The Data Told a Story, But It Didn’t Make Sense
You build the dashboard. It looks clean, runs fast, and the numbers check out—until someone asks, “Why are weekend sales always zero?” You look again. It’s not a rounding issue. The data literally stops every Friday at 5 PM and picks up again Monday morning.
You’ve just met one of the most frustrating issues in analytics: when the data tells a story, but it’s not the right one.
This happens more than people think. A pipeline runs late. A data source is archived but still queried. A field is renamed but never updated in downstream jobs. From the analyst’s perspective, everything looks logical. The query runs. The numbers line up. The story makes sense. But it’s fiction.
It’s not that the tools are broken. The tools are doing exactly what they’re told. The problem is the data doesn’t reflect reality. And if you don’t catch it, decisions get made based on patterns that aren’t real—like assuming customers don’t shop on weekends when really, the ingest job is scheduled Monday at 2 AM.
Here’s how to keep this from happening:
- Sanity check edge cases. Look at time series for sudden drop-offs or regular gaps.
- Ask, “Where does this data come from, and when does it land?” Timing matters more than most people think.
- Trace one real-world example end to end. If a user signs up today, can you follow that event through to the report?
In the next post, we’ll look at what changes when the analyst gets direct access to the source system—and finally sees how the data is created in the first place.
