Most data professionals work with multiple datasets scattered across teams, systems, and formats. But without a clear modeling strategy, the result is often chaos: mismatched schemas, fragile pipelines, and a constant fight to make sense of the noise. This essential guide offers a better way by introducing a practical framework for designing high-quality data models that work across platforms while supporting the growing demands of AI, analytics, and real-time systems.
Author Jun Shan bridges the gap between disconnected modeling approaches and the need for a unified, system-agnostic methodology. Whether you're building a new data platform or rethinking legacy infrastructure, Universal Data Modeling gives you the clarity, patterns, and tools to model data that's consistent, resilient, and ready to scale.

• Connect conceptual, logical, and physical modeling phases with confidence
• Apply best-fit techniques across relational, semistructured, and NoSQL formats
• Improve data quality, clarity, and maintainability across your organization
• Support modern design paradigms like data mesh and data products
• Translate domain knowledge into models that empower teams
• Build flexible, scalable models that stand the test of technology change