WDQI uncertainty shouldn’t delay
workforce data modernization
Recent Workforce Data Quality Initiative rounds have typically opened in the spring. But as of early May 2026, no new WDQI competition has been posted, and the U.S. Department of Labor’s FY 2026 budget justification proposes folding WDQI into a broader Make America Skilled Again (MASA) grant structure.
That uncertainty has created understandable questions for state workforce agencies. Will WDQI return as a standalone funding opportunity? Will modernization funding move into a different structure entirely? Will priorities shift?
But waiting for perfect clarity is not a strategy.
Regardless of what the next funding vehicle looks like, the operational challenge facing states has not changed. Agencies are still under growing pressure to connect workforce and education data, improve outcome reporting, and support more evidence-based workforce strategy across programs and partners.
The states that prepare now will be in a much stronger position if funding is released later this year, whether through WDQI, MASA, or another successor program.
The need has not changed, even if the funding vehicle might
WDQI exists to help states build the data infrastructure that makes evidence-based workforce strategy possible.
Whether or not WDQI grants materialize, agencies are under growing pressure to connect training activity to employment outcomes, track performance across systems, and give leaders confidence that investments are producing results.
Linking workforce and education data, strengthening Eligible Training Provider reporting, supporting research and evaluation, and giving leaders and the public better visibility into program performance aren’t niche grant priorities. They’re the same capabilities states need to meet current accountability expectations, support Workforce Pell implementation, and answer basic questions with confidence:
- Are training programs producing employment outcomes?
- Which pathways work for which populations?
- Where are the gaps?
In most states, the data needed to answer those questions exists. It’s just spread across systems that weren’t designed to talk to each other. Workforce program participation data lives in one place, unemployment insurance wage records live in another, and education and training provider data sit in separate platforms, sometimes split between credit and noncredit systems. Answering those questions requires stitching together extracts from multiple systems, often by a small team that’s already stretched beyond capacity.
The grant name may change. The readiness challenge doesn’t.
Why agencies should prepare now, even when the timing is unclear
The uncertainty around a 2026 WDQI competition is real. But from a practical standpoint, that uncertainty is a reason to prepare, not a reason to pause.
Agencies that compete well in these grant cycles share a few characteristics.
- They have a clear narrative about why their current infrastructure falls short and what they’re trying to change.
- They’ve identified the specific data linkages that matter most for outcomes measurement.
- They have defined partners across workforce, education, and unemployment systems.
- They’ve translated that into an implementation plan that funders can believe.
This takes time to develop. Agencies that start developing it now, regardless of whether a competition is posted this year, will be faster and more credible when one appears.
Agencies that haven’t will spend the first several weeks of an application window doing the foundational thinking that should have happened months earlier.
What becomes possible when workforce data is finally connected
Agencies that struggle to confidently answer performance questions usually have the same underlying problem: their data exists, but it isn’t connected.
We’ve helped states address this directly. In Indiana, we helped the Department of Workforce Development integrate over a decade of workforce, wage, and education data from the state’s longitudinal data system. The result was Pivot, an award-winning AI-powered recommendation engine that matches workers to jobs and training pathways based on real career data.
Early outcomes showed workers who followed Pivot’s top recommendations saw average hourly wage increases of nearly $4, compared to $1.42 for those who didn’t use the tool.
We’re working with agencies now to develop modernization readiness by clarifying the business case, mapping current system limitations, identifying high-value linkages, and shaping a narrative that connects infrastructure investment to measurable policy and operational outcomes. If a funding opportunity appears, they’ll be ready. If it doesn’t, the work still has value.
Read the full client story about our work with Indiana’s DWD, or reach out to talk through what data modernization preparation looks like for your state.
About the author
Zak Aker
Client Partner , Workforce and Economic Development @ Resultant
