Like many parents in the last year, I’ve spent more time with my kids’ learning materials, re-familiarizing myself with the ins and outs of intermediate school math, social studies, and more. One of the concepts that sticks out to me in science is atomic particles, because it has a very clear tie to how we make talent decisions at work.
Atoms are the smallest individual components of an element. They have the necessary characteristics to tell us what the element is and what its properties are, and you can’t break one into smaller parts.
Skills have the same function in the workplace.
- Hiring: are we selecting the right skills we need to grow as an organization?
- Development: are we growing the right skills our people need to remain competitive?
- Mobility: do we use skills as the common language for career growth and promotions?
- Performance: are the skills of the workforce the focus of performance processes?
- Diversity and inclusion: do we use skills to drive more inclusive cultures and practices across the business?
Skills can even be part of the bigger engagement picture. In a recent study on talent development and employee mobility, we found that one in five workers didn’t know if their employer had any idea what their skills were. How do we expect those employees to be engaged if they think their company and leaders don’t even know their capabilities?
This is a short version of a much longer list of applications for skills within the business. Put simply, skills are the currency of the modern organization, and they have the opportunity to add value and objectivity to every practice that involves skill data.
Objectivity and Subjectivity in Skills Identification
Skill data are a powerful, objective layer of information if used appropriately. However, our research shows that the number one way employers measure skills is through manager observations. While not inherently bad, it’s important that observations aren’t the only method for evaluating skills and proficiency.
According to data on cognitive bias, humans have nearly 200 different types of biases that affect our everyday decision-making. Don’t you think that if those biases affect what kind of laundry detergent we buy at the grocery store, then they would also affect how we identify, measure, and evaluate the skills of the people around us at work? Pick almost any bias on the list and it’s easy to see how it could factor into skill identification. However, this isn’t just about telling you what doesn’t work: as a part of a bigger skills identification effort, manager observations can be helpful data.
We see in our research two key levers for creating more accurate and actionable skill data:
- Vectoring. Vectoring simply means using multiple elements to get closer approximations of the truth. When a pilot is flying a plane, air traffic controllers can offer different directions over time that progressively guide the pilot closer to the destination. In a skills context, we can use self-identified skills, assessments, and even manager observations or 360-degree feedback to help vector in on actual skills for an individual.
- Technology. Speaking of bias, we overestimate what we can remember and recall and underestimate the amount of information that we don’t know about a subject. In a practical sense, if you were asked to list the top three skills of your closest three friends, you might be able to do that. But what if you were asked for the top seven skills for your closest 10 friends? It gets progressively harder the farther you go. The good news is that technology doesn’t have that limitation and can create accurate pictures of skills at scale.
Once you have actual skill data on the workforce, it opens up some valuable business applications. Depending on how much you have thought about this concept in the past, you may have some questions about how it works or what barriers might exist. The list below includes some of the common discussion points and advice relating to a skills-based talent strategy.
4 Common Questions and Issues on Skills-Based Strategies
1) How do we agree on a skill definition or a common language? While many organizations have created their own library of skills and competencies internally (about 40%, in our newest data), others use existing skills data sources, technology partners, or a combination of different methods to agree on a set of skills. Overall, we have seen companies be successful by starting and iterating instead of just waiting and waiting until everything is perfect.
2) Should we allow workers to self-identify skills? Absolutely! Having people share their own skills on their employee profile is just like crowdsourcing suggestions for a problem. Everyone gets to contribute, and that saves HR and talent teams from trying to have to do this manually. Even if self-identification is just a starting point, it still can help to populate skill data as a jumping off point.
3) How do we get enough skill data? Starting with self-identification, there are a few different methods for driving additional skill data volume. For example, you can allow people to put in aspirational skills, not just existing strengths. You can also encourage them to share their preferences, such as openness to relocation or interest in leadership roles. The bottom line is that once people see that data being referenced or acted upon for talent decisions, they will want to share more information. Speaking recently with an executive for an enterprise aerospace company, he explained that he updates his own skills in his employee profile because it has led to promotion and internal mobility discussions over the last 18 months.
In our newest Reskilling, Mobility and Talent Development 2021 study (new report coming next week!), 91% of workers told us that they would be interested in a skills-based career development tool. Helping them to understand how the information is used can lead to better and more consistent participation.
4) For companies that have skill data, how are they using it? We will go into greater detail on this element later in this series, but one company leader told us they were using skills to “engage workers in their career journeys – skills [are] the connection for learning, internal jobs, mentors, gig opportunities, development, etc.” This information enables a host of talent decisions that don’t just benefit the workforce, but the company as well.
In the next piece in this series, we’ll take this discussion further and explore a real use case for applying skill data in an organizational context, diving deep into the intricacies of utilizing skill data to solve longstanding problems that every company faces.
Ben Eubanks is the Chief Research Officer at Lighthouse Research & Advisory. He is an author, speaker, and researcher with a passion for telling stories and making complex topics easy to understand.
His latest book Talent Scarcity answers the question every business leader has asked in recent years: “Where are all the people, and how do we get them back to work?” It shares practical and strategic recruiting and retention ideas and case studies for every employer.
His first book, Artificial Intelligence for HR, is the world’s most-cited resource on AI applications for hiring, development, and employee experience.
Ben has more than 10 years of experience both as an HR/recruiting executive as well as a researcher on workplace topics. His work is practical, relevant, and valued by practitioners from F100 firms to SMB organizations across the globe.
He has spoken to tens of thousands of HR professionals across the globe and enjoys sharing about technology, talent practices, and more. His speaking credits include the SHRM Annual Conference, Seminarium International, PeopleMatters Dubai and India, and over 100 other notable events.