AI can only be as intelligent as the data behind it.
For years, organizations have invested in data platforms, governance programs, catalogs, quality dashboards, metadata, and regulatory controls. Yet many still struggle with inconsistent definitions, unclear ownership, fragmented reporting, weak lineage, stale metadata, and data that cannot be fully trusted.
Data Management 3.0 argues that the age of AI requires something more.
Michael Ivie presents a practical vision for the next evolution of enterprise data management, organized around three paradigms:
- Data Management 1.0 established foundational control.
- Data Management 2.0 scaled governance, stewardship, metadata, operating models, and regulatory expectations.
- Data Management 3.0 moves toward semantics, data products, active metadata, continuous certification, knowledge graphs, AI-assisted governance, and enterprise intelligence.
Inside, you'll find practical frameworks for data management maturity, a six-domain capability model, a six-step data certification lifecycle, roadmap guidance, operating model considerations, ROI and value realization approaches, and insights into data products, semantic layers, ontologies, knowledge graphs, master and reference data, industry frameworks, and AI readiness.
For Chief Data Officers, data governance and technology leaders, risk and compliance executives, architects, practitioners, and transformation teams, Data Management 3.0 provides a roadmap for turning data management from a fragmented discipline into an enduring foundation for enterprise intelligence and responsible AI.
