Intermediary Audit System for Back-End Data Synchronization
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Solution Overview
Problem
Inconsistencies in data across disparate back-end systems can lead to improper billing, mismatched service provisioning, and increased disputes between service providers and customers, disrupting service quality and reducing revenue.
Innovation Solution
An audit system periodically gathers and analyzes data from multiple back-end systems to detect and correct inconsistencies in customer-related information, including billing, provisioning, and contract data, ensuring data integrity and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple disparate back-end systems are used to manage different aspects of network services, then functional versatility and operational capability are improved, but data consistency and reliability deteriorate
Solution Approach 1:
The patent introduces an intermediary reconciliation system that acts as a mediator between disparate back-end systems. This intermediary collects data from multiple independent systems (billing, provisioning, inventory, contracts), detects inconsistencies through correlation analysis, and triggers automated reconciliation processes to harmonize the data, thereby maintaining data consistency without requiring consolidation of the underlying functional systems.
Solution Approach 2:
The patent implements continuous feedback mechanisms where the system periodically monitors data across back-end systems, detects discrepancies, and automatically initiates correction processes. The feedback loop includes detection of inconsistencies, analysis of root causes, execution of reconciliation actions, and verification of resolution, creating a self-correcting system that maintains data reliability while preserving system independence.
2Measurement precision
If data from multiple back-end systems is collected and correlated to detect inconsistencies, then data accuracy and reliability are improved, but system complexity and computational resources increase
Solution Approach 1:
The patent segments the data correlation and inconsistency detection function into modular components that operate independently on specific data types (billing data, provisioning data, inventory data, contract data). Each module processes its designated data category using specialized algorithms, reducing overall system complexity while maintaining comprehensive data accuracy through distributed specialized processing rather than monolithic analysis.
Data Source
AI summary
An arrangement collects data from disparate system back-end sources, such as a contracts system, a billing system, a service provisioning system, and analyzes the data to determine whether any inconsistencies exist. If so, the system issues a modification request to compensate for the inconsistency.