Modern Data Architecture
Open and governed data foundations should help organizations adapt to new workloads while improving how people work with data.
Beyond the technology.
Design for change
Data systems need to evolve as business questions, applications, and AI workloads change. Avoid architectures that require rebuilding the same foundation for each new consumer.
Connect openness with governance
Open data formats are useful, but discoverability, clear ownership, permissions, and shared definitions are equally important to an architecture people can depend on.
Measure what matters
Throughput and infrastructure cost are necessary measures. So are time to insight, confidence in answers, and the effort required to turn data into an action.
Explore related thinking in my selected essays ↗.
Enterprise AI Architecture ↗
Building trustworthy enterprise AI requires business context, evaluation, governance, and workflows that connect answers to decisions.
Semantic Layers & Enterprise Ontology ↗
A consistent business vocabulary connects analytics, applications, and AI agents to the same trusted enterprise meaning.