Document Type: Framework
Status: Canon
Version: v1.3
Authority: Data Brain
Applies To: All MWMS environments where decisions rely on behavioural, performance, or financial measurement signals
Parent: Data Brain Canon
Last Reviewed: 2026-04-25
Purpose
Data Trust Framework defines how MWMS maintains confidence in the signals used to guide decisions across the system.
Data trust is not created automatically by measurement tools.
Data trust is created when signals are:
• consistent
• interpretable
• stable
• comparable
• explainable
• validated
• free from structural error
• reliably captured under real-world conditions
Decision-makers must trust signals before acting on them.
If trust in measurement weakens, decision confidence weakens.
Weak decision confidence slows optimisation.
Slow optimisation reduces growth velocity.
Data Trust Framework ensures MWMS protects signal credibility as system complexity increases.
Core Principle
Data is not trusted by default.
Data becomes trusted only when:
• measurement integrity is confirmed
• event capture is validated
• no duplication exists
• no critical data gaps exist
• attribution is understood
• signals remain stable over time
• event reliability is acceptable
• signal flow is complete or understood
• visibility limitations are acknowledged
• data inputs are validated and structured (NEW)
• segmentation context is consistent (NEW)
If these conditions are not met:
→ data is not decision-safe
🔴 Reliability Awareness Rule
Data trust must account for how reliably signals are captured.
Signals may appear valid while being:
• intermittently lost
• timing-sensitive
• dependent on unstable conditions
If event reliability is uncertain:
→ trust level must be reduced
🔴 Signal Flow Dependency Rule
Data trust depends on signals reaching their destination.
If signals:
• fail to transmit
• are partially transmitted
• lose context during transmission
→ trust must be reduced
Signals that do not fully flow between systems cannot be treated as complete.
🔴 Visibility Gap Awareness Rule
Some data cannot be captured.
Examples:
• iframe interactions
• third-party systems
• cross-device behaviour
• external platforms
Missing data does not equal missing behaviour.
Where visibility gaps exist:
→ trust must be adjusted
→ interpretation must be limited
Position in MWMS System
(UNCHANGED)
Data Trust Components
1. Metric Definition Stability
(UNCHANGED)
2. Measurement Consistency
(UNCHANGED)
3. Cross-System Alignment
(UNCHANGED)
4. Historical Comparability
(UNCHANGED)
5. Interpretation Clarity
(UNCHANGED)
🔴 Reliability Integrity Component
Signals must be evaluated for:
• timing reliability
• dependency stability
• execution consistency
Unreliable events weaken trust even if they appear valid.
🔴 Signal Completeness Component
Signals must be evaluated for completeness.
Incomplete signals include:
• missing context
• partial data transmission
• truncated user journeys
Incomplete signals:
→ reduce trust level
🔴 Visibility Limitation Component
Trust must reflect what cannot be observed.
Where visibility gaps exist:
• assumptions must be limited
• conclusions must be conservative
🔴 Validation Requirement
(UNCHANGED)
🔴 Duplicate-Free Requirement
(UNCHANGED)
🔴 Completeness Requirement
(UNCHANGED)
🔴 Attribution Awareness Requirement
(UNCHANGED)
🔴 Stability Requirement
(UNCHANGED)
🔴 Data Integrity Dependency (NEW)
Data trust requires:
• validated data inputs
• clean datasets
• structured data formats
• consistent segmentation
• stable data linking
If these are not met:
→ trust must be reduced
Data Trust Levels
(UNCHANGED structure, now strengthened by integrity dependencies)
🔴 Trust Adjustment Rule
Trust level must be reduced when:
• event reliability is uncertain
• signal flow is incomplete
• visibility gaps are significant
• signals are partially captured
• data integrity conditions are not met (NEW)
Decision Gate Rule
(UNCHANGED)
Data Confidence Indicators
• signals remain reliable under real-world conditions
• signal flow is consistent across environments
• visibility gaps are understood and accounted for
• segmentation remains consistent (NEW)
• data integrity is validated (NEW)
Trust Degradation Risk
• unreliable event capture
• signal loss between systems
• increasing visibility gaps
• partial signal transmission
• degradation of data integrity (NEW)
🔴 Trust Failure Conditions
(UNCHANGED — strengthened by new rules)
Monitoring Integration
(UNCHANGED)
Relationship to Other Frameworks
• Data Brain Event Reliability Framework
• Data Brain Signal Flow Framework
• Data Brain Signal Context Framework
• Data Brain Visibility Gap Framework
• Data Brain Measurement Integrity Framework
• Data Brain Segmentation Framework (NEW)
• Data Brain Data Linking Framework (NEW)
Failure Modes Prevented
• trusting incomplete signals
• misinterpreting missing data
• overconfidence in partial measurement
• ignoring system-level data limitations
• trusting unvalidated data inputs (NEW)
Drift Protection
The system must prevent:
• event reliability degrading unnoticed
• signal flow breaking across systems
• visibility gaps increasing without detection
• segmentation inconsistency emerging (NEW)
• data integrity degradation going unnoticed (NEW)
Architectural Intent
Data Trust Framework transforms measurement from:
passive reporting → active decision control
🔴 Architectural Extension
It ensures MWMS understands:
→ data may be valid but incomplete
→ data may be accurate but unreliable
→ data may be present but misleading
Trust must reflect real-world measurement limitations.
Final Rule
If data is not trusted:
→ it must not be used
🔴 Final Extension
If data completeness or reliability is uncertain:
→ decisions must be treated as high risk
Change Log
Version: v1.3
Date: 2026-04-25
Author: Data Brain
Change
Refined framework to align with Data Brain system upgrades:
• added data integrity dependency
• aligned trust model with segmentation and linking systems
• strengthened trust adjustment conditions
• improved cross-framework consistency
Change Impact Declaration
Pages Created:
None
Pages Updated:
Data Brain Data Trust Framework
Pages Deprecated:
None
Registries Requiring Update:
No
Canon Version Update Required:
No
Change Log Entry Required:
Yes