In our last blog post, we explained the key terminology around skill data and offered some definitions to help you understand and use it in your organization. This blog post will go beyond the basics and discuss the different methods of structuring your organization’s skill data.
There are many overlapping (even contradictory) ideas about what it means to have a skills strategy and how that strategy relates to a taxonomy or an ontology of skills. These ideas aren’t inherently simple, but they don’t need to be overly complicated. That’s why we’re breaking them all down for you here.
Definition: A talent development strategy that prioritizes skills as a means of measuring the capacity of your staff. This metric is aligned with the work your organization needs to do and the career opportunities that exist internally. Competency strategies can vary widely from company to company and can use any combination of skill development technologies, skill taxonomies, skill ontologies, skill clouds, or none of the above. .
Why is this important: Using a skills strategy as opposed to a skills model (or in tandem with one) can help make your workforce more agile and provide opportunities for internal mobility and career development.
Definition: A hierarchical classification system that can categorize and organize skills into groups or âskill clustersâ. A skills taxonomy is structured and will usually include the skills most important to business objectives, sometimes with the definitions of the skills as well.
Why is this important: It can help workers understand what skills they have learned from taxonomy, how those skills relate to organizational needs, and what they should learn next. The goal of the framework is not to capture all skills, but to capture information on the most essential skills relevant to your business strategy.
Definition: A set of skills and their interrelationships.
Why is this important: A skills ontology allows organizations to define and measure the relationships between skills (and even jobs and people). It helps to create a common language and an understanding of skills across different dimensions or platforms. Another way of looking at an ontology is that it is an âintelligent systemâ that allows the maintenance, aggregation and simplification of skill data within a taxonomy.
Definition: A skills graph shows the relationships between other skills and determines how skills correspond to roles, content, and other features related to skills. It is often simply a visual representation of an ontology of skills.
Why is this important: Understanding how different skills relate to each other (and how closely related they are) can explain how artificial intelligence and models provide opportunities for skill enhancement and mobility.
Skill Cloud (also called skills inventory or skills register)
Definition: An inventory of skills across organizations that includes all known skill terms. This is the dataset that is used to assess the skills to be included in organizational skills lists, ontologies or taxonomies. Essentially, this is a single source of truth for any skill, but it does not order or categorize skills like a taxonomy does.
Why is this important: A skills cloud helps organize and standardize skills within an organization, but on its own, it does not make those skills actionable. They’re just sitting in the cloud.
Skills I / O
Definition: A skills I / O manages the skills, skills data and structures mentioned above. You can use skill I / O to create taxonomies, manage multiple skill sources, integrate different taxonomies, and modify skills in your organization.
Why is this important: While taxonomies, ontologies, and charts help us understand skills in relation to our business goals, skills I / O puts these concepts into practice together.
For more information on skills data, download The Ultimate Skill Data Handbook.