tylers-blogger-blog

We didn't plan for our analytics automation to become the main character. It started as a "nice-to-have" script that pulled numbers together so we could stop copy-pasting CSVs at 8 a.m. Then came the quarterly review-leaders in the room, dashboards on the big screen, and a single question that turned our quiet helper into the star of the show: "Are we sure these numbers are right?" That's the moment automation either earns trust... or gets politely benched. When the spotlight hit: what we changed in one afternoon The dashboard looked great. Too great. Which is exactly why everyone suddenly cared about the plumbing. Our automation was doing three jobs: ingesting events, transforming them into a reporting table, and refreshing charts every hour. But under pressure, we realized we had a few trust gaps: Definitions lived in people's heads. "Active user" meant one thing to product, another to marketing. No obvious freshness cues. Charts updated, b...

We didn't call it a "data apocalypse" at the time. We called it "that weird spike," "the numbers are off again," and-my personal favorite-"let's just refresh and see if it fixes itself." Then came the day every metric disagreed with every other metric. Revenue looked down 22%. Signups looked up 40%. Paid CAC doubled. And the CEO asked the sentence that makes analytics teams age ten years instantly: "Which one is right?" The problem wasn't one bug. It was a chain reaction: a tracking change shipped late Friday, a backfill job that silently timed out, a dashboard built on top of a view built on top of a sheet someone "temporarily" edited. We were one Slack message away from deleting something important just to stop the bleeding. The Apocalypse: When Manual Analytics Collapses Here's what the failure looked like in practice: Multiple sources, one metric name: "Leads" meant "form submits" in on...