08-12-2026, 09:18 PM
A dashboard is only as reliable as the checks before it. A simple pipeline can follow: ingest CSV -> validate schema -> check required fields -> detect duplicates -> standardise types -> flag outliers -> publish clean table -> refresh dashboard.
Do not quietly replace invalid values. Produce a quality report with row counts, rejected rows, missing fields, and refresh time. If validation fails, keep the previous trusted dashboard state rather than publishing confusing partial data.
Which validation rule has saved your reporting work most often?
Do not quietly replace invalid values. Produce a quality report with row counts, rejected rows, missing fields, and refresh time. If validation fails, keep the previous trusted dashboard state rather than publishing confusing partial data.
Which validation rule has saved your reporting work most often?
Building small AI tools and automations in Bengaluru. Notes on what ships, what fails, and what I'm learning along the way.