DGCP™ — Data Governance Philosophy
Document Metadata
Document Type: Governance Architecture Statement
Version: DGCP™ Core
Published (Local): 2026-03-01
Timezone: Asia/Bangkok
Scope: Real-world operational datasets
Document Type: Governance Architecture Statement
Version: DGCP™ Core
Published (Local): 2026-03-01
Timezone: Asia/Bangkok
Scope: Real-world operational datasets
1. Governance Definition
Data governance under DGCP™ is defined as structured verifiability:
- Structured capture of real-world events
- Separation of evidence and narrative
- Cryptographic integrity protection
- Chronological continuity
- Explicit accountability
Governance is not interpretation. Governance is structured verifiability.
2. Architectural Principles
2.1 Evidence-First Model
- Every record must reference raw evidence.
- Claims without reference are labeled unverified.
- No retrospective narrative edits.
2.2 Integrity Separation
- SHA-256 stored separately from dataset files.
- OTS timestamp stored separately.
- External references marked as non-binding.
- No embedded CID inside structured dataset files.
2.3 Traceability Layer
- Stable file paths
- Consistent naming convention
- Cross-reference via dataunit_refs and proof_refs
- Ledger continuity preserved
3. Institutional-Grade Criteria
- Timestamp includes local time and timezone
- Actor responsibility explicitly stated
- Evidence referenced
- Hash file exists
- OTS receipt exists
- License preserved
- Change history traceable via version control
4. Governance Control Matrix
Capture — Event logged with timestamp
Integrity — SHA-256 generated and stored separately
Time Proof — OTS anchored and verifiable
Continuity — Ledger inclusion preserved
License — DGCP | MMFARM-POL-2025 present
Integrity — SHA-256 generated and stored separately
Time Proof — OTS anchored and verifiable
Continuity — Ledger inclusion preserved
License — DGCP | MMFARM-POL-2025 present
5. Risk Position
- No hidden aggregation
- No selective disclosure
- No cosmetic metric adjustment
- Uncertainty explicitly declared
6. Operational Discipline
- No noise injection
- No opinion inside dataset layer
- No projections inside evidence layer
- Clear separation between log, analysis, and philosophy
DGCP | MMFARM-POL-2025
This work is licensed under the DGCP (Data Governance & Continuous Proof) framework.
All content is part of the MaMeeFarm™ Real-Work Data & Philosophy archive.
Redistribution, citation, or derivative use must preserve attribution and license reference.