Data Trust Engineering — Build Trust in Data & AI

Data Trust Engineering (DTE)

Data Trust Engineering (DTE) extends the foundations of governance by certifying data systems by use case, risk, and value—making trust measurable, repeatable, and AI-ready through open-source, engineering-driven practices.

Key Takeaway: Governance defines the “why.” DTE delivers the “how”—certifying data and pipelines by use case, risk, and value so trust becomes measurable, repeatable, advanced analytics and AI-ready.

Data Trust Engineering (DTE) is a community-driven movement that helps data teams build trusted, data and AI-ready systems through practical engineering patterns and open-source collaboration. Born from the Data Trust Engineering Manifesto, DTE offers actionable frameworks to certify data and pipelines by use case, risk, and value—blending DataOps principles with hands-on implementation across cloud and hybrid environments.

Our vendor-neutral community shares tools, patterns, and real-world experience to make trust measurable and repeatable. The first artifact, the DTE Trust Dashboard, demonstrates real-time data and AI trust monitoring—with more community-built tools on the way. Join us, contribute on GitHub, and be part of the #DTERevolution.

Join the #DTERevolution

Read the Manifesto Try the Trust Dashboard Contribute on GitHub Join Slack

The Evolution of Data Governance

  • 2001: Enron Scandal — Corporate fraud shakes investor trust worldwide.
  • 2002: Sarbanes–Oxley Act (SOX) — Establishes stronger controls, accountability, and independent oversight (PCAOB).
  • 2003–2008: Consulting Expansion — Major firms (PwC, Deloitte, Accenture) extend governance concepts from finance into master data, metadata, and quality management.
  • 2009: DAMA-DMBOK & Gartner Models — Formal frameworks and enterprise maturity models take hold, setting a common vocabulary for governance (DAMA, Gartner, IBM, CMMI, EDM).
  • 2010s–2020s: Cloud & Big Data Era — Governance expands to cover privacy, security, and AI ethics, while vendors rebrand catalogs, lineage, and quality tools under the governance banner.
  • Today — Governance continues to play a vital role, but its broad scope often makes it difficult for teams to apply consistently across fast-moving, cloud-native and AI environments.

Where do we go from here? Data Trust Engineering (DTE) builds on these foundations by shifting focus from governing data in the abstract to certifying it by use case, risk, and value — enabling trusted, AI-ready systems that complement compliance goals.

How DTE Extends Traditional Data Governance

CriteriaTraditional Data GovernanceData Trust Engineering
ApproachProcess-heavy frameworksEngineering-driven patterns
ImplementationTop-down mandatesCollaborative, iterative
ComplianceDirect ownershipIndirect support through technical excellence
AdaptabilityLimited by rigid policiesFlexible, feedback-driven
Team FocusProcess documentationEngineering delivery
AI IntegrationAfterthoughtBuilt-in monitoring and validation

Together, traditional governance provides the strategic “why,” while Data Trust Engineering delivers the operational “how” — turning intent into measurable, certifiable outcomes in real systems.


How to Contribute

Join the #DTERevolution by contributing to the Data Trust Engineering project. Fork the repo, enhance the dashboard, or add DTE tools. See the Contributing Guide for details.



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