Our industry’s number one analyst, Josh Bersin, recently recorded a podcast dedicated exclusively to skills taxonomies. And the message is landing - 93% of businesses see reskilling and upskilling as the number one priority for 2021.
There’s a growing undercurrent of thinking that a skills-based employment setup is not just economically more efficient, but also more socially equitable. Skills like data, digital, leadership and wellbeing are all becoming essential to the wider picture of organisational capability - and learner engagement.
However, despite all the noise around skills, there’s very little substance behind the discussion. And businesses are not even getting close to the reality of skills. But at Filtered, we work with skills all day every day, and we know what that reality looks like. We’ve condensed what we know into this piece, so read on to find out.
The world of skills is a mess, just as the world of learning content is a mess. There are hundreds of them in some companies’ skills frameworks, skills taxonomies, skills matrices etc. And there are many, many thousands of skills out there. They are badly defined and rarely grouped despite many of them being almost synonymous. User-generated skills clouds amplify the noise 10x. And if skills are a mess, we stand no chance of cleaning up our content.
The problem is that skills are, for the most part, unintentional. Making your skills intentional means going beyond a definition and fully articulating what your business means, by, say, resilience. The boiler-plate definition from the content provider is a start. But their definition of resilience is unlikely to be exactly what you intend for your workforce. Does it include getting a good night’s sleep? Should it? Does it include working well under pressure? Should it? Does it include time management? Again, should it?
The answers to these and other skills questions will be different for each company. They depend on many factors, including the industry, the cultural direction and company mission, what leaders at the company stand for and the motivations of its workforce. But they are questions that must be answered, and answered with nuance.
Other questions you need to answer to solve your skills issues: are we clear on how to define what a skill is, not just each individual skill? There’s a hotchpotch of associated concepts: competencies, capabilities, values, traits, behaviours. What is a skill vs a capability? Or a competency? Then there are hard skills and soft skills and power skills. Which of these are within the remit of L&D / HR / Talent, and which are not?
Every business will have different answers, so I’m not going to try and untangle all this here, but the graphic below may stimulate discussion.
The important thing is to pick a line and stick to it. Take, for example, a business practice I find particularly enjoyable and useful (and one Filtered has a long history of training): spreadsheeting. We hear clients say that Excel is a skill, definitively. And we hear clients saying the exact opposite, equally definitively. I don’t actually think it matters all that much in the broad, high-level industry discussions. But it does matter that everyone in your company agrees. Stakeholder buy-in at a senior level is crucial to achieving consensus.
That also helps break down skills silos. There’s a jumble of skills related to content in one system. Ditto job descriptions in another. Ditto learning objectives and KPIs. Ditto appraisals and performance management. The promise that skills can be that coveted ‘common currency’ all the way along the employee lifecycle is still a lofty aspiration rather than a hard reality.
We often miss skills. Some skills are just willfully or subconsciously overlooked. As Annie Lowrey pointed out last month in The Atlantic:
A great deal of skill is required to change the clothes of an immobilized senior who might not want to have her clothes changed, or to wrangle a class of toddlers, or to clean up an overgrown yard at breakneck pace, or to handle five tables of drunk guys who want their wings yesterday. The kind of patience and equanimity it takes to be a good care worker? Not a skill, apparently. The kind of fortitude it takes to be a fruit picker? Not a skill either.
These and countless other skills often remain effectively unknown to an individual’s manager, colleagues, and employer. The wastage here is lamentable and colossal. And it hamstrings the employer when identifying and developing talent.
Indeed, the role of the organisation is often unclear. People learn all the time, by themselves, for themselves, intentionally and unintentionally. That is one of the properties of a neotenic, lifelong learning species. Through social media alone we consume hours of content each day, much of which is informational and educational and almost all of which is genuinely consumer-grade. How are organisations supposed to help direct that learning at the skills they’ve identified as important?
All of these skills questions are difficult to answer. But I believe we can crack this tough nut with the right combination of pragmatic thinking and practice. And if we do, we’re affecting an agenda that everyone cares about and creating that rising tide that lifts all boats. To that end, a skills data blueprint...
To deal with all of the misgivings described above, we *just* need to do this:
We need to assemble the appropriate data in order to design our skills framework well and also to bring business partners and stakeholders together and convince them. Of course, not all the data we would like will be readily available and some may be entirely inaccessible. But in good, agile fashion we should start with what we have and iterate from there.
Here are some examples.
Job descriptions, especially when they have written thoughtfully, used appropriately in interviews and followed up with through on-boarding and the job can reveal much about the desired and required skills.
Future skills, especially that thinking which pertains to your sector and your company, should also feature for obvious reasons but my advice would be to not agonise over this; it’s more important to construct an extensible framework which can easily accommodate a new, unforeseen skill than to plan the next 10 years.
Existing taxonomies, sometimes developed and used by individual departments, should be the starting point - and maybe with a little repurposing, that’s all you need.
Organic user search queries tend to throw up a lot of expected results (agile, excel, leadership) but some of the less frequent, more nuanced terms can bring more actionable insights. Here’s a typical set of results for organic search on an LXP, SharePoint etc:
How useful is data like this? On the one hand, this is real, unprompted user behaviour. On the other, it’s from a small and possibly unrepresentative sample of the workforce population and a lot of the terms are very generic (eg ‘leadership’).
In short: it’s of some use. So we certainly need to bring in other data sources to ratify, amplify and augment this. But here are some questions this kind of data might prompt:
Synthesise all this data and draw it together into a relevant, robust, flexible and extensible skills framework. In our experience, this tends to be 30-100 skills. The skills need to cover the business priorities today, as a minimum. They also need definitions, either accurate description in natural language or skills graphs (a bit like the Curiosity triangle aboe). They need to be believed in by business partners (and ideally you’ll have a champion per skill high up in the organisation). They may include some levelling. If you have this, you have brought the employer’s needs (the skills required to succeed) to the table which is our role, in L&D. The individual will of course then pick and choose and click and not click and we help them by making clear what skills are important and why.
My co-Founder, Vinit Patel wrote about how to design a skills framework to be transformative. At the top of that piece we shared some research into some of the most popular areas covered by such frameworks today:
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By a distance, the most popular type of framework amongst our clients is some version of digital skills.
And then, within your framework, be sure that you understand - no, be sure that you determine and define each skill. Take curiosity, for example. There’s a lot of interest in this right now as learning to learn and learning agility rise up L&D’s and individuals’ agendas. Well, what do you and your company mean by this? There’s no objective right or wrong. The only misstep you can make here - which is often made - is to not define the skill or behaviour at all.
The graphic below is a crude illustration of what I mean.
Here, the terms at the top are more specifically pertinent to the skill under consideration, Curiosity. So for this (emblematic) this client, asking the right questions and out-of-the box thinking are very much what Curiosity is about, and iteration, resilience, interruption science less so. If you have developed and articulated this understanding of the skill, your human curators colleagues have the instructions to curate intentionally. If you have also codified this understanding of the skill, algorithms have what they need to take the baton (see below).
There are algorithms now which can take a human understanding of skills and apply that to thousands or millions of pieces of content and decide - as well as a human can which skill each relates to. We call this Content Intelligence and one of the outputs looks like this:
Viewing this asset-by-asset gives you a curation accelerant. Viewing the assets in terms of relevance by library informs content buying decisions and negotiations.
Finally, the tags generated by this part-human-expert, part-machine process improve the discovery experience (eg the number of successful searches) for your users. Do you know the search success stats for your workforce today?
To give a specific example from our Content Intelligence technology - we ran our analysis on 6 of the major content libraries' learning assets to assess relevance to your power skill as well as overall quality. Our technology scalably assesses every piece of content that the learning provider has algorithmically to paint an overall picture of the library’s relevance to a specific skill.
In this case, we looked at the nuanced skill ‘Leveraging data’ and assessed its relevance across the learning content that HBR, LinkedIn Learning, Coursera, getAbstract, Mind Tools and FutureLearn have.
After we ran the analysis we discovered that getAbstract, LinkedIn Learning and HBR libraries are the most relevant libraries for businesses that are looking to develop or improve their employees’ skill to leverage data. While not all 6 major content libraries have an equal number of assets in total, if you're using or considering Coursera, Mind Tools or FutureLearn, you may be missing out on assets that rank more relevant for your nuanced skill requirement. So, if your business is looking to focus on leveraging data as one of the top priority skills in your skills matrix, then investing in getAbstract followed by LinkedIn Learning and HBR which have the most relevant content coverage will provide the best ROI.
The skills you’ve put together are useful elsewhere too. The right (data-backed, and carefully synthesised), high-definition picture of skills does of course help with learning and development decisions and activities. It’s also interesting to those working in Talent and C-Suite and PR and really any hiring manager in the entire business. Because there’s at least a popular and growing opinion that skills is the best way to think about workplace productivity and the full employee lifecycle. More tangibly, anyone involved in the process of creating hiring plans, job descriptions and appraisals should find your work on skills and what you’ve done with them useful and compelling.
If you believe in the data-skills-content-rest-of-business approach, outlined above, please be in touch.