Document Type: Framework
Status: Active
Authority: HeadOffice
Parent: Governance
Applies To: All MWMS decisions involving optimisation, scaling, budget allocation, experimentation outcomes, or strategic direction
Version: v1.0
Last Reviewed: 2026-04-23
Purpose
The HeadOffice Data Decision Gate Framework defines the mandatory validation criteria that must be passed before any decision is executed within MWMS.
This framework ensures:
• decisions are based on valid data
• optimisation actions are justified
• scaling occurs only under reliable conditions
• system risk is controlled
This framework acts as the final approval layer before execution.
Core Principle
No decision should be made on unvalidated or unreliable data.
If data fails validation:
→ the decision must not proceed
Position in MWMS System
This framework operates at HeadOffice level and integrates outputs from:
• Data Brain Measurement Integrity Framework
• Data Brain Data Trust Framework
• Data Brain Attribution Reliability Framework
• Experimentation Brain Statistical Confidence Framework
This framework determines:
👉 whether a decision is allowed
👉 whether a decision is blocked
👉 whether further validation is required
Decision Gate Structure
All decisions must pass four core gates:
Gate 1 — Measurement Integrity
Requirement
Measurement must be structurally valid.
Conditions
• events firing correctly
• no duplicate tracking
• no missing critical events
• data capture functioning correctly
Failure Outcome
→ Decision blocked
→ Measurement must be fixed and revalidated
Gate 2 — Data Trust
Requirement
Data must be reliable and interpretable.
Conditions
• data validated
• stable behaviour over time
• no unexplained anomalies
• consistent across systems
Failure Outcome
→ Decision paused
→ further validation required
Gate 3 — Attribution Reliability
Requirement
Attribution must be understood and acceptable.
Conditions
• attribution model understood
• no major cross-platform conflicts
• directional consistency present
• known limitations acknowledged
Failure Outcome
→ Decision downgraded or delayed
→ attribution must be reviewed
Gate 4 — Statistical Confidence
Requirement
Experiment or performance signals must be reliable.
Conditions
• sufficient sample size
• stable signal patterns
• aligned metrics
• behavioural coherence
Failure Outcome
→ No scaling allowed
→ continue testing
Decision Outcomes
Approved Decision
All four gates passed:
• Measurement Integrity → PASS
• Data Trust → PASS
• Attribution Reliability → ACCEPTABLE
• Statistical Confidence → HIGH
→ Decision may proceed
→ Scaling or optimisation allowed
Conditional Decision
Some gates partially satisfied:
• minor data inconsistencies
• moderate confidence
• attribution limitations
→ Decision may proceed with caution
→ reduced scale or controlled testing
Blocked Decision
Any critical gate fails:
• measurement broken
• data untrusted
• attribution invalid
• confidence low
→ Decision must not proceed
Decision Execution Flow
Step 1 — Identify Decision
Define:
• what action is being considered
• what data supports it
Step 2 — Run Gate Checks
Evaluate:
• Measurement Integrity
• Data Trust
• Attribution Reliability
• Statistical Confidence
Step 3 — Assign Outcome
• Approved
• Conditional
• Blocked
Step 4 — Execute or Pause
• execute decision
• delay decision
• return to validation
🔴 Decision Blocking Conditions
Decisions must be immediately blocked if:
• duplicate conversions detected
• key events missing
• tracking breaks after changes
• major platform discrepancies exist
• data is unstable or unexplained
• attribution conflicts are unresolved
🔴 Scaling Rule
Scaling is only allowed when:
• all four gates pass
• signals are stable
• results are repeatable
Scaling without validation increases capital risk.
🔴 Risk Control Rule
Higher risk decisions require stronger validation.
Examples:
• large budget increases
• major campaign changes
• offer scaling
• new funnel rollout
Higher risk → higher confidence required
🔴 Revalidation Rule
If conditions change:
• new campaigns
• tracking updates
• site changes
• anomaly detected
→ decision must be revalidated
Relationship to Other Frameworks
This framework integrates:
• Data Brain Analytics Audit Framework
• Data Brain Measurement Validation Protocol
• Data Brain Measurement Integrity Framework
• Data Brain Data Trust Framework
• Data Brain Attribution Reliability Framework
• Experimentation Brain Statistical Confidence Framework
Failure Modes Prevented
scaling on invalid data
optimising based on false signals
misinterpreting attribution
acting on low-confidence experiments
wasting budget due to poor data
system instability from poor decisions
Architectural Intent
This framework ensures MWMS operates as a controlled decision system, not a reactive system.
It protects:
• capital
• data integrity
• optimisation accuracy
• system stability
Final Rule
If the decision cannot pass all required gates:
→ the decision must not proceed
Change Log
Version: v1.0
Date: 2026-04-23
Author: HeadOffice
Change:
Initial creation of Data Decision Gate Framework integrating measurement, trust, attribution, and confidence into a single decision approval system.
Change Impact Declaration
Pages Created:
HeadOffice Data Decision Gate Framework
Pages Updated:
None
Pages Deprecated:
None
Registries Requiring Update:
MWMS Architecture Registry
Canon Version Update Required:
No
Change Log Entry Required:
Yes