LLM Interaction Metrics for Automated Data Governance
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Solution Overview
Problem
Data governance becomes challenging due to increased user diversity, data ownership turnover, and limited bandwidth of data architects, leading to data silos and inefficiencies in data maintenance and resource utilization.
Innovation Solution
An LLM-powered system that collects interaction information from users to determine metrics on data structures, enabling automated data governance actions without substantive input from data architects, thereby reducing data redundancy and silos.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data governance is performed manually by data architects, then data structures can be maintained with expert oversight, but the process becomes inefficient and creates data silos due to limited bandwidth and user diversity
Solution Approach 1:
The system enables data structures to self-govern by automatically monitoring their own usage metrics and performance characteristics. Data structures autonomously identify when they become redundant or siloed based on interaction patterns, and automatically trigger governance actions without requiring manual intervention from data architects, thus resolving the contradiction between efficiency and complexity
Solution Approach 2:
The system implements continuous feedback loops where interaction information from users is collected, analyzed to determine usage metrics, and fed back to automatically adjust data structure governance. This feedback mechanism enables the system to adapt dynamically to changing usage patterns, improving productivity while maintaining manageable complexity through automated decision-making
2Reliability
If automated data governance is implemented using interaction information, then reliance on data architects is reduced and errors are minimized, but the system requires complex metrics computation and governance action determination
Solution Approach 1:
The system introduces an intermediary layer of usage metric computation that bridges raw interaction information and governance decisions. This intermediary automatically processes interaction data to determine usage metrics, which then guide governance actions, reducing the need for complex human judgment while maintaining reliability through systematic, error-minimized automated processes
Solution Approach 2:
The automated system performs self-governance by autonomously computing its own usage metrics from interaction information and determining appropriate governance actions. This self-service capability reduces reliance on external data architects and minimizes human-induced errors, while the systematic automated process manages complexity through consistent, rule-based decision-making
3Measurement precision
If user interactions are extensively monitored to determine data structure metrics, then data governance decisions become more accurate, but the amount of interaction information collected and processed increases
Solution Approach 1:
The system extracts only the essential interaction information needed to determine usage metrics, rather than monitoring all possible user interactions. By selectively extracting relevant usage patterns and behaviors, the system achieves accurate measurement of data structure utilization while minimizing the volume of interaction information that must be collected, stored, and processed
Data Source
AI summary
A system for interaction-based data governance may obtain interaction information associated with a data structure in a plurality of data structures. The interaction information may be associated with user interactions with the data structure enabled using a large language model (LLM). The system may determine a metric associated with the data structure based on the interaction information associated with the user interactions. The system may perform a data governance action associated with the data structure based on the metric associated with the data structure.