Document Type: Framework
Status: Active
Authority: Data Brain
Parent: Data Brain Architecture
Applies To: All MWMS environments where analytics, experimentation, or performance data is used for analysis or decision-making
Version: v1.0
Last Reviewed: 2026-04-23
Purpose
The Data Brain Raw Data Access Framework defines how MWMS accesses, validates, and uses raw event-level data from underlying data systems.
Most analytics platforms present:
• aggregated data
• sampled data
• estimated data
• thresholded data
These representations are useful for trend analysis but may not reflect exact behaviour.
Raw data access provides:
• full event-level visibility
• exact counts
• complete behavioural logs
• direct query control
This framework ensures MWMS uses raw data where accuracy is required, especially for experimentation and decision-critical analysis.
Core Principle
Interface data is not guaranteed truth.
Raw data provides the closest representation of actual behaviour.
If decision accuracy requires precision:
→ raw data must be used
Position in MWMS System
This framework operates within:
• Data Brain → data access and validation
• Experimentation Brain → test analysis
• HeadOffice → decision control
It supports:
• Data Trust Framework
• Measurement Integrity Framework
• Attribution Reliability Framework
• Statistical Confidence Framework
Raw Data Definition
Raw data refers to:
• event-level records
• unaggregated behavioural data
• system-exported logs
• direct database tables
Examples:
• GA4 BigQuery event tables
• database event logs
• server-side tracking logs
Raw data represents:
→ what actually happened at the event level
Interface Data vs Raw Data
Interface Data
Characteristics:
• aggregated
• sampled
• estimated
• simplified
Advantages:
• fast
• easy to interpret
• accessible
Limitations:
• may not be exact
• may differ over time
• may hide small differences
Raw Data
Characteristics:
• unaggregated
• event-level
• exact counts
• fully queryable
Advantages:
• high accuracy
• full flexibility
• complete visibility
Limitations:
• requires technical querying
• higher complexity
• requires interpretation
Raw Data Requirement Rule
Raw data must be used when:
• analysing A/B tests
• validating measurement accuracy
• investigating anomalies
• resolving discrepancies
• validating attribution differences
• making high-impact decisions
Interface data may be used for:
• trend monitoring
• directional insights
• high-level reporting
🔴 Critical Use Case — Experimentation
A/B testing requires precise data.
Small differences in:
• conversions
• users
• events
can change test outcomes.
If interface data is:
• sampled
• thresholded
• estimated
→ test results may be incorrect
Therefore:
→ raw data is required for experiment validation
🔴 Critical Use Case — Discrepancy Resolution
When systems disagree:
• GA4 vs Ads
• analytics vs backend
• dashboards vs reports
Raw data must be used to:
• identify source of discrepancy
• validate counts
• confirm correct behaviour
Raw Data Access Methods
1. Data Warehouse Access
Primary method for raw data access.
Examples:
• BigQuery
• data warehouses
• internal databases
Provides:
• full event logs
• structured tables
• query flexibility
2. Query-Based Access
Data is retrieved using structured queries.
Examples:
• SQL queries
• filtered extraction
• aggregation queries
Allows:
• precise data selection
• custom segmentation
• experiment analysis
3. Export Pipelines
Raw data may be accessed through:
• automated exports
• streaming pipelines
• data integrations
Purpose:
• continuous data availability
Raw Data Structure Awareness
Raw data is often structured as:
• event logs
• timestamped records
• nested parameter structures
Important considerations:
• events may contain multiple parameters
• parameters must be extracted correctly
• data may require transformation
Incorrect interpretation of raw structure leads to incorrect analysis.
🔴 Nested Data Rule
Raw data may contain nested structures.
Examples:
• key-value parameters
• event attributes stored within columns
These require:
→ correct extraction logic
If nested data is not handled properly:
→ analysis results become incorrect
Raw Data Validation Rule
Raw data must still be validated.
Raw does not mean automatically correct.
Validation includes:
• event integrity checks
• duplication checks
• missing data checks
• timestamp validation
• session validation
Raw Data Limitations
Raw data may still have limitations:
• tracking gaps
• missing events
• system-level visibility gaps
• delayed data availability
• export limitations
Raw data improves accuracy, but does not eliminate all limitations.
Data Latency Consideration
Raw data may not be immediately complete.
Examples:
• delayed exports
• backfilled data
• streaming vs batch differences
Recent data must be treated cautiously.
Cost Awareness Rule
Raw data querying may incur:
• compute costs
• storage costs
Queries must be:
• efficient
• targeted
• controlled
Automation and Monitoring Integration
Raw data should feed into:
• automated reporting systems
• dashboards
• experiment monitoring
• decision surfaces
Automation reduces manual querying.
Relationship to Other Frameworks
Supports:
• Data Brain Data Trust Framework
• Data Brain Measurement Integrity Framework
• Data Brain Attribution Reliability Framework
• Data Brain Signal Flow Framework
• Experimentation Brain Statistical Confidence Framework
Failure Modes Prevented
relying on estimated data
incorrect test conclusions
misinterpreting small differences
ignoring discrepancies
over-trusting interface reports
Drift Protection
The system must prevent:
• reliance on interface data for critical decisions
• ignoring raw data availability
• degradation of query accuracy
• incorrect interpretation of event structures
Architectural Intent
The Data Brain Raw Data Access Framework ensures MWMS operates with:
→ evidence-based decision inputs
It transforms:
analytics consumption → data interrogation
Final Rule
If decision accuracy requires precision:
→ raw data must be used
Change Log
Version: v1.0
Date: 2026-04-23
Author: Data Brain
Change:
Initial creation of Raw Data Access Framework defining how MWMS accesses and uses event-level data for accurate decision-making.
Change Impact Declaration
Pages Created:
Data Brain Raw Data Access Framework
Pages Updated:
None
Pages Deprecated:
None
Registries Requiring Update:
MWMS Architecture Registry
Canon Version Update Required:
No
Change Log Entry Required:
Yes