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Painting it Gold: Mining Data from the PIRL to Make Informed Decisions

This article is part of a series where we periodically feature guest-authored blog posts from leaders within workforce development to share their insights and perspective. This week, we hear from Geoff Smith of FutureWork Systems. Since the early 2000s, Smith has helped states and workforce development boards manage, track, report, and analyze performance data.

By Geoff Smith

Did you know that there are 488 data fields in the Workforce Innovation and Opportunity Act’s (WIOA) Participant Individual Record Layout (PIRL)?

Created through a collaborative partnership between the U.S. Departments of Labor and Education, the PIRL is a common set of definitions that are shared across multiple programs for performance reporting.

Within the almost 500 fields of data are irresistible nuggets of gold that can be mined to paint colorful -- and powerful -- performance pictures. These visualizations can be used by a diverse set of organizations, including all DOL regions, individual states, workforce development boards, American Job Centers, and eligible training providers, to go above and beyond the required WIOA performance indicators.  

And while the PIRL data allows us to adequately report on the essential WIOA performance indicators, we can dig much deeper by using advanced business intelligence (BI) tools. Using these tools allows us to gain greater insight into participant activities, characteristics, and outcomes which ultimately help inform our program and policy decisions.

For example, data is collected on participants who receive training services funded by the WIOA. When participants receive training, we record in the PIRL the occupation that best applies to the training they receive. By recording that information, we can not only see how we are performing on the WIOA indicators by those trained for certain occupations, but we can also monitor the participant flow activity within these occupations trained, and even calculate new, custom indicators, such as training completion rates by occupations trained. 

The number and variety of questions we can explore just by focusing on 1 of the 488 PIRL data fields can be eye opening. Are people being trained in occupations that are part of their sector strategy? Are they getting the outcomes they want or expect? How many are completing training? How many withdrew? How does this break down by demographics?

It’s easy to see that many paths of analysis can be explored, and examining specific fields within the PIRL data such as Occupational Information Network (O*NET ) Training 1, can yield exciting insights.

To illustrate this point, we created the following interactive dashboard by tapping into the DOL’s national PIRL data, and using FutureWork Systems’ Performance Matters National BI Application. It examines WIOA Title I participants who received training services from April 1, 2017 through March 30, 2018, and cross references the occupation code classification of the training they received.

The dashboard displays the top 25 occupations trained by state, including the top 10 job families. You can use the state drop down list to choose the state(s) that interest you, adjust the veteran filter to focus on veteran vs non-veteran, or leave the data unfiltered to see data for the entire population.

Filter settings and state selections carry over into the WIOA Training Grid tab on the dashboard. The grid shows the percent share and total count of WIOA program participants who exited the WIOA program from April 1, 2017 through March 31, 2018, and who received training services by these job families and occupations.

Click below to interact with the dashboard in a new browser tab:

This dashboard is just one example of how data mined from the PIRL can yield valuable insights. See more examples of how BI tools can radicalize the data by attending this year’s Workforce Technology Conference.

About the Author: Geoff Smith has spent his career advocating for the use of advanced web-based technology tools and services that can leverage data to inform program and policy decision-making in workforce development. He has assisted numerous state and local boards in their move from a reports-driven culture, to one of democratized data and evidence-based decision support. He continues to play an active role in FutureWork Systems’ marketing, business development, client relations, product design, and development.



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