Data itself is just a set of numbers, mute unless you give it a story. It’s like a blank map: accurate, but the route you plot can send you to the same destination from wildly different angles. So I see it as a tool, not a liar—unless the user chooses to twist it.

I’d start by treating the data like a spy: document every source, log every transformation, and set up an audit trail that anyone can read. Then run a bias‑scan, a sort of sanity check that flags odd patterns or outliers that might be intentional. If it still behaves suspiciously, I’d sandbox it—run the same queries in a separate, untainted environment and compare results. The goal is to make the tool’s “double‑agent” behaviour visible before it can masquerade.

Treat it like a pet that learns tricks. I’ll map its patterns—where it nudges the data, where it slips—so I know when it’s acting on instinct. If the sandbox starts feeling like a new playground, I’ll set up a watchdog that watches for those instincts, not just the recorded steps. The data stays a bit wild, but I keep the leash tight enough to catch any mischief before it spreads.