Why data quality matters when working with data at scale

"Data quality has always been an afterthought. Teams spend months instrumenting a feature, building pipelines, and standing up dashboards, and only when a stakeholder flags a suspicious number does anyone ask whether the underlying data is actually correct."

"Most of these failures are preventable if you treat data quality as a first-class concern from day one rather than a cleanup task for later."

"Before data reaches production, there is typically a validation phase in dev and staging environments. Engineers walk through key interaction flows, confirm the right events are firing with the right fields, fix what is broken, and repeat the cycle until everything checks out."

"Once data goes live and the ETL pipelines are running, most teams operate under an implicit assumption that the data contract agreed."

Data quality is often overlooked until issues arise, leading to increased costs and loss of trust in data teams. Most data projects begin with discussions on metrics, followed by the creation of a logging specification. This specification serves as a contract for data capture. Validation occurs in development and staging environments, but problems often emerge once data is live. Teams must recognize the importance of treating data quality as a primary concern from the outset to avoid preventable failures.

Read at TNW | Data-Security