Data asset management applies the principles of enterprise asset management to data: treating datasets as valuable organizational assets that require ownership, documentation, maintenance, retirement policies, and value tracking. Just as physical assets like manufacturing equipment have owners, maintenance schedules, and depreciation policies, data assets (Iceberg tables, virtual datasets, ML feature sets) require comparable governance.
Key Data Asset Management Practices
Effective data asset management in an Iceberg lakehouse includes: Ownership assignment: every Iceberg table has a designated team or individual accountable for its quality and freshness. Documentation: table descriptions, column business definitions, and usage guidance stored in the catalog. SLA tracking: freshness and quality SLAs monitored by observability tools, with automated alerts when SLAs are breached. Usage analytics: query engine logs provide data on which tables are most queried, enabling prioritization of maintenance and documentation efforts for high-value assets. Lifecycle management: Iceberg snapshot expiration policies archive or delete tables that have reached end-of-life, reclaiming storage costs. Data catalog platforms like Atlan, DataHub, and Collibra provide tooling for implementing these practices at enterprise scale.
