Executives are demanding more data-driven company cultures. But 67 percent of leaders say they are not comfortable accessing or using data from their tools and resources, according to Deloitte.

As pressure builds on learning and talent leaders to develop their people faster, the importance of skill data is undeniable. But the industry around talent development technology has become noisy, and the terminology has become bloated and confusing. That is why we’ve created this resource—to help you and your team better understand how to talk about and use skill data.

Skill Data

Definition: A data point that relates to the capability, demonstration, or definition of a skill. In short, skill data is the measurement of what your people can do.

Why It Matters: This data can include individual skills, their definitions, skill assessments, skill ratings, skill inferences, or the relationships between these data points. This can inform business, hiring, performance, and talent strategies. And using this data can enable more precisely targeted investments in recruiting, workforce planning, capacity management, and change management.

APIs

Definition: The letters stand for application programming interface, but what does that really mean? An API is an intermediary to connect two or more systems so they can communicate with one another.

Why It Matters: An open upskilling platform that works with APIs can share and consume skills, skill ratings, and skill data from any other platform that also has the necessary APIs. Skill data from APIs are typically used to keep a user’s profile in sync with other enterprise applications or to communicate their skills across their organization.

Skill Model

Definition: A process that takes a set of inputs (data) and makes predictions of future user behavior based on a historical dataset of similar users.

Why It Matters: Sophisticated skill models that use data algorithms can also use the data of a specific user to help identify relevant and personalized content, recommend new skills to learn, find subject matter experts to follow, and more. These models will be most helpful when they have access to large amounts of user data. That data is generated by people consistently using the platform. This means that the more engaging your upskilling platform is, the more data it will acquire and the better it will perform.

Data Science

Definition: A variety of scientific methods used to find insights from large amounts of structured and unstructured data. It is dedicated to collecting, storing, and analyzing information about people, machines, and the wider world.

Why It Matters: Data science is about enabling companies to make key strategic decisions based on informed analysis. It defines and trains skill models (defined above) that upskilling platforms use to personalize content and experiences for users.

Artificial Intelligence (AI)

Definition: Software sophisticated enough to replicate the abilities of humans. Machines learn from experience, adjust to new inputs, and complete tasks via AI.

Why It Matters: Sophisticated systems can use skill models and data science to continue personalizing the learning experience through AI. It can do things such as auto-populate learning plans or suggest internal candidates for open positions or projects.

Machine Learning

Definition: Machine learning is a subset of AI. It can leverage data to identify patterns and make decisions with little or no human intervention.

Why It Matters: An upskilling solution that uses machine learning will be able to provide better content recommendations but also help identify emerging skill sets your workforce needs, find skill gaps, and indicate weaknesses to focus on. It keeps your people moving forward.