Multi-Channel Data Reconciliation Using Intermediate Channel Data
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Solution Overview
Problem
Managing data consistency and discrepancies across diverse channels in a multi-channel pipeline is complex due to varying data structures, formats, and latency issues, leading to inefficiencies in data migration and integration operations.
Innovation Solution
A data management device that collects intermediate data from each channel, performs data normalization operations, and identifies data discrepancy events to ensure end-to-end visibility and traceability, thereby coordinating data mapping and transformation across channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is transferred through multiple channels in a multi-channel pipeline, then data integration capability and versatility are improved, but data consistency and reliability deteriorate due to varying data structures, formats, and latency issues across channels
Solution Approach 1:
The system performs preliminary data normalization operations on intermediate data from each channel before comparison. By normalizing data formats, structures, and timestamps in advance, the system enables reliable cross-channel data comparison while maintaining the ability to integrate diverse data sources through multiple channels
Solution Approach 2:
The system introduces an intermediary reconciliation process that mediates between multiple data channels. This intermediary layer normalizes and compares data from different channels, identifying discrepancies and resolving conflicts, thereby ensuring data consistency while preserving the versatility of multi-channel data integration
2Reliability
If comprehensive data comparison and reconciliation operations are performed across all channels, then data accuracy and reliability are improved, but processing resources and computational overhead increase
Solution Approach 1:
The system extracts and compares only the intermediate data from each channel that is relevant to the source data being reconciled. By taking out only the necessary data portions for comparison rather than processing all data comprehensively, the system maintains high data accuracy while reducing processing resource consumption
Solution Approach 2:
The system performs partial comparison operations focused on specific data elements and channels that are most likely to contain discrepancies. Rather than exhaustively comparing all data across all channels, the system applies targeted reconciliation operations that achieve sufficient data accuracy with reduced computational overhead
3Measurement precision
If intermediate data from each channel is collected and compared, then data discrepancy detection capability is improved, but device complexity and operational complexity increase
Solution Approach 1:
The system segments the data reconciliation process into distinct operations: collecting intermediate data from each channel, normalizing the data, comparing normalized data to identify discrepancies, and resolving conflicts. By segmenting the complex reconciliation process into manageable stages, the system achieves high discrepancy detection capability while keeping operational complexity可控 through modular processing steps
Data Source
AI summary
In some implementations, a device may obtain, via a source platform, source data that is to be provided to a target platform to be stored in one or more databases included in the target platform, wherein the one or more databases are accessible via a data integration pipeline. The device may provide the source data via the data integration pipeline to cause input data to be provided to the target platform. The device may obtain sets of intermediate data associated with respective channels of multiple channels included in the data integration pipeline. The device may determine, based on a comparison of the source data, the sets of intermediate data, and the input data, data reconciliation information indicating one or more data discrepancy events and channels associated with respective data discrepancy events of the one or more data discrepancy events. The device may perform, based on the data reconciliation information, an action.