#80 The Rise of Hybrid Jobs & the Future of Data Skills (with Matt Sigelman)

The Power of Skills Over Job Titles: A Culture Transformation in the Era of Rapid Change

Companies are reporting earnings misses because they can't actually produce, and all of a sudden it's like where do I find the town well I can't buy it how do I build it how do I build it up from within. This realization is a turning point for companies to change their culture. The other golden moment we have is around equity because I think companies around the world are increasingly aware that they need to build more inclusive workforces. If you take a zero-sum game mindset, that is to say there's a finite amount of diverse talent and all I can do is just try to compete more aggressively 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 than the top, more 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 unlock the power of that talent? You can wind up within not only an organization that can find the talent it needs but it can also build the equity that it wants to display.

Creating Skill Pathways for Inclusive Workforces

The key to building inclusive workforces is creating skill pathways. Instead of focusing on filling quotas, companies should focus on unlocking the potential of their existing employees. This requires a shift in mindset from focusing on individual skills to understanding how different skills fit together to create a more comprehensive skill set. By doing so, companies can create a culture that values diversity and inclusion, not just as a marketing gimmick but as a core part of their identity.

The Importance of Data-Driven Career Planning

To find jobs that are meaningful and provide upward mobility, individuals need to develop a data-driven approach to their career planning. This means building a personal career map, where you identify the skills needed for success in your industry and start working on those skills. It's like building a predictive model about your career, where you forecast where opportunities will arise and run towards them.

Skills vs Job Titles: A Liberating Approach

The future is not about job titles but about skills. This shift in perspective allows individuals to take control of their careers and build their own destiny. By focusing on developing transferable skills that are in high demand across industries, individuals can adapt to changing market conditions and get ahead of the curve. The world is rapidly changing, and the ability to adapt not only adapt to it but to get in front of it will put you in the driver's seat in your career.

The Power of Learning and Reinvention

Historically, when the Apollo mission was underway, computers were seen as a novelty, and computational math at scale was seen as a person's job. However, the women portrayed in the movie "Hidden Figures" could have found themselves displaced and instead reinvented themselves by acquiring new skills and keeping ahead of the market. This is an inspiring story that highlights the power of learning and reinvention. By focusing on developing their skills, individuals can overcome any obstacle and achieve great things.

Democratizing Data Skills for Everyone

Data science and analytics are rapidly shaping every aspect of our lives and businesses. However, not everyone has access to the skills needed to work with data in the real world. This is where companies like Data Camp come in. Data Camp upskills companies and individuals on the skills they need to work with data in the real world, learn more at datacamp.com.

Conclusion

The future of work is all about skills, not job titles. By focusing on developing transferable skills that are in high demand across industries, individuals can adapt to changing market conditions and get ahead of the curve. Companies that prioritize diversity, inclusion, and skill development will be the ones that thrive in this rapidly changing world.

"WEBVTTKind: captionsLanguage: enyou're listening to data framed a podcast by data camp in this show you'll hear all the latest trends and insights in 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 in-depth discussions with data and analytics leaders at the forefront of the data revolution let's dive right in hello everyone this is adele data science evangelist and educator at datacamp you know it's no secret that data science jobs are on the rise but data skills across the board in every profession are rising as well leading to what today's guest matt siegelman calls hybrid jobs this will require a paradigm shift in how we think about jobs skills and education matt siegelman is the president of the burning glass institute and chairman of mz 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 position themselves to address these tectonic shifts in the job market and more now let's dive right in but it's great to have you on the show i am super excited to discuss with you all things future work your work at mz burning glass the importance of data skills and increasingly changing labor market and all that fun stuff but before do you mind giving a brief introduction about mz burning class and what you guys do so mz 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 in the way that 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 plan 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 interview i was in awe of the level of depth and care you and the mz burning glass team practice when were 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 uh it's an interesting because we started not as a a data company we started as an lp company we had developed a really good engine for recruitment that uses advanced nlp to be able to structure people's cvs 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 of large recruitment companies and hr management systems and the like but after a time we sort of came to realize the constraints of this because on 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 cvs and job postings and 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 we go walk into a cafe and you see somebody's you know a server who's she's fabulous right 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 in a given day and whoever happens to be looking for talent in a given day and so what we realized was that part of the reason why that's always been the case is that there's no market map it's hard for an individual job seeker to know what's all what are all the opportunities out there employers likewise and so you can't plan for a market you can't connect effectively within a market which you don't understand so what we did is we said hey look actually the world has evolved to in most industrial economies to where most hiring is happening online most job postings are online and so instead of waiting for uh just processing the data that our clients receive what do we go out and actually scrape kind of all the the job poisoning you find a lot of by the way kind of labor economists around the world use our data set we find that a sort of general consensus estimates about 85 percent of all job postings in the 55 countries that we we cover are actually in our database in the employee glass 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 define the experience levels credentials and so forth across dozens of dimensions and then we use our nlp expertise to then be able to to create to attach 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 an analogy for what what we've done is uh some work that's being done by an economist at mit by the name of alberto cavallo several years ago he was a young economist at uh 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 um and it totally transformed them we actually were doing this before alberto did his work but i always find that's like a he's now running something called the billion prices project empty burning glasses you could call it the the many the many billions of jobs project and it's been transformative in how we understand the economy and what 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 economics approach and what type of value does it offer that wasn't previously attainable so a traditional labor economist is usually transacting in survey-based data so all the kind of things we see the monthly job numbers or whatever they're based actually on a survey the it's a very you know 1950s 60s 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 we'll construct it we'll 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 a benchmark uh in being able to to validate what we're seeing from a broad trend perspective but the weakness of that kind of approach is that 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 u.s 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's a whatever computer programmer it is every kind of computer programmer 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 trend line don't mess with with the buckets right so i think only about three or four years ago i may be slightly off on that did the bureau of labor statistics actually start to recognize that there's a 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 was so breakthrough about this is not even just that it gave a more real-time view of the economy because obviously most of all that survey work takes a lot of time too 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 do jobs that are ruby developers right ruby developers in one industry have actually a slightly different skill mix from ruby developers in another industry 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 institutions 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 today'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 on 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 since 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 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 in a whole range of now uh you know a whole range of jobs are becoming what i would call data enabled 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 450 000 jobs last year up from about 350 000 just before the pandemic so literally in the space of two years you said about a 40 growth in demand for these skills which is think about how fast or not labor markets tend to move which is a couple percent a year and think about you know a 40 growth in the space of two years a tremendous growth in in that demand now when you think about how we characterize that demand 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 vary fairly differently fairly significantly rather based on the kind of job that 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 sets so common across all of them is python not all data analysts need python but even a growing number of data analysts need them python seems to be winning out a little bit over our um both 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 data engineer needs ztl skills um a data scientist generally does not a data scientist a growing percentage of data scientists now need ml skills five years ago that were true it's true today but you don't see uh ml skills showing up in data analyst jobs and and in a relatively small percentage of data engineering jobs so those kinds of differences are increasingly you know sort of coming into 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 isn't huge growth in data science jobs but in fact here's a little fun fact for you in 2010 there are only 150 job postings in the united states for data for data scientists last year there are about 50 000 and there were another 50 000 jobs for data engineers and the engineers i think probably i don't know when you started to see but i would bet even in 2014 or 2015 they were probably pretty close to zero so you're seeing huge rates of growth and what i also love about breaking down skills as a methodology is that it also shows you a proxy of where organizations are on the data maturity curve right so for example you mentioned machine learning skills are are much more in demand today than they were let's say five years ago it's because five years ago i don't think a lot of organizations knew how to operationalize machine learning like they do today so it shows you where the organizations lie on the data 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 role what are we looking for in that role and how is that different from what our best practice peers are looking for in the same role if you would look at for example product managers at city or jpmorgan chase and look at product managers at amazon amazon product managers are data enabled product managers and a lot of mainline companies or not so you can kind of benchmark yourself around like hey what are the skills that we're going to need to be able to manage transformation in our in our industry part being on something you mentioned here is that we don't want to constrain this conversation just to roles we want to focus on skills so i don't think many people today define this as brilliantly as you do and hear the concept of hybrid jobs can you walk us through how you define hybrid jobs especially when it comes to data skills yeah absolutely and and first you all define 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 notion of you work in the office some of the time and and work remotely some of the rest of time but when we first started tracking what we call hybrid jobs we were tracking a really fascinating and and i think actually a really disruptive trend that we've been increasingly seeing in the job market and that's the the tendency of jobs to blend skills from across domains 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 we found that the average job across all jobs like we're not even just talking about tech jobs or data jobs the average job has seen about a third of its skills replaced over the last decade you know and that pace has been quickening or through the pandemic by the way huge implications for that because think about university of traditional university-based kind of learning structures like have they changed a third of their curriculum in the last 10 years don't think so and and by the way think about employers as well okay has have the skills of of our workforce genius has our workforce changed a third of its skill base and probably not and so you know raises real questions that what we were just talking about a minute ago about obsolescence but you know so that provides an overall frame of the pace 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 tech stacks 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 domains essentially you know jobs are having sex i'll give you an example i think you actually you point to this example before you think about a marketing manager we all know plenty of people who are marketing nice folks they tend to be pretty right brain marketing because you know they understand people and their psychology and they can communicate to them and whatever increasingly we want marketing people to enable to be able to manipulate customer data well guess what you need some data skills to do it and so a marketing manager who has sql skills i'm not even talking about hardcore data skills right but a marketing map who has sql skills and we just manipulate a customer database gets paid about 40 more than you know one who does not have those same sets of skills so you know it sort of 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 know think about that example right brain person left brain skills you're creating an 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 cat bird seat in the in the economy that's really awesome so i want to kind of focus in on the data skills of hybrid 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 to reinforce your point right part of the reason why jobs can hybridize in 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 a data scientist compared to you know kind of the quant jobs that preceded a data scientist actually is very strong in fact it's a great example of a hybrid job because data scientist needs significant programming skills which are you know 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 they need to understand those problems but you know look when i think about the world of of data skills or of hybridization and hybridization of of data skills and others one thing that's important in framing our understanding of 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 i'm an old guy so this is where i get to do my you know when i was a boy i used to walk back you know up up hill both ways to school and barefoot in the snow kind of moments but my original work and data was in in fortran and an spss that must have been challenging you know there was a it was a much less accessible language by the way i'll give you just diagnosis off on the side but but it makes me makes me laugh to remember it there was a a command when you're working with sort of big data sets was sort of like call tape whatever the the tape number was and it was only you know i realized that like half the time you call that it was very slow i figured that was just a processing issue and and so half the time it would hang and i only later discovered that when you type call tape whatever it it rang a bell in at somebody's desk and someone had to go and fetch a tape and load it so if they're away from their at their desk or whatever they would like you know they just that's why it would hang right because they just come across oh wow that's hilarious but you know so look what when we look at the state of a lot of tech stacks 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 those skills because you don't need a you know a deep specialist in order to be able to to use data skills use an example marketing we were talking about before 10 almost 10 percent of the jobs that ask for data science skills not just data skills but data science skills rn 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 can be 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 python and r are open source there's you no longer need to pay to license like similar that you had with spss or sas 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 finance or 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 a across professional jobs you know we recently looked at the digitalization of of what are sometimes called middle skill jobs that means north of of secondary school and and south of university and what we found is that about eight and ten of them today are digitally intensive and digitally intensive middle school jobs are twice as likely to pay a living wage they're growing twice as fast the two and ten the middle school jobs that are not digitally intensive are increasingly just in construction and transportation so so this is this is really broad-based you know most job i think one of the things that we've been looking at recently in in our data is trying to identify what are the foundational skills of the new economy what are the sets of skills that are broadly in demand and you know i think traditionally if you looked at foundational skills you would say okay it's the human skills it's what people like to call the soft skills um but actually we're seeing at least as much as broad-based demand for for data skills for digital skills for business skills so i want to 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 in this hybridized economy do you mind expanding on that notion yeah this is a fascinating you know conclusion we sort of 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've got carbohydrates down here and then the important stuff is protein that's the technical skills actually your career 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 and one of the other things that we found is that the more hybridized the 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 edited the more tech enabled the job is the more data driven a job is the more intensive its 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 about twice as like value collaboration skills about 50 more likely to require writing skills crop quality skills research skills so i think you're you're exactly right there's a an economist at the the harvard kennedy school who's been doing a lot of work inside the mt burning glass state and has found that the jobs that are growing the fastest and their highest value are the ones that combine deep technical skills with with human skills with judgment skills and the like and so when 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 extent you know a 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 solid communication skills the ability 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 you're seeing kind of two things happening at one one you know a range of careers that are increasingly being enabled by tech and data skills that are being rendered more valuable through tech and data skills and by the way they're becoming more future proof and and robot proof because they have those tech and data skills at the same time you're seeing 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 and other 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 data analysis you have a certain degree of skill obsolescence that wasn't necessarily true in other professions before do you mind expanding into 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 rules themselves we're we're used to hearing these kind of hyperbolized statistics about well hey by 2030 70 of us aren't going to work in jobs that have been born yet it's total nonsense but the skills themselves of roles actually change much faster than that so 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 ten-fold increase in demand inside inside data scientist jobs which themselves are growing right but for data visualization skills for deep learning skills for nlp skills a bit of 500 percent increase in demand and big data skills and then at the same time things like pearl scripting like matlab and c plus significantly declining in demand 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 that data scientists or folks can adopt to keep their skills competitive if you were a data scientist 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 you need to be able to use data to be able to understand what skills are emerging within your field 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 sense the hybridization of jobs and the you know increased velocity of skills transformation and the rise of data and digital skills you know have ushered in this paradigm shift within the 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 to managing such a change how do you see education evolving to address the skills transformation we're witnessing 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 in 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 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 in the format and structure of education because right now education is for the most part in most countries a once and done phenomenon right you go to school you slide 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 10 years where if you look at a job like a data sign 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 over the last decade and so we sort of think about that that imperative it says that the structure we need is not once and done that increasingly we need to be able that people need to have access to shorter form programs to programs that are adapted to learning on the fly and that enable people to acquire um 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 titrated 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 quantity of an analog financial quantitative analyst i can't even say that would go on to become a computer scientist because they had you know sort of c plus and matlab skills and other things like that increasingly the skills that they have they need to have position them to be a data scientist 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 transition 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 sense this is a bit of a controversial question but do you think that the business model of universities today is geared towards this transformation i think it's going to challenge uh higher education but it wouldn't count it out i think that i think you're right 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 company countries a lot of continental european countries you know university just gets a a budget from the state every year from the from the nation from the national government every year and not a lot of impetus to try to drive enrollment in a lot of places like the uk in the us on the netherlands there's more incentive to drive around enrollment and and i'll give you an example in the u.s today there's 17 million people enrolled in higher education programs in nutritional colleges and universities i would argue that that number needs to be more like 30 million the growth of that market and 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 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 we need you know something we experience at data camp we work with a lot of organizations trying to fill their talent gap right with upskilling there's tons of discussion today on the role of organizations and learning and development teams and reskilling and upscaling their workforce to accommodate these hybrid jobs and the increasing 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 i mean 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 don't anymore but you know what they don't actually know is what skills you have and in fact most companies are don't even necessarily even know um what skills they need so one of the first steps that companies are starting to go down the the road of today is to define a role architecture what are the skills that i need role by role in my company because if 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 its obsolescence risk where am i do i need it to go and so they're sort of starting 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 or 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 notion that okay well you know we'll let people learn stuff and maybe they'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 who probably aren't universities and to be able to say okay how do we how can we create those very specific learning those skill pipelines that enable internal mobility and to your 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 pipeline for them i think it requires a you know a another cultural change as well which is a belief in your workforce and in people's future potential because it's 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 a 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 how do we how do we invest in it it's much more humanizing inclusive in my opinion because it creates a much stronger and more engaged culture exactly and i think the good news is i think we've got two you know we've got a golden moment right now there's two key imperatives that are going to drive companies to rethink their talent and to rethink how they invest in skills one is again town 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 see this all the time today a lot of companies that are are reporting earnings missing misses because they can't actually produce and so all of a sudden it's like where do i find the town well 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 workforces 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 of diverse talent and all i can do is just try to compete more aggressively 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 than the top more 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 of that that talent you can wind up within not only a an organization that can find the talent it needs but it can find the you can build the equity that that it wants to display 100 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 final question 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 data driven and managing your own career right so you know you need to develop essentially a 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 and do the underlying data science to be able to say where is opportunity where is opportunity today but more importantly where is opportunity going to be look data scientist data sciences in 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 i'd offer is when you start to think in terms of skills not jobs then it makes those trends it makes running that distance much more achievable because you start to think about how you can construct a transition or a set of transitions skill by skill instead of feeling wow how do i leap tall buildings in a single bound well you don't need to that is 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 the future is about skills not not job titles probably 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 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 the movie hidden figures um i really recommend it and you know for for those who haven't seen it if you rewind the tape to you know by 60 years to you know as we were to the age of the you know the apollo mission and and the like uh a computer didn't mean a thing that was on your desk a computer was was a person who was doing computational math at scale and i bring that up here because it says that skills have always been changing you know the pace of that may have increased but it's an inspiring movie because the women who they whom the the movie portrays 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 matt and thank you for coming on data framed so enjoy this likewise data camp's 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 aspects 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 upskills companies and individuals on the skills they need to work with data in the real world learn more at datacamp.com 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 timeyou're listening to data framed a podcast by data camp in this show you'll hear all the latest trends and insights in 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 in-depth discussions with data and analytics leaders at the forefront of the data revolution let's dive right in hello everyone this is adele data science evangelist and educator at datacamp you know it's no secret that data science jobs are on the rise but data skills across the board in every profession are rising as well leading to what today's guest matt siegelman calls hybrid jobs this will require a paradigm shift in how we think about jobs skills and education matt siegelman is the president of the burning glass institute and chairman of mz 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 position themselves to address these tectonic shifts in the job market and more now let's dive right in but it's great to have you on the show i am super excited to discuss with you all things future work your work at mz burning glass the importance of data skills and increasingly changing labor market and all that fun stuff but before do you mind giving a brief introduction about mz burning class and what you guys do so mz 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 in the way that 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 plan 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 interview i was in awe of the level of depth and care you and the mz burning glass team practice when were 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 uh it's an interesting because we started not as a a data company we started as an lp company we had developed a really good engine for recruitment that uses advanced nlp to be able to structure people's cvs 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 of large recruitment companies and hr management systems and the like but after a time we sort of came to realize the constraints of this because on 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 cvs and job postings and 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 we go walk into a cafe and you see somebody's you know a server who's she's fabulous right 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 in a given day and whoever happens to be looking for talent in a given day and so what we realized was that part of the reason why that's always been the case is that there's no market map it's hard for an individual job seeker to know what's all what are all the opportunities out there employers likewise and so you can't plan for a market you can't connect effectively within a market which you don't understand so what we did is we said hey look actually the world has evolved to in most industrial economies to where most hiring is happening online most job postings are online and so instead of waiting for uh just processing the data that our clients receive what do we go out and actually scrape kind of all the the job poisoning you find a lot of by the way kind of labor economists around the world use our data set we find that a sort of general consensus estimates about 85 percent of all job postings in the 55 countries that we we cover are actually in our database in the employee glass 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 define the experience levels credentials and so forth across dozens of dimensions and then we use our nlp expertise to then be able to to create to attach 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 an analogy for what what we've done is uh some work that's being done by an economist at mit by the name of alberto cavallo several years ago he was a young economist at uh 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 um and it totally transformed them we actually were doing this before alberto did his work but i always find that's like a he's now running something called the billion prices project empty burning glasses you could call it the the many the many billions of jobs project and it's been transformative in how we understand the economy and what 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 economics approach and what type of value does it offer that wasn't previously attainable so a traditional labor economist is usually transacting in survey-based data so all the kind of things we see the monthly job numbers or whatever they're based actually on a survey the it's a very you know 1950s 60s 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 we'll construct it we'll 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 a benchmark uh in being able to to validate what we're seeing from a broad trend perspective but the weakness of that kind of approach is that 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 u.s 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's a whatever computer programmer it is every kind of computer programmer 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 trend line don't mess with with the buckets right so i think only about three or four years ago i may be slightly off on that did the bureau of labor statistics actually start to recognize that there's a 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 was so breakthrough about this is not even just that it gave a more real-time view of the economy because obviously most of all that survey work takes a lot of time too 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 do jobs that are ruby developers right ruby developers in one industry have actually a slightly different skill mix from ruby developers in another industry 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 institutions 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 today'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 on 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 since 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 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 in a whole range of now uh you know a whole range of jobs are becoming what i would call data enabled 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 450 000 jobs last year up from about 350 000 just before the pandemic so literally in the space of two years you said about a 40 growth in demand for these skills which is think about how fast or not labor markets tend to move which is a couple percent a year and think about you know a 40 growth in the space of two years a tremendous growth in in that demand now when you think about how we characterize that demand 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 vary fairly differently fairly significantly rather based on the kind of job that 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 sets so common across all of them is python not all data analysts need python but even a growing number of data analysts need them python seems to be winning out a little bit over our um both 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 data engineer needs ztl skills um a data scientist generally does not a data scientist a growing percentage of data scientists now need ml skills five years ago that were true it's true today but you don't see uh ml skills showing up in data analyst jobs and and in a relatively small percentage of data engineering jobs so those kinds of differences are increasingly you know sort of coming into 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 isn't huge growth in data science jobs but in fact here's a little fun fact for you in 2010 there are only 150 job postings in the united states for data for data scientists last year there are about 50 000 and there were another 50 000 jobs for data engineers and the engineers i think probably i don't know when you started to see but i would bet even in 2014 or 2015 they were probably pretty close to zero so you're seeing huge rates of growth and what i also love about breaking down skills as a methodology is that it also shows you a proxy of where organizations are on the data maturity curve right so for example you mentioned machine learning skills are are much more in demand today than they were let's say five years ago it's because five years ago i don't think a lot of organizations knew how to operationalize machine learning like they do today so it shows you where the organizations lie on the data 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 role what are we looking for in that role and how is that different from what our best practice peers are looking for in the same role if you would look at for example product managers at city or jpmorgan chase and look at product managers at amazon amazon product managers are data enabled product managers and a lot of mainline companies or not so you can kind of benchmark yourself around like hey what are the skills that we're going to need to be able to manage transformation in our in our industry part being on something you mentioned here is that we don't want to constrain this conversation just to roles we want to focus on skills so i don't think many people today define this as brilliantly as you do and hear the concept of hybrid jobs can you walk us through how you define hybrid jobs especially when it comes to data skills yeah absolutely and and first you all define 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 notion of you work in the office some of the time and and work remotely some of the rest of time but when we first started tracking what we call hybrid jobs we were tracking a really fascinating and and i think actually a really disruptive trend that we've been increasingly seeing in the job market and that's the the tendency of jobs to blend skills from across domains 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 we found that the average job across all jobs like we're not even just talking about tech jobs or data jobs the average job has seen about a third of its skills replaced over the last decade you know and that pace has been quickening or through the pandemic by the way huge implications for that because think about university of traditional university-based kind of learning structures like have they changed a third of their curriculum in the last 10 years don't think so and and by the way think about employers as well okay has have the skills of of our workforce genius has our workforce changed a third of its skill base and probably not and so you know raises real questions that what we were just talking about a minute ago about obsolescence but you know so that provides an overall frame of the pace 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 tech stacks 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 domains essentially you know jobs are having sex i'll give you an example i think you actually you point to this example before you think about a marketing manager we all know plenty of people who are marketing nice folks they tend to be pretty right brain marketing because you know they understand people and their psychology and they can communicate to them and whatever increasingly we want marketing people to enable to be able to manipulate customer data well guess what you need some data skills to do it and so a marketing manager who has sql skills i'm not even talking about hardcore data skills right but a marketing map who has sql skills and we just manipulate a customer database gets paid about 40 more than you know one who does not have those same sets of skills so you know it sort of 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 know think about that example right brain person left brain skills you're creating an 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 cat bird seat in the in the economy that's really awesome so i want to kind of focus in on the data skills of hybrid 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 to reinforce your point right part of the reason why jobs can hybridize in 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 a data scientist compared to you know kind of the quant jobs that preceded a data scientist actually is very strong in fact it's a great example of a hybrid job because data scientist needs significant programming skills which are you know 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 they need to understand those problems but you know look when i think about the world of of data skills or of hybridization and hybridization of of data skills and others one thing that's important in framing our understanding of 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 i'm an old guy so this is where i get to do my you know when i was a boy i used to walk back you know up up hill both ways to school and barefoot in the snow kind of moments but my original work and data was in in fortran and an spss that must have been challenging you know there was a it was a much less accessible language by the way i'll give you just diagnosis off on the side but but it makes me makes me laugh to remember it there was a a command when you're working with sort of big data sets was sort of like call tape whatever the the tape number was and it was only you know i realized that like half the time you call that it was very slow i figured that was just a processing issue and and so half the time it would hang and i only later discovered that when you type call tape whatever it it rang a bell in at somebody's desk and someone had to go and fetch a tape and load it so if they're away from their at their desk or whatever they would like you know they just that's why it would hang right because they just come across oh wow that's hilarious but you know so look what when we look at the state of a lot of tech stacks 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 those skills because you don't need a you know a deep specialist in order to be able to to use data skills use an example marketing we were talking about before 10 almost 10 percent of the jobs that ask for data science skills not just data skills but data science skills rn 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 can be 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 python and r are open source there's you no longer need to pay to license like similar that you had with spss or sas 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 finance or 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 a across professional jobs you know we recently looked at the digitalization of of what are sometimes called middle skill jobs that means north of of secondary school and and south of university and what we found is that about eight and ten of them today are digitally intensive and digitally intensive middle school jobs are twice as likely to pay a living wage they're growing twice as fast the two and ten the middle school jobs that are not digitally intensive are increasingly just in construction and transportation so so this is this is really broad-based you know most job i think one of the things that we've been looking at recently in in our data is trying to identify what are the foundational skills of the new economy what are the sets of skills that are broadly in demand and you know i think traditionally if you looked at foundational skills you would say okay it's the human skills it's what people like to call the soft skills um but actually we're seeing at least as much as broad-based demand for for data skills for digital skills for business skills so i want to 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 in this hybridized economy do you mind expanding on that notion yeah this is a fascinating you know conclusion we sort of 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've got carbohydrates down here and then the important stuff is protein that's the technical skills actually your career 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 and one of the other things that we found is that the more hybridized the 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 edited the more tech enabled the job is the more data driven a job is the more intensive its 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 about twice as like value collaboration skills about 50 more likely to require writing skills crop quality skills research skills so i think you're you're exactly right there's a an economist at the the harvard kennedy school who's been doing a lot of work inside the mt burning glass state and has found that the jobs that are growing the fastest and their highest value are the ones that combine deep technical skills with with human skills with judgment skills and the like and so when 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 extent you know a 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 solid communication skills the ability 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 you're seeing kind of two things happening at one one you know a range of careers that are increasingly being enabled by tech and data skills that are being rendered more valuable through tech and data skills and by the way they're becoming more future proof and and robot proof because they have those tech and data skills at the same time you're seeing 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 and other 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 data analysis you have a certain degree of skill obsolescence that wasn't necessarily true in other professions before do you mind expanding into 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 rules themselves we're we're used to hearing these kind of hyperbolized statistics about well hey by 2030 70 of us aren't going to work in jobs that have been born yet it's total nonsense but the skills themselves of roles actually change much faster than that so 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 ten-fold increase in demand inside inside data scientist jobs which themselves are growing right but for data visualization skills for deep learning skills for nlp skills a bit of 500 percent increase in demand and big data skills and then at the same time things like pearl scripting like matlab and c plus significantly declining in demand 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 that data scientists or folks can adopt to keep their skills competitive if you were a data scientist 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 you need to be able to use data to be able to understand what skills are emerging within your field 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 sense the hybridization of jobs and the you know increased velocity of skills transformation and the rise of data and digital skills you know have ushered in this paradigm shift within the 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 to managing such a change how do you see education evolving to address the skills transformation we're witnessing 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 in 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 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 in the format and structure of education because right now education is for the most part in most countries a once and done phenomenon right you go to school you slide 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 10 years where if you look at a job like a data sign 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 over the last decade and so we sort of think about that that imperative it says that the structure we need is not once and done that increasingly we need to be able that people need to have access to shorter form programs to programs that are adapted to learning on the fly and that enable people to acquire um 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 titrated 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 quantity of an analog financial quantitative analyst i can't even say that would go on to become a computer scientist because they had you know sort of c plus and matlab skills and other things like that increasingly the skills that they have they need to have position them to be a data scientist 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 transition 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 sense this is a bit of a controversial question but do you think that the business model of universities today is geared towards this transformation i think it's going to challenge uh higher education but it wouldn't count it out i think that i think you're right 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 company countries a lot of continental european countries you know university just gets a a budget from the state every year from the from the nation from the national government every year and not a lot of impetus to try to drive enrollment in a lot of places like the uk in the us on the netherlands there's more incentive to drive around enrollment and and i'll give you an example in the u.s today there's 17 million people enrolled in higher education programs in nutritional colleges and universities i would argue that that number needs to be more like 30 million the growth of that market and 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 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 we need you know something we experience at data camp we work with a lot of organizations trying to fill their talent gap right with upskilling there's tons of discussion today on the role of organizations and learning and development teams and reskilling and upscaling their workforce to accommodate these hybrid jobs and the increasing 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 i mean 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 don't anymore but you know what they don't actually know is what skills you have and in fact most companies are don't even necessarily even know um what skills they need so one of the first steps that companies are starting to go down the the road of today is to define a role architecture what are the skills that i need role by role in my company because if 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 its obsolescence risk where am i do i need it to go and so they're sort of starting 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 or 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 notion that okay well you know we'll let people learn stuff and maybe they'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 who probably aren't universities and to be able to say okay how do we how can we create those very specific learning those skill pipelines that enable internal mobility and to your 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 pipeline for them i think it requires a you know a another cultural change as well which is a belief in your workforce and in people's future potential because it's 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 a 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 how do we how do we invest in it it's much more humanizing inclusive in my opinion because it creates a much stronger and more engaged culture exactly and i think the good news is i think we've got two you know we've got a golden moment right now there's two key imperatives that are going to drive companies to rethink their talent and to rethink how they invest in skills one is again town 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 see this all the time today a lot of companies that are are reporting earnings missing misses because they can't actually produce and so all of a sudden it's like where do i find the town well 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 workforces 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 of diverse talent and all i can do is just try to compete more aggressively 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 than the top more 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 of that that talent you can wind up within not only a an organization that can find the talent it needs but it can find the you can build the equity that that it wants to display 100 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 final question 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 data driven and managing your own career right so you know you need to develop essentially a 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 and do the underlying data science to be able to say where is opportunity where is opportunity today but more importantly where is opportunity going to be look data scientist data sciences in 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 i'd offer is when you start to think in terms of skills not jobs then it makes those trends it makes running that distance much more achievable because you start to think about how you can construct a transition or a set of transitions skill by skill instead of feeling wow how do i leap tall buildings in a single bound well you don't need to that is 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 the future is about skills not not job titles probably 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 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 the movie hidden figures um i really recommend it and you know for for those who haven't seen it if you rewind the tape to you know by 60 years to you know as we were to the age of the you know the apollo mission and and the like uh a computer didn't mean a thing that was on your desk a computer was was a person who was doing computational math at scale and i bring that up here because it says that skills have always been changing you know the pace of that may have increased but it's an inspiring movie because the women who they whom the the movie portrays 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 matt and thank you for coming on data framed so enjoy this likewise data camp's 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 aspects 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 upskills companies and individuals on the skills they need to work with data in the real world learn more at datacamp.com 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\n"