Document Type: Standard
Status: Canon
Authority: HeadOffice
Parent: Governance
Applies To: All MWMS tracking systems, event pipelines, analytics integrations, and measurement implementations
Version: v1.0
Last Reviewed: 2026-04-23

Purpose

The MWMS Measurement Control Standard defines how all data must be validated, controlled, and governed before being accepted into MWMS measurement systems.

Tracking systems generate data.

Uncontrolled data introduces:

• inaccuracies
• duplication
• missing context
• compliance risks
• incorrect interpretation

This standard ensures all data entering MWMS systems is:

→ validated
→ controlled
→ enriched
→ decision-safe

Core Principle

Data is not trusted by default.

Data becomes trusted only after control.

Scope

This standard governs:

• data validation before dispatch
• data enrichment and transformation
• duplication prevention
• compliance enforcement
• signal filtering
• measurement integrity controls

This applies to:

• custom events
• conversion events
• attribution data
• behavioural signals
• system-generated data

Measurement Control Overview

All measurement data must pass through a control layer before entering analytics systems.

Control Flow Model

Event Capture
→ Signal Transport
→ Measurement Control
→ Analytics / Storage
→ Interpretation
→ Decision

Measurement Control Functions

1. Data Validation

Ensures data is correct before dispatch.

Validation Checks

• required parameters exist
• data types are correct
• values are within expected ranges
• event structure matches standard

Core Rule

Invalid data must not be sent.

2. Data Enrichment

Adds necessary context to events.

Examples

• attaching traffic source
• adding funnel step
• including experiment identifiers
• adding session context

Core Rule

Enrichment must improve signal clarity without altering meaning.

3. Data Cleaning

Removes or corrects problematic data.

Examples

• removing malformed values
• standardising formats
• correcting known errors

Core Rule

Cleaning must preserve signal integrity.

4. Data Filtering

Prevents low-quality or irrelevant data from entering systems.

Examples

• blocking internal traffic
• suppressing duplicate events
• filtering non-actionable signals

Core Rule

Noise must be removed before data enters the system.

5. Duplication Control

Ensures events are not counted multiple times.

Risks

• inflated conversion rates
• incorrect performance metrics
• misleading experiment results

Core Rule

Duplicate events must be prevented or corrected.

6. Compliance Enforcement

Ensures data meets privacy and platform requirements.

Examples

• removing PII
• enforcing data privacy rules
• ensuring platform compliance

Core Rule

Non-compliant data must not be transmitted.

7. Data Transformation

Converts data into standardised structure.

Examples

• mapping parameters to MWMS payload standard
• renaming fields
• restructuring data objects

Core Rule

All data must conform to MWMS standards before dispatch.

🔴 Pre Dispatch Control Requirement

All control actions must occur before data is sent to analytics systems.

Core Rule

Once data is sent:

→ it cannot be corrected

🔴 Event Quality Requirement

All events must be:

• meaningful
• complete
• structured
• context-aware

Core Rule

Low-quality events must not enter the system.

🔴 Dependency Awareness Rule

Measurement control may depend on:

• event parameters
• session state
• user state
• configuration data

Core Rule

Control logic must account for missing dependencies.

🔴 Execution Order Rule

Control processes must execute in the correct sequence:

Validation
→ Cleaning
→ Enrichment
→ Transformation
→ Filtering
→ Dispatch

Core Rule

Incorrect order leads to incorrect data.

🔴 Monitoring Requirement

Measurement control must be monitored for:

• errors
• unexpected outputs
• data inconsistencies

Core Rule

Control failures must be detected early.

🔴 Testing Requirement

All control logic must be tested under:

• normal conditions
• edge cases
• high-volume scenarios

Core Rule

Unverified control logic must not be deployed.

🔴 Data Trust Integration

Measurement control directly supports:

• Data Trust Framework
• Measurement Integrity Framework
• Attribution Reliability Framework

Core Rule

Data trust depends on control effectiveness.

🔴 Signal Flow Integration

Measurement control must integrate with:

• signal transport systems
• data layer structures
• analytics pipelines

Core Rule

Control must not break signal flow.

Relationship to Other Standards

Supports:

• MWMS Tracking Architecture Standard
• MWMS Custom Event Payload Standard
• MWMS Pre Conversion Signal Tracking Standard
• MWMS Attribution Preservation Standard
• Data Brain Custom Task Control Framework

Failure Modes Prevented

invalid data entering system
duplicate events
missing context
compliance violations
incorrect analysis
poor decision quality

Drift Protection

The system must prevent:

• uncontrolled data entering systems
• degradation of validation logic
• silent failure of control mechanisms
• inconsistent control across environments

Architectural Intent

The MWMS Measurement Control Standard ensures MWMS operates with:

→ controlled, validated, and decision-safe data

It transforms measurement from:

data collection → data governance

Final Rule

If data is not controlled:

→ it must not be trusted

Change Log

Version: v1.0
Date: 2026-04-23
Author: HeadOffice

Change:
Initial creation of Measurement Control Standard defining how MWMS validates and governs all measurement data.

Change Impact Declaration

Pages Created:
MWMS Measurement Control Standard

Pages Updated:
None

Pages Deprecated:
None

Registries Requiring Update:
MWMS Architecture Registry

Canon Version Update Required:
Yes

Change Log Entry Required:
Yes