•   Article   •   5 mins

Skill Data: Reducing Bias in Career Opportunities

Imagine you’re dropped into the middle of a city that has 36,000 residents. There’s no internet. You don’t even have a phone book. You barely know anybody.

You do, however, have a goal: Give people the opportunity to join a team that’s going to create and sell a new product. You’ll need designers and a product manager with applicable experience. You’ll need marketing pros who understand communication, sales reps with chain store connections, and someone in finance to track all the revenue.

Though unpredictable and inefficient, activities like walking from one street corner to the next, knocking on doors, and arbitrarily approaching people at the mini-mart are among your best shots at finding your team. You might muster the help you need eventually, but only in neighborhoods reachable by foot. Do the best, brightest, and most qualified individuals cross your path? You’ll never know. 

A similar dynamic unfolds every day inside companies large and small. All too often, leaders and managers who need to look beyond their regular orbits — to other teams or departments for help — rely on word of mouth or anecdotal impressions to find someone who can kick start a new initiative or contribute the right skills to a special project.

When you rely on word of mouth, you’re shortsighted. You’re not stepping out of your workplace “neighborhood.” Even with the best of intentions, you simply can’t gain the visibility you need to build a stellar team. Whether you realize it or not, your process of connecting people to career opportunities becomes biased. There’s a better way.

Enter Skill Data

More than half of business leaders (53%) say a lack of visibility into skills is their top barrier to workforce transformation.

Top Barrier to Workforce Transformation

Learning transformation relies on several factors, but chief among them is skill data, according to HR Technologist. “The primary responsibility of L&D at any organization is first to identify which skills employees need to develop to stay relevant to the business objectives. Then, they need to build an appropriate program to close this skill gap.”

Skill data helps your organization assess its skill landscape. In Degreed, it represents the skills your workers have, their levels of proficiency, and what skills they are currently learning. And because our platform is designed to help people learn continually, it captures this development on an ongoing basis.

Embracing Skill Data

So what does it look like when companies embrace skill data — in ways designed to reduce bias — and use that information to enable a more equitable form of internal career mobility?

Think about our city analogy and the challenge of assembling your ad hoc business team on foot. Now, imagine that you do have a phone book and internet. You can call across town and reach anybody in seconds.

Now let’s imagine you’ve got a list of all the city’s residents, the top five skills of each, and more. Let’s say you know their certifications and what they learned last week. You know what their interests are, what they care about, and what their goals are.

Suddenly, you can contact whoever you need. That’s what skill data unlocks.

skill data breaks down barriers.

Reducing Bias for the Better

In systems that don’t use data (or accurate data), people rely on anecdotal experiences or word of mouth. That’s when influences like recency bias come into play, because we all tend to remember or notice things that are important to us at the moment, but aren’t objective.

Used wisely, skill data can eliminate the guesswork from internal mobility initiatives. You no longer need to rely on implicit assumptions about people or contextualize their experiences to understand what kind of work they can do. You can see what their skills are.

When you have a platform like Degreed, which consistently engages people through learning, skill-building, and career opportunities, valuable skill data becomes readily available. That data becomes an extremely powerful resource for informing the talent needs of your organization.

Avoiding Skill Data Pitfalls

Be aware that skill data is not infallible. It’s not a magical solution. In fact, you need to be careful that your data itself is not biased — and you need to know how to use it correctly.

Three skill data pitfalls

Pitfall No. 1:  Incorrect Skill Level Distribution

Representing data in certain ways can actually introduce bias where none existed before. This can lead to inappropriate or unwarranted assumptions about how well your data represents reality. 

This depends on how thoroughly skill levels are tracked across your organization, which will create your skill level distribution. If a handful of people rate their proficiency in a particular skill as high, and you assume that this group is representative of your company overall, then you might think you’ve got an advanced workforce ready to go to work on your next-gen initiatives. But if the hundreds (or thousands) of other workers who haven’t rated themselves have lower skill levels in these areas, your workforce might require a lot of upskilling before those important initiatives can get underway.

In the context of our city example, it’s like looking around your neighborhood and upon not seeing any pharmacies, deciding that there can’t be any in the city. Or, on entering an area with many artists, assuming that you can find an artist whenever you need one.

Pitfall No. 2: Reducing People to Numbers

Let’s say you conduct a ranked search to find people proficient in three separate skills. You do a little math to combine their three scores, and it turns out that the results for two individuals are close but not exactly the same.

Putting too much credence in the slightly higher number would probably be a mistake. The precision of the inputs might not be commensurate with the calculated precision of the output. The trap of false precision is a common one. You can find yourself thinking it’s telling you about some meaningful and important difference between people, but it’s usually not. Reducing people to a numerical skill metric risks making your process arbitrary.

Pitfall No. 3: Oversimplification

Avoid taking a reductionist view of skills. Software development is software development, right? It’s not that simple. Any discipline has genuine nuances and gradations. Saying “I just need three software developers” is an oversimplification, because a software developer who focuses on user interface and design has a different set of skills than one who specializes in database transactions. 

Connecting Skill Data to Mobility

Degreed was founded on a vision of jailbreaking the college degree, to enable people to continually grow and advance their careers based on their skills. So it follows that skill data is a key component of our products, and that it will play an increasingly important role in how our clients benefit.

Last month we announced Degreed Career Mobility, a new product designed to help organizations establish an internal marketplace that uses data about workers’ existing skills and ongoing skill development to connect people to real-time, in-house opportunities — so they can explore stretch assignments, gigs, mentorships, and more.

Skill data powers all of that.

If you want your organization to create real business value by connecting workforce learning to opportunities — and if you want to do it in a less biased, more equitable way — it’s time to step outside your neighborhood.

To learn the skill data basics (and more), download The Ultimate Skill Data Handbook today!

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