Unified Data Catalog for Enterprise Compliance Tracking
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Solution Overview
Problem
Existing data management systems for enterprises lack transparency and accountability, particularly for Global Systemically Important Banks (G-SIB) that need to comply with strict data regulation requirements to avoid financial crises and maintain system stability.
Innovation Solution
A computing system with a unified data catalog that utilizes platform and vendor agnostic APIs to collect metadata, data use cases, and governance policies, allowing for the creation of data domains aligned with regulatory requirements and providing tools for executives to track compliance and defects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a unified data catalog with automated framework is implemented, then transparency and compliance tracking are improved, but device complexity increases
Solution Approach 1:
The patent introduces a unified data catalog as an intermediary layer between raw data sources and executive decision-making. This catalog aggregates metadata, data quality metrics, and compliance information from multiple sources, providing a single point of access that improves transparency without requiring executives to directly manage the complexity of underlying data infrastructure.
Solution Approach 2:
The automated data management framework implements self-service capabilities by automatically collecting metadata, assessing data quality, tracking compliance status, and generating reports without requiring manual intervention. This automation handles the complexity internally while presenting simplified transparency metrics to users.
2Reliability
If strict data management practices are imposed to meet regulatory requirements, then reliability is improved, but ease of operation deteriorates
Solution Approach 1:
The system implements continuous feedback mechanisms that automatically monitor data quality and compliance status across the enterprise. By providing real-time feedback on compliance metrics and automatically adjusting data management processes, the system maintains high reliability while reducing the operational burden through automated monitoring rather than manual compliance checking.
Solution Approach 2:
The framework performs preliminary actions by pre-establishing data governance policies, pre-assessing data quality against compliance requirements, and proactively identifying potential compliance issues before they become problems. This advance preparation ensures reliability while simplifying operations by preventing issues rather than requiring extensive manual intervention to address them.
3Reliability
If data is partitioned into multiple domains with executive accountability, then reliability is improved through accountability, but device complexity increases
Solution Approach 1:
The patent segments the enterprise data landscape into distinct data domains (e.g., customer data, financial data, operational data), each with designated executive accountability. This segmentation improves reliability by assigning clear ownership and responsibility for data quality and compliance within each domain, while the unified data catalog provides the infrastructure to manage this segmented structure without excessive complexity.
Data Source
AI summary
An example computing system includes: a memory storing a plurality of data assets; and a processing system of an enterprise, the processing system comprising one or more processors implemented in circuitry, the processing system being configured to: maintain a plurality of data domains, each of the data domains being managed by an executive of the enterprise, and each of the domains having one or more subdomains; maintain the one or more subdomains of each of the plurality of data domains, each of the plurality of data domains being associated with one or more data use cases, one or more data sources, and one or more risk accessible units; and track defects of the data assets in each of the plurality of data domains.