Central Data Hub Rationalization for Multi-Source Data Migration
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Solution Overview
Problem
Conventional software solutions for enterprise data management face inefficiencies, inaccuracies, and high processing power consumption due to the need to compare and transform data from multiple internal and external sources with inconsistent formats, leading to costly licenses and cumbersome processes.
Innovation Solution
A computer-implemented system and method that migrates data from various electronic data sources into a central hub, performing data enhancement and linkage to create a consolidated view, using a common data model for efficient data analysis and management, and decommissioning legacy components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional software solutions are used to manage data from multiple sources with inconsistent formats, then data compatibility can be achieved through comparison and transformation, but processing power consumption increases significantly and the process becomes cumbersome
Solution Approach 1:
The patent applies preliminary action by pre-defining data formats and structures before data ingestion. The system establishes a centralized data warehouse with predetermined schema and formatting rules in advance, so that when data arrives from multiple sources, it can be directly loaded into the pre-configured structure without requiring complex real-time comparison and transformation operations, thereby reducing processing power consumption while maintaining data compatibility
Solution Approach 2:
The patent introduces a centralized data warehouse as an intermediary between multiple data sources and various data consumers. This intermediary layer receives data from diverse sources in their native formats, transforms them into a unified internal representation, and serves all consumers through this standardized interface. The intermediary absorbs the complexity of format differences, allowing consumers to access data without encountering the underlying format inconsistencies, thus reducing overall system processing requirements
2Adaptability or versatility
If conventional software solutions transform fields and tables to make data compatible, then data from different sources can be integrated, but the process takes significant time and becomes inefficient
Solution Approach 1:
The system performs preliminary action by establishing a predetermined data warehouse schema and formatting framework before data ingestion occurs. This pre-configured structure defines how data from multiple sources should be organized, transformed, and stored in advance. When data arrives, the system can directly map source data to the pre-defined schema rather than performing complex real-time transformation, significantly improving processing speed while maintaining comprehensive data integration capabilities
Solution Approach 2:
The patent merges multiple data sources and their diverse formats into a single unified data warehouse that maintains a consistent internal representation. By combining all source data into one centralized repository with standardized structures, the system eliminates the need for separate transformation processes for each data source. The merged data can then be efficiently queried and analyzed as a single cohesive dataset, improving overall productivity while achieving complete data integration
3Quantity of substance
If conventional solutions retrieve and load data from multiple source systems, then comprehensive data can be obtained, but expensive licenses are required making the solutions uneconomical
Solution Approach 1:
The patent implements a universal data warehouse platform that can ingest, store, and manage data from multiple diverse sources through a single unified system. This multi-functional platform replaces the need for multiple separate specialized solutions, each requiring its own expensive license. The universal system handles various data types and sources through common infrastructure and processing mechanisms, achieving comprehensive data coverage while significantly reducing licensing costs through consolidation
Data Source
AI summary
Disclosed herein are embodiments of systems, methods, and products comprises an analytic server for data rationalization and migration. The server receives data from a plurality of electronic data sources and stores the received data into a database within a central hub. The server demobilizes the data and performs enhancement on the data to prepare the data for complex calculation. The server copies the enhanced data to an operational data store, performs various calculation functions that filer, rationalize the data, and create data linkage. The server copies back the calculation result to the database for further processing and downstream consumption. The calculation results provide a consolidated view of data from different source systems and satisfy the reporting needs for different consumers. The server uses common data model to generate reports for different consumers. After the data migration and consolidation into a central hub, the server decommissions legacy components.