an intentional choice and they know there's a need and they know that the company's investing in giving them the chance to do it. Was there anything that you were most proud of that came out of that—any business impact, or something where you're like, wow, that was just cool?

Heather Whiteman:

A lot; I don't know if I have a single line or more than one… there are plenty of things that were cool. So not exactly from the GE example, but my PhD dissertation was something that I felt I really wanted to know the answer to, and so I did take the same kind of Skills-based approach in looking at data. And what I found in that was that individuals’ Skills, competencies, capabilities, pick your term, I wanted to know if they would be more predictive of future performance than past performance alone. So I had found myself meeting people who would get promoted, get promoted again, get promoted again, and they sort of have this like mark on them of this is the person who gets promoted. Oh, and that person, well, they got passed up last time, so they get passed up again. And that happens just because of natural human biases, so what I wanted to know is, is past performance really the best predictor of future performance, or is it Skills? And so in my work, I went through thousands of data points to try to predict what would tell us who was going to be the best performer in the future. And they both were predictive, so past performance does predict future performance—but Skills adds an extra level. It said that those people who have higher skill levels, smaller gaps to where they're trying to get to, do in fact, perform better than past performers and that there is what we've called a ‘mediating effect,’ meaning that your performance goes through the Skills, and then gets amplified. And that is something that I thought was great, because you can go talk to someone and say, look, I know that last round of work didn't go the way you thought, but if you work on your Skills, if you build them, it's not just lip service, you actually will be able to perform better, you really will be able to get to that next stage. It's also the opportunity to talk to managers and say, give a double-check to the person who has been working on their Skills, because just because you maybe didn't give that employee a very high rating last year, they've put some Skill work in; they might more than exceed your expectations this next year—go ahead and give them the opportunity to do so. So that's something that I think was really interesting that I was able to dig into the data on and find.

Stacia Garr:

Well, I know we're getting to the end of our time, so I want to ask, we've talked a lot about Skills and Skills taxonomies have been here longer than some of us have been alive, but we're at this interesting place. So as you look to the future, what do you see is the future and what do you think we'll be doing in five years that we're not doing today?

Heather Whiteman:

I'm not sure it's necessarily a Skills comment, but what I hope we're doing in five years—or at least I hope any student who goes through my class is doing it five years—is, I want to see us doing more ‘human in the loop’ type activities. And what I mean by that is, we have gotten really in love with data and technology, and for good reason, I love it—but we tend to start with the data first, and then we wait to see what the data tells us. I think we need a little bit more of sure, go ahead and build your data but put some human back into it. Meaning if I build you a predictive algorithm based on Skills of who's going to be the right candidate, I need you to come back and tell me how that math did: don’t just trust the math, come back, assess them all, then tell me if my math is good or bad and why, and let's add it in. Too often, we run the math, try to make a business decision and keep moving and we need more human-entered, new math, new tech, human-entered, new math, new tech, human-entered, especially with individuals who hopefully have Data, Design and Delivery for a More Just World in their thought process when they do that, which is if your algorithms are a little biased one way or the other, it's not mad, bad math to monkey with it a little bit and be like, let's maybe tweak it so it stops only giving me certain types of people. Tthat would be what I hope we're doing more of in the next five years.

Stacia Garr:

Wonderful. Can you tell us how people connect with you and your work?

Heather Whiteman:

Yeah, I am sort of around: podcasts like this, LinkedIn is usually the best way to find me, so if you just want to find me on LinkedIn—I do have a little YouTube channel, but I'm hit or miss on what I put up videos, but I'm always happy to connect and chat throughout.

Stacia Garr:

Wonderful, and then we asked this question of every guest who comes on the show and that's our purpose: so tell us, why do you do what you do?

Heather Whiteman:

I do what I do because there is immense power in data, but there's even greater power in people. And if we can take people data and start to build the world that we hope to see, I think that's where we'll get further, and I am a believer of all of that in a very practitioner world, so to all my HR colleagues, all my managers, all my leaders: the power of a tiny change in any one of our workplaces is huge. If we just made one aspect of HR or being a business leader just 1% better, the impact that would have on hundreds and thousands of people is huge. So small changes in wherever you work, wherever you are, that impact, that's what I think using analytics, using Skills, building more fair places can have this ripple effect. And that's kind of what I'm hoping for.

Dani Johnson:

I love that.

Stacia Garr:

Heather, thank you so much for the time today: we are truly appreciative of your broader perspective, your specific examples, and your overall generosity and focus on doing this with us.

Heather Whiteman:

Thank you; thanks all.

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