Three Metaphors for Skills and Competencies

It is still early in the year, a time to plant seeds that will grow over the year and into the future. So we are pausing here to look more deeply into the ideas behind skill and competency management and the approach we are taking at Ibbaka.

Metaphors are central to thought. There is a rich body of research showing that how we think is guided by the metaphors we use. Cognitive scientists such as Mark Turner, Gilles Fauconnier and George Lakoff have a lot to teach us on this.

One can even argue that a good competency description is a kind of metaphor. It does not describe all of the skills or behaviors needed for performance, nor all the details of the context in which the performance takes place. The competency definition is meant to make the skills, behavior and performance context meaningful, even to tell a story that helps people make sense of their work. Metaphors make the story of the skill and its performance easier to understand. Metaphors from sports, music and more recently machines and robotics are deeply coded into many competency descriptions. It is worth rereading Tom Gilbert’s classic Human Competence: Engineering Worthy Performance with this lens and noting all of the mentions of sports, coaching and practice.

Ibbaka’s job though goes beyond authoring and implementing competency models. Our mission is to create the platform where such models can be

Designed (note that we say ‘design’ and not ‘author’)
Discovered
Applied
Evolved (a metaphor creeps in)

What metaphors do we use to inform our work?

We started with a chemical metaphor

We started with a chemical metaphor. Skills are the atoms of competencies. They combine with different types of bonds into larger structures. The bonds are things like parent skills and child skills (this makes it possible for us to build hierarchies of how skills fit together), associated skills (skills often found together in the same person) and complementary skills (skills often used together on the same project but not by the same people, like UI designers and front end-engineers or the grill chef and the pastry chef. A lot of the power of the Ibbaka approach comes from the bonds we have to connect skills together.

The larger structures can be formal things like competency models or frameworks and job architectures. But they are also the hidden structures that enable team performance and connect people together in communities of practice. A list of skills or competency definitions will not solve the critical problems that skill and competency frameworks are meant to address. Questions like

What skills are needed to achieve are goals?
Where do we have skill gaps that we need to fill?
What skills drive our competitive differentiation?
Are there potential skills, that we could develop our put to work?

All of these questions require large connected skill graphs to answer.

Ibbaka thinks of skills as existing in, and becoming meaningful, in the context of a skill graph. In a skill graph, skills are the nodes or vertices and the connections between skills are the edges. Taking this approach lets us apply all the power of graph theory and networks to skills.

Chemists have also found graph theory useful in understanding the physical world.

Atoms are distributed in the environment in various concentrations. The same is obviously true of skills. Understanding what skills are present in an environment suggests what kinds of performance are possible. Just as atoms combine to provide the nutrients needed for organic growth, skills are the nutrients needed for performance.

We also use an evolutionary metaphor

The limit of of the chemical metaphor is that it does not give us a very good way of thinking about how skill and competency models change over time. The evolutionary metaphor helps us shift from a static understanding of skill and competency models to a dynamic approach in which the model or framework changes over time, becoming better adapted to the environment and responding to changes in the environment like new competitors.

Genes work together with other genes, interacting with the environment, to shape the phenotype. The same is true of skills. Skills combine together to create competencies. The environment determines what skills can be expressed and applied to work. Many of us have skills that we do not have the chance to apply in our current working situation. One could say that …

Skills are to genes as competencies are to the phenotype.

There is a large pool of genes that go unexpressed in any genome. These are a reservoir for potential change. New phenotypes can emerge as genes (including genes from the pool of unused genes) get combined in new ways. The same is true of skills. A lot of innovation comes not from new skills, but from combining existing skills in new ways. One can see this happening with artificial intelligence and machine learning. The skills that were put together to enable the current progress in deep learning began when graph theory was combined with probability to create Bayesian networks. Most computer scientists did not think of probabilities as something to embed in code, this seemed like a path to chaos, but a few people did make this conceptual leap and, as a natural product of competition, these skill sets are becoming more and more common. Before you try to understand AI you may want to learn probability theory and graph theory. As a leader developing your organization’s capability in this area, you can look for people who have these two skill sets.

The phenotype is not limited to the body (how one's own skills are expressed) but extends out into the environment in an extended phenotype. This term was coined by Richard Dawkins in his book The Extended Phenotype. His point is that genes in one organism coevolve with those in other organisms to cocreate the environment. Dawkins sums up the central idea of the extended phenotype as follows:

An animal's behaviour tends to maximize the survival of the genes "for" that behaviour, whether or not those genes happen to be in the body of the particular animal performing it.

One can apply this to skills as well. It is in my interest to invest in the skills of other people that complement my own skills. I don’t want to, and cannot, have all the skills needed, and it is often more effective to help other people to acquire a skill than to acquire it myself. The skills needed to help other people acquire skills are an important skill in their own right.

What skills would you like the people around you to acquire?

How are those skills evolving?

The neural metaphor

The chemical and evolutionary metaphors get us part of the way there, but there is another metaphor that we are beginning to explore. Can we also think of skills as neurons? Activating one skill leads to the activation of other skills, just as when one neuron fires it generally leads to a whole cascade of other neurons firing. In a very simple sequence, writing triggers copy editing, then proofreading, followed design and publishing, and then promotion (this is an extended phenotype, as these skills are not generally held by the same people). Invoking any one skill frequently leads to other skills getting used.

Skills, like neurons, function as networks, with feed forward and feedback loops. The key to activating a certain skill, say critical thinking, is most often not to try to trigger it directly, but to take a more oblique approach (John Kay talks about this in his article on obliquity). When presented with a new idea, rather than asking if it is true, ask what else has to be true for this to be true.

When connections between skills are not kept active there is decay. Keeping skills connected to each other, within a person and on teams, is one of the keys to performance, and a good skill and competency management system can encourage this.

Learn about the Ibbaka Talent Platform

As with neural networks, where any one neuron can be lost without any degradation in memory or performance, when one skill is absent there is generally a work around (performance does not depend on any one skill but on the connections between skills and how skills can substitute for each other).

As new neural networks are overlaid on existing neural networks, the older neural networks do not disappear, but often become homeostatic. They become routine, necessary for functioning, but not differentiating. The same is true of skills. Old skills, that have become routine, are generally still ticking away under the surface, supporting connections in the skill web. All those Excel spreadsheet skills remain useful, they are just not the things you want to highlight in your skill profile. This is why we recommend not deleting skills from your skill profile, but we do let you hide them, and over time skills tend to drift down the skill list as they become routine.

We hope you have enjoyed this ramble through some of the metaphors we use at Ibbaka. Using metaphors, and combining them in new ways, are central to how we approach innovation.

What metaphors inform your own work?

What do you learn when you surface these metaphors and explore them?

Ibbaka Posts on Competency Models and Competency Frameworks

Competency models as learning plans