Your organisation needs certain skills to perform and reach its goals.
If you don’t know what they are, it becomes a lot harder.
And if you haven’t got visibility into the skills you already have…
You’ve got no clue which skills gaps exist or need closing.
THAT is why an employee skills assessment is so critical!
We need to get clarity on the skills we need and to what extent.
We need to measure the skills we already have and to what proficiency.
And only then can we find the skills gaps that are holding us back.
When we speak with organisations, this is the thing that simply takes too long.
Nine to 12 months too long in most cases.
And it’s because most people try to map skills manually, relying on individual interviews, building a skills taxonomy from scratch, and gathering it all in a spreadsheet that ends up gathering dust.
We’ve got a full guide on skills mapping, but here’s what you need to do in a nutshell.
Using HowNow AI, we analyse real-time job market data - looking at adverts to understand the tasks and responsibilities associated with jobs on the market.
Now you can infer which skills are required and the proficiency levels needed for someone to be able to do that task and responsibility.
Now we have a core skills taxonomy for the jobs that are available in the market. And the goal is to contextualise that based on jobs that exist within your organisation.
Integrating with your HR system is a great way to do this because we can look at your jobs you have, the roles people previously had, and their responsibilities to further contextualise the required skills.
This is skills mapping with a strong data foundation, benchmarking against the wider market, but bringing in the context from within your organisation.
This is another really simple yet effective way to bring in business context as you go through the skills mapping process.
Because we can essentially work back from the goals we need to hit, and establish the goals needed to hit them.
And if we do it this way, prioritisation is baked in. We have to hit these goals commercially, so we ultimately have to have those skills.
We often hear that buy-in is a challenge for L&D teams, but this is a great way to win people over because our initiatives our geared toward business critical skills.
So, we understand the skills we need and the levels of proficiency required.
Now we have to measure the skills we currently have and to which extent.
That’s how we find our skills gaps, and there’s a simple three-step way of looking at this.
Don’t do this:
Like skills mapping, we want to be smarter and more efficient than just interviewing everyone in the organisation.
Do this:
And that means leveraging the existing data you have.
On a personal level, what do you know about someone’s work history, what they’ve been learning, their knowledge contributions in the business?
We can use this to infer the skills they have, and it’s also something we use HowNow AI to do at speed and scale.
Productivity and performance data offer another objective route to measuring skills.
Imagine your average sales cycle is three months. If you’ve got a sales rep who continually closes within that time and others who don’t, that data can start to give us an inference around what skills people actually have.
Don’t do this:
Rely on time-consuming tests and exams, where we assume skills level based on performance.
Do this:
Leverage 360-degree assessments to combine self-review with insights from peers and managers.
This paints a clear picture that pulls together your beliefs about your own performance and insights from people who witness your performance and execution on the job.
At HowNow, we use the Dreyfus Model of Skill Acquisition - measuring proficiency on a five-point scale from Novice to Expert.
We then use AI to combine that with performance data and project outcomes, so that we can answer questions like:
Did this person complete that project on time? And did they complete it to a high degree?
You can integrate with the systems that contain this performance data and make a data-driven assessment on where someone's proficiency is for this particular skill.
Don’t do this:
Try to close every single gap at once! Not all skill gaps are created equally…
Do this:
Be objective! And try not to solve the skills gaps as you analyse the data. Remember:
So we need to look for obvious and concerning gaps.
And ultimately, this is an exercise in prioritisation, which you can learn how to do in our guide to skills-first L&D.
From mapping and measuring skills to closing your gaps and showing ROI, this is your step-by-step guide to skills-led L&D!