Skip to content
Boollean

Data Engineering · 8 May 2026 · 6 min read

Data Quality Comes Before Dashboards

A dashboard built on unvalidated data does not create insight — it creates confident, well-designed misinformation.

Every organisation that has abandoned a reporting project can usually point to the same moment: someone noticed a number was wrong, and after that nobody trusted any of them.

01

Define the metric before building the chart

Ambiguous definitions — what counts as an active customer, when revenue is recognised — are the most common source of disputed dashboards. Write the definition down and attach it to the view.

02

Validate at ingestion

Checks belong at the point data enters the pipeline: schema validation, range checks, duplicate detection and freshness monitoring.

  • Reject or quarantine rather than silently coerce bad records
  • Track freshness so a stale source is visible on the dashboard itself
  • Keep raw data so any transformation can be re-run

03

Design for the decision

A view that nobody acts on is maintenance cost. Start from the recurring decision, then show only what changes it.

The takeaway

Agree definitions, validate at ingestion and build views around real decisions — trust is harder to rebuild than to establish.

Next step

Have a problem worth solving?

If this article describes something you're dealing with, we can tell you in one conversation whether it's worth building.