Data Governance Service With Automated Policy Violation Alerts
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Solution Overview
Problem
Traditional data governance systems struggle to keep up with the rapid changes in cloud data storage and agile development, leading to inefficiencies, compliance issues, and increased penalties due to reactive governance approaches, lack of clear roles and responsibilities, and inadequate data ownership models.
Innovation Solution
Implementing Data Governance as a Service (DGaaS) using trained machine learning models to centralize data governance, automate processes, and enforce policies across multiple data subsets, ensuring proactive management and standardized data ownership.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional reactive data governance approaches are used, then implementation simplicity is maintained, but compliance reliability deteriorates due to inability to keep up with rapid changes in cloud data storage and agile development
Solution Approach 1:
The patent implements dynamic data governance by transitioning from static, reactive policies to continuous, adaptive governance. The system continuously monitors data flows, dynamically applies policies based on real-time conditions, and adapts to changing cloud storage environments and agile development patterns, ensuring compliance reliability without requiring overly complex manual intervention
Solution Approach 2:
The patent enables self-service data governance through automated policy application and continuous monitoring. The system autonomously identifies compliance requirements, applies appropriate governance policies, and enforces them across data subsets without requiring constant human intervention, thereby improving compliance reliability while managing system complexity
2Productivity
If centralized automated data governance is implemented, then productivity is improved through automation, but device complexity increases due to the need for trained machine learning models and centralized infrastructure
Solution Approach 1:
The patent implements a universal centralized governance system that handles multiple data subsets across different cloud storage environments and agile development workflows. The system provides multi-functional capabilities including policy management, compliance monitoring, and automated enforcement through a single unified platform, improving governance efficiency while consolidating rather than multiplying complexity
Solution Approach 2:
The patent replaces manual, mechanical governance processes with automated machine learning-based systems. Trained models automatically analyze data patterns, apply policies, and enforce compliance, substituting human-driven mechanical processes with intelligent automation that improves productivity while managing infrastructure complexity through centralized intelligence
3Loss of time
If reactive governance approaches are used, then ease of operation is maintained, but loss of time increases due to inability to proactively manage data compliance
Solution Approach 1:
The patent implements preliminary action by continuously monitoring data flows and proactively applying governance policies before compliance violations occur. The system anticipates potential issues, pre-configures appropriate policies, and enforces them in advance, dramatically reducing compliance response time while maintaining operational simplicity through automated processes that require minimal manual intervention
Data Source
AI summary
Systems and methods for performing data governance as a service is disclosed and may include onboarding a database to a governance as a service system, the database including at least one data set, executing, via the data governance as a service system, at least one core capability, wherein the at least one core capability is applicable to the at least one data set, determining data governance metadata for the at least one data set, wherein the data governance metadata is a subset of a data set metadata, applying at least one policy rule of the core capability to the data governance metadata, determining a violation based on applying the at least one policy rule, generating a first alert based on the violation, and transmitting the first alert to a designated system.