Automated Parameter Adjustment for Data Policy Compliance
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Solution Overview
Problem
In enterprise data systems, the lack of data sharing between internal groups within a corporation leads to resource wastage and lower consumer satisfaction due to data providers' reluctance to share data, hindering a streamlined and efficient environment.
Innovation Solution
A system that automatically modifies computer parameters based on data usage metrics and desired vectors by calculating scores and providing incentives or penalties to clients, such as financial credits, bandwidth changes, or priority adjustments, to encourage compliance with data policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data providers safeguard their data and do not share with other providers, then data security and control are improved, but data sharing and resource utilization deteriorate
Solution Approach 1:
The system dynamically adjusts computer parameters (bandwidth, priority levels, financial credits) based on real-time scoring of data usage behavior. This dynamic adjustment creates flexible incentives that adapt to each provider's compliance level, transforming the static data sharing environment into a responsive system that rewards sharing while maintaining security controls.
Solution Approach 2:
The patent changes key parameters of the computing system associated with data providers based on their compliance scores. Parameters such as bandwidth allocation, priority levels, and financial credits are modified to incentivize desired data usage behaviors. This parameter-based control mechanism resolves the contradiction by allowing security policies to remain intact while productivity improves through incentivized compliance.
2Productivity
If data is shared between providers to reduce resource wastage, then resource utilization is improved, but data hygiene and security control deteriorate
Solution Approach 1:
The system implements a feedback mechanism where data usage behavior is continuously monitored, scored against desired vectors, and used to adjust computer parameters. This closed-loop feedback ensures that data hygiene standards are maintained while resource utilization improves, as providers receive real-time feedback on their compliance and adjust their data sharing practices accordingly.
Solution Approach 2:
The patent replaces manual data hygiene control mechanisms with an automated scoring and parameter adjustment system. Instead of relying on manual review and control of shared data, the system uses automated metrics, scoring algorithms, and dynamic parameter changes to enforce data hygiene standards while enabling broader data sharing for improved resource utilization.
3Reliability
If manual monitoring and control of data usage is implemented, then data policy compliance is improved, but system complexity and operational overhead increase
Solution Approach 1:
The system enables self-service compliance monitoring where data providers are automatically scored based on their usage patterns against predefined desired vectors. The computer parameters are automatically adjusted based on these scores, eliminating the need for manual monitoring and reducing system complexity. The automated feedback loop handles compliance enforcement without human intervention.
Solution Approach 2:
The patent uses parameter changes as an automated control mechanism rather than manual processes. By modifying computer parameters (bandwidth, priority, financial credits) based on automated scoring, the system achieves policy compliance with minimal complexity. The parameter-based control system replaces complex manual monitoring procedures with simple, automated adjustments.
Data Source
AI summary
One embodiment of the present invention provides a system for automatically modifying computer parameters as an incentive for complying with data policies. During operation, the system receives, at a computing system, a data policy definition, wherein the data policy definition comprises: one or more metrics associated with data usage, and one or more desired vectors associated with the one or more metrics. Next, the system receives, at the computing system, usage statistics for data, wherein the usage statistics comprise the one or more metrics for one or more clients. The system then analyzes the usage statistics to determine, at the computing system, a first score indicating how a first client in the one or more clients is performing compared to the one or more desired vectors for the one or more metrics. Finally, the system automatically modifies a parameter of the computing system associated with the first client based on the first score.