You are building a visualization workflow for a product analytics team that publishes dashboards to internal users and customer-facing reports. The pipeline pulls from operational databases, event streams, and warehouse tables, then feeds curated datasets into the visualization layer. The main problem is that bad upstream data, missing fields, duplicate rows, and late-arriving records can reach end users before anyone notices.
How would you design a visualization workflow that keeps data quality issues from reaching end users?
