Data Quality Rule Framework for IT Resource Allocation Accuracy
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Solution Overview
Problem
Accurate measurement and allocation of IT resource utilization in IT infrastructure are hindered by varying data quality, leading to inefficiencies and errors in decision-making and cost transparency.
Innovation Solution
An automated data quality servicing framework that determines storage systems for IT resource utilization data, establishes and modifies data quality rules to detect and rectify discrepancies, and ensures data integrity across storage systems, thereby improving data quality and reducing unallocated costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data quality rules are established to control storage of IT resource utilization data, then data integrity is improved, but device complexity increases
Solution Approach 1:
The framework performs preliminary data quality assessment and validation before data is stored in the storage system. Data quality rules are established and applied in advance to prevent poor quality data from being stored, thereby ensuring data integrity without requiring complex post-storage processing mechanisms
Solution Approach 2:
The system automatically assesses data quality, detects discrepancies, and remediates issues without requiring manual intervention. The automated data quality servicing framework monitors and maintains itself, reducing the operational complexity that would otherwise be required to manage data quality manually
2Measurement precision
If automated data quality servicing is implemented to detect and remedy data discrepancies, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The framework continuously monitors data quality and performs automated assessment and remediation in an ongoing manner rather than through periodic batch processing. This continuous operation ensures measurement precision is maintained without significant time loss, as the system is always ready to detect and correct discrepancies immediately
Solution Approach 2:
The system implements feedback loops where data quality metrics are continuously measured, compared against defined standards, and used to automatically trigger remediation actions. This closed-loop approach ensures high measurement precision while minimizing time loss through automated real-time correction rather than manual review cycles
3Reliability
If data quality rules are modified to prevent data discrepancies, then reliability is improved, but ease of operation decreases
Solution Approach 1:
The framework automatically generates, updates, and modifies data quality rules based on detected patterns and discrepancies. The system self-adapts by learning from data quality issues and automatically adjusting rules to prevent future problems, eliminating the need for manual rule management while maintaining high data quality
Solution Approach 2:
Manual rule management and modification processes are replaced with automated computational mechanisms. The system uses algorithms to analyze data quality patterns and automatically generate appropriate rules, substituting mechanical manual operations with automated electronic processes that are both reliable and easy to operate
Data Source
AI summary
A method includes determining one or more storage systems storing information technology (IT) resource utilization data, establishing a set of data quality rules for controlling storage of the IT resource utilization data in the one or more storage systems to reduce unallocated IT resource utilization, and analyzing the IT resource utilization data stored in the one or more storage systems to detect data discrepancies affecting allocation of the IT resource utilization data, adjusting the IT resource utilization data stored in one or more of the storage systems to remedy a given data discrepancy associated with the IT resource utilization data, and modifying the set of data quality rules for controlling storage of the IT resource utilization data in the one or more storage systems to prevent the given data discrepancy from occurring on subsequent storage of the IT resource utilization data in the one or more storage systems.