Carbon-reduction projects and independent verification taught me to test whether a claimed change was real and supported by evidence that could withstand challenge. Think globally, act and change locally connected practical interventions to wider outcomes.
About Steven Zhang
Trusted Data & Transformation Consultant
My career has centred on the points where data, governance and transformation have to move together. In regulated and data-intensive organisations, that is often where unclear meaning, ownership or evidence starts to slow decisions and delivery.
I work with the people already accountable for the change: clarifying what matters, introducing practices that can work in delivery and building capability as we go. The aim is not dependency on me, but clearer decisions and reusable assets the team can own and continue.
My strongest established commercial work is in enterprise data governance, data quality, metadata, migration, reconciliation and transformation delivery. Financial services is my strongest recent commercial context, alongside direct experience across energy and sustainability, public-sector climate programmes and higher-education research and data work. Research and independent work add spatial, semantic and AI-oriented perspectives without replacing that enterprise core.
Professional continuity
Four stages of the same trust problem.
The terminology has changed across my career, but the practical question has remained consistent: how can important change be supported by information that stays connected to reality, meaning, evidence and accountability?
Funded research connected more than 30 major datasets from over 50 evaluated sources to the same homes, places and times. It developed my focus on identity, provenance, spatial-temporal context and how different digital records remain connected to the reality they represent.
BI and analytics led into enterprise data quality, metadata, ownership, governance, migration, reconciliation and cross-team Agile Delivery. In regulated financial services, I contributed to the design and led implementation of a Snowflake-based data-quality framework and rule engine, helped establish operational exception management, supported metadata and observability, and led migration analysis and rapid reconciliation approaches. I also coordinated plans, dependencies and delivery across business, governance and technical teams. This is the strongest established commercial core of my work: turning policy, definitions and controls into practical transformation delivery.
Current independent work extends the same questions into AI-ready data, semantic discovery and agent-assisted delivery: source authority, permissions, provenance, conflicting evidence, accountability and human review. It extends my Trusted Data practice; it is not a claim of multiple enterprise AI production deployments.
Recognition and professional context
Research depth with a practical enterprise centre.
In 2020, Tech Nation endorsed me under the Global Talent Exceptional Promise route in Digital Technology, with the official field recorded as Geospatial Data Science and Engineering. I have also served as an elected Council Member of the Association for Geographic Information.
I am interested in contract, advisory and principal-level work where data governance, quality and transformation need to support reliable decisions. For asset-, place- and infrastructure-intensive problems, my spatial background adds a specialist perspective on identity, context and the real world.
How I work
Connecting what organisations and systems keep separate.
I bridge business, data, governance, architecture and delivery without blurring who is accountable. I keep meaning, evidence and accountability explicit as information moves, and use outcomes and new evidence to improve the next decision.
Every new connection needs a clear purpose, accountable authority, sufficient evidence, understood impact and a practical way to correct or reverse it.
Explore the thinking
The Framework provides depth when the problem needs it.
The Trusted Data Framework is my current synthesis of the recurring questions across assurance, research, analytics, data governance and transformation. It was not the historical name of the earlier work.
Explore the Trusted Data Framework, see how the delivery lifecycle connects questions to decisions, or use the Data Kitchen to understand why trusted preparation should be shared rather than repeated by every consumer.
Contact
Start a conversation about a role, project, or advisory need.
A useful first conversation can start from a role brief, a decision that depends on data you cannot confidently explain, a governance or data-quality challenge, a migration problem, or a defined AI use case that needs a stronger data foundation.
Email: steven.zhang@trusted-data.tech
LinkedIn: linkedin.com/in/steven4320555