Automated Metatagging for Enterprise Data Elements
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current data tagging systems in enterprises lack efficiency in automatically characterizing and managing data elements, particularly in applying relevant metatags based on access metrics and identifiers, and often require manual intervention for validation and application.
Innovation Solution
A method and system for characterizing data elements by ascertaining access metrics and data identifiers to automatically apply or recommend metatags, with owner validation and database maintenance for access and metadata, enabling automated metatag application and recommendation across an enterprise file system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual intervention is used for metatag application and validation, then accuracy and control are improved, but productivity and time efficiency deteriorate
Solution Approach 1:
The system performs preliminary actions by automatically applying metatags to data elements before human review. The automated metatagging system analyzes data characteristics, access metrics, and identifiers to pre-apply appropriate metatags, which then require only human validation rather than complete manual application, thus improving both productivity and accuracy.
Solution Approach 2:
The system implements feedback mechanisms where automatically applied metatags are submitted for owner validation. The feedback loop allows data owners to review and correct automated metatagging decisions, ensuring accuracy while maintaining high throughput. This feedback mechanism resolves the contradiction by combining automated efficiency with human oversight for precision.
2Productivity
If automated metatag application is implemented, then productivity is improved, but reliability and control deteriorate
Solution Approach 1:
The system enables self-service through automated metatagging that operates independently without continuous human intervention. The automated system serves itself by analyzing data elements, access metrics, and identifiers to apply metatags autonomously, improving productivity while the built-in validation mechanism ensures reliability through owner confirmation.
Solution Approach 2:
The feedback mechanism allows data owners to validate and correct automated metatagging decisions. This feedback loop ensures reliability by catching errors in automated metatag application, while the system maintains high productivity by processing large volumes of data elements that would be impractical to manually tag, thus resolving the contradiction between speed and accuracy.
3Reliability
If comprehensive data characterization is performed, then data organization and security are improved, but device complexity and implementation difficulty worsen
Solution Approach 1:
The system achieves multi-functionality by using a unified automated metatagging platform that handles multiple tasks: data characterization, access metric analysis, metatag application, and security enhancement. This universal approach consolidates what would otherwise require multiple separate systems into a single integrated solution, improving data organization and security while managing implementation complexity through consolidation rather than multiplication of components.
Data Source
AI summary
A method for characterizing data elements in an enterprise including ascertaining at least one of an access metric and a data identifier for each of a plurality of data elements and employing the at least one of an access metric and a data identifier to automatically apply a metatag to ones of the plurality of data elements.