As we continue to explore the role that learning analytics plays in the shift toward skill-based organizations, one small but important detail is emerging. A successful approach to data-driven insights requires vast swathes of reliable data.
No surprise there, of course. But how do you get that information when you’re capturing the individual skills of thousands of employees across complex learning ecosystems? If that all sounds a bit data-centric, we hear you. But read on because creating an engaging employee experience that emphasizes career progression is at the heart of this.
This blog post explores the importance of nurturing a culture shift that encourages employees to actively enrich your learning ecosystem through a bottom-up approach to skills data capture. Successfully upskilling and reskilling your workforce depends on it.
5 considerations when developing a culture of employee skill self-assessments:
As CEOs increasingly recognize the importance of shifting from role-based people strategies to skill-based ones, this directive will land on the learning and HR functions to deliver. So how does L&D promote a bottom-up culture where your employees accurately assess their own skills? Consider these potential factors:
- We’re seeing a shift from role-based people strategies to skill-based ones.
- There’s a trust barrier to overcome.
- A bottom-up approach engages employees and gives you credible data to future-proof your skills strategy.
- Verifying skills encourages a greater depth of learning ecosystem data.
- There is no universal ‘right’ answer; each organization has unique verification thresholds and skills ecosystems
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We’re seeing a shift from role-based people strategies to skill-based strategies
Getting a grip on the challenges of upskilling and reskilling is rapidly approaching the top of many a CEO’s agenda. This, in turn, puts renewed focus on CLOs to deliver.
This isn’t even a case of needing to adapt to grow in the future. The need is here and now—we see it in our active discussions with learning leaders across the globe. CEOs are recognizing that to remain competitive, they need a robust skills strategy. — Piers Lea, LTG’s Chief Strategy Officer
The shift toward skills-based strategies (as opposed to the traditional role-based approach) is so vivid that within three months of presenting a skills data and analytics webinar with our friend Steve Boucher at Degreed, we had to adapt that content when we gave a similar skills conversation to the Chief Learning Officer audience.
As part of Degreed’s Strategic Solutions and Advisory Team, Steve’s finger is well on the pulse, and he had seen an increased emphasis on Talent Mobility as a driver in client conversations.
The following example report shows an ideal end-goal dashboard that reports on Talent Mobility metrics. Percentage swings in specific skill acquisitions can be filtered by role, region, department, timeframe, etc to offer leaders a quick pulse check on their core Talent Mobility metrics.
There’s a trust barrier to overcome
Encouraging employees to adopt a mindset focused on skill capture can address concerns or hesitations about why organizations are interested in their data. Think about it: if you excel at Excel, why would your employer want to know if it’s not directly related to your current job?
Historical fears still linger that organizations may use skill data punitively (e.g., during job cuts), but it’s crucial to recognize the potential benefits of capturing these skill sets. You can gain employee trust and stakeholder buy-in by applying marketing and storytelling techniques to convey the reasons behind your learning initiatives effectively.
The first step will be to start driving a culture that encourages employees to feel comfortable about skills and explains why you want to capture this information. Introduce skills as a mechanism and a tool for them to continue to grow their careers. Even if you are passively collecting the data to start with, it will be there for you to leverage in the future. — Steve Boucher, Director of Strategic Advisory & Solutions at Degreed.
A bottom-up approach engages employees and provides credible data to future-proof your skills strategy
To improve your organization’s skill health, having a broad set of data that evolves over time is essential. It allows you to benchmark your current state of play and then assess the progress of future initiatives, ultimately enabling you to align your skills data with your learning strategy.
And building trust is key to enabling a training program that verifies employee skills. A flow for verifying your skills data can include some (rarely all) of these steps:
- Employees submit self-assessments
- Peer reviews
- Manager reviews
- Course completions
- Badges and certifications
- On-the-job observations*
- On-the-job assessments
*See this transformative Nebraska Medicine / Xapimed observations case study.
So, if you can foster a culture whereby employees are motivated to input their skills, you can build a wealth of insights across your ecosystem. Achieving an organizational view of employee knowledge is challenging without the help of your learners.
However, suppose employees are motivated to do so as they see the potential upskilling opportunities and the link to career progression. In that case, you future-proof your data banks at the same time as addressing the skills conundrum. It’s an absolute win-win scenario.
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Verifying skills encourages a greater depth of learning ecosystem data
Your skills data may live in several places across your ecosystem. And while some of that data may live in your HRIS systems, it rarely contains everything you need. Aggregating the data across your ecosystem is essential to building a well-rounded view of your organization’s skills and capabilities and identifying gaps.
The following image shows an example of a modern learning ecosystem and the numerous platforms that may contain essential elements you need to build a rounded picture. The more systems and platforms you use, the more reliable your insights will be.
Every organization has its way of organizing data, but it's important to gather and present all the data in an easily accessible format, resulting in comprehensive reports and dashboards.
There will be challenges you’ll face when aggregating your data, such as dealing with overlapping skill categories in different systems, and ensuring learner records all point to a single ID to avoid duplications. These data cleansing challenges should be handled when connecting your individual data sources to your Learning Analytics Platform.
There is no universal right answer; each organization has unique verification thresholds and skills ecosystems
When we ran our webinar, A Skills Analytics Journey: Turning Data into Meaningful Insights, we noticed a lot of questions focused on the ins and outs of skills taxonomies:
- What happens when skills have similar names across different systems?
- Is 360 feedback a suitable way of verifying skills?
- Are proficiency ratings based on self-reported values, and how are these being calculated?
All are completely valid questions, but not necessarily ones with universal answers. Each organization will have different thresholds that work for them. Likewise, how you verify skills will depend on the tools and platforms in your ecosystem.
For some skills, manager verification may be enough. For skills that perhaps play a role in more crucial operations, you may want verification to include passing an assessment and on-the-job application.
How do you know if you have the right thresholds? The only way you’ll know is to try it and see if the outcomes support the desired business goals.
Take this example. Say you have a skill that’s associated with electric battery repairs in the automotive sector. If your analytics show that dealerships have repeat returns following these repairs, the data can flag an issue with the effectiveness of training.
And if that training forms part of a skills verification process, you may say, Hang on, these mechanics all have a verified skill in this area. Why are we still seeing repeat repairs?
This indicates that either the training needs to be revisited (if that is deemed to be the root cause), or you may increase your skill verification thresholds to put further emphasis on “on-the-job application,” for example. Either way, you end up adjusting the verification process of the skills to meet a satisfactory business outcome (this process is also known as skills calibration).
The wider point here is that we see many people daunted by the challenges of skills taxonomies, and many questions seek a universal answer for best practice. Surely, the best approach boils down to making sure your learning outcomes (in this instance understanding your skill verification thresholds), are aligned with business goals.
Navigating the brave new world of skills
There’s a bold new world of skills out there, just waiting for learning, HR, and people leaders to embrace. And it’s all underpinned by capturing data now to make decisions in the future.
Want to find out more about how you can take advantage of your skills data? Join Brandon Brodkin and Steve Boucher as they take a transformative journey into the world of skills analytics in this webinar.
About the author
Having worked in almost every job going in marketing, Ash loves the diversity and variation of challenges marketing handles. From acknowledging pain points to genuine, straightforward messaging, there’s a lot to be said and many ways to say it!
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