Apply product management principles to data assets, ensuring high quality, clear schemas, and robust pipelines.
Treating Corporate Data as a Product
Historically, analytical data has been treated as a byproduct of application databases, resulting in messy schemas and untrusted reports. Treating data as a product means assigning clear ownership.
Each data product must have documented schemas, defined SLAs for freshness, and clear interfaces, allowing analytics and AI teams to consume records without complex cleaning cycles.
Automated Data Quality Testing
Data pipelines require automated quality checks just like software applications. We insert validation steps into ingestion pipelines to check for duplicate keys, null values, or schema drift.
Invalid data is automatically quarantined, and pipelines alert operators to prevent corrupt records from reaching downstream business dashboards and machine learning models.
Data Lineage and Observability
When a KPI in an executive report shows unexpected figures, tracing the calculation source manually can take days. End-to-end lineage mapping logs every transform step in the pipeline.
Observability portals allow teams to trace metrics back to raw source tables, ensuring transparency, simplifying audits, and facilitating database debugging.