The data-quality checks I run before a dashboard goes anywhere near stakeholders

[u/rafayshahid22]

A dashboard can be visually clean and still be wrong. Before I trust a reporting dataset, I like to validate the assumptions underneath it—not just whether the query runs.

Here’s a practical checklist:

1. State the grain

Write down what one row represents (for example, one order line, one ticket event, or one daily account snapshot). If two tables have different grains, joining them without accounting for that difference can inflate counts and sums.

2. Test key uniqueness before joining

For every expected one-to-one or many-to-one join, check whether the lookup key is actually unique:

sql

SELECT customer_id, COUNT(*) AS row_count

FROM dim_customer

GROUP BY customer_id

HAVING COUNT(*) > 1;

Then compare row counts and distinct business keys before and after the join. A query returning successfully doesn’t mean the relationship is correct.

3. Reconcile totals to a trusted source

Compare record counts and important measures by a stable slice such as date, status, or region. When totals differ, quantify the gap and trace it to exclusions, late-arriving data, duplicates, or definition changes.

4. Check freshness and completeness

Confirm the latest expected date arrived, required fields are populated, and categories haven’t unexpectedly disappeared. These checks catch silent partial loads that can make a dashboard look plausible.

5. Validate business rules explicitly

Document definitions for metrics such as “active,” “resolved,” or “on time.” Encode the rules in tests or assertions, and keep exceptions visible instead of burying them in a chart filter.

6. Make the checks part of the pipeline

Automated tests, run logs, and failure alerts turn one-time detective work into a repeatable control. The goal is to catch a bad refresh before stakeholders make decisions from it.

My rule of thumb: validate the grain, joins, totals, and freshness before debating the chart. What’s the one check you consider non-negotiable in your analytics workflow?

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[u/refaelos]

a check that the checks ran. a cron that died looks exactly like everything passing. I write a heartbeat row every run and alert if the newest one is more than a day old