Data Warehouse Synchronization with Version History Tracking
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Solution Overview
Problem
In the pharmaceutical sales industry, there is a need to synchronize data between customer relationship management (CRM) systems and data warehouses to ensure compliance and avoid regulatory penalties, as non-compliant call reports can expose companies to legal liabilities, and existing methods lack efficient mechanisms for real-time data synchronization and version history management.
Innovation Solution
A method for synchronizing data between a CRM system and a data warehouse involves creating lists of items to monitor and ignore, generating a synchronization table, extracting metadata, and dynamically updating the data warehouse with changes, including version history and schema adaptations, to maintain a running history of data and reflect changes in the CRM system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data synchronization between CRM system and data warehouse is implemented using existing methods, then data consistency is improved, but real-time synchronization capability deteriorates and version history management becomes inefficient
Solution Approach 1:
The patent pre-configures monitoring lists (first list for monitored items, second list for ignored items) and synchronization tables with metadata repositories before data synchronization occurs. This preliminary setup enables the system to immediately recognize and process only relevant data changes in real-time, rather than processing all data changes sequentially, thus achieving both real-time synchronization speed and data consistency.
Solution Approach 2:
The patent segments the data synchronization process by creating distinct monitoring lists that categorize different data items (monitored vs. ignored) and organizes metadata into structured tables. This segmentation allows the system to selectively synchronize only necessary data elements in real-time, improving both synchronization speed and version history management efficiency while maintaining data consistency.
2Reliability
If comprehensive data monitoring and version history tracking is implemented, then data integrity is improved, but system complexity increases
Solution Approach 1:
The patent introduces a metadata repository as an intermediary layer between the CRM system and data warehouse. This repository stores pre-defined synchronization rules, monitoring lists, and version history metadata, allowing the system to automatically manage complex data integrity requirements without increasing operational complexity. The intermediary handles version tracking and data validation automatically, maintaining data integrity while simplifying the overall system structure.
3Measurement precision
If automatic monitoring of all CRM items is implemented, then data synchronization accuracy is improved, but processing time and resource consumption increase
Solution Approach 1:
The patent extracts and separates data items into categorized lists: a first list containing only the specific CRM items that require monitoring and synchronization, and a second list containing items to be ignored. This extraction process enables the system to achieve high data synchronization accuracy for critical items while significantly reducing processing time by excluding irrelevant data from the synchronization workflow.
Data Source
AI summary
Systems and methods for synchronizing data between a customer data management system and a data warehouse system. A data warehouse server may constantly monitor a dynamic metadata flow from the customer data management system, compare it with the metadata in the data storage device, and dynamically update the metadata in the data storage device. The data warehouse server may track activities over time and accumulate a long running history, which may include multiple versions of accounts in the customer data management system, e.g., the account as of today, the account as of yesterday, and another version that was the account two weeks ago.