Zone-Based Data Governance with Automated Dataset Classification
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Solution Overview
Problem
Existing data governance systems lack efficient and automated processes for managing and governing digital assets, leading to unnecessary time and resource expenditure in understanding data interconnectedness without proper visual or structural representation.
Innovation Solution
Implementing a zone-based database management system that creates and manages dataset zones, including transient, raw, trusted, refined, and analytical workspace zones, with data pipelines and machine learning for automated data classification and policy enforcement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data governance processes are used without automated classification and zone-based management, then data can be managed with simpler systems, but time and resource expenditure increase unnecessarily and data interconnectedness cannot be properly understood
Solution Approach 1:
The patent segments data into different zones (raw zone, trusted zone, refined zone, analytical workspace zone) based on data quality, processing stage, and trust level. This segmentation enables automated classification and management, improving productivity while reducing time expenditure through systematic organization rather than manual analysis of data relationships.
Solution Approach 2:
The system implements automated data classification and zone assignment that operates without manual intervention. The automated classification engine automatically categorizes data and places it in appropriate zones, enabling self-service data management that improves efficiency and eliminates time-consuming manual processes for understanding data interconnectedness.
2Reliability
If manual data governance processes are used, then system complexity is reduced, but resource expenditure and risk of improper data usage increase
Solution Approach 1:
The patent implements feedback mechanisms where the automated classification engine continuously monitors data characteristics, processing stages, and quality metrics to dynamically adjust zone assignments. This feedback loop ensures compliance and security requirements are met while managing system complexity through automated decision-making rather than manual governance processes.
Solution Approach 2:
The system changes parameters such as data quality thresholds, processing stage indicators, and trust level metrics to automatically determine appropriate zone assignments. By monitoring and responding to parameter changes, the system maintains reliability and compliance while managing complexity through automated parameter-based classification rather than manual governance.
3Productivity
If data is not classified and organized into zones, then storage and access are simpler, but data asset value cannot be maximized and governance policies cannot be effectively enforced
Solution Approach 1:
The patent applies preliminary action by pre-defining zone structures, classification rules, and governance policies before data is ingested. The automated classification engine uses these pre-established parameters to immediately categorize and place data in appropriate zones upon arrival, maximizing data asset value and governance effectiveness without requiring complex post-ingestion management.
Solution Approach 2:
The zone-based management system serves multiple functions simultaneously: it classifies data, enforces governance policies, manages security permissions, tracks data lineage, and optimizes data access. This multi-functionality maximizes data asset value and governance effectiveness while the standardized zone structure prevents excessive complexity in management.
Data Source
AI summary
Systems and methods are disclosed for generating dataset zones, wherein a plurality of datasets from a plurality of sources are received and stored into a data catalog. The method further includes: (1) generating a first zone comprising a transient zone and configured for storing the sourced data from the plurality of datasets; (2) generating a second zone comprising a raw zone and configured for storing raw data generated from the sourced data after it has been ingested and organized; (3) generating a third zone comprising a trusted zone and configured for storing standardized data generated from the raw data after it has been ingested and organized according to one or more data governance policies; and (4) generating a fourth zone comprising a refined zone and configured for storing business-specific data generated from the standardized data.