How are skills understood in the world of work?
A post by Violetta Yan
HR professionals and project managers constantly seek the most effective way to understand and assess people’s skills. We are all under more and more economic pressure to be competitive. Companies are dependent on the skills of their people to be competitive and individuals need to be able to demonstrate their skills to earn a living.
As a result, we will most likely tolerate less and less ambiguity about skills and seek for new ways to define them.
Why is there ambiguity?
There are several reasons that skills claims are ambiguous. Each of us is unique, has unique experiences, and defines skills differently. As an MBA student with extensive experience working in international non-profits, when I say ‘design thinking’ do I mean the same thing as a person who has graduated from an art university and worked in a digital agency? Even when we agree on definitions, which are fluid, we go about applying our skills in very different ways. This is true even for skills where there are formal certifications. Too lawyers, expert in torts, will draft a legal brief very differently. We are all creative (or not) in our own ways. We apply skills in different contexts and, consequently, have our own variations of skills. Not surprisingly, this makes it hard to distill the universe of human accomplishments and capabilities to discrete skills definitions. Therefore, a broader skills
get grouped into broader categories, like ‘project management’ or ‘front-end web development.’
The other reason for ambiguity is the profound subjectivity of skill assessment. Employers use various metrics to guess at their people’s skills. Some metrics are more objective than others (number of shots on goal by a soccer player). But objective measures are not always meaningful. Attendance at meetings, or even the number of hours spent at work can be poor indicators of contribution. Other metrics can be more informative (when, for example, we evaluate how well someone facilitates a roundtable), but are seen as inherently subjective.
Lingering uncertainty
Knowing these pitfalls, recruiters look at a resume and try to guess the likelihood of the resume owner’s ability to perform certain tasks. They are trying to make guesses about relevant skills and the level at which the skills have been demonstrated. Job titles, some brief descriptions of job functions and, down the road, references (two-three maximum usually) are the traditional toolkit companies rely on to guess at skill levels. Interviews do provide useful insights, but are also limited and heavily dependent on the job candidate’s ability to present herself and the questions asked. Tests and examinations are the last resort to validate someone’s skills, but yet are a poor way of grasping a candidate’s skills history and mastership, and how they will actually perform on a team.
None of approaches give a picture of the actual abilities acquired through our systematic and sustained efforts to smoothly and adaptively carryout a whole range of complex projects and activities. Given this, will a deliberate focus on skills add value? I believe yes. It will go beyond job titles and momentary impressions to the core of person’s proven and sustained abilities.
A skill-centric lens may provide unexpected benefits, bringing into focus multidisciplinary expertise and experience combinations that companies can leverage for greater innovation.
The alternative
To make this skill-centric possible, a new approach to skill measurement is needed. It should provide a deeper insight into skill mastery regardless of how tangible or intangible the skill is and despite inevitable biasness of people’s perceptions of each other’s skills. TeamFit uses some fancy math to do this, a combination of machine learning and Bayesian probabilities (a Bayesian probability measures both the level of a skill on a scale of 1 to 5 and confidence in that estimate).
This is the SkillRankTM approach invented by TeamFit. Here, the ranking for the skill “Patents” is 3.3. This is based on the limited number of ratings by teammates. But the Bayesian algorithm weights the ratings given based on each indivudals own ratings on this and related skills and on each individuals typical beahviors (does the person tend to rank high or low, how much other experience do they have working with this person, and so on). SkillRank thus measures both a skill level and likelihood that the skill rank is accurate.
One way to increase confidence is to increase the number of raters and to capture ratings at different times and in different contexts. We created TeamFit as a social platform and encourage people to rate each other on a regular basis and to have more open and honest conversations about each others skills. By building up the habit of rating each other and having transparent conversations, we will improve both our ability to assess others’ skills and our ability to assess our own capabilities.
We are building TamFit around a project-based profile. Skills are mutually assessed in the context of a project that we are working on together. This has two advantages. It is more granular, I am claiming to have a skill in the context of a specific piece of work that I am doing, and people who have actually worked with me are making the assessments. Besides, there is a shift towards looking for people who know how to apply their skills in the context of a team.
Conclusion
It may be that skills ambiguity is not such a problem as it is made out to be. Sometimes we may pretend that we do not care about other people’s rating us, or that active networking and resumes are adequate substitutes for defining our value securing work. But we know in our hearts this is not true. And companies need granular, in context information about people’s skills in order to get the right people on project teams.
A more objective and proactive measurement of skills – social, contextual and refined through machine learning – will become the primary and preferred mode of skill assessment. Once adopted by many, it will be the most collaborative system ever built.
Use TeamFit to test your assumptions about yourself and your teams. Let it shake your perceptions. We invite interested professionals to discuss this approach, as our team is passionate about this discourse and finding the best way to assess and onboard skills.
