The Struggle is Real: Bridging the Gap between Data Leaders and Business Leaders

As we explore the current state of data analytics, it's clear that there's a significant gap between data leaders and business leaders. This disconnect leads to a lack of strategic direction, resulting in wasted resources and a failure to deliver value from data initiatives.

The issue lies in the fact that data leaders often struggle to articulate the value of their work, making it difficult for business leaders to understand why certain decisions need to be made. On the other hand, business leaders may not know how to set up their organizations to support effective data leadership, leading to a lack of clarity on what skills and expertise are required.

This creates a vicious cycle where data leaders can't articulate the value they're creating, while business leaders aren't setting them up for success. It's almost as if they're being set up to fail. As one expert noted, "it becomes a bit of a vicious cycle... it's like, how do we bridge that gap?" The answer lies in recognizing the importance of strategy and tying data initiatives back to business goals.

The current state of data analytics is characterized by a focus on building infrastructure without strategic direction. This leads to data becoming seen as a cost center rather than a valuable asset. As a result, organizations often appoint the wrong people for data leadership roles, making it difficult to deliver value from their work.

To bridge this gap, organizations need to focus on creating well-balanced teams that incorporate diversity of thought and experience. This means looking beyond technical expertise and considering the broader skill set required to drive business outcomes. It's not just about having a team with diverse backgrounds, but also about ensuring that they have a range of perspectives and approaches.

One key trend that will shape the data space in the next few years is the rise of data products. As organizations look for ways to adopt data analytics more effectively, they'll need to create compelling narratives around why certain data-driven decisions are essential. This involves articulating the value that data can bring to the business and creating a clear vision for how data will be used to drive outcomes.

Attraction and retention will also become increasingly important as organizations seek to build teams with diverse skill sets. Rather than focusing solely on technical expertise, they'll need to balance their teams to ensure they have a range of perspectives and approaches. This means bringing in people from different backgrounds and industries, rather than simply relying on internal talent.

Another key trend is the increasing recognition that data analytics should be treated as an asset. As organizations look for ways to derive value from their data initiatives, they'll need to develop a clear understanding of how data can drive business outcomes. This involves creating a compelling narrative around the value of data and ensuring that it's visible and impactful within the organization.

The industry needs to tackle this challenge in order to reach stability point for the data analytics industry. As one expert noted, "I think we need to figure out a way of how does the data analytics community start to articulate the value that it's creating?" By developing clear messaging around the value of data and its role in driving business outcomes, organizations can attract and retain top talent.

Ultimately, the key to bridging the gap between data leaders and business leaders lies in recognizing the importance of strategy and tying data initiatives back to business goals. By focusing on creating well-balanced teams with diverse skill sets and articulating the value of data, organizations can drive real change and deliver value from their data analytics efforts.

**Trends to Watch**

In the next few years, several trends will shape the data space. The rise of data products is likely to be a key driver, as organizations look for ways to adopt data analytics more effectively. Attraction and retention will also become increasingly important, as organizations seek to build teams with diverse skill sets.

The increasing recognition that data analytics should be treated as an asset is another trend worth watching. As organizations develop a clear understanding of how data can drive business outcomes, they'll need to create compelling narratives around the value of data and its role in driving business success.

**Creating Well-Balanced Teams**

Creating well-balanced teams is essential for bridging the gap between data leaders and business leaders. This means looking beyond technical expertise and considering the broader skill set required to drive business outcomes. Rather than focusing solely on internal talent, organizations should consider bringing in people from different backgrounds and industries.

A balanced team will include a mix of skills and perspectives, ensuring that there's no single point of failure. By incorporating diversity of thought and experience into their teams, organizations can create a more effective and efficient data analytics function.

**The Value of Data**

Treating data as an asset is becoming increasingly important for businesses looking to derive value from their data initiatives. As the industry continues to grow and evolve, it's essential that organizations develop a clear understanding of how data can drive business outcomes.

By recognizing the value of data and its role in driving business success, organizations can attract and retain top talent. They'll also be better equipped to create compelling narratives around the benefits of data analytics and drive real change within their organizations.

**Conclusion**

Bridging the gap between data leaders and business leaders requires a fundamental shift in approach. By recognizing the importance of strategy and tying data initiatives back to business goals, organizations can drive real change and deliver value from their data analytics efforts.

As the industry continues to evolve, it's essential that we prioritize creating well-balanced teams with diverse skill sets and articulating the value of data. Only by doing so will we be able to unlock the full potential of data analytics and drive business success in the years to come.

"WEBVTTKind: captionsLanguage: enyou're listening to data framed a podcast by datacamp 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 foreign this is Adele data science educator and evangelist at data camp over the past year hosting data framed I've had the privilege and honor of interviewing a lot of data leaders and practitioners on how to become data driven one common theme is that I always boils down to the people side of things whether upskilling or hiring becoming data driven is a talent strategy just as much as it is a technology strategy this is why I'm excited to have Kyle winterbottom on today's episode Kyle is the host of the driven by data podcast and the founder and CEO of orbition Italian Solutions provider for scaling data analytics and artificial intelligence themes across the UK Europe and the US throughout the episode we touched upon a few themes that are really relevant to building a holistic Talent strategy for data teams including hiring and recruiting improving retention for data teams and how to approach upscaling and more if you enjoyed this episode make sure to rate and subscribe to the show but only if you liked it also make sure to check out the content we have in store this month on building a data driven organization it ties a lot to the themes we talk about on the show and now on today's episode Kyle it's great to have you on the show thank you very much for having me looking forward to it I am very excited to discuss with you all things building effective data teams how organizations should be approaching hiring retaining recruiting data talent and your work leading orbitrin and hosting the throne by data podcast but before maybe can you give us a bit of a background about yourself so I've been in the data and analytics talent and recruitment space for the last 12 years I guess our mission is a boutique kind of Talent Solutions business that operates exclusively in this world so we're headquartered here in the UK but we have a presence out in the in the states and do quite a lot of work across the UK Europe and America and I guess really the bulk of what we do is done at the mid to senior level and often done at scale so helping organizations typically to build out their data leadership and management teams and then the senior technical teams that that fit beneath them so that could be I don't know working with a ftse 100 or Fortune 100 100 as an example that want to hire 40 50 60 people over a 12-month period it could be a smaller to mid-sized Enterprise that wants to hire eight to ten people over the next two to three months it could be an organization looking to appoint a chief data analytics officer as an example so the three components to to our business really and then yeah as you mentioned outside of the day-to-day the business really is underpinned by the community element that we try to to serve so we have our podcast we have our event series we have our mentorship scheme you know Partnerships with universities to try and raise awareness and things like that so yeah roughly speaking that's a little bit about about me that's really great and there's definitely a lot to cover so I'd love to set the stage for today's conversation by first trying to understand what the hiring and retaining landscape looks like for data talent in 2022 we're at the backdrop of a pandemic a rise of remote work the great reshuffle the great resignation and now a downturn in the economy so maybe in your own words how would you describe the landscape for hiring and retaining data talent in 2022 and how has it evolved over the past few years it's an interesting question I think the the last 18 months have been a few words to sum it up in one word would be crazy right you know so I think um it's been interesting because I have conversations on a daily basis with people that might have not been in the job market since before before the pandemic and normally one of the first questions I get asked is how is the market now after the pandemic and it's interesting because I think really data and analytics was was very fortunate so a lot of the work that we were doing with with organizations even after the pandemic started obviously there was an natural pause so while people figured out what's going on how long is this going to last Etc but within a matter of months recruitment in this space was back to normal levels and then obviously you know start of 2021 and ever since it's been on a trajectory like like that so it's certainly been crazy but also just the level of growth and the amount of hiring we were always going to see in natural correction so we talk about now the great resignation we talk about a potential downturn in the economy we've definitely seen a bit of a Slowdown over the last couple of months but I don't think that's necessarily related to the economic situation I think that's more of a natural correction because just the the scale and the pace of hiring in this space just wasn't sustainable to keep going the way it had been going for the last 18 months and and then I think anything outside of that which I'm sure we're going to get into in a little bit more detail but there's been a lot more Focus I'd say over the last 12 months especially around trying to maybe realign our focus on getting the foundations of data analytics right as opposed to focusing on the the shiny technical AI type stuff which many businesses unfortunately got kind of distracted by us yeah definitely I'd love to like talk about that trap at a later point in the conversation but maybe on mentioning the crazy and unsustainable pace of hiring over the past 18 months what were the drivers behind such high demands of hiring in the data space especially over the past 18 months well I think if every business leader now knows that for them to continue to thrive and be fit for the future as we're starting to refer to it as that data is going to play An Almighty important role in that unfortunately that's easier said than done right so the execution becomes really difficult but I think what we've found is that you're getting more organizations actually starting that data analytics journey and then you're also getting organizations that are already in the midst of it throwing even more money at it so it was a combustion of two narratives coming together at the same time which meant there was a an awful lot of businesses looking to hire people and not just ones and twos you know multiples so I don't think I've spoken to a business over the last got 18 24 months that's saying you do we just want to hire one or two people it's normally you know we need to hire 10 engineers and four data governance people and yeah they are the other so that combustion of those two narratives the demand and the lack of Supply I think all together just created this this big kind of Boom really that's very exciting so let's definitely go into the details about hiring and recruiting data Talent you've as you've mentioned you've worked with quite a few organizations over the past few years on filling their talent Gap walk us through maybe some of the main challenges data leaders are facing and filling their talent Gap and how have you addressed these challenges I think there's a there's a few key things and probably to frame and give you a bit of context around the question I've used the analogy that data leadership is almost coming like football slash soccer management or I guess management of any sports team right where the demand for instant return is so high that a few months of instability or unsung successful results and people's heads are on the chopping block unfortunately so what that created is this environment where organizations and their data leaders need to hire people that can come in who can hit the ground running because Unfortunately they just don't have the time or usually the energy the finance the resource to bring on more Junior people more entry-level people and wait for them to be developed and trained as unfortunate as that is so at that mid to senior level that's where every business is trying to recruit people from and obviously as I just mentioned before the amount of businesses looking to hire the fact that they're trying to hire in multiples the fact that there isn't enough people that certain job disciplines more so than others for sure but that's led to this point now where there's just such a demand that there's there's not enough people there so that's been the challenge is that we're in a talent short Market but the really the only way that data leaders can go on start to add value quickly is by hiring people that are of a certain level that know the job that can come in and just hit the ground running but there aren't enough of those people so that's certainly a challenge I think the second part of this challenge that most data leaders face and where we try to start to then take conversations is do you have a strategy around your talent and your talent attraction and your talent acquisition because what we found is that most businesses don't unfortunately you know most are very reactive to their own needs so hence why 9 out of 10 conversations we have when you say okay well when would you like this person or these people to start it's a case of yesterday right and and I always try to kind of use the analogy of if you were to ask a business or a data leader how do you go about acquiring your customers for example if you talk about new customer acquisition they'd be able to give you a pretty compelling answer around that you know where we do this and we do this and the whole nurturing process and we've multiple touch points and we try to engage them and all of that type of stuff that's exactly what organization should be doing with their with their talent but they're not they only think about it when there's a big gaping hole and the reality is obviously depending upon where you're located but there's an interview process length is a notice period to work so often especially here in the UK right you get organizations that they want to hire someone who they would love to have started yesterday but the reality is is they're not going to have anyone a seat for probably five months which obviously causes challenges right so that whole Talent strategy of well when are you going to need skills why do you need them what should them skills look like and then how do we start to go and engage those types of people so that leg work and groundwork has been done so that when the time comes for us to actually make a hire we are much further along that process so that's the the thing that we where we usually start and then I think that the next piece around that is do you have a compelling enough narrative you're competing with hundreds if not thousands of other organizations for the very same Talent at the very same level it's not just about are you paying well Etc using the the shine instant latest tech so talk about compelling narrative but you know why should someone join your organization over the other 10 to 15 options that they might have because we're Talent short and there's high demand so that whole compelling narrative and creating that Narrative of what's important to that audience that's something where we we focus a lot of time and attention and I think to bring that all together there's really the the landscape of data analytics Talent has changed and their their wants and needs and desires have changed over the last few years especially where many unfortunately many kind of went into organizations where they were told you were a data-driven business in quotation marks and they realized when they got in there actually that that wasn't the case they felt like they were on a production line of projects where a project runs on their desk it gets done it goes off into the ether and they never see or hear about it ever again so they don't know if it was good whether it was bad whether it was used whether it had any value whether it had an impact Etc so we try to tie that compelling narrative back to how are you apart from how are you different and how is that compelling but how can you make the day-to-day work of these people be visible be valuable and be impactful across the organization Asian because Beyond just salary location technology exciting projects that's the other part of the equation that people are now starting to look at organizations and assess them in terms of the people looking to enter those businesses that's really great I love that answer and there's so many things that I'm back here I want to maybe start off with the town strategy component right and to go on a bit of a tangent you mentioned here that very few organizations have a talent strategy similar to their or at least a strategy that is as robust as their customer acquisition strategy maybe walk us through what does a robust Talent strategy look like for an organization in the data space well I think that the fact that so few organizations actually put any time and attention into this it's kind of one of those things where anything is better than nothing right at this stage so I I think it's just trying to get ahead of the curve on this right because as I said most businesses are really reactive so it's a case of based on that data strategy that hopefully is being compiled to operate in tandem with the business strategy and then the operating model around that okay well where are the current gaps in this team are those gaps causing those problems right now and how do we prioritize which of those gaps are really important to us that then gives you a list of prioritization and then it's often a case of whether you believe it or not in fact we do a lot of work around actually you think that you need this skill set but you actually don't really what you'd be better off with if this is this skill set so for example you know data science being the sexiest job of the 21st century unfortunately many organizations went out and thought they needed a data scientist if they were going to become data driven again in quotation marks so and I've spent many many meetings trying to convince Business Leaders that you don't need to hire a data scientist at this point in time all that is going to do is mean that you pay 20 to 30k more than you actually need to for the skill set that you really require at this time and they're going to come in and get really bored and leave which is a very expensive hire and not a very efficient strategy so I think it's about prioritizing the gaps that you have and therefore when you need to recruit them and then working backwards from that point so if you know that in January 2023 you're going to need I don't know a data engineer for example to do x y and z well really you should have already started that process last month right because by the time you factor in notice periods the interview process etc etc unfortunately it doesn't work like that there'll be hundreds of organizations that get to January and go okay we need a data engineer and they won't have one until May to June so I think I think that that Talent strategy and then tie that in with how do we make our proposition compelling to the audience that we're trying to engage with it's really interesting because it's really similar to like a marketing department in some sense where you need to build like a predictable pipeline of talent create an entire marketing narrative around why you need to join this organization and all these pieces need to fall together to be able to create that predictable pipeline of talent yeah that's exactly what it is yeah absolutely that's really great so given your Vantage Point in the market you know what are the different data roles that organizations are currently hiring for and maybe more importantly how has this skill set or requirements of these roles have evolved over the past few years yeah so obviously I think we've seen a big shift in the last two years especially around around the market and again to go back to something that I said earlier I think many organizations again unfortunately jumped feet first into the wrong areas so they made investments into areas like you know Advanced analytics data science AI ml when they almost saw that I guess as a bit of a silver bullet and they really didn't have their house in order right they just weren't ready for that type of activity and and initiatives so we've seen and probably just by again natural correction unfortunately you know many businesses have spent an awful lot of money and not got a lot of value out of what they were expecting in comparison to what they were expecting so therefore now I've had to trace their tails back and things like okay data engineering is really important and we hired a load of data scientists and we couldn't get data out of one system to the next and for them to do any data science on so data engineering has been Far and Away alongside architecture the biggest area of growth in terms of demand for sure same conversation goes for data governance and data management again many businesses didn't have their foundations in place and therefore they didn't get the value they were expecting so data governance and data management has has certainly been an area of of high investment and where there's been a lot of a lot of hiring and I think the thing that's probably changed the most if you if you look at transformation and innovation of our sector we're starting to see now a real big drive towards a product mindset our product thinking to treat data as a product or data as a product or data products or whatever terminology we're using around that on the basis that as we all know you know the stats have shown us that many many data analytics initiatives don't add the value they were expecting to add and obviously then you trace that back to things like culture and adoption within organizations the data products thinking and mindset seems to have driven better level of adoption and engagement from those business businesses and business users so I think that's going to be something that is going to be really prevalent over the next 12 months or so so we're getting to the point where there's a few key things I'd say that have really changed over the last of the last 12 months that's really awesome and I love that last notion on approaching data as a product maybe walk us through what does that skill set look like in practicality for organizations right and what are the roles that are usually reserved for this type of skill set yeah so it's interesting so I I personally think the the title that most have adopted is data product owner and and really we've heard the term coin the data translator right that's being branded and at one point in time that almost threatened to become an actual real job title which would have been interesting right so it's to kind of have that as an official title but I think the date of product owner is effectively become that person right they are the person that normally comes from a background where they at least understand the technical Concepts so that they can sit between the business and the business users and the data analytics team and they can ensure that the products that the data team are creating are actually being embedded and used properly within the business I think it's more of a a mindset thing than anything else to be honest with you I don't think there's any kind of huge revelations in fact you probably hear many conversations in the industry where this stuff almost already happens but probably just with not the the rigor around it so if you talk about data translators they were often responsible for going out and just trying to make sure that the whole business was adopting whatever the data team were producing I think this has become a little bit more focused so you might have someone that's responsible for a single type of product as far as making data a product and really becoming an expert in that product and being known for that product in and around the business so I'd say that's the key thing around that is the ability to translate technical lingo into I don't know someone that works in a marketing team or someone that works in an accounting team for example understands what what's the premise of me using this product why should I do anything different I think that's that's probably a good point to highlight right many businesses have been operating very successfully forever without data being at the core of their business right so effectively we're asking people to change and transform their behavior to suit what we're trying to do so there needs to be that level of buying and I think historically speaking the whole soft skills again in quotation marks because the soft skills tend to be the the the hardest skills to to master I think they're the things that we're really starting to to focus on if we can get that right then the rest of it takes care of itself because eight to nine times out of ten the thing that we struggle with is often the softer skills it's not it's not the building of the data lake or the dashboard or the model we're pretty good at that right it's getting them to to use it getting it to be embedded into the culture getting it to be adopted so that actually you can see some value out of the other side of that whereas what's often happened is we've built great Solutions these Solutions either it's trying to answer a problem that we don't have or it's not the right problem to be addressing at this point in time and therefore it's not used and therefore it's been a big cost with very little return I love how you define the data Proctor owner as the next iteration of the analytics translator because I do think that there is some specialization to be had in that field and more maturation of the processes and kind of the Notions of what makes an embeddable machine learning or data science solution as our best practices are evolving so given that let's also talk about retention I think retention has never been more important to think about today especially given the great resignation the great reshuffle that we've experienced over the past year how have you seen the great reshuffle affect data roles and how have you seen data leaders trying to address the retention problem so I think retention is probably one of if not the biggest problem that a data leader faces right because if you think about it really logically every business is going to have some level of attrition that's just they've got to factor that in and most of them do factor that in however in a market where there's been such a shift right so just to give you a very plain example part of the problem and challenge that most data leaders have now is that if they've had someone that's worked for them for three or four years there will be a certain level with a certain salary for them to go externally to the market and bring the same person in at the same level they're probably going to have to pay them twenty thousand pounds more because there's been a shift in the market which obviously causes a lot of problems internally Politics as I'm sure anyone would if someone's working alongside a team member that's doing the same job and finds out that they're getting paid twenty thousand pounds more for doing the same job but probably isn't going to be happy about that so in my eyes it makes absolute sense that rather than having to go to market for additional recruitment why don't you focus your time and attention first of all on keeping the people you've got happy and retaining them because the other side of the coin is that when they're not happy and there's not being a leveling up let's say in terms of salary and remuneration to what the the new market kind of standards are those people are also Sitting Ducks right because other organizations will just go and pluck them straight out because the individual knows that the market is shifted by 20 000 pounds as an example they're not getting that at their current organization and someone else is more than willing to to pay them to do that so the retention problem is is very real often though I think the unfortunate reality is is that most people on an individual basis are are better off by moving jobs in terms of they will get so bigger titles more responsibility bigger pay packets by moving roles than they will do by staying with an organization even if they get promoted you know they might get a percentage uplifting salary but it's never going to equate to what a move will get you so I think there just needs to be some thought around around that right the reason why that happens is companies internally are dealing with politics red tape salary bandings job title levels it's a really difficult process to navigate but unfortunately it's one that's happening and there's not really a right or wrong answer I think it's I think it's something that most data leaders struggle with struggle with a lot to be honest with you yeah I can imagine this is something that we've definitely seen over the past two years what have you seen to be the differentiator between organizations suffering from retention issues versus those that are not so I think natural things like are they willing to have a conversation with us about leveling up salaries to do market conditions I think that that's that's a given obviously the whole move to having a more flexible Workforce and work-life balance I think that's been something that's you know if if there was something out there that has dramatically changed since pre-covered to now it's that I think most people are more aware than ever that they want to be in a place where they can add value so their work is visible valuable impactful but they have a great work-life balance and they're not needing to be on the road five days a week so I think it's businesses that have really tried to cater to the needs of the market in regards to that kind of flexibility I think is the differentiator between those that have retention issues and those that don't and businesses that fundamentally at their core look at okay how do we make sure that the work of our team is being used it's visible it's valuable and it's impactful for them that they're having an impact on this organization and they're not just coming to work doing a job that they never see you feel or hear about so I think that's that's the key thing we talked about the compelling narrative earlier from an attraction standpoint that compelling narrative also plays out from a retention standpoint right because if an organization is able to articulate over and over again look this is why we feel that we're better as an employer in the data analytics industry over you going down the road and getting a twenty thousand pound pay rise but what you're doing here is actually going to be better for you longer term it's going to look better on your CV you're going to actually add some value to an organization which you know is going to help your growth and development and and all of that type of stuff I think it comes down to organizations that are really Forward Thinking about this stuff and trying to think about actually what are our people fundamentally interested in and how do we cater to that as opposed to to taking very hard lines on will we want people back in the office three or four days a week and it needs to be prescribed on these three or four days a week because ultimately that's probably not flexibility either yeah I'm really looking forward to expand on that flexibility notion but maybe let's pause a bit and discuss the making work visible valuable impactful I think a lot of data leaders struggle with this and I think this gets easier as you go along the data maturity curve and actually become a data driven organization so maybe for those organizations at the beginning of that Spectrum right that are still struggling and getting value out of data how do you create a culture that makes the work of your data professionals visible valuable and impactful it's a really good question because it's so it's as we know as an industry it's really difficult to actually be able to put quantifiable tangible value to the data analytics initiatives I often think this comes down to if I'm being really candid the ability of the of the data leader within that organization to spearhead that the the whole all the whole team and really put it in in the midst of the business to to be seen and to be heard I think there's a few very obvious ways that you can do that one thing that I really like which I don't think really gets as much air time as it should we speak off the cuff around communication right and obviously Communication in our industry is is really important to be able to make sure that the business understands what we're doing and how are we translating it and where do we get the value from and how do we articulate our role that we've played in that value but often that's where it starts and ends it's almost like a flippant comment that communication is important and it is what I've seen work really well is where organizations have put together a communication strategy so with their internal PR team for example where they have a budget assigned to Communications it might be an internal podcast where they talk about what's going on with data it might be where they've built a bit of a data Academy that's for the business users to come in and and start to look at well how can I get more involved in creating my own dashboards for example you know it's I think it's the trying to bring awareness and and literacy if we want to use that that term right around how do we bring all of that to the Forefront because ultimately that just raises the profile of the data team within the organization so I think that's one one really useful example where I've seen it work quite well and harping on that notion of dedicating a small budget for communications and public relations or internally are of the data teams work one example I've seen that works really well comes from New York Life Insurance actually where we had on the podcast Glenn Hoffman the chief data analytics officer of generic life insurance what they do there for example is that for each new project there is a dedicated landing page internally with like really high quality videos explaining the new project what it's about Etc and that is very effective at creating excitement within the organization and driving adoption and making sure that the work is visible valuable so another thing that I'd really love to discuss here that you mentioned is flexibility right one thing that you mentioned here is on remote work and creating work-life balance of course remote work has been quite on the rise over the past two years and is one of the differentiators that we see more and more so on the market as to why candidates Stay or Leave an organization a great example would be Apple's director of machine learning in Goodfellow left Apple because of the return to office policy maybe walk us through how remote work in your perspective has impacted organizations ability to find and retain data Talent yeah so I think this is a really really interesting topic because obviously you've got two very clear distinct sides of the fence here right you know you've got the individual that naturally wants as much flexibility and work-life balance as possible and and rightly so I think if the pandemic has taught us anything it's that we'd probably had an unhealthy Obsession most of us at work so that the whole work-life balance thing has been has been a real positive that's come out of the of the pandemic I think how how businesses have tackled this so I think there's there's been this General misconception in the the general employment market right especially for people that do office based roles of course that once the pandemic ended that everyone was gonna just stay 100 remote and I think that's that's obviously been proven to be a myth like we we work with very few organizations that are willing to appoint people on fully remote contracts right I think most organizations want people to be visible in in some internet in some aspects that could be a day a week it could be four days a week whatever like each each company obviously sets its own policy but naturally that has a knock-on effect to how businesses can either retain or retract new Talent right because if the work-life balance and remote working aspects are really important then obviously no one's going to be choosing to go and work for for Apple right as per as per your example so I think that's that's been something there I think what it's done outside of that obviously it's quite interesting because I think most organizations operate within their own kind of locational bubble right because that's how they've been used to thinking and operating so I don't know if you're based in in New York City there's a radius right from a postcode perspective let's say of really where someone would be willing to travel from and to and you could pretty much Hazard a guess is where that would be that's obviously changed now so in in essence the candidate pool is as great as you want it to be right it's really up to your the organization on what their party line is on how how willing they are to appoint people in fully remote roles or not so that plays a part because Therefore your candidate pool is greater equally on the other side of that coin that your your competition is greater I think that's something that a lot of organizations didn't really think about I think they thought okay well now we can appoint someone I don't know if we're based in London we can point someone based in Spain find that our candidate pool has grown but actually that same person in Spain could also be be employed by Google in San Francisco so so so therefore your candidate pool there's your competition has also grown despite the fact that the candidate pool's grown so I think I think those things have been everything that that plays into it the attraction of talent is probably not as easy as most organizations thought it would be on that basis the retention of talent is is all comes down to now how flexible is the organization actually willing to be in and then you know because let's be honest most people don't make a decision solely based on if they can work remotely or not there's a lot of other factors to it but obviously it's a factor that still is very high up on most people's agenda okay that's really great and I love that holistic perspective I love how you showed the other side as well given that we discussed as well how to make business users more engaged as well with the data analytics teams projects how to get business users involved I'd love to talk about as well hybrid roles right and how organizations have been filling them to give a bit more context earlier in the year we had Matt siegelman on the podcast who's the chairman of burning glass Institute and they do quite a lot of natural language processing on open job descriptions across the internet and one of the main insights that they have found is that there's a hybridization of roles where a lot of data skills are becoming standard as part of a traditional business role such as marketing operations etc for example think of roles such as business operations analyst marketing analysts Revenue operations analysts walk us through from the vantage point of data leaders how have they been filling up these worlds as well this is really interesting because I think it shows that we are moving it's in the right direction we're starting to to think about there's roles in our organization that don't have to be plugged by pure technical data people which I think is a really good thing I've become probably famous on LinkedIn for talking about how bad most job descriptions have been over the last several years even if you think about the chief data officer role normally the first requirement was to be able to code in Python I got this kind of like well yeah it shouldn't probably be the case so I think it shows that we're moving in the right direction it also shows that there's more awareness and literacy around the role that data is going to be playing within those individual domains now naturally you will get areas that marketing and finance are probably two areas to to pick on here a little bit because they are typically more data literate in the sense of they're used to using data to measure and manage performance I think the reality of hiring those people is then quite different because whilst the notion and the concept of hiring people with hybrid skill sets I think what it does as I mentioned before is the whole thing around soft skills and you know the commercial skills and the persuasiveness and the influencing and the adoption and the communication all of that stuff that stuff that historically and not exclusively but historically you know a lot of data teams have struggled with so you can acquire a lot of skills from outside of the pure data analytics world that really works better in that space But then obviously there's certain things that those people need to be need to be upskilled in maybe from more of a more of a data and analytics analytics space for example so I think it's definitely a step in the right direction it's probably a lot easier in theory to say than it is to to execute on because obviously that they've typically been in most businesses been two separate roles right you would have maybe a data analyst that comes in and just sits within Finance or just sits within operations or just sits within supply chain and then you would have someone on from the business again in quotation marks that they almost become business partners to each other I think we're starting to see that these business partners can learn some of the core fundamentals or you're getting data people that are becoming better communicators influencers and then can start to act more as the business so yeah in theory it works finding those people is really difficult I'd say yeah that's really great insight and you mentioned here the importance of upskilling maybe where you see expanding on that where do you see the role of upscaling and internal promotion when filling these types of roles most disciplines of data analytics there's more demand than there is Supply right so theoretically here there's there's two options we need to get more people into the industry from less conventional areas of study so if you think about I don't know computer science is a great example just by the sheer demand and the growth trajectory of data analytics people students that study in that area will just get sucked in to our industry just by the sheer demand and the amount of money that's being paid and all of that type of good stuff they're they're an abundance of students out there that study things that might be seen as less conventional so social sciences is a great one students that study criminology or sociology or psychology students that study geography as an example they're all used to using data to analyze what they're doing and often incorporate that into their studies so in theory they have the the foundational skills to enter our industry which makes absolute sense to look at those areas because we have a talent shortage across most areas right unfortunately that doesn't happen so we need to get better at how well how do we get out and raise awareness as an example I do a lot of public speaking at certain universities I speak at a social sciences faculty and these people are doing quantitative analysis as part of their social sciences degree like they're using certain tools yet when I go in and do speaking it's probably the most diverse room in terms of gender religion ethnicity background everything you can think of and I'd say no 95 of them don't even know that there's an industry in data analytics they could go and get a job in so that's problem number one that we need to address right the second thing is definitely retraining there are going back to a previous question about these hybrid roles now right there's the data analytics industry as being guilty of the whole softer skills maybe not being quite to the standard that we might expect or want or need that's been a bit of a problem for us historically and therefore there are people that work in the business that have an appetite to get more into the data analytics side that we can start to cross-train or retrain from other areas of the business that's another way to try and plug the Gap but again the problem often being is that that takes time that's not a quick fix it's just going to happen overnight so there needs to be a real initiative around well how are we going to do this how are we going to execute it again it's another idea that's great in theory but the reality of actually implementing and seeing results is quite difficult and because most data leaders don't have the time and resource and energy to develop these people whichever side of that coin that you're looking on it often gets neglected a little bit really and that's why you see these types of data academies that pop up mainly in big organizations right because they have the money to throw at this stuff and some you know they'll be happy to pay someone to manage that right so I think the whole upskilling piece is going to be absolutely critical for us as an industry to move forward because we already know that there's a lack of talent at most areas and is probably a large portion of that talent that are approaching a certain age now so we'll start losing people at the other end of that funnel so it's a big area and I think obviously one of the reasons why businesses like like yours do really well I completely agree and yeah the upselling component I completely agree on the notion that the transformational aspect of it is definitely something that needs patience it's it's essentially a culture change project and should be trade like treated as such now of course Kyle as we reached end of our podcast I'd also be remiss not to mention your podcast driven by data I think any data leader listening to this conversation would benefit from subscribing maybe walk us through some of the learnings that you've had from hosting the podcast over the past how long has it been running a year now two years yeah yeah so we concluded season two a few weeks back so we did 50 episodes in season one 50 episodes in season two we're about to launch season three soon so a lot of interviewing and a lot of learning I think the there's a few key things that I've learned throughout all of these conversations and and that's typically that most organizations suffer with the same problems and challenges just on different scale in terms of their business and their size which is is really interesting actually I think the second thing is despite all of the talk and the Press around the importance of data I think there's only really a few organizations that have mastered how to actually drive value out of data I think everyone else is still very much on on that maturity journey and trying to figure this this out as as they go and I think beyond all of that I I see this there's almost this Chasm that's being created to be honest with you between the ability of the data leader and and their team and then the business so I think it's been quite broadly reported that the data Community often struggles to articulate and quantify the role that they've played in creating and realizing value for the business right there's a lot of factors around that but allocation might be might be a problem if a data analytics team helps the sales team to generate 10 million dollars more in sales than well this naturally the sales team wants the credit for that right so it's about how do we get front and center as a team to create relationships to say well look if it wasn't for us that number wouldn't have been that it wouldn't have been that big or that wouldn't have happened so I think we need to as a data Community get better at how do we articulate the role that we've played in that kind of value realization but the flip side of that is that organizations and Business Leaders that have made the decision about investing into data analytics often don't know a what they're trying to do therefore they don't know what type of person they should hire to run that function with them and often jump feet first into some kind of technical initiative so the amount of times that I've sat in rooms where it's almost a notion of well the business leader knows there's value in here somewhere so why don't we just start to build a data Lake because we're going to need a data lake at some point in time so they'll build a data Lake and then they try to piece it as they go which becomes really problematic because there's no strategic direction that ties back the data initiative and strategy to what the business is trying to achieve and that's I think why we end up in this place where a lot of money is being spent but it's not being spent strategically to help the business it's being spent just building infrastructure and that data becomes seen as a cost center so because they are starting from a place of strategy and tying it back to the business they don't know who to appoint for that data leadership role they don't really know what that role should be why they want it what that person should be delivering so they often appoint the wrong person it's almost like they're being set up to fail so there's this big gap right data leaders can't articulate the value well enough but Business Leaders aren't setting them up to do that if that makes sense so I think that's been the thing out of all these conversations and all the events that we've run I often just end up back at that place wondering well how do we bridge that Gap to be honest with you it becomes a bit of a vicious cycle yeah that's really something interesting and I think definitely something that the industry needs to tackle in the next few years in order to reach that stability point for the data analytics industry so given this perspective of you interviewing data leaders what do you think are top trends that will affect the data space in the next few years and how are data leaders approaching hiring and building data teams yeah so as I said earlier I think the data product thing will be will be a real key component to driving adoption and changing culture which is is really the thing that needs to happen if we're going to continue on this journey to get value from it I think back to the the kind of Attraction and retention piece it's about having well well-balanced teams I think we've been guilty of building very highly technical teams and that's fine but I think we've realized now that there needs to be a balance around that and not just diversity in the in the traditional sense but diversity of of thought and experience and perspective so people are bringing people in from different backgrounds that have come from different places that look at challenges and problems in in different ways and have just a different breadth of skill so I think we're in a place now where not everyone needs to be a python wizard there might be someone within a team that whose job is actually to go and translate technicalities into business and I think that's I think that's fine so yeah I think I think those are going to be the key things and then getting to the point of the overarching thing for me is I think we need to figure out a way of how does the data analytics Community start to articulate the value that it's creating once we tackle that and we figure out okay what's the correct starting point for this and there's enough use cases out there where other organizations can almost lean on past experiences to make decisions in terms of where do they start with data analytics what's the right place who's the right type of people how big does the team need to be what skills do you need what tech do you buy etc etc I think we'll be in a much better place so I think the whole treating data as an asset and the value and how you value that I think that's going to be one of the the big trends over the next 12 to 24 months that's really great I love this perspectives now Kyle as we wrap up our podcast you have any final call to action before we wrap up today's episode called so I should know I don't think so I think all I'd encourage anyone if they're in the process of trying to build data analytics teams I would say just touch back on the points of create well-balanced teams around diversity of thought and experience create that compelling narrative around someone should join your organization over over someone else because I think that's the thing where most businesses in my experience fail you know when I ask them that question straight up I often get very blank stares back right which is is a problem and it's something so simple to do yet so few organizations actually sit and think about what that message and narrative should be so that's something that the businesses definitely should be doing and then articulating how the work of that team is visible valuable impactful within the organization because that's become a real driver for the individuals that they'll be targeting that's all really great thank you so much Kyle for coming on data friend no problem at all thanks for having me foreign you've been listening to data framed a podcast by datacamp 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 datacamp 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 foreign this is Adele data science educator and evangelist at data camp over the past year hosting data framed I've had the privilege and honor of interviewing a lot of data leaders and practitioners on how to become data driven one common theme is that I always boils down to the people side of things whether upskilling or hiring becoming data driven is a talent strategy just as much as it is a technology strategy this is why I'm excited to have Kyle winterbottom on today's episode Kyle is the host of the driven by data podcast and the founder and CEO of orbition Italian Solutions provider for scaling data analytics and artificial intelligence themes across the UK Europe and the US throughout the episode we touched upon a few themes that are really relevant to building a holistic Talent strategy for data teams including hiring and recruiting improving retention for data teams and how to approach upscaling and more if you enjoyed this episode make sure to rate and subscribe to the show but only if you liked it also make sure to check out the content we have in store this month on building a data driven organization it ties a lot to the themes we talk about on the show and now on today's episode Kyle it's great to have you on the show thank you very much for having me looking forward to it I am very excited to discuss with you all things building effective data teams how organizations should be approaching hiring retaining recruiting data talent and your work leading orbitrin and hosting the throne by data podcast but before maybe can you give us a bit of a background about yourself so I've been in the data and analytics talent and recruitment space for the last 12 years I guess our mission is a boutique kind of Talent Solutions business that operates exclusively in this world so we're headquartered here in the UK but we have a presence out in the in the states and do quite a lot of work across the UK Europe and America and I guess really the bulk of what we do is done at the mid to senior level and often done at scale so helping organizations typically to build out their data leadership and management teams and then the senior technical teams that that fit beneath them so that could be I don't know working with a ftse 100 or Fortune 100 100 as an example that want to hire 40 50 60 people over a 12-month period it could be a smaller to mid-sized Enterprise that wants to hire eight to ten people over the next two to three months it could be an organization looking to appoint a chief data analytics officer as an example so the three components to to our business really and then yeah as you mentioned outside of the day-to-day the business really is underpinned by the community element that we try to to serve so we have our podcast we have our event series we have our mentorship scheme you know Partnerships with universities to try and raise awareness and things like that so yeah roughly speaking that's a little bit about about me that's really great and there's definitely a lot to cover so I'd love to set the stage for today's conversation by first trying to understand what the hiring and retaining landscape looks like for data talent in 2022 we're at the backdrop of a pandemic a rise of remote work the great reshuffle the great resignation and now a downturn in the economy so maybe in your own words how would you describe the landscape for hiring and retaining data talent in 2022 and how has it evolved over the past few years it's an interesting question I think the the last 18 months have been a few words to sum it up in one word would be crazy right you know so I think um it's been interesting because I have conversations on a daily basis with people that might have not been in the job market since before before the pandemic and normally one of the first questions I get asked is how is the market now after the pandemic and it's interesting because I think really data and analytics was was very fortunate so a lot of the work that we were doing with with organizations even after the pandemic started obviously there was an natural pause so while people figured out what's going on how long is this going to last Etc but within a matter of months recruitment in this space was back to normal levels and then obviously you know start of 2021 and ever since it's been on a trajectory like like that so it's certainly been crazy but also just the level of growth and the amount of hiring we were always going to see in natural correction so we talk about now the great resignation we talk about a potential downturn in the economy we've definitely seen a bit of a Slowdown over the last couple of months but I don't think that's necessarily related to the economic situation I think that's more of a natural correction because just the the scale and the pace of hiring in this space just wasn't sustainable to keep going the way it had been going for the last 18 months and and then I think anything outside of that which I'm sure we're going to get into in a little bit more detail but there's been a lot more Focus I'd say over the last 12 months especially around trying to maybe realign our focus on getting the foundations of data analytics right as opposed to focusing on the the shiny technical AI type stuff which many businesses unfortunately got kind of distracted by us yeah definitely I'd love to like talk about that trap at a later point in the conversation but maybe on mentioning the crazy and unsustainable pace of hiring over the past 18 months what were the drivers behind such high demands of hiring in the data space especially over the past 18 months well I think if every business leader now knows that for them to continue to thrive and be fit for the future as we're starting to refer to it as that data is going to play An Almighty important role in that unfortunately that's easier said than done right so the execution becomes really difficult but I think what we've found is that you're getting more organizations actually starting that data analytics journey and then you're also getting organizations that are already in the midst of it throwing even more money at it so it was a combustion of two narratives coming together at the same time which meant there was a an awful lot of businesses looking to hire people and not just ones and twos you know multiples so I don't think I've spoken to a business over the last got 18 24 months that's saying you do we just want to hire one or two people it's normally you know we need to hire 10 engineers and four data governance people and yeah they are the other so that combustion of those two narratives the demand and the lack of Supply I think all together just created this this big kind of Boom really that's very exciting so let's definitely go into the details about hiring and recruiting data Talent you've as you've mentioned you've worked with quite a few organizations over the past few years on filling their talent Gap walk us through maybe some of the main challenges data leaders are facing and filling their talent Gap and how have you addressed these challenges I think there's a there's a few key things and probably to frame and give you a bit of context around the question I've used the analogy that data leadership is almost coming like football slash soccer management or I guess management of any sports team right where the demand for instant return is so high that a few months of instability or unsung successful results and people's heads are on the chopping block unfortunately so what that created is this environment where organizations and their data leaders need to hire people that can come in who can hit the ground running because Unfortunately they just don't have the time or usually the energy the finance the resource to bring on more Junior people more entry-level people and wait for them to be developed and trained as unfortunate as that is so at that mid to senior level that's where every business is trying to recruit people from and obviously as I just mentioned before the amount of businesses looking to hire the fact that they're trying to hire in multiples the fact that there isn't enough people that certain job disciplines more so than others for sure but that's led to this point now where there's just such a demand that there's there's not enough people there so that's been the challenge is that we're in a talent short Market but the really the only way that data leaders can go on start to add value quickly is by hiring people that are of a certain level that know the job that can come in and just hit the ground running but there aren't enough of those people so that's certainly a challenge I think the second part of this challenge that most data leaders face and where we try to start to then take conversations is do you have a strategy around your talent and your talent attraction and your talent acquisition because what we found is that most businesses don't unfortunately you know most are very reactive to their own needs so hence why 9 out of 10 conversations we have when you say okay well when would you like this person or these people to start it's a case of yesterday right and and I always try to kind of use the analogy of if you were to ask a business or a data leader how do you go about acquiring your customers for example if you talk about new customer acquisition they'd be able to give you a pretty compelling answer around that you know where we do this and we do this and the whole nurturing process and we've multiple touch points and we try to engage them and all of that type of stuff that's exactly what organization should be doing with their with their talent but they're not they only think about it when there's a big gaping hole and the reality is obviously depending upon where you're located but there's an interview process length is a notice period to work so often especially here in the UK right you get organizations that they want to hire someone who they would love to have started yesterday but the reality is is they're not going to have anyone a seat for probably five months which obviously causes challenges right so that whole Talent strategy of well when are you going to need skills why do you need them what should them skills look like and then how do we start to go and engage those types of people so that leg work and groundwork has been done so that when the time comes for us to actually make a hire we are much further along that process so that's the the thing that we where we usually start and then I think that the next piece around that is do you have a compelling enough narrative you're competing with hundreds if not thousands of other organizations for the very same Talent at the very same level it's not just about are you paying well Etc using the the shine instant latest tech so talk about compelling narrative but you know why should someone join your organization over the other 10 to 15 options that they might have because we're Talent short and there's high demand so that whole compelling narrative and creating that Narrative of what's important to that audience that's something where we we focus a lot of time and attention and I think to bring that all together there's really the the landscape of data analytics Talent has changed and their their wants and needs and desires have changed over the last few years especially where many unfortunately many kind of went into organizations where they were told you were a data-driven business in quotation marks and they realized when they got in there actually that that wasn't the case they felt like they were on a production line of projects where a project runs on their desk it gets done it goes off into the ether and they never see or hear about it ever again so they don't know if it was good whether it was bad whether it was used whether it had any value whether it had an impact Etc so we try to tie that compelling narrative back to how are you apart from how are you different and how is that compelling but how can you make the day-to-day work of these people be visible be valuable and be impactful across the organization Asian because Beyond just salary location technology exciting projects that's the other part of the equation that people are now starting to look at organizations and assess them in terms of the people looking to enter those businesses that's really great I love that answer and there's so many things that I'm back here I want to maybe start off with the town strategy component right and to go on a bit of a tangent you mentioned here that very few organizations have a talent strategy similar to their or at least a strategy that is as robust as their customer acquisition strategy maybe walk us through what does a robust Talent strategy look like for an organization in the data space well I think that the fact that so few organizations actually put any time and attention into this it's kind of one of those things where anything is better than nothing right at this stage so I I think it's just trying to get ahead of the curve on this right because as I said most businesses are really reactive so it's a case of based on that data strategy that hopefully is being compiled to operate in tandem with the business strategy and then the operating model around that okay well where are the current gaps in this team are those gaps causing those problems right now and how do we prioritize which of those gaps are really important to us that then gives you a list of prioritization and then it's often a case of whether you believe it or not in fact we do a lot of work around actually you think that you need this skill set but you actually don't really what you'd be better off with if this is this skill set so for example you know data science being the sexiest job of the 21st century unfortunately many organizations went out and thought they needed a data scientist if they were going to become data driven again in quotation marks so and I've spent many many meetings trying to convince Business Leaders that you don't need to hire a data scientist at this point in time all that is going to do is mean that you pay 20 to 30k more than you actually need to for the skill set that you really require at this time and they're going to come in and get really bored and leave which is a very expensive hire and not a very efficient strategy so I think it's about prioritizing the gaps that you have and therefore when you need to recruit them and then working backwards from that point so if you know that in January 2023 you're going to need I don't know a data engineer for example to do x y and z well really you should have already started that process last month right because by the time you factor in notice periods the interview process etc etc unfortunately it doesn't work like that there'll be hundreds of organizations that get to January and go okay we need a data engineer and they won't have one until May to June so I think I think that that Talent strategy and then tie that in with how do we make our proposition compelling to the audience that we're trying to engage with it's really interesting because it's really similar to like a marketing department in some sense where you need to build like a predictable pipeline of talent create an entire marketing narrative around why you need to join this organization and all these pieces need to fall together to be able to create that predictable pipeline of talent yeah that's exactly what it is yeah absolutely that's really great so given your Vantage Point in the market you know what are the different data roles that organizations are currently hiring for and maybe more importantly how has this skill set or requirements of these roles have evolved over the past few years yeah so obviously I think we've seen a big shift in the last two years especially around around the market and again to go back to something that I said earlier I think many organizations again unfortunately jumped feet first into the wrong areas so they made investments into areas like you know Advanced analytics data science AI ml when they almost saw that I guess as a bit of a silver bullet and they really didn't have their house in order right they just weren't ready for that type of activity and and initiatives so we've seen and probably just by again natural correction unfortunately you know many businesses have spent an awful lot of money and not got a lot of value out of what they were expecting in comparison to what they were expecting so therefore now I've had to trace their tails back and things like okay data engineering is really important and we hired a load of data scientists and we couldn't get data out of one system to the next and for them to do any data science on so data engineering has been Far and Away alongside architecture the biggest area of growth in terms of demand for sure same conversation goes for data governance and data management again many businesses didn't have their foundations in place and therefore they didn't get the value they were expecting so data governance and data management has has certainly been an area of of high investment and where there's been a lot of a lot of hiring and I think the thing that's probably changed the most if you if you look at transformation and innovation of our sector we're starting to see now a real big drive towards a product mindset our product thinking to treat data as a product or data as a product or data products or whatever terminology we're using around that on the basis that as we all know you know the stats have shown us that many many data analytics initiatives don't add the value they were expecting to add and obviously then you trace that back to things like culture and adoption within organizations the data products thinking and mindset seems to have driven better level of adoption and engagement from those business businesses and business users so I think that's going to be something that is going to be really prevalent over the next 12 months or so so we're getting to the point where there's a few key things I'd say that have really changed over the last of the last 12 months that's really awesome and I love that last notion on approaching data as a product maybe walk us through what does that skill set look like in practicality for organizations right and what are the roles that are usually reserved for this type of skill set yeah so it's interesting so I I personally think the the title that most have adopted is data product owner and and really we've heard the term coin the data translator right that's being branded and at one point in time that almost threatened to become an actual real job title which would have been interesting right so it's to kind of have that as an official title but I think the date of product owner is effectively become that person right they are the person that normally comes from a background where they at least understand the technical Concepts so that they can sit between the business and the business users and the data analytics team and they can ensure that the products that the data team are creating are actually being embedded and used properly within the business I think it's more of a a mindset thing than anything else to be honest with you I don't think there's any kind of huge revelations in fact you probably hear many conversations in the industry where this stuff almost already happens but probably just with not the the rigor around it so if you talk about data translators they were often responsible for going out and just trying to make sure that the whole business was adopting whatever the data team were producing I think this has become a little bit more focused so you might have someone that's responsible for a single type of product as far as making data a product and really becoming an expert in that product and being known for that product in and around the business so I'd say that's the key thing around that is the ability to translate technical lingo into I don't know someone that works in a marketing team or someone that works in an accounting team for example understands what what's the premise of me using this product why should I do anything different I think that's that's probably a good point to highlight right many businesses have been operating very successfully forever without data being at the core of their business right so effectively we're asking people to change and transform their behavior to suit what we're trying to do so there needs to be that level of buying and I think historically speaking the whole soft skills again in quotation marks because the soft skills tend to be the the the hardest skills to to master I think they're the things that we're really starting to to focus on if we can get that right then the rest of it takes care of itself because eight to nine times out of ten the thing that we struggle with is often the softer skills it's not it's not the building of the data lake or the dashboard or the model we're pretty good at that right it's getting them to to use it getting it to be embedded into the culture getting it to be adopted so that actually you can see some value out of the other side of that whereas what's often happened is we've built great Solutions these Solutions either it's trying to answer a problem that we don't have or it's not the right problem to be addressing at this point in time and therefore it's not used and therefore it's been a big cost with very little return I love how you define the data Proctor owner as the next iteration of the analytics translator because I do think that there is some specialization to be had in that field and more maturation of the processes and kind of the Notions of what makes an embeddable machine learning or data science solution as our best practices are evolving so given that let's also talk about retention I think retention has never been more important to think about today especially given the great resignation the great reshuffle that we've experienced over the past year how have you seen the great reshuffle affect data roles and how have you seen data leaders trying to address the retention problem so I think retention is probably one of if not the biggest problem that a data leader faces right because if you think about it really logically every business is going to have some level of attrition that's just they've got to factor that in and most of them do factor that in however in a market where there's been such a shift right so just to give you a very plain example part of the problem and challenge that most data leaders have now is that if they've had someone that's worked for them for three or four years there will be a certain level with a certain salary for them to go externally to the market and bring the same person in at the same level they're probably going to have to pay them twenty thousand pounds more because there's been a shift in the market which obviously causes a lot of problems internally Politics as I'm sure anyone would if someone's working alongside a team member that's doing the same job and finds out that they're getting paid twenty thousand pounds more for doing the same job but probably isn't going to be happy about that so in my eyes it makes absolute sense that rather than having to go to market for additional recruitment why don't you focus your time and attention first of all on keeping the people you've got happy and retaining them because the other side of the coin is that when they're not happy and there's not being a leveling up let's say in terms of salary and remuneration to what the the new market kind of standards are those people are also Sitting Ducks right because other organizations will just go and pluck them straight out because the individual knows that the market is shifted by 20 000 pounds as an example they're not getting that at their current organization and someone else is more than willing to to pay them to do that so the retention problem is is very real often though I think the unfortunate reality is is that most people on an individual basis are are better off by moving jobs in terms of they will get so bigger titles more responsibility bigger pay packets by moving roles than they will do by staying with an organization even if they get promoted you know they might get a percentage uplifting salary but it's never going to equate to what a move will get you so I think there just needs to be some thought around around that right the reason why that happens is companies internally are dealing with politics red tape salary bandings job title levels it's a really difficult process to navigate but unfortunately it's one that's happening and there's not really a right or wrong answer I think it's I think it's something that most data leaders struggle with struggle with a lot to be honest with you yeah I can imagine this is something that we've definitely seen over the past two years what have you seen to be the differentiator between organizations suffering from retention issues versus those that are not so I think natural things like are they willing to have a conversation with us about leveling up salaries to do market conditions I think that that's that's a given obviously the whole move to having a more flexible Workforce and work-life balance I think that's been something that's you know if if there was something out there that has dramatically changed since pre-covered to now it's that I think most people are more aware than ever that they want to be in a place where they can add value so their work is visible valuable impactful but they have a great work-life balance and they're not needing to be on the road five days a week so I think it's businesses that have really tried to cater to the needs of the market in regards to that kind of flexibility I think is the differentiator between those that have retention issues and those that don't and businesses that fundamentally at their core look at okay how do we make sure that the work of our team is being used it's visible it's valuable and it's impactful for them that they're having an impact on this organization and they're not just coming to work doing a job that they never see you feel or hear about so I think that's that's the key thing we talked about the compelling narrative earlier from an attraction standpoint that compelling narrative also plays out from a retention standpoint right because if an organization is able to articulate over and over again look this is why we feel that we're better as an employer in the data analytics industry over you going down the road and getting a twenty thousand pound pay rise but what you're doing here is actually going to be better for you longer term it's going to look better on your CV you're going to actually add some value to an organization which you know is going to help your growth and development and and all of that type of stuff I think it comes down to organizations that are really Forward Thinking about this stuff and trying to think about actually what are our people fundamentally interested in and how do we cater to that as opposed to to taking very hard lines on will we want people back in the office three or four days a week and it needs to be prescribed on these three or four days a week because ultimately that's probably not flexibility either yeah I'm really looking forward to expand on that flexibility notion but maybe let's pause a bit and discuss the making work visible valuable impactful I think a lot of data leaders struggle with this and I think this gets easier as you go along the data maturity curve and actually become a data driven organization so maybe for those organizations at the beginning of that Spectrum right that are still struggling and getting value out of data how do you create a culture that makes the work of your data professionals visible valuable and impactful it's a really good question because it's so it's as we know as an industry it's really difficult to actually be able to put quantifiable tangible value to the data analytics initiatives I often think this comes down to if I'm being really candid the ability of the of the data leader within that organization to spearhead that the the whole all the whole team and really put it in in the midst of the business to to be seen and to be heard I think there's a few very obvious ways that you can do that one thing that I really like which I don't think really gets as much air time as it should we speak off the cuff around communication right and obviously Communication in our industry is is really important to be able to make sure that the business understands what we're doing and how are we translating it and where do we get the value from and how do we articulate our role that we've played in that value but often that's where it starts and ends it's almost like a flippant comment that communication is important and it is what I've seen work really well is where organizations have put together a communication strategy so with their internal PR team for example where they have a budget assigned to Communications it might be an internal podcast where they talk about what's going on with data it might be where they've built a bit of a data Academy that's for the business users to come in and and start to look at well how can I get more involved in creating my own dashboards for example you know it's I think it's the trying to bring awareness and and literacy if we want to use that that term right around how do we bring all of that to the Forefront because ultimately that just raises the profile of the data team within the organization so I think that's one one really useful example where I've seen it work quite well and harping on that notion of dedicating a small budget for communications and public relations or internally are of the data teams work one example I've seen that works really well comes from New York Life Insurance actually where we had on the podcast Glenn Hoffman the chief data analytics officer of generic life insurance what they do there for example is that for each new project there is a dedicated landing page internally with like really high quality videos explaining the new project what it's about Etc and that is very effective at creating excitement within the organization and driving adoption and making sure that the work is visible valuable so another thing that I'd really love to discuss here that you mentioned is flexibility right one thing that you mentioned here is on remote work and creating work-life balance of course remote work has been quite on the rise over the past two years and is one of the differentiators that we see more and more so on the market as to why candidates Stay or Leave an organization a great example would be Apple's director of machine learning in Goodfellow left Apple because of the return to office policy maybe walk us through how remote work in your perspective has impacted organizations ability to find and retain data Talent yeah so I think this is a really really interesting topic because obviously you've got two very clear distinct sides of the fence here right you know you've got the individual that naturally wants as much flexibility and work-life balance as possible and and rightly so I think if the pandemic has taught us anything it's that we'd probably had an unhealthy Obsession most of us at work so that the whole work-life balance thing has been has been a real positive that's come out of the of the pandemic I think how how businesses have tackled this so I think there's there's been this General misconception in the the general employment market right especially for people that do office based roles of course that once the pandemic ended that everyone was gonna just stay 100 remote and I think that's that's obviously been proven to be a myth like we we work with very few organizations that are willing to appoint people on fully remote contracts right I think most organizations want people to be visible in in some internet in some aspects that could be a day a week it could be four days a week whatever like each each company obviously sets its own policy but naturally that has a knock-on effect to how businesses can either retain or retract new Talent right because if the work-life balance and remote working aspects are really important then obviously no one's going to be choosing to go and work for for Apple right as per as per your example so I think that's that's been something there I think what it's done outside of that obviously it's quite interesting because I think most organizations operate within their own kind of locational bubble right because that's how they've been used to thinking and operating so I don't know if you're based in in New York City there's a radius right from a postcode perspective let's say of really where someone would be willing to travel from and to and you could pretty much Hazard a guess is where that would be that's obviously changed now so in in essence the candidate pool is as great as you want it to be right it's really up to your the organization on what their party line is on how how willing they are to appoint people in fully remote roles or not so that plays a part because Therefore your candidate pool is greater equally on the other side of that coin that your your competition is greater I think that's something that a lot of organizations didn't really think about I think they thought okay well now we can appoint someone I don't know if we're based in London we can point someone based in Spain find that our candidate pool has grown but actually that same person in Spain could also be be employed by Google in San Francisco so so so therefore your candidate pool there's your competition has also grown despite the fact that the candidate pool's grown so I think I think those things have been everything that that plays into it the attraction of talent is probably not as easy as most organizations thought it would be on that basis the retention of talent is is all comes down to now how flexible is the organization actually willing to be in and then you know because let's be honest most people don't make a decision solely based on if they can work remotely or not there's a lot of other factors to it but obviously it's a factor that still is very high up on most people's agenda okay that's really great and I love that holistic perspective I love how you showed the other side as well given that we discussed as well how to make business users more engaged as well with the data analytics teams projects how to get business users involved I'd love to talk about as well hybrid roles right and how organizations have been filling them to give a bit more context earlier in the year we had Matt siegelman on the podcast who's the chairman of burning glass Institute and they do quite a lot of natural language processing on open job descriptions across the internet and one of the main insights that they have found is that there's a hybridization of roles where a lot of data skills are becoming standard as part of a traditional business role such as marketing operations etc for example think of roles such as business operations analyst marketing analysts Revenue operations analysts walk us through from the vantage point of data leaders how have they been filling up these worlds as well this is really interesting because I think it shows that we are moving it's in the right direction we're starting to to think about there's roles in our organization that don't have to be plugged by pure technical data people which I think is a really good thing I've become probably famous on LinkedIn for talking about how bad most job descriptions have been over the last several years even if you think about the chief data officer role normally the first requirement was to be able to code in Python I got this kind of like well yeah it shouldn't probably be the case so I think it shows that we're moving in the right direction it also shows that there's more awareness and literacy around the role that data is going to be playing within those individual domains now naturally you will get areas that marketing and finance are probably two areas to to pick on here a little bit because they are typically more data literate in the sense of they're used to using data to measure and manage performance I think the reality of hiring those people is then quite different because whilst the notion and the concept of hiring people with hybrid skill sets I think what it does as I mentioned before is the whole thing around soft skills and you know the commercial skills and the persuasiveness and the influencing and the adoption and the communication all of that stuff that stuff that historically and not exclusively but historically you know a lot of data teams have struggled with so you can acquire a lot of skills from outside of the pure data analytics world that really works better in that space But then obviously there's certain things that those people need to be need to be upskilled in maybe from more of a more of a data and analytics analytics space for example so I think it's definitely a step in the right direction it's probably a lot easier in theory to say than it is to to execute on because obviously that they've typically been in most businesses been two separate roles right you would have maybe a data analyst that comes in and just sits within Finance or just sits within operations or just sits within supply chain and then you would have someone on from the business again in quotation marks that they almost become business partners to each other I think we're starting to see that these business partners can learn some of the core fundamentals or you're getting data people that are becoming better communicators influencers and then can start to act more as the business so yeah in theory it works finding those people is really difficult I'd say yeah that's really great insight and you mentioned here the importance of upskilling maybe where you see expanding on that where do you see the role of upscaling and internal promotion when filling these types of roles most disciplines of data analytics there's more demand than there is Supply right so theoretically here there's there's two options we need to get more people into the industry from less conventional areas of study so if you think about I don't know computer science is a great example just by the sheer demand and the growth trajectory of data analytics people students that study in that area will just get sucked in to our industry just by the sheer demand and the amount of money that's being paid and all of that type of good stuff they're they're an abundance of students out there that study things that might be seen as less conventional so social sciences is a great one students that study criminology or sociology or psychology students that study geography as an example they're all used to using data to analyze what they're doing and often incorporate that into their studies so in theory they have the the foundational skills to enter our industry which makes absolute sense to look at those areas because we have a talent shortage across most areas right unfortunately that doesn't happen so we need to get better at how well how do we get out and raise awareness as an example I do a lot of public speaking at certain universities I speak at a social sciences faculty and these people are doing quantitative analysis as part of their social sciences degree like they're using certain tools yet when I go in and do speaking it's probably the most diverse room in terms of gender religion ethnicity background everything you can think of and I'd say no 95 of them don't even know that there's an industry in data analytics they could go and get a job in so that's problem number one that we need to address right the second thing is definitely retraining there are going back to a previous question about these hybrid roles now right there's the data analytics industry as being guilty of the whole softer skills maybe not being quite to the standard that we might expect or want or need that's been a bit of a problem for us historically and therefore there are people that work in the business that have an appetite to get more into the data analytics side that we can start to cross-train or retrain from other areas of the business that's another way to try and plug the Gap but again the problem often being is that that takes time that's not a quick fix it's just going to happen overnight so there needs to be a real initiative around well how are we going to do this how are we going to execute it again it's another idea that's great in theory but the reality of actually implementing and seeing results is quite difficult and because most data leaders don't have the time and resource and energy to develop these people whichever side of that coin that you're looking on it often gets neglected a little bit really and that's why you see these types of data academies that pop up mainly in big organizations right because they have the money to throw at this stuff and some you know they'll be happy to pay someone to manage that right so I think the whole upskilling piece is going to be absolutely critical for us as an industry to move forward because we already know that there's a lack of talent at most areas and is probably a large portion of that talent that are approaching a certain age now so we'll start losing people at the other end of that funnel so it's a big area and I think obviously one of the reasons why businesses like like yours do really well I completely agree and yeah the upselling component I completely agree on the notion that the transformational aspect of it is definitely something that needs patience it's it's essentially a culture change project and should be trade like treated as such now of course Kyle as we reached end of our podcast I'd also be remiss not to mention your podcast driven by data I think any data leader listening to this conversation would benefit from subscribing maybe walk us through some of the learnings that you've had from hosting the podcast over the past how long has it been running a year now two years yeah yeah so we concluded season two a few weeks back so we did 50 episodes in season one 50 episodes in season two we're about to launch season three soon so a lot of interviewing and a lot of learning I think the there's a few key things that I've learned throughout all of these conversations and and that's typically that most organizations suffer with the same problems and challenges just on different scale in terms of their business and their size which is is really interesting actually I think the second thing is despite all of the talk and the Press around the importance of data I think there's only really a few organizations that have mastered how to actually drive value out of data I think everyone else is still very much on on that maturity journey and trying to figure this this out as as they go and I think beyond all of that I I see this there's almost this Chasm that's being created to be honest with you between the ability of the data leader and and their team and then the business so I think it's been quite broadly reported that the data Community often struggles to articulate and quantify the role that they've played in creating and realizing value for the business right there's a lot of factors around that but allocation might be might be a problem if a data analytics team helps the sales team to generate 10 million dollars more in sales than well this naturally the sales team wants the credit for that right so it's about how do we get front and center as a team to create relationships to say well look if it wasn't for us that number wouldn't have been that it wouldn't have been that big or that wouldn't have happened so I think we need to as a data Community get better at how do we articulate the role that we've played in that kind of value realization but the flip side of that is that organizations and Business Leaders that have made the decision about investing into data analytics often don't know a what they're trying to do therefore they don't know what type of person they should hire to run that function with them and often jump feet first into some kind of technical initiative so the amount of times that I've sat in rooms where it's almost a notion of well the business leader knows there's value in here somewhere so why don't we just start to build a data Lake because we're going to need a data lake at some point in time so they'll build a data Lake and then they try to piece it as they go which becomes really problematic because there's no strategic direction that ties back the data initiative and strategy to what the business is trying to achieve and that's I think why we end up in this place where a lot of money is being spent but it's not being spent strategically to help the business it's being spent just building infrastructure and that data becomes seen as a cost center so because they are starting from a place of strategy and tying it back to the business they don't know who to appoint for that data leadership role they don't really know what that role should be why they want it what that person should be delivering so they often appoint the wrong person it's almost like they're being set up to fail so there's this big gap right data leaders can't articulate the value well enough but Business Leaders aren't setting them up to do that if that makes sense so I think that's been the thing out of all these conversations and all the events that we've run I often just end up back at that place wondering well how do we bridge that Gap to be honest with you it becomes a bit of a vicious cycle yeah that's really something interesting and I think definitely something that the industry needs to tackle in the next few years in order to reach that stability point for the data analytics industry so given this perspective of you interviewing data leaders what do you think are top trends that will affect the data space in the next few years and how are data leaders approaching hiring and building data teams yeah so as I said earlier I think the data product thing will be will be a real key component to driving adoption and changing culture which is is really the thing that needs to happen if we're going to continue on this journey to get value from it I think back to the the kind of Attraction and retention piece it's about having well well-balanced teams I think we've been guilty of building very highly technical teams and that's fine but I think we've realized now that there needs to be a balance around that and not just diversity in the in the traditional sense but diversity of of thought and experience and perspective so people are bringing people in from different backgrounds that have come from different places that look at challenges and problems in in different ways and have just a different breadth of skill so I think we're in a place now where not everyone needs to be a python wizard there might be someone within a team that whose job is actually to go and translate technicalities into business and I think that's I think that's fine so yeah I think I think those are going to be the key things and then getting to the point of the overarching thing for me is I think we need to figure out a way of how does the data analytics Community start to articulate the value that it's creating once we tackle that and we figure out okay what's the correct starting point for this and there's enough use cases out there where other organizations can almost lean on past experiences to make decisions in terms of where do they start with data analytics what's the right place who's the right type of people how big does the team need to be what skills do you need what tech do you buy etc etc I think we'll be in a much better place so I think the whole treating data as an asset and the value and how you value that I think that's going to be one of the the big trends over the next 12 to 24 months that's really great I love this perspectives now Kyle as we wrap up our podcast you have any final call to action before we wrap up today's episode called so I should know I don't think so I think all I'd encourage anyone if they're in the process of trying to build data analytics teams I would say just touch back on the points of create well-balanced teams around diversity of thought and experience create that compelling narrative around someone should join your organization over over someone else because I think that's the thing where most businesses in my experience fail you know when I ask them that question straight up I often get very blank stares back right which is is a problem and it's something so simple to do yet so few organizations actually sit and think about what that message and narrative should be so that's something that the businesses definitely should be doing and then articulating how the work of that team is visible valuable impactful within the organization because that's become a real driver for the individuals that they'll be targeting that's all really great thank you so much Kyle for coming on data friend no problem at all thanks for having me foreign you've been listening to data framed a podcast by datacamp 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"