Data Trust Engineering + EMM 2.0
Complete Data Trust Solutions: DTE certifies data systems by use case, risk, and value. EMM 2.0 manages enterprise metadata through modern graph technologies. Together, they provide comprehensive data trust solutions for AI-ready systems.
Two Complementary Frameworks
Data Trust Engineering (DTE)
Certifies data systems by use case, risk, and value through practical engineering patterns and open-source collaboration. DTE extends governance foundations with measurable, AI-ready trust solutions.
- ✅ Real-time trust monitoring and certification
- ✅ 19+ practical patterns with runnable code
- ✅ DTE Trust Dashboard for live monitoring
- ✅ AI safety and bias detection patterns
EMM 2.0: Enterprise Metadata Management
Manages enterprise metadata through modern graph technologies with vendor-neutral patterns, reusable code, and user-friendly tools that work across any platform.
- ✅ Graph-native metadata repositories
- ✅ Federated and standalone architectures
- ✅ GraphRAG-powered knowledge discovery
- ✅ OpenLineage and graph metamodel integration
- ✅ Open universal metamodel
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.
EMM 2.0 complements DTE by providing the metadata management foundation through modern graph technologies. It enables vendor-neutral enterprise metadata management with battle-tested patterns, reusable code, and user-friendly tools that work across any platform—from genome research to media assets to financial lineage requirements.
Our vendor-neutral community shares tools, patterns, and real-world experience to make trust measurable and repeatable. The initial artifacts include the DTE Trust Dashboard for real-time monitoring, 19 practical patterns with code covering everything from data quality to AI safety, and EMM 2.0 metadata management tools—with more community-built solutions on the way. Join us, contribute on GitHub, and be part of the #DataTrustCommunity.
Founded by Brian Brewer
Join the #DataTrustCommunity
Read DTE Manifesto Read EMM 2.0 Framework Try the Trust Dashboard Contribute on GitHub Join SlackThe 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. EMM 2.0 provides the metadata management foundation through modern graph technologies. Together, they enable trusted, AI-ready systems that complement compliance goals.
How DTE + EMM 2.0 Extend Traditional Data Governance
| Criteria | Traditional Data Governance | Data Trust Engineering (DTE) | EMM 2.0 |
|---|---|---|---|
| Focus | Process-heavy frameworks | Engineering-driven certification | Graph-native metadata management |
| Implementation | Top-down mandates | Collaborative, iterative | Vendor-neutral, flexible |
| Technology | Relational databases | Cloud-native, API-driven | Graph databases, GraphRAG |
| Compliance | Direct ownership | Indirect support through technical excellence | Metadata integrity and lineage certification |
| AI Integration | Afterthought | Built-in monitoring and validation | Native GraphRAG and knowledge graphs |
| Vendor Lock-in | Platform dependent | Vendor-neutral patterns | Zero vendor lock-in |
Together, traditional governance provides the strategic "why," DTE delivers the operational "how" for certification, and EMM 2.0 provides the metadata management foundation—creating complete data trust solutions.
How to Contribute
Join the #DataTrustCommunity by contributing to both Data Trust Engineering and EMM 2.0 projects. Fork the repo, enhance the dashboard, add DTE tools, or contribute to EMM 2.0 metadata management patterns. See the Contributing Guide for details.
Contact Us
- Slack: Join datatrustengineering (#general, #contributions)
- Inquiries: Open a Contact issue or use our contact form
- Issues: Report Issues & Propose Features: Submit bug reports or suggest new artifacts
- Website: datatrustmanifesto.org
- Contribute: Fork the repo or enhance the dashboard. See Contributing Guide