Stop building data products. Start building data services.

Enterprises must shift from product-centric to service-centric data architecture to support rapidly evolving AI agent consumption.

• •Services-based architecture replaces product-per-use-case models, better accommodating AI agents' flexible, unpredictable data composition needs
• •Data quality and mastering should happen near ingestion, not downstream, to accelerate integration and eliminate reconciliation bottlenecks
• •Unified data models and codified taxonomies prevent teams from becoming manual reconciliation engines and eliminate scaling overhead
• •Reusable data assets and standardized pipelines enable scaling from isolated pilots to enterprise-wide capabilities without per-division rebuilding
• •Focus on reducing insight lag (time from data availability to action) rather than data freshness for real-time business decisions