Governed Placement of Analytic Results via Trust Metadata
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Solution Overview
Problem
Existing data analytics result placement is highly ungoverned, leading to potential violations of regulatory requirements, security issues, and lack of compliance with trust and veracity standards, as data scientists and engineers often prioritize exploration over governance and compliance.
Innovation Solution
The implementation of a method that utilizes metadata associated with input data sets to determine a governed placement for analytic results, incorporating veracity scores and trust indices to ensure compliant storage on trusted infrastructure, with a governance selector input to manage placement decisions, thereby ensuring auditable and provable compliance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data analytic results are placed in analytic sandboxes or data lakes for further action, then data exploration and analysis capability is improved, but governance and compliance with trust requirements deteriorates
Solution Approach 1:
The system performs preliminary actions by evaluating trust and veracity metadata of input data sets before analytic processing occurs. Governance selectors are pre-configured to determine placement decisions based on trust requirements, ensuring that data is prepared and categorized before it enters the analytic sandbox or data lake, thereby maintaining governance while enabling exploration.
Solution Approach 2:
The patent introduces an intermediary governance placement system that sits between data source and analytic sandbox/data lake. This intermediary evaluates trust metadata and veracity scores, then mediates the placement decision by routing data to appropriate locations based on governance selectors, thus maintaining both exploration capability and compliance.
2Productivity
If data scientists and engineers focus on exploration and experimentation, then analytical productivity is improved, but compliance with regulatory requirements and security standards deteriorates
Solution Approach 1:
The system implements feedback mechanisms where trust metadata and veracity scores are continuously evaluated during the analytic process. Governance selectors provide feedback on compliance status, and the system automatically adjusts placement decisions or blocks processing when trust requirements are not met, thereby maintaining productivity while preventing regulatory non-compliance.
Solution Approach 2:
The governance placement system operates autonomously by automatically evaluating trust metadata and making placement decisions without requiring manual intervention from data scientists. The system self-regulates based on pre-configured governance selectors and trust thresholds, allowing analysts to focus on exploration while compliance is handled automatically.
3Reliability
If trust metadata and veracity scores are evaluated for each data set, then compliance with trust requirements is improved, but system complexity and processing overhead increases
Solution Approach 1:
The governance placement system is designed as a universal multi-functional component that handles multiple tasks: evaluating trust metadata, calculating veracity scores, determining placement decisions, and enforcing governance policies. By consolidating these functions into a single system, the patent reduces overall complexity compared to having separate systems for each function.
Data Source
AI summary
Metadata respectively associated with one or more input data sets processed by one or more analytic applications is obtained. The metadata for each data set is indicative of at least one of trust and veracity associated with the data set. The one or more analytic applications generate analytic results based on the one or more input data sets. A governed placement is determined for at least the analytic results based on at least a portion of the obtained metadata.