Consent Data Pipeline Architecture for Cross-Source Harmonization
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Solution Overview
Problem
Existing systems struggle to efficiently manage and harmonize customer consent data across multiple sources, leading to difficulties in ensuring compliance with privacy regulations and honoring customer preferences in data processing.
Innovation Solution
A consent data pipeline system that harmonizes consent data from various sources into a standardized model, enabling seamless integration with customer data platforms and applying consent rules to data workflows, ensuring compliance and customer consent is honored.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If consent data is stored in multiple sources with different formats, then data collection capability is improved, but data harmonization difficulty increases
Solution Approach 1:
The patent introduces a consent data pipeline as an intermediary system that includes a mapping component. This pipeline receives consent data from multiple sources in different formats, maps the data to a standardized schema, and outputs harmonized data. The mapping component acts as a mediator that translates various source formats into a common structure, resolving the contradiction between collecting diverse data and maintaining data consistency.
Solution Approach 2:
The patent applies parameter changes by transforming the format and structure of consent data through the mapping process. The mapping component changes the parameters of the input data (format, schema, structure) to match the target standardized format, enabling data from multiple sources to be harmonized while preserving the original collection capabilities.
2Ease of operation
If consent data is mapped to standardized schema, then data usability is improved, but mapping configuration complexity increases
Solution Approach 1:
The patent implements preliminary action by pre-defining a standardized consent data schema and mapping configurations before actual data processing occurs. The mapping between source formats and the standardized schema is configured in advance, allowing the pipeline to automatically process incoming data without requiring complex real-time configuration decisions.
3Reliability
If consent rules are applied to workflows, then compliance accuracy is improved, but workflow processing time increases
Solution Approach 1:
The patent applies preliminary action by pre-processing consent data through the mapping pipeline before it enters workflows. The consent data is harmonized and validated in advance, so when workflows need to access consent information, the data is already in the correct format and ready for use, minimizing additional processing time while maintaining compliance accuracy.
Data Source
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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.