Automated Census Data Reconciliation System
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Solution Overview
Problem
Manual reconciliation of census data between separate computing systems is time-consuming and prone to human errors, leading to discrepancies that can impact decision-making in healthcare and other systems.
Innovation Solution
An automated reconciliation system that detects discrepancies by receiving census events, transmitting reconciliation requests, and processing event histories to rebuild accurate census data, reducing the need for human intervention and improving data accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual reconciliation of census data is performed, then data accuracy can be maintained, but time consumption increases and human errors occur
Solution Approach 1:
The system performs self-reconciliation by automatically detecting discrepancies between census data in different systems and initiating correction processes without human intervention. The automated reconciliation system monitors census data changes, identifies inconsistencies, and resolves them through programmatic operations, thereby eliminating manual reconciliation time consumption while maintaining data accuracy.
Solution Approach 2:
The system implements continuous feedback loops where census data changes are monitored and compared against expected states. When discrepancies are detected, the system generates feedback signals that trigger automated correction processes, ensuring data accuracy is maintained through ongoing automated verification and adjustment without requiring manual review.
2Productivity
If manual reconciliation is used, then system complexity remains low, but productivity decreases due to time-consuming processes
Solution Approach 1:
The patent replaces manual mechanical reconciliation processes with an automated computer-based system. The automated reconciliation system uses software algorithms to detect and correct census data discrepancies, substituting human operators with programmatic operations that execute reconciliation tasks automatically, thereby increasing productivity while managing system complexity through standardized automated protocols.
3Productivity
If automated reconciliation is implemented, then productivity increases, but device complexity increases
Solution Approach 1:
The automated reconciliation system is segmented into distinct functional modules: data collection components that monitor census changes, discrepancy detection modules that compare expected versus actual states, and correction execution modules that apply fixes. This segmentation allows the complex automated system to be organized into manageable, independent components that can be developed and maintained separately, reducing overall system complexity while maintaining high productivity.
Data Source
AI summary
Embodiments herein describe an automated reconciliation system that can detect census data discrepancies between two computing systems and perform automatic reconciliation. A first computing system is a source of truth for the census data, while a second computing system maintains its own copy of the census data. When its census data is updated, the first computing system can push census event notifications to the second computing system so it can update its copy of the census data. In one embodiment, the second computing system includes an error detector to identify when a census event received from the first computing system results in an unexpected census state. In response, the second computing system can perform an automatic reconciliation to correct the discrepancy by requesting a history of census events from the first computing system that can be used to rebuild the census data at the second computing system.