Data Trust Engineering (DTE) Manifesto
Executive Summary
Data Trust Engineering (DTE) is a community-driven approach that extends the foundations of traditional data governance into the engineering layer. Where governance provides the strategic “why,” DTE delivers the operational “how” — certifying data systems by use case, risk, and value through collaborative, open-source patterns.
By blending DataOps principles with practical trust frameworks, DTE helps organizations address the realities of AI readiness, cloud-native scale, and data quality challenges. Rather than replacing governance, DTE complements it: enabling engineering teams to implement measurable trust practices while supporting compliance and strategic goals. This balance ensures organizations can move faster, with greater confidence in the reliability and integrity of their data.
Mission
Data Trust Engineering (DTE) empowers data professionals to build trusted, AI-ready systems through collaborative engineering practices and open-source patterns. This manifesto outlines practical principles for certifying data systems by use case, risk, and value, moving beyond static governance frameworks toward adaptable, engineering-driven solutions. Our vendor-neutral community develops the implementation approaches, creating shared tools and patterns that work across diverse organizational contexts.
Rationale
Data Trust Engineering emerged from the practical challenges data teams face when building reliable systems in complex, fast-moving environments. Organizations can face governance complexity, where compliance requirements sometimes overshadow technical data management needs. Born from collective experience across engineering teams, DTE redefines data management as a collaborative engineering discipline focused on practical certification patterns.
DTE draws inspiration from successful open-source movements and agile development practices, evolving beyond traditional governance’s process-heavy approaches. Instead of vendor-driven frameworks, DTE empowers communities to shape practical solutions through collaborative tools and shared knowledge.
DTE’s foundation rests on three practical insights:
Diverse Organizational Needs: Different organizations require different approaches—enterprises need scalable compliance-adjacent solutions, SMBs seek lightweight implementations, and cloud-native teams require flexible, API-driven patterns.
AI Implementation Challenges: Data quality issues frequently impact AI projects, requiring practical monitoring and validation approaches that integrate with existing engineering workflows.
Technical vs. Compliance Focus: While compliance remains important for legal and audit teams, DTE enables engineering teams to focus on building technically robust, trustworthy systems using proven open-source tools.
By combining DataOps principles with practical trust patterns, DTE bridges the gap between governance aspirations and engineering reality. It provides actionable frameworks that engineers can immediately implement, fostering collaboration rather than imposing top-down mandates.
The Evolution Beyond Data Governance
Traditional data governance emerged in the post-SOX era with good intentions, but has struggled to adapt in some contexts to modern cloud-native and AI-driven requirements. Many organizations find that governance initiatives can create complexity, particularly when compliance requirements overshadow technical data management needs.
DTE represents a practical evolution—an engineering-focused approach that emphasizes implementation over process, collaboration over mandates, and measurable outcomes over theoretical frameworks.
The Problem: Process Over Engineering
Data governance initiatives, while well-intentioned, sometimes prioritize process documentation, which can at times outweigh practical engineering solutions. This approach can create barriers for engineering teams who need to deliver working systems quickly. Organizations frequently encounter implementation challenges when governance requirements become disconnected from actual data workflows.
AI introduces additional complexity—teams need practical approaches for bias monitoring, drift detection, and model validation that integrate seamlessly with existing development practices.
The Solution: Data Trust Engineering
DTE provides practical, engineering-driven patterns for building trustworthy data systems. By focusing on certification by use case, risk, and business value, DTE enables teams to implement trust measures that align with their specific operational needs.
DTE combines DataOps agility with practical trust patterns, ensuring reliable systems for critical use cases like AI model training and regulatory reporting. It separates technical data management from compliance concerns, allowing engineering teams to focus on building robust systems while supporting audit and legal requirements indirectly.
DTE works effectively across cloud-native architectures, data mesh implementations, and real-time analytics environments, addressing practical gaps where operational focus meets governance requirements.
Getting Started with DTE
Organizations can adopt DTE through practical, incremental steps:
Phase 1: Assessment - Identify high-value use cases where trust certification provides immediate benefits. Evaluate existing data quality tools and establish practical baseline metrics.
Phase 2: Pilot Implementation - Select one critical use case and implement DTE patterns using familiar tools like Great Expectations for validation and OpenLineage for metadata tracking. Focus on measurable outcomes that demonstrate value.
Phase 3: Scale and Collaborate - Expand successful patterns to additional use cases, share learnings with the broader community, and refine approaches based on real-world feedback.
Phase 4: Community Integration - Engage with the DTE community, contribute successful patterns, and help shape shared best practices that benefit the entire ecosystem.
Core Principles of DTE
Trust: Data systems must be accurate, secure, and accessible, building confidence across teams and stakeholders.
Engineering Rigor: DTE applies software engineering best practices—automation, testing, and iterative improvement—to data systems.
Adaptability Through Feedback: Continuous feedback loops enable teams to rapidly test, measure, and optimize data trust implementations.
Enablement: DTE empowers data professionals to deliver value, fostering collaboration and knowledge sharing.
Cloud-Native: DTE aligns with modern cloud architectures and shared responsibility models.
Certification by Use Case, Risk, and Value: DTE provides practical assurance frameworks tailored to specific business contexts, supporting compliance goals through technical excellence rather than process overhead.
Technical Debt Management: DTE treats data quality as manageable technical debt, using practical tools to identify and resolve issues early in the development process.
Community-Driven: DTE evolves through open collaboration, welcoming contributions from diverse perspectives and experiences.
A Complement to Governance, Not a Framework: DTE is not a replacement for governance but a practical complement that focuses on engineering implementation.
Not a Tool: DTE is not a specific software platform but a collection of patterns and principles that guide implementation.
Vendor Neutrality: DTE emphasizes open-source tools and community-developed approaches, avoiding vendor lock-in.
Technical Independence from Compliance: DTE provides technical foundations that support compliance without becoming compliance-driven.
Not Policy-Driven: DTE rejects rigid policy mandates in favor of adaptable engineering practices.
Why DTE Succeeds
DTE provides practical value through:
- Technical Focus: Engineering teams can implement trust patterns without extensive process overhead.
- Adaptable Implementation: Feedback loops and iterative approaches ensure solutions evolve with changing needs.
- Cloud-Ready: Patterns work effectively across data mesh, hybrid cloud, and real-time analytics environments.
- Efficiency-Oriented: Practical approaches minimize complexity while maximizing effectiveness.
- AI-Enabled: Proven patterns for fairness, drift detection, and model monitoring that integrate with existing workflows.
- Universal Applicability: Approaches that scale from small teams to large enterprises.
- Practical Certification: Risk-based assurance that supports business outcomes and compliance requirements.
- Technical Debt Discipline: Proactive approaches to data quality that prevent downstream issues.
- Engineering Integration: Trust measures built into development workflows rather than added as afterthoughts.
Observability and Provenance Over Static Policy
DTE emphasizes dynamic, observable approaches over rigid policies. Real-time monitoring, metadata tracking, and feedback loops enable teams to maintain trust as systems evolve and requirements change.
Understanding Traditional Data Governance Context
Data governance originated with good intentions around financial integrity and regulatory compliance. However, many organizations face challenges adapting to modern cloud-native and AI-driven requirements. Implementation challenges are common when governance becomes disconnected from actual engineering workflows.
Some approaches, like data contracts, show promise when implemented as practical engineering patterns rather than theoretical frameworks.
Key Considerations:
- Implementation challenges often stem from complexity rather than technical limitations
- Value depends on organizational context and specific use cases
- Overly broad governance scopes can limit practical implementation
Reframing Data Quality as Technical Debt Management
DTE treats data quality as manageable technical debt rather than an abstract compliance requirement. Using practical tools and early validation approaches, teams can identify and resolve quality issues before they impact production systems.
The DTE Framework
DTE provides practical patterns across key areas:
- Data Quality: Automated validation using Great Expectations and similar tools
- Lineage & Metadata: Provenance tracking with OpenLineage and similar frameworks
- Security & Access: Identity and encryption patterns aligned with existing infrastructure
- Scalability: Architecture patterns for data mesh and real-time analytics
- Adaptability: Testing and feedback loops integrated into development workflows
- Certification: Use case-specific validation that supports business and compliance needs
- Technical Debt Management: Proactive quality approaches using dbt and validation frameworks
- AI Governance: Practical monitoring for fairness, explainability, and performance
- Collaboration: Community-driven evolution through shared tools and patterns
Case Studies: DTE in Action
- Healthcare Implementation: Great Expectations validation reduced pipeline errors by 15%, with Fairlearn providing practical evidence for compliance discussions.
- Retail Operations: Data quality patterns and dbt transformations reduced operational issues by 18%, improving overall system reliability.
- Media Platform: Metadata tracking and lineage patterns reduced technical complexity by 50%, improving system maintainability.
How DTE Extends Traditional Data Governance: A Comparison
| Criteria | Traditional Data Governance | Data Trust Engineering |
|---|---|---|
| Approach | Process-heavy frameworks | Engineering-driven patterns |
| Implementation | Top-down mandates | Collaborative, iterative |
| Compliance | Direct ownership | Indirect support through technical excellence |
| Adaptability | Limited by rigid policies | Flexible, feedback-driven |
| Team Focus | Process documentation | Engineering delivery |
| AI Integration | Afterthought | Built-in monitoring and validation |
The Call to Action
Join the DTE community:
- Start Small: Implement one pattern in your current projects
- Share Learnings: Contribute successful approaches to the community
- Collaborate: Engage with other teams facing similar challenges
- Contribute: Share tools, patterns, and practical implementations
Join the Community
DTE provides practical paths forward for organizations seeking effective data trust implementations. Join our community to share experiences, contribute patterns, and collaborate on solutions that work in real-world environments.
References
- MIT Technology Review Insights (2025). AI-Readiness for C-Suite Leaders. Available at: MIT Technology Review.
- EU AI Act (2024). Regulation on Artificial Intelligence. Available at: European Commission.
- Industry Surveys (2024-2025). Compiled from non-vendor sources.
- Enricher.io (2025). The Cost of Incomplete Data. Available at: https://enricher.io/blog/the-cost-of-incomplete-data
- Great Expectations (2025). Down with Pipeline Debt. Available at: https://greatexpectations.io/blog/down-with-pipeline-debt-introducing-great-expectations/
- Cloud Data Insights (2025). Data Pipeline Pitfalls. Available at: https://www.clouddatainsights.com/data-pipeline-pitfalls-unraveling-the-technical-debt-tangle/
- DQOps (2025). Technical Debt in Data Engineering. Available at: https://dqops.com/technical-debt-in-data-engineering/
- Statista (2025). Number of Data Professionals. Available at: https://www.statista.com/statistics/1134896/number-of-data-professionals-eu-uk-2025/
License: MIT License
Contribute: Submit pull requests on GitHub.
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