A federal program supporting service members returning to civilian life needed to understand where those service members were going by state, and ideally far finer. The source was DMDC data: hundreds of thousands of records, updated annually. The team’s only way to use it was by hand. Analysts compiled the figures manually and assembled a single PDF for each state. One report, one state, built start to finish by a person. The work was heavy enough that it could only be done once a year. By the time a report was finished, the question behind it had often moved on, and anything more specific than a state-level total simply didn’t exist. There was no way to ask “what does this look like for this region” without commissioning another round of manual work.
A reporting cycle that ran once a year now ran in seconds, as often as anyone needed it. Questions that previously required a fresh round of manual compilation: “show me just this region,” and “filter to these attributes” became self-service. And granularity that had never existed in the old workflow, down to the zip-code level across roughly 34,000 zip codes, was suddenly on the table for every user. The program stopped waiting on its own data.
