Most analytics failures don’t start with big architectural mistakes.
They start with one bad data point that quietly flows downstream.
By the time leadership notices, dashboards look “off,” trust erodes, and teams start debating numbers instead of decisions.
The most common causes I see are:
- Upstream schema changes with no enforcement
- Late or partial data arrivals treated as “good enough”
- Nulls, duplicates, and outliers silently pass validation
- Business logic living in dashboards instead of pipelines
- No ownership once data crosses system boundaries
The fix isn’t more dashboards or another monitoring tool.
Stable analytics comes from preventive discipline:
- Contract-based data expectations at ingestion
- Row-level and distribution-level validation, not just schema checks
- Explicit freshness SLAs and failure alerts
- Versioned transformations with rollback paths
- Clear data ownership tied to business impact
If your pipeline can fail without anyone knowing, it eventually will.
The question isn’t whether bad data will show up. It’s whether you’ll catch it before it shows up in a board deck.
The Takeaway.
How confident are you that your pipelines fail loudly, not politely?
Thanks,
Tom Myers
P.S. Also, please connect with DIH on LinkedIn.
