Zone-Based Database Governance for 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 includes transient, raw, trusted, refined, and analytical workspace zones, with data pipelines and machine learning algorithms to automate data management and ensure compliance with predefined policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data governance processes are used without automated systems, then data management can be performed with existing tools, but time consumption and resource expenditure increase significantly
Solution Approach 1:
The system enables self-service data governance through automated classification algorithms that independently analyze datasets, determine their characteristics, and assign them to appropriate zones without requiring manual intervention. The machine learning models automatically update and refine their classification capabilities, allowing the system to serve itself and reduce dependency on human resources for routine data management tasks.
Solution Approach 2:
The patent replaces manual mechanical processes of data classification and governance with automated computational systems. Machine learning algorithms and neural networks substitute for human analysts in determining data characteristics, classification, and zone assignment, dramatically reducing the time and effort required while maintaining or improving accuracy.
2Extent of automation
If manual data classification methods are used, then existing algorithms can be applied, but the system lacks automated governance capabilities and requires significant human intervention
Solution Approach 1:
The system segments the complex data classification task into distinct operational zones (transient, raw, trusted, refined) with specific characteristics and requirements. Each zone has defined entry criteria and transformation rules, breaking down the overall complexity into manageable, automated stages that can be processed independently by specialized algorithms.
Solution Approach 2:
The patent introduces machine learning models and neural networks as intermediary components between raw data and final classification decisions. These intermediaries automatically analyze data characteristics, apply classification rules, and determine appropriate zone assignments, reducing the need for direct human intervention while managing system complexity through layered processing.
3Reliability
If data is stored without structured zone classification, then storage simplicity is maintained, but data security and compliance cannot be effectively ensured
Solution Approach 1:
The system applies local quality by assigning different security levels, access controls, and governance policies to different zones based on their specific characteristics. Each zone (transient, raw, trusted, refined) has tailored security measures and compliance rules appropriate to its data sensitivity and processing stage, rather than applying a uniform approach to all data.
Solution Approach 2:
The patent adds a structural dimension to data storage by organizing data across multiple zones with defined relationships and transformation pathways. This dimensional organization introduces security and compliance controls at each level while maintaining overall system manageability through the structured progression from raw to refined data states.
4Reliability
If comprehensive data governance policies are implemented, then data quality and compliance improve, but the time and resources required for implementation increase
Solution Approach 1:
The system performs preliminary classification and zone assignment automatically as data enters the system, establishing governance frameworks and quality controls in advance rather than applying them retroactively. Machine learning models pre-analyze data characteristics and determine appropriate governance policies before data processing begins, reducing implementation time while maintaining quality standards.
Solution Approach 2:
The patent implements continuous automated governance through ongoing machine learning model execution that constantly monitors, classifies, and manages data across zones. This continuous automated action maintains data quality and compliance without requiring periodic manual interventions, thereby improving both reliability and implementation speed.
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.