About Steven Zhang
Building trusted data foundations for decisions, platforms, and spatial intelligence.
I work at the intersection of enterprise data governance, spatial-temporal data, semantic architecture, digital twin thinking, analytics, AI, and decision support.
The Trusted Data Framework is my working framework for one practical question: how can organisations turn fragmented data into trusted meaning, reusable knowledge, and decisions that AI-enabled systems can safely support?
Trusted AI depends on trusted representations of reality, not only larger datasets or newer models.
The work began with data governance and semantic architecture, then expanded into spatial intelligence, digital twin readiness, and workbench prototypes.
Trusted Data is the public home for my framework, teaching models, reference programmes, and demonstrable advisory assets.
Explore reference programmes used to test the framework.What I focus on
From data governance to trusted AI foundations.
My work connects data governance, metadata, lineage, identity, semantic layers, knowledge graphs, spatial data, digital twins, and decision assets. The goal is not to create another abstract framework, but to make trusted data visible, explainable, and usable in real delivery settings.
Next step
Start with a focused conversation or a one-week readiness assessment.
The practical entry point is a small, evidence-based assessment using anonymised architecture, metadata, spatial data, or governance material. The output is a readiness view, control recommendations, and a 2-4 week MVP roadmap.