Episode 80 · 10 months ago

#80 The Rise of Hybrid Jobs & the Future of Data Skills


It’s no secret that data science jobs are on the rise; but data skills across the board are rising — leading to what today’s guest calls “hybrid jobs.”

This will require a paradigm shift in how we think about jobs and skills.  

Today’s guest, Matt Sigelman, President of The Burning Glass Institute & Chairman of Emsi Burning Glass, talks about the difficulties of connecting companies with top talent, the hybridization of many positions, and how to position yourself in the ever-changing market. 

Join us as we discuss:

  • The methodology of using data science on the labor market
  • The demand for data skills & how they’re evolving
  • Blending skills to get ahead in the job market & the rise of subskills
  • How educational institutions can prepare students for hybridization 
  • Advice to the audience on how to structure their approach to skill acquisition 

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You're listening to data framed, a podcast by data camp. In this show you'll hear all the latest trends and insights and data science. Whether you're just getting started in your data career or you're a data leader looking to scale data driven decisions in your organization, join us for indepth discussions with the data and analytics leaders at the forefront the data revolution. Let's dive right in. Hello everyone, this is Adell, data science eventelists and educator at data camp. You know, it's no secret that data science jobs are on the rise, but data skills across the board and every profession are rising as well, leading to what today's guest, Matt Siegleman, calls hybrid jobs. This will require a paradigm shift and how we think about jobs, skills and education. Matt Siegelman is the President of the Burning Glass Institute and Chairman of MC burning glass, a leading labor market analytics firm who, for more than a decade has used data science to truly dive into which skills are in demand and which skills will be in demand in the future. Throughout the episode he talks about the rise of digital and data skills, the increasing demand for data science jobs and roles and what he calls the hybridization of jobs and how organizations, educational institutions and individuals should positions themselves address these tectonic shifts and the job market and more. Now let's dive right in. Matt's great to have you on the show. I am super excited discuss with you all things future work, your work at MC burning glass, the importance of data skills and an increasingly changing labor market and all that fun stuff. But before do you mind giving a brief introduction about MC burning glass and we guys do so. Mc Burning Glass is a company which has brought the data science methods to be able to understand the job market and how it works and how it works at scale and the way the data science does. Our breakthrough innovation was realizing that we actually could understand. We could we could collect data on both job postings around the economy, we could collect data at scale about people in their careers, create effective ontologies to understand what people are expressing, what what signals are coming from the market and provide critical insights that help companies, that help policymakers, that help educators and help workers understand the job market, planned for the job market and make more effective connections within the job market. I want to set the stage for today's conversation. You know, when preparing for this interviews and all of the level of depth and care you in the MC burning glass team practice. When we are you thinking about and speaking about the labor market. You mentioned here the data science methodology underpinning it. Do you mind expanding into that methodology and how you're able to model the job market just so effectively? Yeah, absolutely, it's it's an interesting because we started not as a data company. We started as an LP company. We had developed a really good engine for recruitment that's uses advanced LP to be able to structure people see these and upload all the information in and make and make more effective matches on that basis, and in fact that's technology that's used even today by the great majority of large recruitment companies and natur management systems and the like. But after a time we sort of came to realize the constraints of this because in the one hand we created this better mousetrap that's able to structure these unstructured the unstructured coin of the realm of the job market cev's and job postings in the like and use that to help individual clients. But ultimately the job market still mostly works on cosmic coincidences, which is say, you know, go walk into cafe and you see somebody's you know, a server who's she's fabulous, right, and you said, like this person could be working anywhere. Why is she here? And so we've constructed this job market that works, that only works on the spot market, that that whoever happens to be looking for a job, and even day, and whoever happens to be looking for talent and given day. And... what we realized was that part of the reason why that's always been the cases that there's no market map. It's hard for an individual jobs you're going to know what's all, what are all the opportunities that are out there? Employers likewise, and so you can plan for a market. You can't connect effectively within a market. Would you don't understand. So what we did is we said, Hey, look, actually the world is evolved to in most industrial economies, to where most hiring is happening online, most job postings are online, and so instead of waiting for just processing the data that our clients receive. What do we go out and actually scrape kind of all the job posts, and you find a lot of by the way, can of Labor economists around the world use our data set and find that sort of general consensus estem is about eighty five percent of all job postings in the fifty five countries that we we cover or actually in our database and the empery last database. And so what we then do is we sort of bring those job postings back and we've constructed a really robust set of ontologies that help us define roles, that help us define skills, that help us to find experience levels, credentials and so forth, crossed dozens of dimensions, and then we use our and LP expertise to then be able to to create, to tach those metadata tags to each experience within each CV, to each job posting, and that's really important because it allows us to aggregate up the information. Analogy for what we've done is some work that's being done by an economist and MIT by the name of L Battoka Vao. Several years ago he was a young economist to university in Buenos Aires and he was trying to understand inflation in Argentina and government statistics he felt weren't giving him an accurate read, and so he said look, I can find prices like I don't need the government statistics. The prices are all online and it totally transformed them. We actually we're doing this before our better did his work, but I always find that's like a he's not run thing called the billion prices project, MC burning glasses. You caught the the many, the many billions of jobs project and it's been transformative in how we understand the economy. And when I love about this approach is that, instead of looking at job evolution, it looks like it looks at skill evolution within the labor market, and that gives you a real time view of the job market the skills that are evolving within it. So, given this, what makes this approach different from the traditional labor it can almost approach. And what type of value does offer that wasn't previously attainable? So a traditional Labor economist is usually transacting in survey base data. So all the kind of things we see, the monthly job numbers, whatever they're based actually on a survey. They it's a very you know, s notion that hey, we can't possibly be able to analyze the job market at scale, and so we'll take this very narrow slice and will construct a will use a survey based methodology and and we'll use that to be able to understand what's and, by the way, those surveys are valuable in being able to provide a benchmark. I'm being able to to validate what we're seeing from a broad trans perspective. The weakness of that kind of approach is that, at as you can imagine, you know, when you're doing a relatively narrow sample, it means two things. One, you have to keep your categories, or three things actually. One, you have to keep your categories really broad, so the US Bureau of Labor Statistics continues to track a job called computer programmer. What's a computer programmer? Number two, it means that you have to assume that every job within one of those categories looks the same. So, okay, there is a can, whatever computer, bigram it is, every kind of computer gargrament does the...

...same work, same skills. To your point, you don't get that granularity. And then, third, whatever you do because you're trying to create a macroeconomic trendline, don't mess with the with the buckets right. So I think only about three or four years ago, I may be slightly off on that, did the beer of labor statistics actually start to recognize that this job called the data scientists. You know, they just don't want to recognize it because it messes with their sample. Whereas, to your point, what really would sort breakthrough about this is not even just that it gave a real, more real time view of the economy, because obviously bols of all that survey work takes a lot of time to so not only is it literally that what's going on today in the job market, but what it's doing is it's allowing you to get to all that granularity. So you jobs that are ruby developers right. Ruby developers in one industry have actually slightly different skill mixed from Ruby developers another industry, and so you can see skills emerging within jobs, skills that are are transforming jobs, and you can see the birth of new jobs all together. And I don't want to spoil the rest of the conversation, but the skill based approach sheds light on how traditional jobs like marketing are becoming differentiated or more technical, and you get a view on how roles are evolving and this helps governments, organizations, educational institution is prepare better for the future. Skills folks need right and so I'd love to impact how you see the job market evolving and the type of skills that will define the future of work. Now, of course, digital skills of all types are growing in demand at various rates, but, given the theme of the day's Podcast, I'd like us to focus on the demand for data skills. Can you walk us through just how much data skills are in demand today and, to a certain extent, where you think they're headed? And of course I'm using the umbrella term data skills here, says there are many sub skills that we can further talk about and break down. So data skills are are very much transforming the market and very much transforming it in not just because there's jobs like data scientists, which are which are in huge demand, but because those skills, exactly to your point, are in demand across the economy and you a whole range of now you a whole range of jobs are becoming what I would called data enable jobs. So one things that we're seeing is that that pace continues to quicken. So if you were looking in the US data for example, you would see that the number of jobs that require data science skills, and again I'm using that sort of skill metric, not the job metric. The jobs have also grown. But but data science skills showed up in about four hundred and Fiftyzero jobs last year, up from about three hundred and Fiftyzero just before the pandemic. So literally in the space of two years. You said about a forty percent growth in demand for these skills, which is think about how fast or not labor markets to the tend to move, which is a couple percent a year, and think about, you know, a forty percent growth in the space of two years, a tremendous growth in that demand. Now we think about how we characterize that demand. I think there's a couple things I would point out here. One that the buckets of data skills that that sort of show up are going to very fairly differently, fairly significantly rather, based on the kind of job we're talking about. You know, if you were to look at the skills of a data scientist versus a data engineer versus a data analyst, you know they have very different skill set. So common across all of them is python not all day and less need by thumb. But even a growing number of data and lets need them, pisons seems to be winning out a little bit over our boat prevalence and also in versatility. You know, I think if you're looking at a set of skills that you would see across all three of, for example, those buckets. But here's where you start to see the differences emerge. Right. So a date engineer and needs etl skills, a data scientist generally does not. A data scientist. Growing percentage of data scientists now need ML skills. Five years ago that true, it's true today, but you don't see ML skills showing up in data analyst jobs and and in a relatively small percentage of date engineering job so those...

...kinds of differences are increasingly, you know, sort of coming into to bear. But you know, I think the key here is is really looking beyond the world of data science from a job perspective. And and again, not because there is a huge growth in data science jobs, but in fact here's a little fun fact for you. In two thousand and ten there are only a hundred fifty job postings in the United States for data for data scientists. Last year there are about Fiftyzero and there are another fiftyzero jobs for date engineers and day engineers. I think probably, I know that when you start to see but I would bet even in two thousand and fourteen and two thousand and fifteen or probably pretty close to zero. So you're seeing huge rights of growth. And what I also love about breaking down skills as a methodologies that it also shows you a proxy of rare organizations are on the data maturity curve right. So, for example, you mentioned machine learning skills aren't are much more in the men today than they were, let's say, five years ago. It's because five years ago I don't think a lot of organizations you how to operationalize machine learning like they do today. So it shows you where the organizations lie on the day of maturity spectrum today. That's a really great point because I think one of the things that it provides is a way for companies to assess their future readiness. Hey, where are we and we look at a given roll, what are we looking for that role and how is that different from what our best practice peers are looking for in the same role? If you were look at, for example, product managers at city or JP Morgan Chase, and then look at product managers at Amazon. Amazon product managers are day enabled. Product managers in a lot of mainline companies are not. So you can kind of benchmark yourself around, like he what are the skills that we're on a need to be able to manage transformation and are in our industry? Parping on something you mentioned here, is that we don't want to constrain this conversation just to rolls, but when to focus on skills. So I don't think many people today define this is brilliantly as you do, and here the concept of hybrid jobs. Can you walk us through how you define hybrid jobs, especially when it comes to data skills? Yeah, absolutely, in and first you all to find terms around, around hybrid jobs, because the reality is is that today, in the post pandemic world, I think hybrid jobs are starting to come to refer to something different, which is this noption of you work in the office some of the time and and work remotely some of the rest of the time. But when we first started tracking what we call hybrid jobs, we were tracking a really fascinating and and and I think actually a really disruptive trend that we've been increasingly seeing in the job market, and that's the tendency of jobs to blend skills from across the main overall jobs are changing and changing very fast. So from some forthcoming research that we've done with our colleagues at the Boston consulting group, found that the average job across all jobs, and it's like we're not even just trying about tech jobs or data jobs like Russell. The average job has seen bet a third of its skills replaced over the last decade. You know, in that pace has been quickening or through the pandemic, by the way, huge implications for that, because think about unite, your traditional university based kind of learning structures, like if they change the third of their curriculum in the last ten years? And don't think so. And and by the way, think about employers as well. Okay, has have the skills of our workforce shun? You have it. Has Our workforce change the third of its skill base? And probably not, and so you know, raises real questions. That what we're just trying about a minute ago, but obsolescence. But you know, so that provides an overall frame of the pates of skill change but it's easy to just sort of say, Hey, most of that change is probably just, you know, it's it's people need to learn new text acts. Okay, fair enough, but you can kind of do that on the fly. But what we're really seeing, you know, much more profoundly, is that jobs are absorbing...

...skills from across the mains. Essentially, you know, jobs are having sex. Give you an example. I think you actually you pointed this example before. You know, think about a marketing manager. We all know plenty of people who are marketing. Nice folks. They tend to be pretty right brain the marketing because, you know, they understand people and their psychology and they can communicate to them and whatever. Increasingly, you want marketing people to man to be able to manipulate customer data. Well, guess what, you need some data skills to do it. And so marketing manager who has sequel skills, and even telling about hardcore data skills, right, but a marketing man, through as sequel skills, we just manipulate a customer database, gets paid about forty percent more than, you know, one who does not have those intense of skills. So you know, so to just speaks to how we're creating these intersections of skills that were never seen before and that challenges the job market because apparently, you think about that, example, right brain person, left brain skills, you're creating it and immediate shortage right there. And so for people managing their careers, the ability to blend skills and get ahead of that is the ability to put yourself in the catbird seat and the in the economy. That's really awesome. So I kind of focus in on the data skills of hypod jobs. Of course it's entirely job dependent, industry dependent, specifically what type of data skills are needed within these hybrid jobs, but my hunch is that a lot of these data skills needed within these types of roles are not necessarily, you know, hardcore technical skills like data engineering or machine learning, right. So what do you think are the main data sub skills that are rising within these hybrid job categories? So, first of all, just to reinforce your point, part of the reason why jobs can hybridize, I mean in various directions, by the way, it's not just that data skills are invading other jobs, but in fact, if you look at a job as the data scientists, compared to you know, kind of the quat jobs that proceeded. A data scientist actually, as very strong affect, is a great example of a hyper job, because data scientist needs significant programming skills, which are, you related to, but not the same as as, data skills, but also significant business skills, because we know data scientists need to solve business problems and then need to understand those problems. But you know, look, when I think about the world of data skills or of hybridization and and hybridization of data skills and others, one thing that's important in framing are understanding why this is happening is that skills, even technical skills, have gotten a lot more accessible. You know, I this is where I get to do an old guy, so as where I get to do my you know, I when I was a boy I used to walk back, you know, up he'll both ways to school and barefoot in the snow, kind of moments. But my original work and data was in Fortran and and an spss and yeah, that must have been challenging. You know, there was a it was a much less accessible language. By the way, I'll give you each dignos is off on the side, but but it makes me, makes me laugh to remember it. There was a command when you're working with set of big data sets, was sort of like call tape, whatever the the tape number was, and it was only, you know, I'd realized it. Half the time you call that it was very slow. I figured that was just processing issue and and some half the time it would hang. And I only later discovered that when you type call tape, whatever it, it rang a bell and in at somebody's desk and someone had to go and fetch a tape and loaded so far away from their at their desk or whatever they were doing, like, you know, they just that's why it would hang, right, because they just come across. Oh Wow, that's cellar is. But you know. So look what, when we look at the state of a lot of text acts today, they are more accessible, I might say in some cases, easier to use. They're also more powerful.

But what it means is is that people on a broader range of backgrounds can actually leverage skills could you don't need a, you know, a deep specialist in order to be able to to use data skills. Use the example of marketing. We're turning about before. Ten percent, almost ten percent, of the jobs that ask for data science skills. Not just data skills, but data science skills are in marketing. So that sort of speaks to that accessibility has facilitated this hybridization. Hybridization isn't just data skills, it's also design skills are being used across various programming skills are are in demand across occupations, business skills. Even a nurse, for example, needs project management because she's managing care across providers. And also regulatory skills showing up in a broad array of jobs. Yeah, so this is especially useful for finance jobs where you know, it's heavily regulated industries. And to harp on that point, you know, around modern data tools being accessible. Outside of accessibility, they're also free, you know, bythone, and are open source. There's you no longer need to pay to license, like someonere that you had with SBSS or Sass to start doing interesting data work. And, you know, following up on some of the points here, outside of marketing and some of the jobs you mentioned, do you think only a set of these jobs will become more hybridized, you know, such as financial marketing, or do you think this is a secular trend that's really going to impact most jobs. This is a broad based trend and and by the way, it's not even just across professional jobs. You know, we recently looked at the digitalization of what are sometimes called middle skill jobs, that means north of secondary school and and south of university, and we found is that about eight and ten of them today are digitally intensive, and digitally intensive middle skill jobs are twice as likely to pay a living wage. They're growing twice as fast the two and ten and middle skill jobs that are not digitally intensive or increasingly just in construction and transportation. So so this is this is really broad based. Your most job. I think one of the things that we've been looking at recently in our data is trying to identify what are the foundational skills to the New Economy, what are the sets of skills that are broadly in demand? And you know, I think traditionally if you had looked at foundational skills, you would say, okay, it's the human skills, it's what people like to call this off skills. But actually we're seeing at least as much is broad based demand for for data skills, for digital skills, for business skills. So when a riff on that a bit. You know, you mentioned here soft skills, and I think when people often think of hybrid jobs they immediately assume that these jobs will become inherently more technical, and I'm going to have to become a hardcore programmer right however, one thing I've seen you cover within the concept of hybrid jobs is that hybrid jobs are increasingly become technical, but the more they become technical, the more valuable soft skills are when it comes to succeeding and this hybrid eyes the economy. Do you mind expanding on that notion? Yeah, this is a fascinating conclusion me. To look at these kind of core foundations. Normally, when you use, in fact even just using the vocabulary of a foundational skill, you're expecting that foundational skills are the stuff that's on the bottom. You know, it's think about the Food Pyramid where you get carbohydrates down here and then the important stuff is protein. That's the technical skills. Actually, your career will works exactly the opposite that. You know, the further north you go in your career, the more you advance, the more relative value employers place on core foundational skills. It's also, and I think this is to your point. One of the things that we found is that the more hybridized a job is, that is, you know, and just think you know, hybridization in a lot of ways proxies for the jobs that are most tech enabled, that are most abdited. The more tech enable the job is, more to data driven in job is, the more intensive it's demand...

...for foundational skills, for soft skills. So the most highly hybridized jobs are about three and a half times more likely to value creativity skills, bout twice as like value collaboration skills, but fifty percent more likely to require writing skills, pleming skills, research skills. So I think you're you're exactly right. There's an economist at the Harvard Kennedy School who's been doing a lot of work inside the MC Gurning by state and it's found that the jobs that are growing the fastest and their highest value are the ones that combine deep technical skills with with your skills, with judgment skills and the like, and so and I sort of think about how people are managing their careers. A key thing to remember is that you want to make sure that you're not just acquiring an individual tech skill set, as important as they are, but that that training is baking in the soft skills that actuate that skill set, that technical and this is what gives me hope to a certain extint. You know, major part of the economy are right brain type roles, whether you know marketing roles or even kind of traditional roles, have already embedded within them. You know, sold communication skills, they abil to collaborate with others, and so once you supplement these roles with technical skills, you start seeing a lot more powerful, more effective people in these roles. That's exactly right, I think. You know, you're seeing kind of two things happening at one one you know, a range of careers that are increasingly being being enabled by tecn data skills, that are being rendered more valuable through tech and data skills and, by the way, they're becoming more future proof and robot proof from because they have those tech and data skills. But the same time, you're seeing, I'm a growing number of people in in tech careers who are realizing they need to acquire management skills, that they need to acquire leadership skills, another kind of human skills, to be able to make themselves effective. So one thing that I've also seen you discuss is how technical skills and data skills are quickly evolving over time and that even within the professions that are, you know, highly sought after, like data science and Dad analysis, you have a certain degree of skill. obsoletetions that wasn't necessarily true and other professions before. Do you mind expanding to that notion as well? So so, first of all, I think this is a really important point, because the skills of roles change much faster than the roles themselves, where we're used to hearing these kind of hyperbolized statistics about well, Hey, by twenty, thirty, seventy percent of us aren't going to work and jobs that have been born yet. It's total nonsense. But the skills themselves of roles actually change much faster than that, though. As an example, you know, if you were to look at the skills of a data engineer, or, sorry, of a data scientist, if you were looking just over the last five years, you would see a huge increase in demand, like literally about a tenfold increase in demand inside inside data scientist jobage themselves are growing right, but for data visualization skills, for deep learning skills, for an ELP skills. That a five hundred percent increase in the man and big data skills and then, in the same time, things like Perl, scripting, like Matt Lab and C plus significantly declining in a man within those roles. So those are you know, those are pretty significant transformations that you see. That's great. And what is a strategy? You know, the data scientists or folks can adult to keep their skills competitive. If you were a data scientists, how would you go about your career growth and planning? So I think data scientists are are no strangers to data and my my advice here would be to be data driven in how you manage your own career. So, you know, you need to be able to use data to understand what's the landscape of opportunity. We need to be able to use data to be able to understand what skills are emerging within your field and across fields, and you need to be able to use data to figure...

...out what skills you need to acquire to stay ahead of the game. You know, in some sens the hybridization of jobs and the you know, increase velocity of skills transformation and the rise of data and digital skills, you know, have ushered in this sparadigm shift within the lot of labor market and how we think about skills and jobs. You know, I'd love to Segue here into how you think organizations, specifically educational institutions as well, can adjust a managing such a change. How do you see education evolving to address this skills transformation we're dissing today in the labor market? So I think there's a bunch of things that are going to need to change. So first of all, in the education system as it exists today. Even there, I think into institutions, universities others, need to become dramatically more agile in terms of how they track the landscape of opportunity for their graduates and build skills into their curricula, evaluate their curricula, make sure that they continue to be aligned, make sure that they are building differentiation for their graduates. But I think, more broadly to your point, we're going to see a significant transformation in how education happens, in the format and structure of education, because right now education is, for the most part in most countries, at once and done phenomenon. Right you go to school, you slog through it, you get your degree and you never look back. But think about a world where, you know, a third of the skills of an average job change in the space of ten years, where, if you look at a job like a data side, by the way, data scientists and data engineers were the two jobs that had the greatest pace of skill replacement across thousands of jobs over the last, you know, the last decade. And so we so think about that. That imperative. It says that the structure we need is not once in them then increasingly we need to be able that the people need to have access to shorter form programs, who programs that are adapted to learning on the fly and that enable people to acquire skills from across domains quickly. It also says, by the way, in a world where the job landscape is changing quickly, where a lot of jobs are automating away other jobs are getting born, that one of the things we also need is to develop structures of learning which are tight traded specific to specific transitions. I want to get ahead in my career as a data scientist, what are the sets of skills I need to acquire? Very specific sets of skills or I'm currently working as a as a financial quantitative analyst. By the way, as the skill landscape changes, some of the transitions that available are available change. So used to be a quantitive analyst, financial quantitative and analyst. Can you say that would go on to become a computer scientist because they had, you know, sort of Clus and Matt lab skills and other things like that? Increasingly, the skills that they have they need to have position and to be a data scientists. Okay, I'm a financial quantitative analyst. I said it right this time. I want to transition to be a data scientist. What are the skills I need to acquire to make that position? And and so we're going to start to see learning be much more personalized to the the kind of transitions that enable people in there and empower people in their careers and in some says. This is a bit of a controversial question, but do you think that the business model of university today is geared towards this transformation? I think it's going to challenge higher education, but I wouldn't count it out. I think that I think you're right that that current university business models are structured on once and done and I think you're going to see them be very resistant to change. Now I think you'll...

...see more future for traditional higher education players in countries where higher education revenues are tied to student enrollment as opposed to just kind of government grants. In a lot of countries, a lot of continental European countries, university just gets a budget from the state every year, from the from the nation, from them, from national government every year, and not a lot of impetus to try to drive enrollment. In a lot of places like the UK and the US, on the Netherlands, there's more incentive to drive around enrollment and and I'll give you an example, in the US today there's seventeen million people enrolled in a higher education programs, you know, traditional colleges and universities. I would argue that that number needs to be more like thirty million. Growth of that market. And when? When? especially given shrinking demographics, your only prospects of growing are to grow, to to be able to serve people in the middle of their careers and that's a lot of there's a lot of incentive to do that, but a lot of organizational resistance, a lot of faculty resistance, a lot of business model resistance. That would need is something we experience a data camp. We work with a lot of organizations trying to fill their talent gap right with upscilling. There's a tons of discussion today on the role of organizations and learning development teams and reskilling and upscaling their workforce. To commentate these hybrid jobs and the increasing the demand for data roles. What do you think of how organizations are addressing the skill gap and what are your recommendations here? So I think we're right at the precipice of seeing a significant transformation in terms of how companies manage talent and how they invest in learning. You know, right now most companies haven't the faintest clue who works for them. Look, they know your name, they know your tax ID number, they know how much they pay you. Before the pandemic they knew where you sit. They know anymore. But you know, what they don't actually know is what skills you have. In fact, most companies are don't even necessarily even know what skills they need. So one of the first steps that companies are starting to go down the road of today is to define a role architecture. What are the skills that I need, role by role in my company, because you don't know that it's hard to figure out how do I how do I build up the pipeline of talent? And where is my talent today? You know where is. It's Soolescence risk, where I'm a do I need it to go? And so they're sort of storying to do that initial mapping. Once they do that, I think that's going to change a lot because today a lot of the way that companies think about learning is either systems and compliance. Training a false and I need to be able to make sure that my customer service representatives can use the new reservation system or whatever, or it's these learning as a benefit platforms ocean that okay. Well, you know, we'll let people learn stuff and maybe theyy'll be more engaged and maybe they'll be more likely to stay. There's nothing wrong with engagement and retention as metrics. Think about how much that transforms in an era of talent shortage when I can say, wait a second, I've got the talent that I need. I've got people with a lot of the right skills already in my workforce and instead of firing them over here and hiring more people over here, I can instead build that pipeline that connects the reservoirs of talent to to where I'm having talent droughts and to bring in learning partners. To your point, you probably aren't universities to be able to say, okay, how do we, how can we create those very specific pipe learning, those skill pipelines that enable internal mobility and through point here that the biggest challenge is cultural. How do you create a mindset shift to become a learning organization? And that requires a deep appreciation of the subject matter, expertise your people have and creating that biplane for them.

I think it requires a, you know, another cultural change as well, which is a belief in your workforce and in people's future potential, because it's easy to look at somebody who's doing a job and say she's doing this job and that's that's who she is, but the ability to be able to take more skill based view of somebody is very liberating because it says, Hey, look, there's a broader potential here that each of my workers has, and how do we unleash that potential? We how do we invest in it? It's much more humanizing inclusive, in my opinion, because it creates a much stronger and more engage culture exactly. And I think the good news is I think we've got to you know, we've got a golden moment right now. There's two key im narratives that are going to drive companies to rethink their talent and to rethink how they invest in skills. One is, again, talent shortage. Right if I can't find the skills that I need, and you know, it brings the idea of talent management from being number six on my top five list being a number one, two or three issue, because I literally am, and you you see this all the time to day, a lot of companies that are reporting earnings, missing misss because they can't actually produce. And so all of a sudden it's a a where do I find the town? I can't buy it. How do I build it? How do I build it up from within? And so that's going to be one of the things that changes the culture. The other, you know, golden moment we have is around equity, because I think companies around the world are increasingly aware that they need to build more inclusive work forces. And again, if you sort of take a zero sum game mindset, that is to say that you know there's a finite amount of diverse talent and all I can do is just try to compete more agrestively for it, then you're going to wind up feeling pretty stuck. But if, instead you say, Hey, wait a second, most companies are more diverse at the bottom of the top marks. Companies have more women at the bottom than the top, and you say, Hey, wait a second, how do we create those skill pathways that you know that unlock the power that that talent, you can wind up with him not only an organization that can find that the talent it needs, but it can find the it can build the equity that that it wants to display. One hundred percent, I couldn't agree more, and I really believe in the power of learning and giving people the opportunity to realize their full potential as opposed to just filling a quota right, and that's where the culture transformation is in that sense, the final question we spoope. We spoke about organizations, educational institutions, but how should individuals looking to find jobs that are meaningful and provide upward mobility? How should they approach their own career growth? So I think this is this goes back to this notion of being GATA driven and managing own career right. So you know you need to develop essentially a ways map for your career. Like we we there is no APP like that out there and which means that you need to actually go out and do the underlying data science to be able to say where is opportunity? Where's opportunity today, but, more importantly, where is opportunity going to be? Look, dat it's nineties. Data Sciences in no small part about building predictive models. So build a predictive model about your career. Where where is the ball rolling on the field, and run there and then the the other advice at offer is when you start to think in terms of skills, not jobs, and it makes those transit makes running that distance much more achievable because you start to think about how you can construct a transition or set of transition skilled by skill, instead of feeling wow, how do I leap tall buildings in a single bound? We don't need to that as awesome. Finally, Matt, do you have any final call to action before we wrap up today's episode? So you know, look, we've talked a lot about this idea that that the future is about...

...skills, not not job titles, from not even jobs. And again I think that's a really liberating idea because it means you can control, you can take control of your own career and you can build your own destiny. You know, the world is is is changing very fast and the ability to be able to adapt to it and not only adapt to it, but to get in front of it. It's going to put you in the driver's seat in your career. Now, none of this is new. You know, if you haven't seen been the movie hidden figures, I really recommend it. And you know, for those who haven't seen it, if you rewind the tape to, you know, buy sixty years to you know, as we were to the age of this the you know can the Apollo mission and in the like, a computer didn't mean a thing. That was on your desk. Computer was was a person who is doing computational method scale, and I bring that up here because it says that skills have always been changing at the pace of that may have increased, but it's an inspiring movie because the women who they whom they the movie portraits, could have found themselves displaced and instead they reinvented themselves. They acquired new skills and they kept ahead of the market and wound up having tremendous achievements, and I think I would put that in front of each of us. Thank you so much for this mud and thank you for coming on data framed. So enjoyed this, like was data camps mission is to democratize data skills for everyone, closing data skill gaps and helping make better data driven decisions. Data Science and analytics are rapidly shaping every aspect of our lives and our businesses, and we're collecting more data than ever before, but not everyone is able to efficiently analyze all that data to extract meaningful insights. Data camp up skills companies and individuals on the skills they need to work with data in the real world. Learn more at data campcom. You've been listening to data framed, a podcast by data camp. Keep connected with us by subscribing to the show in your favorite podcast player. Please give us a rating, leave a comment and share episodes you love. That helps us keep delivering insights into all things data. Thanks for listening. Until next time,.

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