Fosway x Filtered | Cutting through content overload to solve your skills challenges

By Marc Zao-Sanders & Fiona Leteney

28 minute read

Remote working, digital transformation, and constant innovation all mandate more skills and more content. But as they pile up, do you know what you actually need and what’s just adding to the chaos?

Fosway Group know more than most about the state of skills in learning. So we've teamed up to help solve content overload.

In this session, Fosway's Fiona Leteney and Filtered's Marc Zao-Sanders help you navigate content overload to build skills, with examples of what the best in the business are doing 📈.

Watch the full recording below as they discuss:

  • Why traditional industry approaches to managing skills are failing to hit the mark
  • How learning systems providers are tackling skills
  • How to optimise your own skills frameworks and content libraries using data
Fosway & Filtered Webinar

 

We have also added the full transcript below so you don't miss a thing!

Fiona Leteney: 
All right. Well, this first slide is just a little bit about Fosway. And so in my background, actually is 20 years in learning systems or in learning technology. And I've either sold, managed or implemented learning systems during that time. And in the last five years, I've actually been working for Fosway. Fosway is the analyst for Europe and Asia in learning technology. And we've got a lot of research and we run advisory boards for both founders and for corporates.

But yeah, I'm going to age myself here when I start to look at the next slide, which is about skills. It's a massive topic at the minute. And when I was thinking about this and how the Half life of skills just seems to be reducing, I was drawn back to when I was actually at school. Now, it wasn't the abacus days, don't get me wrong, but it wasn't quite that far back. I maybe had one as a baby, but I was actually learning log tables.

Now, there might be people on the call that I've never heard of log tables before, but it was used in maths to give more complex multiplication of large numbers. But when you got the calculator into the 70s, you didn't need log tables. And so that's a skill that I learnt at school that is completely useless. Then moved on to university, I was studying engineering and I learnt Fortran for programming when I was at uni. But the way we did it, it was with the data input was via punch cards. And so I had to sit at a terminal and create the cards, hand that over to an operator, onto the computer, let it be run overnight and then come back and find out whether it worked. And more often than not, I'd made a mistake. I left a comma out, a full stop or something, and I had to kind of do the whole process again, it would take forever.

But as I was looking at station punch cards, it was the 1950s, up to the 1980s that they were being used. So gradually we're moving through a set of skills that is reducing significantly. And the other thing I just wanted to kind of this introduction is really sort of talk about over the last 20 years that I have been in the learning industry, we've talked a lot about competency frameworks. And as an analyst firm, we kind of have been a little bit sceptical about whether they have been a great deal of use, because although there's a huge amount of work that goes into actually creating them, they're all that often out of date as soon as you've actually written them.

And what we are seeing now, is morphing into a skills framework. But in the research that we've been doing - what's the difference? How we defined what skills are? Skills are being talked about being the currency for jobs and roles and that kind of thing. But here’s a job there is no longer with us. I mean who doesn't like going bowling.

But that is not the way it's done these days. And those guys are no longer needed. There are no jobs. So the next slide talks about the top and skills over 2015 and 2020. This report from the World Economic Forum and brought out every year, you can see at the top there's a complex problem solving, not with log tables, but is there at the top again and in at number two up from four critical thinking up from ten in 2020 was creativity. New in number six was emotional intelligence and cognitive flexibility came in at number ten. So, things are always moving. 

And so one of the questions that we asked in our Digital Reality Survey was how significant are the skills gaps in your organisations? And there was a 90% belief that the skills gaps are significant. 

Basically, a couple of years ago, I did a report on the talent trends and we found this as well, so this is about CEOs, but we were talking to HR directors and they are seriously concerned about the availability of key skills going into the future. And on another report (Deloitte) on the next slide, I love this conclusion at the end of the report: “In a world where the only constant is change, supporting workers in reinventing themselves often offers organisations a sustainable path forward as we aim to equip our forces to do the work of today - and into the future.”

Free learning content library benchmark

So skills are key. So what works in bridging the skills gaps? And some of the practitioners in our own research have told us when we've asked the question, so “which of the following approaches is your organisation using to help address the skills gaps?” And this is the answer. This is the analysis that we got highlighted online learning courses and digital learning courses. These were the approaches that the respondents were using to address the skills gap and they're at the top there. But also bear in mind, this particular survey was done before COVID-19. And what I would just ask is, if you get the opportunity, perhaps provide the link later on, our current survey is still underway. I've got a sneak peek at one of the stops in there that is quite useful to look at, but it's not completed. We’ve not closed the survey yet. So we would love to hear from you from this perspective.

And so we've still got in there face to face and the formal internal training and the formal external training, which is just because of COVID dropped off the face of the earth, really. But this next slide is really interesting. So, looking at those two underlined ones and we're asking the question: “What has made the biggest impact on the skills gaps?” Digital and online is actually dropped down again. Bear in mind that this is pre-COVID. 

And so, the next slide just gives the top five for those. And we see that formal learning is in the face to face training. But what I want to show you is the next one, which is the sneak peek at this year's stats formal learning, face to face, is obviously out of there. And so we've got still at the number one of the managers developing their people. And I think also that building a learning culture is an interesting one that we don't define when we ask the questions. We don't define what these mean. But for me, that is about giving the learner, empowering them to learn, which goes back to the comment on the Deloitte report. But in at number five here we have that the digital learning resources are actually starting to make an impact. 

We also asked the question, the top five system features that make the most difference to the learning experience. Now, we might come back to this, but basically last year, Fosway undertook a huge amount of research into basically deconstructing what the meaning of learning experience is. And a lot of people tend to look at that UX, UI, where the 50% is. But the learning experience is much more than that. And one of the reports we'll be bringing out soon is about skills. And it's just interesting to see that, you know, even before COVID, one of the features in learning systems that made the most difference was aligning of skills and roles to content.

And I'll take a breath and let Marc have a say now, so I mean, basically, I mean, we see that the skills and content and the alignment of those is hugely important. And I think we will also be talking about the personalisation of the content curation piece as well because I think that's significant and all about data and whatever. So, yeah. What are you guys seeing in terms of skills? Are people able to define those yet?

Marc Zao-Sanders:
We're saying that that's a really important thing to do if you want to understand your content, the content that you have well enough, you can understand your people in terms of the roles they're doing. And if you want to link the two in some way and actually I think the thinking so far and the practise so far is pretty imprecise on skills.

And there are some slides that will show in a bit that I just wanted to just run back on some of the slides that you just showed and just have a little bit of a discussion. I know that's probably not exactly the agenda, but let's just go. So the first was to do with the half life of skills. So this quote, I don't know where it came from, but someone started talking about half lives of skills, which always confused me anyway. It's caught fire. And so everyone's using that. I can definitely accept that especially with tech technical skills, you don't need them for as long as some technical skills, that's true. But I also think there's a large body of skills that they're not reducing half life or life, you know, take resilience or communication skills or persuasiveness or even some technical skills, say, like Microsoft Excel, it's been around for 35 years, could have gone away, hasn't gone away. People still need that skill. So I think it's certainly true of some. But there is some nuance, as always with this with these things. So I just wondered what you thought about that. 

Fiona Leteney: 
We had a round table where we were talking to some of the corporates about skills and what we would ask about skills is “what are we actually talking about?” Is it a very specific thing or is it much more general? I think what you're getting at things like leadership and as you say, the communication, they don't go away. Just, you know, we kind of develop those and those can be something that is so important. There is another slide that we have that has all those very skills in there that I didn't include in the deck about the importance of skills and leadership and some of the power skills I think we're calling them these days, rather than soft skills are absolutely of their own importance.

And if you can highlight which of the ones are important to your organisation in terms of value to your organisation, and then those are probably the ones to kind of to focus on in terms of making sure you've got the right content. 

Marc Zao-Sanders:
Yeah, absolutely. And just picking up on that and you mentioned, say, leadership and I mentioned communications skills. They have a longevity. I mean, probably a permanence in terms of what human beings need to do professionally or in their actual lives. I think there's also a point, so not just about longevity and of some skills, but also the nuance. One of my co-founders, Chris Littlewood, wrote an article on our blog recently about nuanced skills. So rather than just looking at, say, leadership, which is a very, very broad term, to think about some of the perspectives on leadership say that might be, say, authentic leadership, but actually more specific than that, authentic leadership in your industry and at your firm, because the more specific you can be, the more relevant you can get that content to be for the end-users, the learners of what you're doing. So I think longevity, as well as nuance, are important considerations.

 

Fiona Leteney: 
And in all of this is linked with the culture of the organisation, which is going to be different from organisation to organisation. So just maybe picking something that is so generic it might actually miss the mark. 

 

Marc Zao-Sanders:
It almost certainly will. And it won't miss the mark for quite a lot of people at the company because it's generic and if it were generic, it just won't have that impact. And actually, that leads me to another question I had. So, Sam would you move us onto the World Economic Forum slide with the skills?

So, complex problem solving always comes at the top. And it's hard to argue with that - we've got complex problems and we want people to solve them. But the problem is, how can I use this list as well to make the points that I want to make. But in a generic list, which doesn't have the context of a particular industry or the context of a particular firm, still less an individual or a team. The problem with that is that I mean, just look at some of the overlap here, critical thinking, complex problem solving, judgement and decision making. These overlap so much.

I think the relative order of some of these and the change over time to exactly the point you're making on the site, these are all interesting things, but it becomes far more tangible when you apply some of this thinking, but actually get to your firm because then you can unpick. Well, OK, we're going to have complex problem solving at the top. But by that scale, we mean exactly this and therefore we've already included or deliberately excluded judgement and decision making from that. And so there are these other buckets and that works for us in our organisation. So, kind of relates to the question you asked me to start in, how people are starting to think about skills and defining them. Well, they're starting and by thinking about what it really means to their company and what they want it to mean to and their company.

So, yeah, we're seeing it more and more. And we're trying to lead the charge because I think you mentioned the phrase common currency, something like that. And a lot of people talk about skills as being that for us all now. I think it literally is that, though. I mean, if you're trying to describe a job of work to someone, I mean, you could talk about the tasks, I suppose, but that's very, very specific. We can talk about the skills that will be needed and will be needed over time. And you can do exactly the same thing with learning experiences or learning resources. So you can see how it runs through the employee experience. So it really is common currency in a very real sense, certainly how we think about it. And I think it's very important to think about it when there's so much change going on in the world and the need for different skills.

 

Sam Franklin: 
Interestingly, in the chat, there's quite a theme developing along with this as well, which is what is a skill and how does it relate to knowledge? And in L&D, are we a little bit too high up in ivory towers and not actually on the ground working out what a skill is and what it means to people, which I think is where you're going a bit there, Marc, with those nuanced skills, which is like the ability to break down a skill in terms of what it actually means and how you apply it.

But how would you guys define a skill versus knowledge versus problem solving?

 

Marc Zao-Sanders:
That's quite hard. But I think that the thought that occurs to me immediately is with a skill, you can go on refining that skill. So let's take communication skills. That's not something you can just sort of mark as complete and then move on to the next skill. That's just something that we all work on over our lifetimes.

Whereas knowledge, I think is a bit more binary. You know, you've got it or you don't have it can be tested for or easily can be taught a lot more easily. And it's obviously very important. But there is a sense in which if you can develop a skill, it will help you out in a variety of situations. So it's a little bit more flexible. Fiona, what do you think? 

 

Fiona Leteney: 
Yeah, and I think it is really difficult. And I'm not sure that there is a definition out there and we're all going off in different ways for this. So, you know, I mean, I was reminded of something you said earlier about a conversation, again, we had with a corporate recently. And it was the selling skill, if you like, that was being taught. And they'd actually ask somebody to come in and create a piece of content for the sales calls.

When you talk to the business, it was great, of course, but it was too general and it didn't actually dig deep enough into the various skills. It was too generic. And I think it goes back to what you were saying, Marc, which is that, you know, each individual organisation needs to define for themselves. What it is that's important to them and how granular that is and therefore how much those individual skills kind of work across the various roles, because there will be certain skills that actually are repeated across quite a number of roles, whether that's, you know, a manager role or whether it's a team leader or a senior leadership or whatever. Again, within a sales part of the organisation or a technical part of the organisation. Some things will be generic, but you have to dig deeper into the context of where it's been used to actually be able to share that. I'm going off at a slight tangent here, but I'm thinking about the two connecting it with content. Connecting the skills with content is also difficult because if you're trying to create a granular level, you might not actually have the content to meet that and going back to the Excel spreadsheet sort of conversation. Out there, there tend to be courses that are at a basic level, an intermediate level and an advanced level. If I just wanted to know how to do with the pivot table or which level does that come in and is it a skill, I mean, or is it just a piece of knowledge? It's hard to define that.

But also, I would say that, you know, mastery of a skill if you, um, you know, on whatever you're doing that according to Malcolm Gladwell, that's ten thousand dollars worth of work just right there. 

Free learning content library benchmark

 

Marc Zao-Sanders:
I think for a lot of people, I would definitely qualify pivot as a skill. I want to pick up a couple of the things that I know definitely come back to pivot tables. But you mentioned, you know, one of one of your clients wanting to have their sales force or maybe actually just their workforce be a bit more commercially savvy and more sort of sales oriented. And we hear that quite a lot. I mean, especially now everyone needs top-line growth or to maintain their revenues. Sales and being sales in the right sort of way. That's exactly the kind of okay, that's a start. But then what is involved in that? Let's unpack it. So does it include negotiation and then within a negotiation walking away or break up emails? And this actually is there's no right or wrong about what should be included and what shouldn't be. But it relates again to culture, the culture of the industry, the culture of your firm as to what should be included and what shouldn't be. And once you've broken it down, you can say, okay, well, it's definitely it's exactly this. This is what we mean by sales. And that would be unique probably to your company.

And then on one of the other points then that you got to, Fiona, if you're that granular, you can find the content. The answer is you are almost certainly going to find the content if you look at it as if you cast the net wide enough because there are just millions and millions of items, the problem is quite hard to do that. So, I mean, that's something we've been working on for the last year - it’s Content Intelligence is it's partially a solution to that.

But, yeah, you can go granular. You can apply multiple filters to as long as you've got enough content to begin with. But with the web, there really is enough. With the pivot tables, I think that's a great example of where we’re sort of using that as, you know, it's that knowledge or is that a skill?

Because if you went to a greater level of granularity, let's say a specific function in Excel count if OK, that's it's quite hard to say that's a skill you put down your CV. But with pivot tables, there are a number of ways that you can take that. And it's a little bit broader and certainly with it, Microsoft Excel, there are so many things you can do with Excel that I think, you know, that that certainly qualifies as a skill.

So I guess there's something about the breadth of the domain, breadth and depth and size of the domain that would suggest that it would be more skill than knowledge. What do you think, Sam?

 

Sam Franklin: 
I think that you're going nicely, Marc, into how we actually break down these skills into nuanced skills and then match them to content, as Fiona also mentioned as well. So just to bring up the next slide. 

 

Marc Zao-Sanders:
I do think it's good to be as tangible as possible in a webinar so that you can be helpful. And a lot of this is our approach doesn't need to be done with us necessarily. But there's a logic that sort of underpins all of this. So first, is choosing the right skills. What should be in, given business priorities over the next period of time that make sense? Five years, ten years, two years, three years. Choosing the right skills, knowing what you mean by them, so defining them, understanding your content in terms of those skills. Then you can generate the data to bring all sorts of benefits, getting the right stuff, curating it for end users, and then linking content to end users and actually understanding the people in terms of you and your organisation in terms of those skills is very helpful. When you think about job descriptions and some of the documents that lie around, you know, the corporate world, you can infer all sorts of thinking about it in terms of heat maps, you know, where the skill strengths, where the skills gaps in the firm, you if you run algorithms, you can get these answers.

Fiona Leteney:
So in the learning systems market, then some learning systems are obviously talking a lot about skills at the moment. And they are talking about having sort of access to tens of thousands of skills. What's your focus, I mean, from some of the things that you've already said? Just give us an idea of the sorts of things where you would tend to start?

 

Marc Zao-Sanders:
20, 30, 40, not thousands. I mean, we'll take whatever data there is. If there's a list somewhere of thousands of skills, then obviously we'll synthesise that. We'll look at search data, we’ll look at job descriptions, we'll look at anything from which we can get a better sense of what the high priority skills for an organisation should be, including the answer. It might be that they've done all of this work, they hired a consultancy or whatever to give them that and we will still challenge it. But, you know, if that's useful. But in the end, keep in mind the end user. So of course, this needs to work for their company. But if the employee can't get a handle on this list that you've got, then it's going to be a lot less effective. And in the end, do you need more than 30 or 40? Probably not. I mean, even with very large organisations that we're working with one hundred thousand two hundred thousand staff, the IT skills list doesn't extend, not usefully, much beyond 40 or so. I'm being a little bit flippant saying 20, 30 or 40, but I'm trying to just get to tangible answers and numbers to stir up debate. 

So choosing the right skills, I mean, these are just examples from a very long list that we've got from multiple clients, but it's working out how broad that should be. Is it for a particular department or function?

How granular? Similarly granular. So you don't have one skills packet that is huge and others that are much, much, much smaller. And when I said before about, say, search data being an input for this, I mean, it really is you look at the search data on your LXP or LMS or SharePoint and what comes up? Does data science come up? Does time management come up? What's the relative ordering? And that's one of the inputs that you could use to inform what goes into this list.

For each one of those skills, take resilience, know what you mean by it. For a given company thinking about resilience rather than just ending the conversation, we've got the skill. Resilience is really important to our workforce at the moment. So that's it. The end of the conversation. What are the different component parts of resilience and be intentional, be deliberate about what should be included and what shouldn't be. And even the relative weights, I mean, I think an interesting example here is sleep. Sleep isn't always included in conversations about resilience and maybe you wouldn't want it to be at your firm. I think the hope of it should play a part, but you can be intentional. You can say, look, sleep in or sleep is out. So all of the recommendations about, say, sleep app or books about sleep would be included, or therefore not be included. The point really is to be intentional, like we think about it, what's the list? And then for each item in the list, what do you mean by it? And here's an example of this. So we would do this through just conversations with the client. Because once you know this, you can start setting algorithms to your content to find out, OK, given what these guys really mean by resilience, this is the content that they need to you know, that's really important. And so you have a much more nuanced list once you've been through this sort of process. 

 

Fiona Leteney:
It's not a quick fix then. It's not 'buy a system that kind of deals with skills'. You really have to kind of dig deep. The devil's in the details. You've got to go into and look at your organisation, and I think that's the issue, that it's hard, it's not an easy thing to deal with. But I imagine it’s worthwhile.

 

Marc Zao-Sanders:
It's still a very efficient and effective process, but yeah, it's not a turnkey or we've got the system now and it's going to deliver everything. A lot of our work is algorithmic. A lot of the value that we add is algorithmic and increasingly, firms are kind of going that way. But the algorithms have to work off of something. And that's something for us anyway, is human understanding of what these skills are after back and forth with the client about, um, about the definition and challenging them and bringing some suggestions like self-esteem, sleep. Do you want to bring that into your definition of this particular skill, for example? So to really know what you mean by them, by the skill, I'm going to keep using these two words, but it may be intentional and be deliberate about it. So say what you mean, mean what you say is the thing.

 

Sam Franklin:
I think that's a bit of a feedback loop there as well. Right. Because all of the data that we're getting from doing this over and over again is then informing those human decisions as well. So the more you do this kind of thing, you can kind of rely on the expertise you build from doing it. But from what we've seen, there aren't actually that many people doing it at this kind of scale. Less is more. I don't know what you guys have seen, but the average kind of amount of skills in a framework is massive, especially where it's been populated by users in the system. For example, we've seen some frameworks with like 10K skills in them. So at that point, it's almost unusable. And we're actually seeing that quite a lot in terms of content overload. I don't know if that resonates, Fiona, with your clients. Do you see the same kind of thing?

 

Fiona Leteney:
When we talk to the vendors and looking at those that have the sort of tens of thousands of skills and some of the sort of the blue sky thinking is that we certainly are a little bit sceptical at the minute. In the sense of - it sounds like it's a good step, so when I was talking previously about competency frameworks and that they're always out of date, then some of these that are using AI/machine learning algorithms to kind of date by scraping from the Internet, their skills and whatever, and increasing that that skills framework sounds great. And it sounds as though it's solved a problem, but I'm not sure whether we've actually seen it working yet in earnest. I think when we had the roundtable on skills with the corporates, there were some large organisations asking each other, we were just facilitating the conversation and they were asking each other, so what have you done? And, oh, I need to talk to you afterwards because we need to kind start to put our arms around this. They even got the issues where different departments or different sections of the same department were going off in different tangents in terms of sort of defining the skills and what they want to do. And I think going back to the devil is in the detail because it's, you know, the data that you have there is there's got to be good. I think that's probably jumping ahead. 

 

Marc Zao-Sanders:
That was a good point about data because the data isn't always good. I mean, it is always not perfect. And so the job is not just the conversations. But, you know, in trying to understand what a company means by resiliency is a conversation with some of the relevant parties there. But it's also looking at such data, job descriptions, other forms of data. Some of these data points are more reliable and up to date than others. And so you've got to apply relative weighting to that in order to get to the output, which is basically a skills taxonomy at the end of it, but a skilled taxonomy that just makes the appropriate use of data. I guess the point I'm really trying to make is that it's never perfect, but it's also never completely imperfect. So as long as you make some sensible decisions about what to take and how to use it, you can end up with something very sensible and useful by the end.

 

Fiona Leteney:
I think the other conversation, though, is that if you're feeding data into a black box, an algorithm, there's a lot of fear about that. Going back to my previous company when I was a System Administrator on a learning system, we had a learning system that did have algorithms and intentionally dense because we knew that our data wasn't up to it at that stage. We've got a lot of work to do first. And I think, you know, there's a fear factor, I think, within the corporate market, not understanding what's in that black box. It's interesting to hear you say that it doesn't have to be perfect in terms of the data, but, you know, the worry is that somebody is going to be recommended something that is a compliance course when actually they really don't want to be recommended that. But, yeah, that there is a concern, I think, about this unknown that they're feeding data and hoping that something good is going to come out of it.

 

Marc Zao-Sanders:
Yeah, I think that's one of the problems with neural networks and AI, it feels like it's just a black box. So you give some inputs and then there are some outputs, but what's the connection and what's going on in between? I mean, one of the things that, you know, in the design of Content Intelligence from the start is to make it as interpretable as possible. So even with this process, you can see on this tweet slide here, you could include emotional well-being in a definition of resilience and you could exclude it and you could see the differences in the results, in the curated results and then agree with them or disagree with them.

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But there's quite a lot that is because there's so much human input here. You're reaching a lot further into the black box. And then and then when you see the results, you can play around with it and see. So inseparability is something that's been really important to us for the first time. And it's just one of the guys in the team, Georgie, who's written a paper on exactly this on this topic. But yeah, I think it's a fear that a lot of people have. And actually any company that's claiming to sell AI should do something to alleviate that concern because the black box is off-putting for a lot of people.

 

Fiona Leteney:
One of the other roundtables was about intelligence and that was something that came out there was a definite learning curve needed for L&D professionals because they just don't understand it. We need to help in this area. So hopefully that will come out. 

 

Marc Zao-Sanders:
And so know what you mean by them. And then content terms of skills. So what I'm saying here is that your TED comes up. So TED is a great provider of learning content, I think, because it is B2C, it's that sort of standard. It's consumer-grade to use the lingo. But increasingly firms are interested. And for that reason, the firms are increasingly interested in having this content for their staff.

The point of this slide in trying to make here is that if you can understand what I mean by understanding content in terms of your skills is for a given learning asset, as we call it, learning object/piece of learning material/thing from which you can extract some learning. How much is it about each of the items in your skills framework and can you calibrate that? Can you add some sort of scale? So I mean, we do with a number between zero and one for the skills that have been chosen by a client. So these are just some illustrative numbers here about a TED talk about building confidence. So you'd expect that it would be about resilience and adaptability and confidence, obviously, and a lot less about data reporting, but maybe not zero about data reporting. It depends on what is in this talk. The point here is there is a step which is running algorithms through tens of thousands or hundreds of thousands or even millions of items of content and coming up with some answers, which are to present each one of those assets in terms of relevance to a particular skill because that brings all sorts of benefits. I mean, the curation benefits, the choosing your library benefits. 

On the right-hand side, what you've got here is mean, it is just to screenshots of our software content intelligence, but ultimately whether it's using our software or finding a way to do this in a different way, you're still going to one output that looks a bit like this. I mean, it's an ordered list of the content that's you know, this case is relevant to empowering teams. You can see that skill on the top right-hand side. It's an audit list. It's on the basis that middle field relevance. And there are some surprises in there - that's what you want with some you know, if machines are with the right human inputs at the start coming up, we are going through lots and lots of data to return some results. There'll be some nice surprises, though. There's one in here. I don't know if you can see it, but there's item number 10 - Harvard Business Review ‘Case study: Is Holacracy for Us? That's not something that is necessarily an item the curators would go for first when thinking about empowering teams, a term like Holacracy, but it gets picked out by the algorithms and it might be very relevant for this particular firm. So that's one kind of output that you can get from algorithmically generated data. The other one is on the left-hand side.

It's not very easy to see here with the screenshot, but basically that if you do this, if you aggregate up the provider level and you've got a very good idea of the skills are important for your company and you could do this manually, it would take a long time, but you could do this manually. We think about it certainly manually. You can get a sense of, okay, well, given all of the providers out there and what I need to do, these ones are super relevant and these ones are less relevant than these ones. These ones I should argue with more, negotiate with them with harder. And these ones I should consider and I have to consider them before. And this better use of the web we could now make the algorithms are nowhere to be able to do this kind of thing. And so these benefits are you know, those are important benefits. 

 

Fiona Leteney:
For the algorithms that you're using that within somebody's library or going out onto the Internet? 

 

Marc Zao-Sanders:
Both. Say in general, they'll say, OK, look, when you're one on Content Intelligence you’ll go through our stuff and that sort of point number one. But while you're at it, we could probably make better use of the website or we were considering such and such library. So could you run an analysis that says both, you know, what they've got and what they might have got there?

 

Fiona Leteney:
I would imagine it tends to work better when you've got a larger organisation that's got a library that goes back a long way. And so there's a lot of content rather than somebody that's in a start up mode with their first learning system and with not a great deal of content.

 

Marc Zao-Sanders:
The truth is, although we have a process and we've spent years building these algorithms, there's only an advantage to be had with algorithms if you've got sent ten thousand or more learning assets, which is a lot of companies. I mean, if you actually look at what's on SharePoint, what's on this LMS or this other LMS, it includes proprietary content that they've created themselves, there's quite a lot of companies, but it's not every company like you say.

This last one is you know, it's I think it's a big part of the future, actually, of what we're doing and where the industry will go ahead. So rather than just thinking about content, in terms of skills, if you apply the same sort of technology and approach to job descriptions and appraisals, you can build up a picture of a really granular and comprehensive view of the skills in an organisation. And then, of course, you know where you are now, where you want to be. And then the content to get that. Do you have the right sort of content to get from one heat map to the future state heat map? And this is actually experimental for us at the moment, but the early results were really, really positive. 

 

Fiona Leteney:
So it's really looking there at the department level. You mentioned going into job roles.

 

Marc Zao-Sanders:
Yeah, I mean, that's just the cost of the data. So it could be job roles. This is actually run on thirty thousand job descriptions from just sample data. We've done it for some of our clients as well. And the results are also interesting. But as long as you've got in the metadata something on the department or the business unit or the geography, or you can make that cut and run it that way, it depends on what's going to be most interesting for you and who's in charge of what. So which cut is most useful will depend on the use case. 

 

Fiona Leteney:
I think that aligning the skills and the roles to consent, you know, a lot of people have that maybe the less it's a competency framework or skills list and then they've got this library of legacy or current content. Bringing those two together, to use a learning system that's kind of standard learning system, I suppose, that has AI as part of it and recommendation/personalisation, there has got to be going back to that, there's some work to do before you can really kind of switch it on and let it go. And you have a new way to do that manually or you have other ways to do it. It's that piece that's missing, it's not all blue sky just at the minute, it's a great idea. There's a picture being painted about being able to solve the skills gaps and that kind of thing. But actually, we are where we are. We've got legacy stuff and we need to kind of get from where we are to and move forward. It's not a quick fix.

Free learning content library benchmark

Marc Zao-Sanders:
It's not a quick fix. And that's why we say that there are a synthesis and sort of an understanding that needs to be developed for the various data sources that could help us with this, you know, skills, skills issue and actually also in the process. Because if you're looking at, say, search data on SharePoint or Microsoft teams, that's not learning data as such. I mean, it might be some curiosity that's expressed. So it relates to learning, but it's just someone in the organisation who outside of the learning context, just wants to know about something. Job descriptions - that's not learning data. So one of the other benefits of this kind of approach is not just that we get more reliable data, but that we're lifting ourselves out of the L&D filter bubble and having a louder voice because we're using data sources that actually the business is using. So it will make a bigger impact.

 

Fiona Leteney:
I mean, I think that's the key ultimately where we need to get to from an L&D perspective, is being able to measure that impact, being able to make an impact on the business, the business bottom line. It's not an easy thing to do. We can measure bums on seats in a learning system, but it's actually not going to move the business forward. 

 

Marc Zao-Sanders:
That's the big point with the linking to business outcomes. But since you mentioned the bottom line, I mean, that's one of the other benefits of an analysis like this that you can generate some data that will say, well, you know, you really need this library, but you don't need such and such library. And there's some bottom-line saving. And, you know, you've got the data to back it up. It's just less relevant. And you hear the numbers rather than what has happened in many of our clients and non-clients - we just see it in the industry. Their content is inherited by the people that have inherited it are enthusiastic about some, not so much others. And then there's inertia. And so it's not very it's really not intentional, deliberate as to what stays and what goes. And that's not very meritocratic. It doesn't actually push the content vendors onto producing what clients actually need.

So this blog provides the data to make and make better decisions on that front and maybe some cost savings, too. 

 

Sam Franklin:
You mentioned the ten thousand threshold idea - one client had 250 K assets. The last one we did. I'm also wary of content overload in our specific case because we're running up close to time. And I just had one question I wanted to ask both of you guys, which is, how do you keep skills fresh? Is there a kind of bottom up approach or how do you keep updating these skills and definitions as you go? 

 

Marc Zao-Sanders:
Well, I think there are two elements. One is what goes on the list, and then two is like how each skill is being defined. So like, for example, with data science, that's moving quite quickly. So you probably want to keep data science for the next number of years, maybe permanently. But what is included, programming languages, for example, that are included would change.

That's got to be partly a manual process and human process with the thinking that goes into market changes and the long term strategy of the company. But I think some of it can be crowdsourced as well. Like search data is effectively crowdsourced data sets. So again, I would just put this in with the synthesis of multiple data sources that need to be run every so often, probably every six months, but maybe annual.

 

Fiona Leteney:
And I think each team, whatever they are, I'm thinking about tech teams, where things are moving so quickly and they have to develop what are the skills that their particular team needs. I think a lot of them have agile coaches and things like that to be able to define what those are and then they end up sort of teaching themselves. So it's almost like you've got to potentially give those teams the ability to do that. So you've got to give them the tools and then stand back and let them do what they do. Because keeping abreast of everything else and HR cannot do that themselves. They have to kind of devolve that responsibility out to the business.

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