01 · Why it matters
A decision engine is only as good as its data.
ZONA's job is to answer one question in real time: where is tonight actually happening. That answer collapses the moment a venue's hours are wrong, its status is stale, or its details do not match reality. At a handful of venues you can hand-fix it. Across a catalog of 400-plus, manual upkeep is not a strategy, it is a slow-motion failure of the core promise.
The catalog carried the usual entropy of any real dataset: missing statuses, inconsistent names, stale or absent hours. Left alone, each one quietly sends someone to a closed door, and a closed door is a lost user the marketing already paid for.
02 · The work
Reconcile against a source of truth, and keep it that way.
I treated the venue catalog as a data pipeline, not a spreadsheet. Reconciled it against authoritative sources like Google Places so hours, location, and details come from a system of record instead of memory. Resolved status across the entire catalog so every venue is explicitly classified rather than ambiguous. Normalized the messy fields, names and contact details, that quietly break search and display.
The goal was never a one-time scrub. It was making the catalog converge on correct and stay there, so the data layer holds as the catalog grows instead of decaying back into noise.
03 · Outcome
A catalog the product can trust.
04 · Behind the work
How I think about it.
Data infrastructure is invisible when it works and catastrophic when it does not. Nobody opens an app to admire accurate venue hours; they just quietly leave when they are wrong. Building the layer that prevents that is exactly the kind of work that never shows up in a demo and decides whether the product is actually real.
I also kept it close to the source. Syncing from an authoritative system instead of hand-entry means the catalog gets more correct over time, not less, which is the only way data integrity survives growth.