Measuring Durable Skills

In conversations about skills-based organizations, I’ve learned that many people define skills as being technical rather than durable (or soft or power or whatever is your favorite descriptor), under the assumption that durable skills are ineffable or nebulous. As a former researcher of squishy things, I couldn’t disagree more: I think durable skills are easier to assess than hard or technical skills! Let me illustrate via a thought experiment on empathy.

Who is the least empathetic person you know? How do you know this person is lacking in empathy? What happens during your interactions with them that leads you to label them as such? Make a list of five examples of this behavior.

I’ll wait right here.

Chances are high that you were able to write down five specific behaviors or utterances the person exhibits routinely toward you. Now imagine your list becomes a survey of sorts that you can use to assess an employee, Sandra’s, empathy: In your experience, how often does Sandra exhibit the following behaviors or say things similar to the statements in the list below? Always, Often, Sometimes, Rarely, Never. You administer your survey once a month, for three months, to ten people who interact with Sandra on a regular basis in a variety of settings. What patterns might you see from the responses? What conclusions can you draw?

That’s how to assess durable skills—by asking people.

Our brains process the behaviors and utterances of others and match them to patterns we’ve created that signal certain characteristics. A person who actively listens, looks you in the eye and provides comforting gestures, shares personal experiences, and offers support and encouragement is perceived as empathetic because they do what our brains read as empathy. A person who doesn’t do these things is categorized as lacking empathy.

Empathy, like all durable skills, is something we can see, hear, and document. In other words, perceptions are data, which means that durable skills are measurable and should be included in the data tracked by skills-based organizations.

“But wait,” you might be thinking. “Just because someone is showing up as empathetic doesn’t mean they are really feeling it.” This is entirely true. I’ll share a story: I have a colleague who has autism and genuinely believes she doesn’t feel empathy. But every single one of her direct reports finds her empathetic—I know because I’ve asked. How? When she was a child, she learned to carefully study the behaviors of others and mimic them to “fit in.” As a result, colleagues experience her as empathetic and feel heard and accepted. At the end of the day, I think this is what we’re trying to capture and develop—how individuals are perceived by those around them.

To be clear, I’m not suggesting we encourage people to fake it. Not only is that disingenuous, but most people are poor actors, so pretending to be something they are not will only worsen a bad situation. I'm saying that general feedback, like “you need to work on being empathetic,” is not at all helpful for most people. The purpose of collecting durable skills data (or any skills data, really) is to develop employees. Think back to the exercise you completed earlier: The awesome thing about that empathy survey is that it can help Sandra see how others perceive her and give her specific actions to work on.

Before you apply this to your own team, there are several important things to note.

• The survey needs to be good, meaning you need to think carefully about the questions, create a coding guide or well-defined rubric, pilot it with several people using cognitive interviewing techniques or a similar technique, conduct item analysis, and so on. The quality of the tool drives the quality of the data output.
• You need lots of data under a variety of circumstances. If you only collect data on Sandra’s empathetic behaviors from one person, in one specific context, or only on one day, your data are potentially suspect. To create signal from noise, you need to ask several people who interact with Sandra in different ways and ask several times in regularly-spaced intervals. This is true of any measure, including technical skills (anyone can have a bad day, and no measure is perfect), but it is vital for durable skills.
• There are a bunch of other things to consider when creating a measure, like parsimony, and you should probably enlist experts to help you develop anything you create in-house. There are also reliable and valid measures in psych and related literature, so you don’t need to start from scratch.

?EPAM includes durable skills in our ontology and validates them in a variety of ways. However, I’ll be straight and say that we can do better here. As a tech company, EPAM has been oriented toward technical skills for obvious reasons, but it’s becoming increasingly clear to leaders that durable skills are just as, if not more, important to individual and organizational success. In fact, we’re expecting to announce a big durable skills initiative soon and I’m really excited.

For the many people on their own skills journey, I hope this was helpful. And, as always, a caveat: We don't live in a skills utopia. EPAM’s systems are far from perfect and we are continuously improving the quality and quantity of data; despite our AI-enabled foresight, we can't predict the future, and we have stumbled many times in our skills journey. The goal here isn't to pretend everything is hunky dory. It's not. But I hope there's something in our experience that will help you see the art of the possible and learn from our trials and triumphs.