Skills data is complicated. Talking about it and researching it can help, but you’ll often find jargon, acronyms, and even conflicting ideas. Cutting through all this noise is important because understanding skills data is invaluable to your organization. To help you harness its power, we’ve simplified the terminology in our three-part series, The skills data dictionary (see part 1 and part 2).
Competency data is extremely versatile; you can develop future skills, find growth opportunities, identify organizational strengths and risks, and more.
To understand how to start using skill data, start by thinking of it as medical data. When choosing which metrics to review and how to use medical data, you must first identify your health goals. Are you trying to lose weight or gain muscle? Running a marathon or re-educating your knee? Lower your cholesterol or increase your stamina? These goals will require different (sometimes opposing) actions and strategies.
When you identify your goals, you can begin to research and track the metrics that matter to you. We recommend the same approach when working with skills data: allow your organizational goals to help you identify where to start. When you have a feel for your goals, you can start extracting insights and insights to help inform your strategy on how to achieve them.
How to use your skills data
- Definition: Make sense of the skills data collected
- Why is this important: Analysis can help you identify patterns, trends, strengths, weaknesses, and other important metrics to market your organization as a whole and your employees.
- Definition: Inference or prediction of what comes next based on data analysis
- Why is this important: Data can’t tell you how to make improvements to your existing processes. What makes the difference is how you interpret your data, apply it to your business, and enable these models and metrics to help you meet the new challenges, opportunities and needs of your workforce.
- Definition: The process of collecting and presenting an accurate analysis of the data collected
- Why is this important: Collecting, analyzing and extracting information from the data will not make it actionable. Learning to report and present your results in an accessible way allows you to communicate the importance of the changes you want to make and demonstrate the value of skills data.
Integration of skills data
- Definition: Communication between tools that produce or store skills data, including human capital management systems and skills assessment tools
- Why is this important: The integration of skill data enables a more complete and accurate view of individual and organizational skill levels by pulling skill data from systems into the workflow. It also enables “skill signals” that describe a user, providing a richer picture of their skill levels.
For more information on identifying, generating, managing and using your organization’s skills data, download our Skills Data Manual.