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[AI-powered skills attribution]

Here’s how Encube works, but in this video I want to focus on a few specific things: how we attribute project-verified skills to employees using our AI engine, and how we facilitate skill pathing via our software skills ontology. First, let’s look at the skills attribution process. It starts by connecting to the project tools that software developers use on a daily basis. Developers simply do their daily work with the tools they normally use, like JIRA and GitHub, and nothing more.

Then, the tasks and Git check-ins from those systems come through our AI engine, which uses natural language processing to infer skills based on text from task descriptions, file names and file types, and repository names. Those skills are categorized and attributed to the employees who are either assigned a specific task or who committed something to a code repository.

This brings us to the real-time talent profiles and skills ontology. Encube’s talent profiles serve as a source of project-verified skills. These skills are captured based on actual work done at the company, which provides a more evidence-based view of each individual’s work--and, collectively, the profiles help form a growing repository of institutional skills and knowledge for the company. But we don’t stop there. We’ve also built a skills ontology to capture the complex relationships between different software skills to deliver skill or career pathing based on what someone knows, wants to learn, or could improve on. Before we dig into that, let’s first go a little deeper into the AI-powered skills attribution process.

You see that project tasks and git commits go through the AI engine and skills are then attributed to employees as a result. These skills are separated into several categories, including high-level categories like ‘tech stack’, which captures technologies and frameworks; ‘technical competencies’ that covers technical areas that cut across different tech stacks; ‘functional areas’ that pervade the programming world across different tech stacks; as well as “industry experience” that covers domain areas related to specific industries. This skills library a good segue into speaking about the new skills ontology because while it’s adequate for the skills attribution step,it lacks the full depth of understanding of skills as they exist in the software development world, which is multifaceted.

Skills are also interrelated in a variety of ways that we capture in our skills ontology. The skills ontology is a way for us to go well beyond the limitations of a two-dimensional library and capture the multi-dimensional world of technology, where skills and technologies relate to one another in a variety of ways. Our skills ontology identifies complex relationships between thousands of different software skills and technologies, provides a more comprehensive view of skills at a company, and supports more complex talent management programs like individually tailored learning & development programs and mentorship programs..and also more informed decision making, so that if you’re in the midst of a hiring freeze, we can provide a more detailed view of what skills they have available and, based on the relationships between different skills, identify opportunities for re-skilling or upskilling when hiring outside people really isn’t an option.

Let me explain with a simple example. Let’s take Javascript. We establish relationships to it in a variety of ways. It could be in a natural learning progression. If you’re a budding front end dev and you know HTML and CSS, you could learn Javascript. Or, if you’re a manager, you think of it the other way around--that is, if you want JS resources, you can look for people who have already learned HTML and CSS and train for it. Or, if they already know JS, they can learn a popular JS framework like ReactJS. Or, relationships can be made up and down a tech stack, like with Node.js on the back end or a web prototyping tool like Sketch on the front. Or, there can be different competing technologies that could be a substitute for JS like Elm and Typescript, which compile to JS but are nonetheless separate skills that can be substituted for someone coding in actual JS.

In another example, say you’re looking for Scala resources. Maybe you don’t have many of them, but what you could do is see all of the people at the company who know different object oriented programming languages who could be best suited to learn it. Some may be better suited than others as a springboard for learning Scala, and the skills ontology will help you sort out the best opportunities to prepare your software teams for your company’s future direction.