Episode 131: How to Use Talent Intelligence to Support Your Organisation in a Recession (an Interview with Toby Culshaw)
In this episode of the Digital HR Leaders podcast, David is joined by Toby Culshaw, Talent Intelligence Lead at Amazon and author of the book Talent Intelligence: Use Business and People Data to Drive Organizational Performance. Having built successful talent intelligence teams at both Phillips and Amazon, Toby is one of the leading authorities in the talent intelligence space.
With the recently turned upside-down global economy and many large organisations looking at cost-cutting initiatives, Toby will share his insights into how organisations can leverage the power of talent intelligence to push through the heavy headwinds of the recession and come out with a competitive advantage.
The conversation will also cover:
The various types of data sets that can be used to hone talent intelligence
How to set up and position a talent intelligence function
The skills you should look out for to build an effective talent intelligence team
Enjoy!
Support from this podcast comes from TechWolf. You can learn more by visiting: techwolf.ai
David Green: Today, as we kick off series 27 of the podcast, we're going to be exploring the world of talent intelligence with Toby Culshaw. Toby is someone who is one of the leading authorities in the talent intelligence space, having built successful talent intelligence teams, first at Phillips and now at Amazon. With the global economy having been turned upside down, I invited Toby onto the podcast to share his views on how talent intelligence can help companies break through these difficult times and come out with a competitive talent advantage.
Toby Culshaw: Too many companies are seeing the slowdown as times to, particularly within TA, to cut that TA headcount because the rationale being, "Well, if we're not hiring people, we don't need a TA function". But really, this is the time to say, "If we're not all guns blazing hiring, this is the time to improve the functions and improve the systems, processes, tools, upskill people". If you don't have those sorts of capabilities in place, this is a great time to take stock and say what's needed from our business, both now and moving forward, and how do we build that in place now.
David Green: But we don't stop there. My conversation with Toby will also cover the various types of datasets that can be used to hone your talent intelligence, how to set up a talent intelligence function, where the TI function should sit within the organisation and the skills you should look out for to build the perfect talent intelligence team. So, if you're looking for some inspiration on how you can withstand the strong economic headwinds, then this is an episode you won't want to miss. Now, let's start the conversation.
Toby, welcome to the podcast, it's a pleasure to have you on the show. I think that we first met at a conference in Barcelona. Maybe we'd met before that, but I certainly remember meeting you at a conference in Barcelona and I had the pleasure of introducing you on the stage. Before we dive into the interview, could you please share with our listeners a little bit about yourself?
Toby Culshaw: Yes, I lead Talent Intelligence for Worldwide Amazon stores at the moment. People that don't know what that means, it's essentially everything you're going to buy from Amazon, so the ecommerce platform, amazon.com, through to the retail stores, right through to how that gets to your door. So, it could be anything from airlines through to robotics through to logistics, operations, warehousing; everything apart from that massive workforce of the associate population. So I don't cover the associate population, everything else within that spectrum falls within my remit and I have a centralised team that looks across talent intelligence, and we can discuss what that means for us, but we look across talent intelligence for the whole of that customer base.
Prior to this, I'd come from TI. We like to refer to fungible talent at Amazon; I'm not a terribly fungible talent, I can't be lifted and shifted into different functions and suddenly work for comp and bens, or anything. I've been doing labour market intelligence, talent intelligence, exec research, sourcing intelligence, that sort of space for 15, 20 years, and really specialising in what I consider pure talent intelligence for probably about the last 7 or 8.
David Green: Firstly, congratulations on the recent publication of your book, Talent Intelligence. It's a great book and I think it's the first that I've seen on intelligence as well. Out of curiosity, what inspired you to write the book?
Toby Culshaw: I think there were two main things. Through the last 24 months or so, the whole talent intelligence world has really exploded, it's been a really hot topic. Obviously, we've had a very volatile labour market and lots of rapid growth and lots of competition for talent. More so than ever, teams have had to start looking at that external landscape to say, "What's going on? Why can't we hire? What are the challenges we're facing; what are the options ahead of us?" So, talent intelligence overall has had a real growth period. That's tied in also with the next generations of platforms kicking through in maturity and their user and customer base, etc. So, there's been a whole perfect storm going on around TI.
Through that period, I was having lots of conversations with people around how to set up a TI function, what to expect when you're setting up a TI function, what that could look like. The last question every single time when we had these conversations was, "Look, this is great, is there anything out there I can read to further understand the space and dive into it deeper?" And beyond, you know, Marlieke Pols did a fantastic whitepaper when we were at Phillips diving into talent intelligence, some of the vendors had done some really interesting whitepapers around what talent intelligence was from their perspective; but beyond that, there really wasn't anything I could find or point people to that gave a more holistic view of, "This is the state of the nation of TI".
I'm not saying this is going to be the state of the nation forever, but at this point in time, this is what it looks like and this is what we're thinking. And there wasn't really anything I could point people towards, so I wanted to write something around that, to give something to people to look towards. And also, honestly, I thought it would be an interesting reference point to see how things evolve. So once again, that stock-take in time to say, "This is where we're at. We're at an interesting point in history now where I think we get to essentially craft and create an entire function, feel, industry, whatever you want to frame it as", and you don't get that very often. There aren't many chances in life where you get to set the stall and say, "This is what this function and field is".
For me, that's quite interesting. So, trying to at least help contribute to that in some small way was probably another driver there as well.
David Green: There's a great definition in the book about what talent intelligence is, and for the benefit of our listeners, who you pretty much span across HR, they might have an idea, some of them probably work in talent intelligence and have a very good idea what it is, but there will be lots that have maybe heard of talent intelligence, maybe not quite sure what it is; what's your definition of talent intelligence?
Toby Culshaw: Yeah, I've got to get a better one; the one in the book's too long, if I'm going to be honest! As soon as I wrote it I thought, "It's out of date already". But the one we use in the book, and it's essentially an amalgamation of a few different TI definitions, because we were seeing different definitions from the vendor space, from the practitioner space, from thought leadership, so we tried to bring them together; but it's essentially the augmentation of that internal and external people data, applying some kind of technology science, insights, intel, whatever it may be, and then you're looking at the people skills, jobs, functions, competitors, geographies.
The key thing for me personally is, it's around de-risking and driving those strategic business decisions. So, for me it's all around the broad human capital labour market and data to say, "What are we trying to achieve; and, where are we trying to go with this?" and we can dive into that versus, say, sourcing intelligence and variances there, etc. But yeah, for me it's all around using people data, in whatever guise that is, merge external people data, merging it with the internal and then looking to drive business decisions.
David Green: You mention, Toby, that talent intelligence is an emerging function, obviously one that's gaining more traction and demand in the people space. Again, reference the book, there's a great line graph in the first chapter that shows the growth over the last 20 years, but also that significant amount of growth over the last 18 months, 2 years.
For our listeners who may not have a TI function in their organisation yet, you've talked about definition and I think you've given a clue there, can you tell us a little bit of why talent intelligence is so important and maybe why it's so important now and likely to be in the coming years?
Toby Culshaw: Yeah. For me, I think it really comes down to one word, and that's "context". I think quite often, when we're looking at internal datasets and we're looking at making decisions based on internal datasets, we understand half the story, but we don't understand the context of how that lies and why things are happening in that way. Using external labour market data, whether that's looking at skills and how it's transitioning, whether you're looking at competitors and how they're changing, whether it's location and looking at how does that sit in that location; and what's the feasibility expansion or growth or development in that location; what are the changes in governmental regulations that mean that immigration flow is going to change?
Understanding the context of the external helps frame the decisions you're making in a much clearer way, and give you a much better idea of what's going on and why it's going on. So I'd say context is the key reason; the overlay of that is obviously, the external is changing faster and more aggressively than we've ever seen really in history. We're seeing movements and transformation and change that should take decades, multiple decades, happening within a year, within two years; we're seeing these rapid, rapid changes in transformations.
That means that that stability, and we often talk about the volatile, uncertain world, etc, that instability is meaning that context is more important than ever; companies can pivot really fast; change locations really fast; for M&A deals, suddenly you have lots of redundancies happening at the turn of a coin. So, that context is so aggressive and it's so volatile. Without that, I think teams are seeing half a story, and they can only really get half a perspective of why things are changing within their organisation and why they're having higher attrition rates, why they're having lower morale, why they're seeing it harder to attract people, etc.
If you're not understanding the external context, it's really, really hard to make true decision and true data-led decisions, and particularly to your point around looking around corners and what's coming ahead; that's very hard to do when you're only looking at your internal datasets.
David Green: In the end, I guess it's understanding that flow of talent, where it's flowing to, where it's flowing from, and I guess in the context now, obviously everyone's talking about hybrid work; if we think about that, what that means for organisations that are embracing that, it potentially opens up new locations, new talent pools, I guess, where they can potentially -- and if these are locations where they've not hired before, they're literally going blind, aren't they, without the data to help them to do that?
Toby Culshaw: Exactly, and they're often whole business strategies that are based upon being able to achieve certain elements of growth, if you landing to expand, if you're buying a new company and wanting to expand it, buying a company wanting to integrate it in; without understanding the context and what the feasibility of that is, really those growth plans are all kind of hopes and dreams, unless you want to go from 700 people to 7,000, is that even going to be possible? Are the another 6,000 even in the industry that do that job?
Quite often, the strategy in M&A teams, they're not necessarily looking into that feasibility piece, because it's not really their problem. Once they've done the deal and it's down the pipeline, it's down to TA to try and sort that mess out. So, quite often it's around, look, if we can impact the decisions upstream and settle that world, downstream impact for TA, and that's why you often see these teams being formed in TA, which we can go into in a minute, but that's where the by-product lies. If we make these decisions better, we should actually have an easier life downstream later.
David Green: Obviously, we're both in the UK, lots to talk about recessions and challenging economics periods, and it's not just the UK of course, we're talking potentially about global recession and everything else. How can businesses take advantage of talent intelligence during economically uncertain times?
Toby Culshaw: There's lots we can do. The most obvious, to be honest, is the cost-cutting piece. A lot of companies are looking to look at their employee base and say, "How can we right-size this, right-sure it; are we in the right locations; are we looking at things from the right cost base, whether it's low-cost countries, whether it's hybrid?" there are loads of options around that. But from a pure location cost element, there's a lot that can be done, looking at the org design, looking at hub-and-spoke versus centralisation versus decentralisation; there's lots of work that can be done around that location/workforce benchmarking from a pure cost perspective.
Then I'd say you've got the broader talent marketplace piece. You know the space better than I, but at the moment a lot of companies are obviously starting to put the brakes a little bit on hiring, they don't want to bring in new talent quite so much, so it's understanding the internal talent. But quite often, understanding the skillsets you have within organisations is quite hard. Ironically, it's quite often easier to look using external platforms back into your own organisation so understand what skills you have potentially within that organisation, because those skills aren't being captured within your HCM, they're not being captured within your learning management system, etc.
So, using TI to use the external data to look back into your own organisation, to look at that whole talent marketplace and skills, is really important. I think the future talent is a really important piece. So, we've got a team of economists, for example, that look to the future to say, "What's rolling down the road we should be aware of? What are these changes that are coming three, five years out? How's that going to impact the changes to the economy at the moment? How's that going to change candidate behaviours?"
The obvious kneejerk reaction in a situation like we're seeing at the moment is that that power is shifting from a candidate market to a company-driven market, because we have the jobs, the candidates are going to be looking. That's not necessarily the case. If the skills you're looking for are still in high demand, if candidates are then becoming less risk-averse so they want to move less, suddenly it's going to be even harder to hire, so you're going to have to put even more effort into it. So, it's quite counter to what some leadership are going to be thinking.
Then, to your point around pivoting, I think always on intelligence is one of the most interesting elements of what TI can do. So, if you're permanently scraping competitor job postings, looking into them, what are they doing from a skillset perspective? Are they pivoting as an organisation? Are they opening up new locations? Are they moving from a fixed location to a hybrid model, because you can see all that information from the job postings? Obviously, within job postings in a lot of states now, you can start looking at the salary benchmarking and looking at the salary ranges, how is that changing? I wouldn't necessarily use that as a sole reason at the moment.
But there's lots of things around having that constant eye on competitors to say, "What are they doing? How are they changing?" and you can see that as a trigger of, "When are they starting to come out of this? When are they starting to ramp up hiring again; and if they are, what's our risk exposure? If we know certain competitors hire aggressively against whatever function in a given location, are we risk-exposed; are we then going to see an attrition spike off the back of that?" All of that's the sort of thing you can really dig into.
But they, I think honestly within this kind of period as well, the other arguably most important thing is, if recruitment volumes are a little lower, if things are stabilising internally a little more, because the activity levels aren't quite as high, use this as the time to build those foundations out, use this as the time if you don't have a TI function, get that in place. I think too many companies are seeing the slowdown as times to, particularly within TA, to cut that TA headcount because the rationale being, "Well, if we're not hiring people, we don't need a TA function". But really, this is the time to say, "If we're not all guns blazing hiring, this is the time to improve the functions and improve the systems, processes, tools, upskill people; this is the time for that".
So I'd say, yeah, if you don't have those sorts of capabilities in place, this is a great time to take stock and say what's needed from our business, both now and moving forward, and how do we build that in place now.
David Green: I think you make several really important points, but a couple around how talent intelligence supports the creation and sustaining the talent marketplace, and we think about workforce planning and if we think about retention, we think about understanding the value of your employees' skills on the marketplace as well and who's hiring them, because as you said, that can support our comp and ben strategies.
I think the other thing that's really interesting there is, it's important to understand what competitors are doing. Companies are always looking at what products and services their competitors are putting out there, but actually if you look at who they're hiring, then you can probably get ahead of that, because you can understand what they're potentially going to be developing before they actually come to market with it, which is quite interesting.
Toby Culshaw: Absolutely. So, we used the phrase at Phillips that was essentially a talent radar, there was an early-warning threat detection of what was happening, and you can see this in a number of ways. Whether it's execs and VPs being hired into spaces that have never been hired into before; we had situations where market intelligence teams that cared about a certain customer base didn't care about the individuals, because they were moving from pan-industry. If we'd have spotted that, we saw individuals moving into this competitor that essentially triggered and highlighted they were opening a whole new product range into a core product for us; no one picked it up.
If you role forward 18 months, the intellectual property and the IP team were saying, "Yes, there are patents being filed around certain products, but there's no conflict of interest", so they're not flagging it to anyone. It was only 18 months after that that we were starting to get competitors hiring specific roles and specific teams out of our production facilities that we then, as a TI team, reversed back and said, "When was the earliest trigger we could have caught this?" Three years earlier, if we'd have seen that VP moving, we'd have seen that they're definitely moving into a new product range that they've never done before.
I think that whole early-warning threat detection is really important. You can really see, are companies pivoting; are they launching into new locations? The business as usual, we don't care about. If certain competitors are hiring 20,000 software engineers a year, you don't care because they always are. If they're opening new sites, if they're pivoting skillsets, are they shifting from an on-prem sales model to a cloud sales model? Well, that's going to fundamentally change the sales people they have. Are they moving from product sales to solution sales? Okay, that's going to fundamentally change.
It's those changes you're looking for, and I think that's super-powerful, when you can start seeing changes in the labour before they're telling the market that they're changing as a company, I think that could be really powerful.
David Green: Yeah, and you can straightaway see the business value of it, it's not just date for data's sake. There is so much data in the labour market, which leads nicely to this question, that we can use to gain that competitive advantage, and you talked about some of them there. But many companies out there aren't utilising or gathering this data. Maybe a TI team that's just getting established, or a company that's just establishing a TI function, what data could or should they be gathering to really put forward a business case for improving talent initiatives, for example?
Toby Culshaw: I think that's a great question. I think I'd always say, we back to context, it depends what your business really cares about. Some companies are going to be very M&A-hungry, so looking into, "What do we need to know about those companies we're buying; why are we buying those companies? Are we buying them for market penetration; are we buying them just for the IP; are we buying them to integrate? What are we buying them for?" That could be a great thing to tie into to say, "Okay, if we're looking to land and expand, this is not feasible. If we're looking to do this over the next three to five years, this is what we need to change internally, etc, looking at the footprint of the existing company, looking at the skills they have, the job levelling, the framing of the architecture, etc.
All of that, although it sounds difficult, it's relatively low-hanging fruit in terms of the sort of work you're going to be doing. You can get a lot of that information from their LinkedIn profiles or XING, or going on the company website corporate structures, etc. There's a lot of low-hanging fruit there to understand competitors and how they work, from what's publicly available. I think more broadly, you really need to work out what your leaders care about, what are their really big ticket items, and work backwards from that; because if it's a pure TA issue, you just need to get that funnel conversion higher, if you need to get more people in that funnel, if you need to target the recruitment marketing intelligence in a smarter way, then suddenly that's a very different problem and it's all around, how do we activate people in a more effective way; how do we target our competitors in a more direct way?
You could go upstream and say, "Well in actual fact, to address this and to do this, we need to change our location strategy, we need to change our office culture to a hybrid model". You can always pivot back and go upstream to work out what do we need to do to affect things downstream. But I'd say really, it's purely around understanding what you're trying to achieve, because to your point, there's so much data out there. It could be grabbing macro data, it could be going to one of the many vendors.
One of the reasons we saw the hockey stick of TI is the vendor landscape radically changed. We saw a real upskilling of the vendor side of simplifying product for us. So it went from really data-heavy products that you had to be very, very data-literate to use, to products with a very good user interface that suddenly, you didn't have to be a hardcore data scientist or an economist to dig into this, they were doing the heavy lifting for you, which meant that the actual product range enabled a lot of the transformation internally.
So, it's not as scary as people think. Quite often they're seeing this stuff and thinking there are some terribly deep, dark, terrifying datasets out there. There are, if you want to dig into that; but the reality is, by using the sources most people already have, LinkedIn Recruiter, the social platforms, looking at GitHub, looking at reports people are publishing, looking at Bureau of Labour Statistics or Office of National Statistics. There's a lot of information there that's at people's fingertips. And I'll tell you, that low-hanging fruit is a lot easier than people think. TA functions generally will have a lot of this information; they're just not very good at bubbling it up, they're not very good at telling the leaders, "This is what we're seeing on the ground". The information flow gets stuck and so it never really bubbles up properly.
David Green: You mention some of the vendors out there, spoke about the vendors, but what role does technology play in collating all this data and helping to scale talent intelligence in general?
Toby Culshaw: I'm always a consultant first-type model, so we can talk about the evolution of HR analytics, people analytics, versus TI and how they're slightly different in my mind. So I'd say that technology as an output is not necessarily always the right way I'd go; self-service dashboarding, tooling, etc, isn't always the way I'd recommend going. But technology in and of itself, absolutely vital. Can you go and, I don't know, manually scrape every job posting for a competitor, put it into Excel and manually go through and track that and analyse it, etc? Absolutely, yeah, but you're going to be there for years, literally years. You couldn't scrape it as fast as they're posting, you'd be there forever.
Technology to scrape that stuff, to analyse that stuff, absolutely vital. Technology for visualisation of these datasets, absolutely vital. There's the actual platform side and the vendor side as I mentioned, they've come on immeasurably in the last five years, huge amount of VC investment in the last couple of years, so we're seeing lots more players coming into that space. The maturity of that is moving ahead rapidly. It's still challenging, is the honest answer, because there is so much variance in the external marketplace, are different platforms using the same taxonomies and definitions; how does that compare to the governmental data; how does that compare with your internal job classification and job taxonomies?
So, it's still messy data. We're still at the stage where there are no universal datasets, there aren't universal agreements on taxonomies, so it's still quite messy, but it's absolutely vital. Without technology, a huge chunk of what we do within TI just isn't possible, and I think that's also one of the dangers some TI teams can fall into, is if their output is pure data and they're not being consultative, they're not doing the advisory piece, they're not doing the analysis, they're not having an opinion. Then eventually, that will just be automated as well. As all these platforms mature and the product and the offering matures, if you're not having that consultation piece and the advisory, your whole workstream's going to be absorbed and automated into that as well.
David Green: Often, there's a misconception out there that talent intelligence and people analytics are the same thing. I mean, I suppose the names themselves could arguably be quite similar. And of course, there are similarities, but they're also very different, and you present this wonderfully in the book. But for those who haven't yet read the book, could you share your thoughts on the differences between the two?
Toby Culshaw: There are some operational differences I see, and there are almost philosophical differences. I'd say operationally, generally, people analytics, and I bow to you on this one because you know this space way better than me, but people analytics generally I'd say is looking at the internal efficiencies, the internal metrics, internal KPIs, looking at your attrition rates, your talent flows, etc, internally and saying, "How are we performing; how is this going?" And it's that internal reflection. Obviously you've got the HR analytics, people analytics maturity curve, where you go from reporting to analysis through to more intelligence and advisory, etc. But generally speaking, it's looking at internal data and trying to understand our internal landscape and how that sits.
Generally speaking, TI is the external piece and it's looking at the external lens, the external landscape. Where the grey area comes is when they merge because neither of them have complete power unless you bring their stuff together. And I think you see some subfunctions where this merges faster and much more acutely, so things like talent acquisition analytics. Quite often, it's a subfunction, quite often it's not sat within people analytics, HR analytics, where I think it should sit personally, but that's a very obvious starting point where you can say, "Actually, we can see this internally from the CRM systems and the ATS systems. This is external; all right, we need to get that together", and I think we're going to see more and more of those grey areas, and we can talk about the evolution in a second.
But I think generally, I'd say HR analytics, people analytics, the primary is internal and the secondary maybe some external context; TI is just the opposite. Primary is the external, you're going to use some internal for context. I'd say the more philosophical piece as well though is generally with HR people analytics, I'd say if you're looking into a problem statement, it's a hypothesis-first methodology, so you've got a hypothesis and then you're looking to the data to prove or disprove that hypothesis, and it's a much more academic approach. Whereas generally in TI, it's more of a business problem that you're trying to solve. So it's a broader inquisitive study that you end up going down more rabbit holes, and you're never quite as tight on the parameters.
So, it's a slightly different methodology, both have their pros and cons, but I'd say generally most TI teams are coming from that slightly less academic and hypothesis-based approach, but it's more of an inquisitive study; whereas in general, I would say people analytics, HR analytics have that slightly tighter rigour on that academic approach.
David Green: Yeah, it's really interesting actually, and I think in the book you talk about obviously the very fact of what you've just said there; the skills to do the various roles in people analytics in some respects differ from the skills to do talent intelligence, and that's not always understood. So I guess when you bring it together, and I think you talk about where it is brought together here, a lot of that, you need the leader of that function, whatever it's called, people intelligence maybe, to actually understand those differences so both can thrive.
One really obvious point I think, when you were talking about the talent acquisition analytics, if you're only looking at the internal data, or you're only looking at the external data, you're only getting half the story and as you said, you need to bring them together, don't you, to give the whole story, depending on what it is you're trying to solve for?
Toby Culshaw: 100%, and I think it's some of the nuances around the maturity of the markets as well. So, HR analytics is a much more mature market, it's a much more stable market in terms of the datasets, etc. So you'll quite often find that people that are very strong practitioners and producing really good, powerful HR analytics in delivery, when they go into the TI world, there's a bit of a learning curve, or relearning curve, because the data isn't as structured, it's messy. So, from a pure data science perspective, they can't just jump in and do some really amazing data science work, because there's a whole kind of ETL-type piece at the beginning where it's just actually, we need to work out what we're even looking at, because all that maturity from the last 20 years or so, that you've had within HR analytics, putting the systems in place, having the structured data fields, having all that structured approach and the data architecture and the engineering, we just haven't had that in TI.
So it is very much a bit more like the Wild West, where there's all this opportunity and people can go out and just stake their claims and build things; but there aren't really as many controls in place, and that's one of the aspects of the book I really wanted to highlight, but I think it probably needs to be highlighted more. It's the whole kind of data ethics and the data security aspect, where I think HR analytics, people analytics, are probably tighter on that. They've got really close controls on personal identifiable information, or DE&I data, etc; they've got those control mechanisms in place to really have that structure.
I think that's still finding its feet in most TI teams, where because the data is publicly available, because the data can be accessed, there's almost this assumption that, "Well, it's out there, we can use it, we can grab it, we can process it", and I think there's a whole data ethics piece around there of, "Just because you can, doesn't mean you should". And I think that's the evolutionary phase, where we really need to focus. We can build this stuff, we can do this stuff, doesn't really mean we should be doing this stuff, either as practitioners or even within the vendors and the supply chain. There are some real elements where I think I'm not convinced that's the appropriate way for us to move as an industry.
David Green: Well, what's interesting actually in the book, and I kind of knew this, but I didn't know the extent of this actually, around 50% of TI functions actually currently report to the talent acquisition function, which is typically a separate function, well it is a separate function from people analytics. For the further evolution of talent intelligence, where do you think TI should sit within an organisation, and why, because I guess you've worked in talent intelligence functions that are separate, their own functions in their own right?
Toby Culshaw: That's a great question. So, when I was at Phillips doing talent intelligence, we sat within talent acquisition. Within Amazon, my TI team sits under talent management programmes. The other talent intelligence teams sit within talent acquisition, there's multiple TI teams over here. Interestingly, with the Talent Intel Collective, which we touched on very briefly earlier, we did a benchmarking survey last year and out of the benchmarking respondents, 80% sat still within talent acquisition; but of that 80%, 85% didn't want to sit within talent acquisition and they didn't think it was the right home, which was fascinating.
I think for me, it kind of depends on how the evolution of the overall HR analytics, people analytics, TI, strategic workforce planning, talent acquisition analytics, that whole piece, I think there's a lot happening in pockets and silos that probably could come together into one more holistic -- in the book I talk about the WAIFS function, so the Workforce Analytics Intelligence Forecasting and Strategy, which is a terrible name; definitely have to rephrase that and rename that for next time! I think Josh Burton touched on it recently and he used the name "TI", but I think actually what it is is workforce intelligence overall. It's that broader, actually if we're looking at any datasets within our talent landscape and the workforce landscape, that's probably all one beast. So, I think we could all come together under one more holistic HR, science-type, workforce intelligence function.
Equally, I think there's probably an argument to be had around whether centralised intelligence is the way forward. We're not talent intelligence practitioners, we're not people analytics practitioners, we're analytics practitioners and intelligence individuals. There's actually a lot of similarities if you look at our worlds versus, say, marketing intelligence or market intelligence or business intelligence or threat intelligence. The mechanisms we use and the skillsets we have, there are very, very similar career paths; there's very, very similar levelling; there's very, very similar actual techniques and processes.
So, there's also I think an argument to be had around having a centralised intelligence function and you just have the subspecialisations within of, you're a subject matter expert in talent, you're a subject matter expert in human capital, you're a subject matter expert in marketing. So, I think there's a whole piece around central intelligence and that whole knowledge transfer, and how do these datasets combine. If we know a competitor is bidding against us for a certain piece of work and from an exec research perspective, we can see they've just lost their head of programmes that leads up that delivery, that function, well from a sales perspective, we now know there's an angle to sell against, because they're not going to have the person to deliver that.
If we can see attrition's high in certain delivery centres, for example, the sales team can know that to go in and counter-sell and say, "Well actually, we know that competitor can't deliver there, because they've just had a huge attrition issue", or whatever it may be. I think there's so much power in combining these data sets, and at the moment we're still very, very siloed, both within HR but even more so when we expand and look at the broader business.
David Green: What I think would be really interesting now, given that talent intelligence is still relatively new in many organisations, obviously you mentioned that the majority still sit within talent acquisition, but let's say there's a company out there listening to you going, "Okay, I've listened to Toby, I'm now going to create a workforce intelligence team", so whether it's workforce intelligence or talent intelligence. For those that are looking to do this, how can they go about that? We've talked about focusing on the business challenges and stuff and before you start thinking about the data, but from a skills perspective, what skills should they look out for; and how should they structure the team?
Toby Culshaw: Assuming you've got the problem statements in the business, etc, as we say, reverse that back in to understand what the customer needs are, I'd say there are two things that I always look for from a soft-skill perspective, and that's passion and productivity, both very hard to actually assess for, from a talent assessment perspective; nightmare, I guess! But I'll tell you, with TI, both passion and productivity are both really, really high on the agenda, because it's a new field. So having passion for the field, understanding what it is, being hungry to learn about it, being hungry to learn what others are doing, that's really important, because there isn't a path out there that's well-trodden that everyone can follow. So, you're going to have to have that level of passion to really want to dig into it yourself.
The productivity piece, no one knows they necessarily need this data yet. Quite often, you'll be going to VPs and they don't know that this even exists, because it hasn't existed before. So you need to be really, really proactive about pushing it out, about not being scared to knock on doors, go into meetings, be exploratory. There's almost a business development, sales aspect to it, where you've got to be really keen to get out there and talk to the business and understand them one-to-one.
I think from a technical skills perspective, I'd say there's two angles you can go. You can either go data-first route and hire some BIEs and DEs and data scientists, etc, and go a similar path to people analytics and build out that scaled solutioning. Or, you generally see teams that will say, "Actually, rather than hire people in, I'm just going to pivot what we've got. So, I'm going to use someone from SWP, or I'm going to use somebody from real estate, or I'm going to use somebody from recruitment and we'll flex and use their skills".
There's a lot of very low-lift products out there, so whether it's any of the big vendors, like your ENSI, your MZ, you've got your Horsefly, you've got Stratagem, you've got LinkedIn Talent Insights that huge swathes of recruiters will be using. There's lots of vendors out there that are very easy to digest and it's very easy if you're starting in a TA function, as I say most do, it's very easy for them to get that data, understand that data and it feels familiar. It's sourcing intelligence, it's that data they use every day; it's largely about repositioning that and just saying, "If we're struggling at sourcing it for this software engineer in Cambridge", for example, "it's probably likely that others in our team are too, so let's just talk to them. And then, how do we scale that to the leadership team in Cambridge to say, 'What do we change?' Are we changing the location; are we changing the strategy; are we changing whatever?" and then it's just about scaling up in the business.
I think too many people are nervous about taking that data and scaling it through the business but from a skillset perspective, honestly I'd say it's low lift. I don't think you need highly, highly technical individuals initially. There's a lot of platforms out there to do the heavy lifting for you.
David Green: It leads quite nicely to the final question actually, Toby. So every series of the podcast we do, we ask the same question; there's five episodes, we ask the same question to each of the five guests. So, how can HR help the business identify and prioritise the critical skills it needs for the future? Kind of in your wheelhouse, this one!
Toby Culshaw: Do you know, it's a funny one. I was thinking about this recently and I think there are two elements really. There's the, "What is the market doing?" because I think that external context is vital. We can say we need skill X, but if the entire market's shifting to skill Y, we probably need to change our strategy on it. So, I think the whole market analysis piece, assessing where we're going. Obviously the overlay of that is how your own internal population is changing and how they're developing their skillsets away from necessarily what you're trying to drive. So, are they trying to shift their skillsets away from what you want to do?
Once again, you can use things like LinkedIn, reverse it back into your own company and see what skills people are developing that you might not necessarily have thought they're developing. I think obviously the overlay there as well is what the industries are changing as well. So, hot topic at the moment, metaverse, where's that going to go; understanding the skills needed there and potential expansion path, and that's when you get to the volume of hiring analysis, etc. So, I think there's a few things from the macro level.
But I think one of the most important things for me is whether businesses actually understand what critical skills are. I think quite often we say "critical skills" and it's really senior individuals, or the skillsets we think are a hot topic or a hot priority. But for me, hot and important and critical are different, and I think there are actually probably only a handful of really critical skills within most organisations. Most organisations, if you lose a huge chunk, it will be painful, but it's not going to be actually business critical.
So, I think really working with the business to say, "What are our truly, truly business-critical roles? Why are they business-critical? Is it an individual; is it a skillset; is it something that can be de-risked by moving across different functions, different locations, etc?", because those single points of failure, those real, true criticality pieces, they can be sometimes very long-tenured individuals that you sit there and go, "Well, if they retire in the next five years, you're in trouble". That de-risking element can be really important. So I'd say, yeah, there's probably a slight difference in my take in terms of the criticality versus the volume and the scale, I guess.
David Green: Great answer, Toby, and a nice way to finish our discussion. Can you let listeners know how they can stay in touch with you, as you're pretty active on social media, how they can follow you on social media, how they can get involved in the Talent Intelligence Collective and find out more about your work and obviously, the book?
Toby Culshaw: Yeah, for sure. As you say, I'm quite visible. So, Toby Culshaw, you can find me on Twitter, on LinkedIn, or wherever you want. Reach out whenever you want, I'm very open on these things. The Talent Intelligence Collective, primarily a Facebook group. We have got a LinkedIn group, but it's not terribly active. So primarily Facebook, or through there you can pop into the WhatsApp channel; that's very busy as well. We do monthly meetups, we obviously do a podcast and newsletter as well, but yeah, pretty much anywhere you google me, you'll be able to find me somewhere.
David Green: And the book is available now, isn't it? It's on Kogan Page.
Toby Culshaw: Yeah, Kogan Page for the book, or we'd be remiss not to say you can get it on Amazon if you wish as well!
David Green: It would be remiss of you, so I'm glad you added that one in there, Toby! Toby, it's been a pleasure talking to you, as ever. Thank you very much for being a guest on the Digital HR Leaders podcast.
Toby Culshaw: Thank you so much for having me, David, really appreciate it.