Liberating Skills Data So We Can Better Tell Our Stories

Episode Transcript

[00:00:00]

Kelly:   Welcome back to Let’s Talk About Skills, Baby, the podcast where we discover what skills can help you live your best life. I am your host Kelly Ryan Bailey, and each week I chat with inspiring visionaries about the skills that make them successful. You’ll get a firsthand account of how they develop those skills, as well as their innovative approaches to improving skills-based hiring and learning around the world. Now let’s talk about skills, baby.

We’re joined this week by Matt Gee. Hi Matt, thanks so much for joining us.

Matt Gee: Hey, Kelly, I’m really looking forward to it.

Kelly: Me too. Well, let me introduce everyone to Matt. Matt Gee is an entrepreneur, data scientist, and researcher. He’s the co-founder and CEO of BrightHive, a public benefit corporation building data collaboratives and data trusts that [00:01:00] increase economic mobility. He is also a data and society fellow at the University of Chicago’s Knowledge Lab.

He was the co-founder of Data Science for Social Good, which has paired over 500 data science fellows with over 150 national state and local government organizations and NGOs to build data-driven solutions to social sector challenges. Matt’s work has focused on finding new ways to increase individual efficacy and equality of opportunity through the creation of new linked data and application of new tools like AI and machine learning to societal problems. This is so great. Matt, I love to hear a little bit more about your journey. What led you to starting BrightHive?

Matt Gee: Oh, sure. Well, first it’s such a pleasure be on this podcast. Kelly, you are such an inspiration and always pleasure to talk to so I’m glad we get to spend this time today together.  I’m just your friendly neighborhood data nerd.

I think I’m more of a kind of data diplomat, but disposition. And in my daily [00:02:00] work, think I find myself often as a type of data hostage negotiator because the way that  I see the world is one where there’s so much untapped potential, in figuring out what questions we can answer.

What problems we can solve, what kind of value we can generate from society, if we can just figure out how the data that you’ve got, and the data that I’ve got, can be responsibly combined. And for me, the epiphany that led me on that journey, started actually, when I was at my first job out of undergrad when I was at the US treasury during the credit crisis from 2007-2010.

I saw firsthand the enormous benefit that can happen when you have a data literate person in public service that could talk to computers and not just gesture at them, really trying to help rebuild systems that were clearly broken. Like the system we were using to see which banks were screwing over people for [00:03:00] loans.

But also how out of whack society had gotten with this major power imbalance around data as  the fuel or this really valuable asset. And I remember one meeting I was in where it was me and one other person from the Treasury representing the people, and on the other side of the table were 30 PhDs in astrophysics and statistics that were representing the banks.

And that moment for me was my call to action. It felt like oh, wow. My profession, so many of my friends, were just optimizing ad clicks all day for Facebook.

And that had the same skills and I wanted to figure out how we could get more data folk,  focusing on really critical problems.

Like how do we make it easier for someone who’s unemployed to get reemployed. How do we help them find the skills that will help them increase their economic mobility and opportunity? How do we help them express in language that employers will really understand and resonate with their own [00:04:00] capabilities, knowledge, skills, and abilities.

All of those problems were problems that data folks could be working on, but instead they were just trying to get folks to click on mortgage ads. And so for me, it felt like a moral calling at that point in time.

And I had the luck of being at University of Chicago at the right time, meeting up with someone who’s now a really close friend, a guy named Ray Gani, who was at the time was the chief scientist for the Obama campaign. And we started talking about how we get more data scientists working on things like skills data, and we were lucky enough to get some funding from a guy named Eric Schmidt to start a program called Data Science for Social Good.

And that was actually when I got the skills data bug. Because I was talking with a bunch of different folks at the Department of Labor about one of the most valuable datasets that the Department of Labor puts out. This dataset called ONet, that has this knowledge, skills, and ability for a bunch of different [00:05:00] occupations across the US economy. And them sharing some of their challenges and frustrations and managing, maintaining that data, wishing there were better ways to tap into the massive troves of data  that are housed by private entities and an online platforms, to make that data more up-to-date and more dynamic.

And make it feel more like a national weather system of skills data. And in that very first year, that’s when realizing that problem, that problem of figuring out how to combine these private sources of data and these public sources of data to create better, more openly available national skills data utilities.

I got the bug then, and it’s been with me ever since.

Kelly: Now, I know you don’t work only with skills data, but as long as I’ve known you, you definitely seem to have an affinity towards it.

Matt Gee: That’s right. Yeah. I think a lot about the problem of in today’s society, we’ve got a bunch of things that should be happening with data that aren’t. And a bunch of things that shouldn’t be happening with our data, that are [00:06:00] happening all the time. And we’ve got to renegotiate the social contract around data. And I actually think it starts with skills data.

Because skills data, if we think about it, are our most valuable personal assets. They’re with us all the time. We don’t lose them if we go bankrupt.

We’re all kind of 1960s Batman, with utility belt around us.

Kelly: Yes.

Matt Gee: We’ve got that right. We’ve got the skills utility belt. And yet when we think about how we prove the things that are in that skills utility belt, the wacky thing is that we have to pay other people for access to that data. I have to pay my university to give me kind of a verification that yes, I got a degree that has these following certificates and stuff, just to prove that I know those things.

And major online platforms that I volunteer to put my data in, they own that data, not me. They’re the ones making [00:07:00] money off of it. I’m not. And I mean, imagine if 1960s Batman had to pay Alfred every time he wanted take something out of his utility belt and use it to fight crime. It’s just crazy. So I think skills data is it’s incredibly valuable.

It’s also incredibly disconnected and inequitably distributed and inequitably accessible. And so I think if we can fix the dynamics around skills data, we can fix it around a bunch of other things.

Kelly: So true. I love your analogy with Batman, because I was actually reading something the other day that was just talking about super powers. If you read some of the old comic books, I was a big comic book fan growing up. And the ones that you didn’t know, like not Superman, it was the wasp and those, they always had to describe their super powers because people were like who are you?

The hardest thing to describe. If you’re not Batman, everybody knows. But it was funny when put in the context of skills, how to try to describe something when you’re not Batman or Superman is so difficult. [00:08:00]

Matt Gee: Yes. And it makes me think of, we’ve been doing some great work with the Rework America Lines. I mean, we’re having a set of conversations. This work group that has been engaged with lot of the career counselors that work on the ground, in great networks like the network and the national urban league network and the Goodwill network.

And we were talking about the aha moments that they create for the people that walk in the door. And one of the first most important aha moments that does counselors create, is just helping an individual realize they’ve got more skills than they may have even known they had. And equipping them with the ability  and the language to describe those skills in a way that that will really resonate with employers.

That to me is that, what is your super power problem?

I love all of these. It seems like every CW superhero show is the canonical first [00:09:00] episode, when the superhero first discovers they have super powers.

Kelly: Yeah.

Matt Gee: And we’ve got people everyday who are working to help folks discover their superpowers. These folks in a Goodwill center, I think that is some of the coolest work that’s happening right now. And it’s also so unheralded.

Kelly: I was just going to say that like they’re superheroes in and of themselves. to be there with those people to help them. And actually, you’re just making me think ahead. It’s funny when you introduced yourself and you said your friendly neighborhood data geek.

It’s so funny how the majority of people might be intimidated by that. But when you break it down to something that actually has to do with your everyday life, and you can explain it through the education that you’ve had and through the work experiences that you had, and in many cases through the life experiences that you’ve had, and you can make that real for someone, that’s a big deal. And I think that’s what’s so exciting about skills to me is that we tend to think of it as like, oh my gosh. Data. But it’s not that.

[00:10:00] Matt Gee: It’s stories. Data is most powerful when it helps us tell better stories to each other. And I think one of the most important stories we can tell about ourselves is, this is what I know. This is what I’m capable of. This is what we can do together.

And that’s a story. That’s a story of skills. That’s a story of experience. And I feel like we’ll be successful in the work that we do if ultimately, we help unlock just a little bit more information that helps each individual better tell their own story and have control over their own story.

I think that’s a really important aspect of this is that, liberating skills data is a key part of helping everyone have more kind of direct saying control over their own stories.

Kelly: So true. And when you say liberating skills data, I know a lot of the people that listen to this podcast are likely working with us on projects, practitioners in this space. But just to be [00:11:00] clear, I mean, I think that you’re saying, is there is a lot of data that’s being held in a proprietary way by many organizations.

Is that what you mean when you say skills data?

Matt Gee: Yeah. I look at great projects like the work that all the different members of the T3 innovation network, who’ve been doing this as a network innovation network run out of the US Chamber of Commerce, where a bunch of folks got together and said, if we want the learner and worker to be at the center of a new skills-based kind of labor market and learning ecosystem.

One of the ways we empower that individual learner worker is by empowering them with their own data. With their own educational records, their own work records. In Emsi, you guys often talk about these kinds of skills clusters, or skills groupings. Skills portfolio is something that every person should have ownership over and control and be able to carry with them, just like you carry your wallet.

 And we [00:12:00] don’t have that. But folks are working on that. And I think that that ability for digital expression of your skills to be something that you actually own and control and can move around, is I think potentially one of the biggest innovations of the next couple of years.

And so that work of creating these portable learning implant records, is I think of the ways that we liberate skills data. Put it in the hands of each individual learner and worker.

Kelly: Well, now that you mentioned one of the most important innovations,  I’ll mention another one because I read an article recently that data trusts was listed as one of the most important recent innovations. And obviously that is what you do.

Did you always know you wanted to be an entrepreneur to start a business around this, or did you try to figure out something before you went there?

Matt Gee: That’s funny. I did not know I wanted to be an entrepreneur. I discovered it halfway into my PhD. I’m a PhD dropout and that’s important. I [00:13:00] discovered that I loved knowledge work and I loved creating new ideas, but I loved even more turning those ideas into action. And I caught the entrepreneurial bug while I was in the middle of my PhD program. 

I hopped over to the business school at University of Chicago and started going to be entrepreneurial classes and pitch competitions and all that. And for me, I felt an energy there that I know where I just felt more at home.

But I always felt where I could contribute to the world the most with an entrepreneurial mindset is bringing some of these newer ideas from academia that were really well thought out, but we’re still just things on paper figuring out how to make them work in the real world.

And one of those ideas, a number of years ago was the set of ideas around different ways of organizing, controlling, and governing our data, our shared data. This notion of a data trust. And it’s a great idea, right? I want to make clear, it’s not my idea.

A number of academics [00:14:00] came up with it first, but we wanted to do some of the first work, figuring out how to make it happen in the real world. And the core of the ideas is simply this, that data Is something that an individual company may have in their database, but it’s actually about you.

 And so the question we’ve been grappling with society is like,  if data is about you, is it your data or is it their data? What are their responsibilities to you? And then importantly, your data is not just one company’s database or one government agencies database. It’s in several government agency databases and companies’ databases.

The question is how do they relate to each other? If they all have data on Kelly Bailey, what is their responsibility to you? What is your responsibility in deciding what can and can’t happen with that data? And that societal contract around data hasn’t been fully worked out yet, but one way of treating a differently is thinking about data as a shared asset and [00:15:00] establishing, putting a trust around it and saying, we’re going to govern this together.

We’re going to put some limits, some ethical limits around what can and can’t be done. We’re going to require auditing of some of the AI algorithms that are being used where that this data is fueling. And wrapping all of those key things around responsible data use into a structure that works.

It’s where data and society meet. And that’s what a data trust is. And we’ve been lucky enough to work with some really innovative folks over the last few years, who’ve been willing to try that out right. To put a data trust into practice. Some of the folks I most admire are innovators within government who’ve been willing to step into that.

So the state of Colorado is one of the first states. They’re an incredibly innovative state.

And one of the first states to say, we want to try this out. We want to work together and work with you at BrightHive to establish data trust among several key agencies that we’re all [00:16:00] working with and helping to support individuals who are out of work, high school students that we’re moving into kind of a post-secondary or workforce pathways or military pathways, and figure out how that their disparate data could be combined.

But governed in a way that was responsible, that was serving those people’s needs and had the right limits around it. And so helping the state of Colorado set up their data trust and see it really start to serve Coloradans, has been one of the most rewarding things that I’ve had the privilege of seeing and being part of last few years.

Kelly: Wow. So what are some of the benefits you’ve started to see in a state like Colorado for people?

Matt Gee: Going back to the story we talked about with Goodwill and Unidos, and career counselors that are helping folks discover their own skill superpowers.  That’s really important work and it’s also really hard to scale that up to millions of people.

Because those [00:17:00] conversations happen kind of on a individual by individual basis. I was talking with one team that said we have on average in peak time, seven minutes per person that comes in the door to help them. Seven minutes to help you discover your superpowers. But that’s so that they can serve everyone who needs it.

And there is still tens of thousands over the course of the year and we have millions of folks who need that help? And so we’ve got to figure out how to help more folks digitally, to discover their skill superpowers.

And the state of Colorado realized that a few years ago. They realized they could build a free public capability to have help each individual discover their own skills, strengths, and map out their own journey. But to do this, they’re going to need a great technology partner to help them build out that skills navigation experience.

And they’re going to have to figure out how all the different data sets necessary [00:18:00] to map out those journeys could come together

Kelly: Hmm.

Matt Gee: and support those decisions. That was the first driving purpose for them setting up a data trust was to support an application, a free application built by a company called Perrin, with the state of Colorado, called My Colorado Journey.

And it’s already been used by and helped tens of thousands of out of work Coloradans since its launch. But the really exciting thing that happens is once you set up that container, we were actually just on a call yesterday where the great innovators at the state and the partners that they’ve brought together are already thinking about what can we do next?

How can we support more folks? What are the key insights that each individual needs more depths on? And what are the additional kind of datasets that can be brought together to support those individuals? So seeing that kind of innovation flywheel get going is I think one of the coolest things.

Kelly: It sounds like when I make the comparison from the way you described getting your entrepreneur [00:19:00] bug, to the way you just described this sort of like innovation, you know, getting the innovation wheel going. It actually reminded me that when you get it and you start to see that you’re helping people, all of that.

It’s something that it’s hard to stop, which is really exciting for states, and it’s amazing that they started this pre-COVID cause there’s so many regions and states that had not necessarily considered these pieces before such large unemployment numbers. Are you seeing a lot of the same type of innovation happening more broadly now?

Matt Gee: Yeah, COVID has shifted so much in our society. And one of those shifts has been a real reckoning with government agencies in realizing the cracks in a system that they, for better or for worse, had to figure out ways of papering over just to make work.

But the fact that we didn’t really have an unemployment entrance system that worked for part-time workers in the gig economy, and we had to figure out [00:20:00] very rapidly how to set that up and states had to figure out a way to get millions of dollars of aid out to folks for the first time, for a class of workers that we didn’t have any way of really supporting in the past.

I read somewhere one in four American workers have relied on unemployment aid during this pandemic at some point in time. One in four.

Kelly: Wow.

Matt Gee: Just about all that money has had to flow through these state systems. And there’s been just amazing Herculean work by teams figuring out how to stand that up quickly.

I think of, there’s this great nonprofit called RIPL. Research and improving people’s lives. And they worked with the state of Rhode Island to spin up a system in just a few weeks that would get these checks out to these part-time workers. These gig economy workers that were now unemployed.

But every state has had to clutch together a system to do that. And I think now there’s a realization [00:21:00] that we’ve got some systemic things we got to fix.

Kelly: Yeah.  I hear that. So I know that you love helping people, which is so fantastic. So I want to lean back into the personal side of your story for a moment. You had mentioned this entrepreneurial mindset, but it was fascinating to me that you had this academic background on one hand and then decided to make this shift.

And I feel like a lot of people right now are in a place where they may be trying to figure out their own transition. And so I’d love to hear what’s your super power belt? What are your things in your belt? And also when you make that comparison for people that are trying to decide, do I continue education versus do I potentially take this other route?

Was there something in that, in your experience that you can share with others that could help them?

Matt Gee: Oh, goodness.  Yeah, I think one thing I realized about myself that  the, not unique but relatively novel combination that I’ve cultivated in my life and part of which I was born with is, the ability to see connections.

And it [00:22:00] just so happens that my particular journey through learning meant that I saw two groupings of connections. The ability to kind of connect people with similar interests and data with a similar kind of meaning. And that in connecting kind of people and data and people with data, I could create a lot of new knowledge and new value. And I initially thought that, okay, the best place for me to do that would be in academia.

Academia is all about creating novel connections between two ideas to create kind of your own new idea. But when I was in the middle of grad school, I was looking out and seeing increasingly just the knowledge economy didn’t have academia and the connections that were being made in academia as the most impactful, at least near term or medium term, connections being made.

But there was those high value connections were happening outside of academia. They were new novel combinations of people in data that were creating [00:23:00] new companies.

That were solving new problems building new public utilities. And I realized for me, I really wanted to have impacts in the short and medium term. And that the potential for impacts academia tended to be more longer-term impacts.

And so maybe it’s just because I was impatient and short attention span. So I decided great, I know I’m good at connecting people in data. Let me do that as an entrepreneur and build a business around that rather than within an academic institution. So what can the other people learn?

One is just discovering and starting to be able to through self-reflection and conversations with your friends and mentors, having kind of a mirror held up to help you identify and name those tool belt skills. And then asking yourself hard questions about what matters most to you on what time horizon?

Do you want to change the world? And you’re okay if you’re a Van Gogh, tragic figure where no one recognizes you now, but in a hundred years everyone’s [00:24:00] going to be praising your name. Or do you want to see something change tomorrow? And are you looking to affect entire systems or individual people’s lives?

Where do you get your energy? And then with that self-reflection about your tool belt, what really drives you. And lastly I think it’s really important to look out at the market and say what’s changing? Lift up just a little bit, get good advice about where things are headed.

So that you can say, all right there’s going to be this trend. For me, it was just trends in data as an emerging area of power and influence and also societal conflict. And where that conversation was happening? It was happening outside of academia, not as much in it.

That allowed me to say, okay, that’s where the puck is headed. I’m going to skate to there. Figure out your tool belt figure out really what motivates you and brings you energy on a daily basis. And then if you have the luxury, to see just a little more than six months ahead and say where might things be going? [00:25:00]

I recognize that I am also talking about this from a place of extraordinary privilege. Like I was in grad school and had all the time in the world to think about these things. And if you are one of those 10 million people that are unemployed right now looking for work, all three of those things feel like luxuries.

But one of the things that I think I’m really encouraged, about talking with folks who are serving the unemployed every day and supporting them and helping them through the work that we do this new crop of digital tools that are coming out are intended to provide, at  least some of those three things to you.

That to a much broader set of folks who don’t need to think about it for two years, like I did to decide what’s next. They can help you figure out your tool belt faster, certainly faster than it took me, and also help you see where things are headed, what skills are going to be in demand in six months, which ones might be kind of sunsetting, in terms of the relative importance.

And I think that’s [00:26:00] one of the most valuable things that, again, liberating skills data can do. If we can provide everyone with that level of insight into themselves and the market, then we’ll end up with a lot more folks, not just gainfully employed, but  loving what they do and able to adapt to changes as they come.

Kelly: That is some wonderful information to offer. And I agree with you too. I understand that not everyone is at that place right now, but the great thing that Matt just mentioned with these new tools that are coming, is that you can start to think through all of those skills you’re gaining and whatever opportunity is, and whatever life experiences you’re having, and how that can help you navigate to the next step. Even if it’s not the step you want to take right now.

Matt Gee: Right.

Kelly: So last question for you, Matt. I know we’ve been talking a little bit about you personally, which thank you so much for sharing with us.  But just to end for our listeners, before I tell [00:27:00] them where to find all the information about BrightHive and all that good stuff, we’ll end on the personal note for a moment, which is just what can people do in their daily lives to continue to grow, besides understanding what’s in their tool belt?

Matt Gee: This is going to be an odd thing, but I think it’s actually really important and that is, you should know and demand that the data about you and your skills, is yours. Ask for it, get it, and ask more of the companies you work for, the institutions you go to for training and education and of your state governments, national governments that your skills data is yours.

Because  I think that will help each individual, definitely. And I think it will help each individual be part of and contribute to a necessary shift in society. Which is that, if we’re going to create a more connected, participatory and equitable digital economy, it’s going to start with each of us expecting [00:28:00] more of data about us and about our skills.

So I think, don’t put up with crappy digital experiences. Don’t put up with having to pay for access to your own data. This is this is yours. They are your skills. It’s part of your tool belt. You should be able to have it on you at all times. And you should to be able to build it and grow it, everything that you say about what’s digitally in your skills portfolio. So I think that’ll help both you and it’ll help make a critical change in society.

Kelly: That is such great advice. I’m just going to add one thing to that for anyone that’s like, why would we ask for this? Think of it like your medical records, right? I mean, you wouldn’t just be like, oh, I can’t have access to my medical records? This is you.

Matt Gee: Right. Like I have on my iPhone, Apple worked it out and a bunch of hospitals systems and other folks worked out so that I can have my medical records on me. I can know more about myself, my health, what’s going well and what’s not.

 I don’t have that for skills [00:29:00] and that is a shame. Every single one of us should have that. Until we do, I’m going to keep beating that drum.

Kelly: I love it. I’ll keep beating it with you.

Matt Gee: Sounds good.

Kelly: Well, thank you so much again, Matt. I also want to encourage anyone that may be interested in pulling together collaboration’s where data is of utmost importance, especially from a work and learn perspective, to contact Bright Hive Data Trust.

It’s such an important aspect and Bright Hive does it really, really well.  You can find Bright Hive at brighthive.io.

Also on all of the socials as well. And I encourage you to touch base with Matt. He is on LinkedIn and Twitter at Matthew Gee, but we were just chatting that Clubhouse is his new favorite hangout, so he’s there at Matt Gee. And I do agree it’s a fun spot, if anyone is on Clubhouse to go in and check that out. And maybe we can continue this discussion at some point. [00:30:00]

Matt Gee: Sounds good. Thanks so much, Kelly. It’s been a pleasure.

 Kelly: You’ve been listening to Let’s Talk About Skills, Baby, a Growth Network Podcasts production. If you enjoyed this episode, make sure to subscribe to the podcast and share it with your community. Ratings, reviews and suggestions are great sources of feedback and always appreciated. Please reach out and connect with me on social at Kelly Ryan Bailey.

I’d love to meet you and continue the conversation. We’ll be back next week with a new episode, so until then keep growing your skills and have a great day.