Zone-Based Database Management for Automated Data Governance
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Solution Overview
Problem
Current data governance systems lack an efficient and automated process for managing and governing digital assets, leading to inefficiencies and increased risk of data misuse.
Innovation Solution
The implementation of a zone-based database management system that categorizes datasets into distinct zones (transient, raw, trusted, refined, and analytical workspace) with predefined policies for data processing and governance, allowing for streamlined data management and governance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data governance systems are used, then data can be managed with basic policies, but the process is inefficient and manual, increasing the risk of data misuse
Solution Approach 1:
The system divides data into distinct zones (raw zone, trusted zone, refined zone, analytical workspace) with different access levels and governance policies. This segmentation enables automated policy enforcement at each zone boundary, improving both efficiency through structured workflows and reliability through enforced compliance
Solution Approach 2:
The system automatically enforces data governance policies by monitoring data movement between zones and applying predefined rules without manual intervention. The automated workflow includes policy validation, access control enforcement, and compliance verification, reducing manual overhead while maintaining high compliance standards
2Loss of time
If manual data governance processes are used, then flexibility in policy creation is maintained, but the time required for data management increases
Solution Approach 1:
Governance policies are predefined and configured in advance for each data zone before data ingestion occurs. The system automatically applies these pre-established policies to incoming data, eliminating the need for manual policy creation during data management operations and significantly reducing processing time
Solution Approach 2:
The system provides automated feedback on policy compliance status at each zone boundary, allowing operators to quickly identify and resolve issues. This feedback mechanism maintains operational simplicity by automatically guiding data management decisions while reducing the time required for manual intervention
3Reliability
If data moves freely between storage locations, then data accessibility is improved, but the risk of improper data usage increases
Solution Approach 1:
The system introduces governance policies as intermediary controls between data zones. Each zone boundary acts as a mediator that automatically evaluates access requests against predefined policies, enabling secure data movement while maintaining convenience through automated decision-making
Solution Approach 2:
The system dynamically adjusts data access permissions based on the source zone, destination zone, and user authentication status. This dynamic access control maintains data security by adapting policies to specific contexts while preserving ease of operation through automated policy application without manual configuration
Data Source
AI summary
Systems and methods are disclosed for using AI and machine learning to generate a dataset zone by receiving a plurality of datasets and storing them in a data catalog. A first, second, third, and fourth zone are generated having various levels of policies and permissions. Based on the known policies, the system trains a machine learning program to generate a trained predictive model and deploys the trained predictive model to predict a data privacy policy for a first dataset from the plurality of datasets. The predictive model further predicts a suitable zone for the first dataset based on the predicted data privacy policy and stores the first dataset in the suitable zone. The system then displays a representation of the first dataset in the suitable zone on a graphical user interface.