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Skill Data Dictionary Part 3: Using Your Skill Data

Skill data is complicated. Talking about it and researching it can help, but you’ll often find bloated jargon, acronyms, and sometimes even conflicting ideas. Cutting through all that noise matters, because understanding this data is invaluable to your organization. To help you harness its power, we’ve simplified terminology in our three-part series, The Skill Data Dictionary. (Check out Part 1 on the basics and Part 2 on the organization of skill data.)

To understand how to begin using your data, first think of it like medical data. When choosing what metrics to look at and how to use medical data, you have to first identify your health goals. Are you trying to lose weight or gain muscle? Run a marathon or rehabilitate your knee? Lower your cholesterol or increase your endurance? All of these goals will require different (sometimes opposing) metrics and strategies. 

When you identify your goals, you can begin finding and tracking the metrics that matter to you. We recommend the same approach when using skill data — allow your organizational goals to help you identify your starting point. When you have an idea of your goals, you can start pulling analytics and insights to help inform your strategy about how to reach them.

How to Use Your Skill Data

Skill Analytics

Definition: Deriving meaning from collected data. 

Why it matters: Analytics can help you identify patterns, trends, strengths, weaknesses, and other significant indicators to raise awareness about your larger organization and your people. 


Definition: Inference or prediction of what comes next based on the analysis of data. 

Why it matters: Data itself cannot tell you how to make improvements to your existing processes. What makes the difference is the way you interpret your data, apply it to your business, and allow those patterns and indicators to help you address new challenges, opportunities, and needs in your workforce. 


Definition: The process of gathering and presenting an accurate analysis of the data collected.

Why it matters: Gathering, analyzing, and pulling insights from data will not make it actionable. Learning to report and present your findings in accessible ways allows you to communicate the importance of the changes you wish to establish and demonstrate the value of skill data. 

Skill Data Integration

Definition: Communication between tools that produce or store skill data, including HCM systems and skill assessment tools.

Why it matters: Integration allows for a more comprehensive and accurate view of individual and organizational skill levels by pulling skill data from systems in the flow of work. It also enables “skill signals” that describe a user, offering a richer picture of their skill levels.

For more information on how to identify, generate, manage, and use your organization’s skill data, download our Skill Data Handbook below!

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