Document Type: Framework
Status: Active
Version: v1.0
Authority: Data Brain
Parent: Data Brain
Last Reviewed: 2026-04-25
Purpose
This framework defines how data must be segmented within MWMS to enable meaningful analysis and decision-making.
Segmentation ensures that insights are derived from structured breakdowns rather than aggregated totals.
Core Principle
Insights do not come from totals.
Insights come from segmented data.
All analysis must operate on structured segments.
Segmentation Requirement
All datasets must be segmented across relevant dimensions.
Typical segmentation dimensions include:
• traffic source
• campaign
• creative / angle
• device
• geography
• funnel stage
• time period
Segmentation Rules
• no decision may be made from aggregated totals alone
• segmentation must align with decision intent
• segmentation must remain consistent across comparisons
• segmentation must be defined before analysis
Application Across MWMS
Affiliate Brain
• compare offers across segments
• identify opportunity patterns
Ads Brain
• evaluate creative performance by segment
• identify winning angles
Experimentation Brain
• analyze test results by segment
• detect hidden performance differences
Data Brain
• enforce segmentation consistency
• define segmentation structure
Failure Conditions
The system is considered invalid if:
• only total data is used
• segmentation is inconsistent
• segments are undefined
• segmentation is applied after decision-making
Outcome
When applied correctly:
• insights become clearer
• decisions become more accurate
• hidden opportunities become visible
End of Framework
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Data Brain Data Cleaning Protocol
Document Type: Protocol
Status: Active
Version: v1.0
Authority: Data Brain
Parent: Data Brain
Last Reviewed: 2026-04-25
Purpose
This protocol ensures all data entering MWMS is clean, consistent, and usable before analysis or decision-making.
Core Principle
No clean data → no valid decision.
Cleaning Requirements
All datasets must be processed to:
• remove duplicates
• correct formatting inconsistencies
• eliminate irrelevant entries
• standardize structure
Cleaning Actions
Duplicate Removal
• detect duplicate entries
• remove or consolidate duplicates
Data Structure Correction
• split combined fields
• normalize formats
• ensure consistency
Filtering
• remove irrelevant data
• isolate meaningful data
Enforcement Rule
No dataset may proceed to:
• evaluation
• measurement planning
• testing
without passing cleaning validation.
Failure Conditions
• duplicate data present
• inconsistent structure
• unfiltered noise
• unclear dataset meaning
Outcome
• reliable datasets
• improved decision quality
• reduced system errors
End of Protocol
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Data Brain Data Linking Framework
Document Type: Framework
Status: Active
Version: v1.0
Authority: Data Brain
Parent: Data Brain
Last Reviewed: 2026-04-25
Purpose
Defines how datasets are connected within MWMS to enable cross-system analysis.
Core Principle
Data has value when it is connected.
Linking Mechanism
All data linking must be based on:
• shared identifiers
• consistent keys
• structured relationships
Examples
• campaign ID → performance data
• offer ID → conversion results
• creative ID → engagement data
Linking Rules
• linking keys must be consistent
• no ambiguous joins
• no manual linking without structure
Application
Affiliate Brain
• connect offers to performance
Ads Brain
• connect creatives to results
Experimentation Brain
• connect tests to outcomes
Failure Conditions
• disconnected datasets
• inconsistent identifiers
• manual linking errors
Outcome
• unified data view
• improved decision-making
• scalable system intelligence
End of Framework
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Data Brain Error Integrity Framework
Document Type: Framework
Status: Active
Version: v1.0
Authority: Data Brain
Parent: Data Brain
Last Reviewed: 2026-04-25
Purpose
Defines how MWMS prevents, detects, and manages data errors.
Core Principle
Uncontrolled errors lead to invalid decisions.
Error Types
• input errors
• calculation errors
• structural errors
• data consistency errors
Error Prevention
Input Validation
• restrict allowed values
• enforce ranges
• enforce formats
System Constraints
• predefined options
• controlled inputs
• locked fields where necessary
Error Detection
Cross Checks
• validate totals vs breakdowns
• compare expected vs actual
Logical Validation
• use conditional checks
• detect inconsistencies
Error Handling
• replace raw errors with controlled outputs
• display meaningful messages
• prevent propagation
Enforcement Rules
• no raw system errors exposed
• all errors must be handled
• all critical data must be validated
Outcome
• reliable data
• safer decision-making
• reduced system risk
End of Framework
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Data Brain Signal Highlighting System
Document Type: Framework
Status: Active
Version: v1.0
Authority: Data Brain
Parent: Data Brain
Last Reviewed: 2026-04-25
Purpose
Defines how important data signals are visually emphasized within MWMS.
Core Principle
Important data must be instantly visible.
Signal Types
• high performance
• low performance
• anomalies
• risk indicators
Highlighting Methods
• color coding
• visual markers
• threshold indicators
Rules
• highlighting must be consistent
• thresholds must be defined
• avoid excessive visual noise
Application
• performance dashboards
• evaluation screens
• reporting outputs
Failure Conditions
• important data hidden
• inconsistent highlighting
• visual overload
Outcome
• faster insight recognition
• clearer decision-making
• reduced analysis time
End of Framework
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Data Brain Text Processing Framework
Document Type: Framework
Status: Active
Version: v1.0
Authority: Data Brain
Parent: Data Brain
Last Reviewed: 2026-04-25
Purpose
Defines how text-based data is processed and structured within MWMS.
Core Principle
Unstructured text must be converted into structured data.
Use Cases
• URL parsing
• naming normalization
• campaign structure extraction
• data cleanup
Processing Methods
• extraction
• replacement
• splitting
• formatting
Rules
• text must be standardized
• structures must be consistent
• outputs must be usable for analysis
Failure Conditions
• inconsistent naming
• unstructured text
• unusable data
Outcome
• structured data
• improved analysis capability
• better system integration