SIG-P4B 2026-07-30

rafa interviewed PAtwater to develop an anchoring story for a report on why water-rate data standardization is valuable, why it's painful, and why past standardization efforts stalled. PAtwater walked through his early career (hand-scraping ~200 utilities' water rates as an intern), the purpose of rate benchmarking, the failed/fizzled standardization initiatives in California, and the structural reasons standardization never fully took hold. The arc lands on a thesis: standardizing at the data-extraction level is a dead end, but new AI tooling (à la "Maxwell's" approach) could lower the cost of compliance enough to unlock the long-promised value.

Key points

• PAtwater: ~a decade in water data. First job was an internship at a large water utility hand-scraping the water rates of ~200 Southern California utilities over a summer — cheaper than paying a consultant (~$50–100k) for a periodic survey.
• rafa reframed the core driver as strategic scenario planning (3% vs 5% vs 10% increases) more than individual customer complaints — PAtwater largely agreed. Rate hikes of ~6% vs 3% feel dramatic to the public even though they're not "2x or 10x."
• California Water Data Consortium (created ~5–7 years ago); a water reporting project started ~2022 aimed at harmonizing standards — produced a report and "fizzled."
• Open Water Rate Specification (~2017–2018): won a state open-data challenge, proved the concept, could flexibly represent full rate structures, was used in a Cal-Nevada water rate survey with several hundred utilities specified. But it never usurped existing alternatives; the GitHub repo is ~7 years out of date.
• The "original sin": the system (dating to ~2006) asked for narrow inputs (the "10,000 gallons" figure), then accreted individually-reasonable questions over decades into an unusable, ossified form. rafa's analogy: they built a spreadsheet of hard-coded values instead of one with formulas.