Document Type: Framework
Status: Active
Authority: Data Brain
Parent: Data Brain Architecture
Applies To: All analytics implementations, tracking systems, and measurement environments across MWMS
Version: v1.0
Last Reviewed: 2026-04-23

Purpose

The Data Brain Analytics Audit Framework defines the structured process for evaluating the quality, integrity, reliability, and business alignment of analytics implementations.

The framework ensures that:

• data collected is accurate and complete
• tracking aligns with business objectives
• measurement systems are free from structural errors
• decision-making is based on trusted data
• privacy and compliance constraints are respected
• analytics environments remain stable over time

Analytics without structured auditing cannot be trusted.

This framework ensures analytics systems produce decision-grade data.

Core Principle

Analytics is not trusted by default.

Analytics becomes trusted only after:

• implementation validation
• data integrity verification
• event accuracy confirmation
• attribution understanding
• privacy and compliance alignment

Position in MWMS System

This framework operates within:

• Data Brain → measurement governance
• Experimentation Brain → test data validation
• Ads Brain → campaign performance accuracy
• Research Brain → evidence reliability
• HeadOffice → prioritisation and audit governance

This framework feeds:

• Data Trust Framework
• Measurement Integrity Framework
• Attribution Reliability Framework
• Experimentation Confidence Systems

Audit Scope Definition

Every analytics audit must define scope before execution.

Scope includes:

• platform (GA4, GTM, Ads, etc.)
• environments (live, staging, subdomains)
• data sources (events, parameters, integrations)
• business objectives (conversion, acquisition, retention)
• reporting usage (decision-making outputs)

Without scope definition, audit results are unreliable.

Audit Structure Overview

The audit consists of six structured layers:

Business Alignment Audit
Implementation Audit
Data Collection Audit
Event and Parameter Audit
Data Integrity and Attribution Audit
Privacy and Compliance Audit

Each layer must pass before data is considered reliable.

1. Business Alignment Audit

Purpose: Ensure tracking reflects real business goals.

Evaluation Areas

• Are primary conversions clearly defined?
• Are key user journeys mapped to measurable events?
• Are metrics aligned to business outcomes (not vanity metrics)?
• Are stakeholders using the correct reports for decisions?

Failure Conditions

• tracking exists without defined objectives
• metrics tracked but not used
• reporting misaligned with decision-making

2. Implementation Audit

Purpose: Ensure analytics infrastructure is correctly structured.

Evaluation Areas

• property structure correctness
• data stream configuration
• GTM setup and tag firing logic
• tracking code presence across pages
• duplicate or orphaned implementations

Failure Conditions

• multiple conflicting implementations
• missing tracking on key pages
• duplicated properties or streams
• lack of test/staging environments

3. Data Collection Audit

Purpose: Ensure data is being captured consistently and correctly.

Evaluation Areas

• page views firing correctly
• session tracking consistency
• internal traffic filtering
• referral exclusions
• cross-domain tracking behavior

Failure Conditions

• inflated or deflated traffic
• internal traffic polluting data
• broken session continuity
• misattributed sources

4. Event and Parameter Audit

Purpose: Ensure user interactions are captured accurately.

Evaluation Areas

• correct event structure
• use of recommended event naming conventions
• parameter completeness
• ecommerce event accuracy
• event duplication or missing events

Validation Methods

• GTM preview mode
• GA4 debug view
• data layer inspection

Failure Conditions

• duplicate events
• missing key events
• inconsistent naming
• incorrect parameter values

5. Data Integrity and Attribution Audit

Purpose: Ensure data reflects reality and can support decisions.

Evaluation Areas

• duplicate conversions
• missing conversions
• attribution model limitations
• “not set” / “unassigned” causes
• channel grouping accuracy
• cross-platform discrepancies

Critical Considerations

• GA4 attribution may under-credit non-Google channels
• modeling and thresholding may distort results
• roll-up vs property-level data differences

Failure Conditions

• inconsistent data across platforms
• misleading attribution
• unexplained traffic segments
• decision-making based on flawed signals

6. Privacy and Compliance Audit

Purpose: Ensure analytics operates within legal and ethical constraints.

Evaluation Areas

• cookie consent behavior
• consent mode implementation
• tracking behavior when consent is denied
• PII collection risks
• data retention settings
• Google Signals usage

Critical Rules

• PII must never be collected
• consent behavior must match policy
• analytics must reflect jurisdictional requirements

Failure Conditions

• tracking without consent where required
• PII leakage
• mismatch between policy and implementation
• improper use of personalization data

Audit Execution Methods

Audit validation must use:

• GTM preview mode
• GA4 debug view
• browser network inspection
• data layer inspection
• report comparison
• manual user journey simulation

Automated tools may assist but cannot replace manual validation.

Audit Findings Classification

All findings must be classified into:

• Critical (data invalid / decisions unsafe)
• High (major distortion risk)
• Medium (partial accuracy issues)
• Low (optimization opportunities)

Classification determines execution priority.

Audit Output Structure

Audit results must include:

• issue description
• affected system/component
• business impact
• root cause
• recommended fix
• priority level

Audit output must be actionable.

Monitoring and Re-Audit Requirement

Analytics audits are not one-time activities.

Systems must include:

• periodic audits
• continuous monitoring
• anomaly detection
• alerting mechanisms

Without monitoring, data quality degrades over time.

Relationship to Other Frameworks

This framework supports and integrates with:

• Data Brain Measurement Integrity Framework
• Data Brain Data Trust Framework
• Data Brain Attribution Reliability Framework
• Experimentation Brain Statistical Confidence Framework
• HeadOffice Governance and Prioritization Systems

Key Outcomes

When this framework is applied correctly:

• data becomes decision-grade
• tracking errors are minimized
• attribution becomes more reliable
• experimentation confidence increases
• compliance risks are reduced
• MWMS operates on trusted intelligence

Change Log

Version: v1.0
Date: 2026-04-23
Author: Data Brain

Change:
Initial creation of Analytics Audit Framework based on GA4 audit capability extraction.

Change Impact Declaration

Pages Created:
Data Brain Analytics Audit Framework

Pages Updated:
None

Pages Deprecated:
None

Registries Requiring Update:
MWMS Architecture Registry

Canon Version Update Required:
No

Change Log Entry Required:
Yes