Inactive Data Identification via Access Pattern Monitoring
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Solution Overview
Problem
Organizations face challenges in identifying and managing inactive data, also known as 'dark data,' which is not actively consumed and can provide valuable insights or business benefits, due to subjective methods and lack of reliable quantification tools.
Innovation Solution
A system that monitors data access across data sources, generates data access information, and applies profiles to identify inactive data based on time intervals and other criteria, providing notifications for unused data, thereby enabling its reuse and optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If organizations store all corporate data without regard to derived business value, then the quantity of stored data increases, but the loss of information increases due to inability to identify and reuse valuable inactive data
Solution Approach 1:
The system performs preliminary analysis of data access patterns before data becomes truly inactive. By monitoring and analyzing access metadata over time intervals, the system proactively identifies data that is likely to be inactive, allowing organizations to retain potentially valuable data longer while still managing storage efficiently.
Solution Approach 2:
The system implements continuous feedback loops by monitoring data access patterns and updating the inactive data identification process. Access metadata is continuously collected and analyzed, providing feedback that refines the identification of inactive data over time, ensuring that valuable data is not mistakenly classified as inactive.
2Device complexity
If subjective methods are used to identify inactive data, then the device complexity is reduced, but the reliability of identification deteriorates due to user bias and lack of quantified utilization methods
Solution Approach 1:
The system enables self-service identification of inactive data by automatically analyzing access patterns without requiring manual input from IT personnel. The system autonomously collects access metadata, applies analysis algorithms, and generates notifications about inactive data, eliminating user bias while maintaining operational simplicity.
Solution Approach 2:
The system replaces manual subjective judgment with automated computational analysis. Instead of relying on IT personnel's subjective assessment, the system uses objective algorithms to analyze access metadata and determine data inactivity, significantly improving reliability while keeping the system manageable.
3Loss of substance
If traditional storage cost constraints limit data storage, then the loss of substance increases due to inability to preserve potentially valuable data, but the quantity of stored data must be controlled
Solution Approach 1:
The system changes the parameter used to determine data retention from cost-based to value-based metrics. By analyzing access patterns and determining data inactivity through objective criteria rather than storage cost constraints, the system identifies which data to retain based on potential business value rather than budget limitations.
Data Source
AI summary
According to embodiments of the present invention, machines, systems, methods and computer program products for analyzing data sources for inactive data are presented. Data accesses within one or more data sources are monitored, and data access information is generated based on the monitored data accesses, wherein the data access information indicates accessed and non-accessed data within the one or more data sources. The data access information is applied to a profile to identify inactive data within the one or more data sources, wherein the profile includes one or more criteria for determining inactive data including a time interval for data access. Notifications of the identified inactive data within the one or more data sources are generated.