Data Valuation via Data Protection Ecosystem Analytics
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Solution Overview
Problem
Existing data valuation techniques face limitations such as high processing loads on production environments, inability to track data lifecycle, disconnect from business processes, lack of change frequency tracking, and inability to calculate IT investment in data protection, which affect the accuracy and efficiency of data valuation.
Innovation Solution
The use of a data protection ecosystem to access backup data, metadata, and analytics results, allowing for data valuation calculations that incorporate data protection metadata and analytics, providing richer valuation insights by tracking user provenance, data lifecycle, and infrastructure investment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data valuation is performed using traditional semantic content analysis, then valuation insight is obtained, but processing load on production environments increases significantly
Solution Approach 1:
The patent extracts the data valuation processing from the production environment by utilizing data already present in the data protection ecosystem (backup data, metadata, analytics results). This separation allows valuation calculations to be performed using existing protection infrastructure data without imposing additional processing loads on production systems.
Solution Approach 2:
The patent uses copies of data that already exist in the data protection ecosystem (backup copies, metadata copies, analytics result copies) to perform valuation analysis. These copies are already maintained for protection purposes, so reusing them for valuation avoids additional processing of original production data.
2Measurement precision
If data valuation only considers semantic content, then valuation is obtained, but connection to business processes and data lifecycle is lost
Solution Approach 1:
The patent merges multiple data sources from the data protection ecosystem including backup data, metadata, and analytics results to perform comprehensive valuation. This combination integrates semantic content analysis with data lifecycle information, protection status, and usage patterns to provide richer valuation insights.
Solution Approach 2:
The data protection ecosystem serves multiple functions: it provides data protection (backup and recovery) while simultaneously enabling data valuation analytics. The same infrastructure and data that support protection functionalities are leveraged to generate valuation insights, maximizing the utility of existing systems.
3Measurement precision
If detailed data protection metadata is collected for valuation, then valuation accuracy improves, but system complexity increases
Solution Approach 1:
The data protection ecosystem automatically generates and maintains the metadata and analytics results needed for valuation as part of its normal operation. The system self-provides the necessary data (backup status, protection metadata, analytics results) without requiring additional complex collection mechanisms, thereby improving valuation accuracy without proportionally increasing system complexity.
Data Source
AI summary
A data protection ecosystem-based data valuation methodology includes the following steps. One or more of backup data, metadata, and analytics results maintained by a data protection ecosystem are accessed. The backup data, metadata, and analytics results are obtained during the course of the data protection ecosystem providing data backup and recovery functionalities for a data storage environment that stores one or more data sets. A valuation is calculated for at least one of the one or more data sets of the data storage environment based on at least a portion of the accessed backup data, metadata, and analytics results maintained by the data protection ecosystem.