Consent Data Pipeline Mapping for Cross-Source Compliance
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Solution Overview
Problem
Existing systems struggle to efficiently manage and honor customer consent data, particularly in compliance with evolving privacy regulations like GDPR and CCPA, leading to difficulties in mapping consent information to scenarios and actions.
Innovation Solution
A computerized consent data pipeline architecture that processes and maps customer consent data using a mapping UI, generates metadata based on rule selections, and applies consent rules to workflows, ensuring seamless integration with customer data platforms and AI model training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If consent data is collected and stored in traditional formats, then data collection capability is maintained, but the ability to map consent information to scenarios and actions becomes difficult
Solution Approach 1:
The consent data schema is segmented into standardized purpose-value pairs that can be independently mapped to different scenarios and actions. This segmentation allows flexible combination of standardized elements to create various consent mapping configurations without increasing overall system complexity.
Solution Approach 2:
The system changes the parameter representation of consent data from traditional raw formats to standardized purpose-value pairs. This parameter transformation enables consistent mapping across different scenarios while maintaining data integrity and reducing complexity in the mapping process.
2Quantity of substance
If multiple consent data sources are integrated, then data completeness improves, but mapping and processing complexity increases
Solution Approach 1:
The standardized purpose-value pair format serves as a universal representation that can accommodate consent data from multiple sources with different original formats. This universal schema allows the same processing logic to handle diverse data sources, reducing processing complexity while increasing data volume capability.
Solution Approach 2:
The standardized purpose-value pairs act as an intermediary layer between raw consent data from various sources and the final mapping to scenarios and actions. This intermediary transformation simplifies the processing complexity by providing a common representation format that bridges different data sources.
3Measurement precision
If detailed consent mapping is implemented, then compliance accuracy improves, but workflow configuration complexity increases
Solution Approach 1:
The consent mapping is segmented into discrete purpose-value pairs that can be independently configured and mapped. This segmentation allows precise control over compliance accuracy for each purpose while simplifying the overall configuration process through modular, independent mappings rather than complex monolithic configurations.
Data Source
AI summary
The disclosure herein describes processing consent data and using the processed consent data in workflows. Customer consent data is accessed, wherein the customer consent data includes subject consent instances including associated consent purpose-value pairs. The customer consent data is mapped to a raw consent data schema based on mapping selections made on a mapping UI, wherein the mapping includes mapping consent purpose-value pairs of the consent instances to data columns of the raw consent data schema. Metadata representing one or more consent rules related to the raw consent data schema is generated based on rule selections made on a rule configuration UI and the consent rules are applied to one or more workflows. The disclosure enables consent data in different formats and/or from different sources to be ingested and standardized in a single platform such that consent checking functionality can be provided for applications in a consistent and comprehensive manner.