Data Mapping Framework for Enterprise Data Redundancy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Enterprise data management faces challenges in ensuring data quality and consistency due to differing views of the same information across business groups, leading to issues in data storage and control, which hinders effective decision-making and integration across organizational functions.
Innovation Solution
A framework is developed that includes a taxonomy of data domains and authorized data sources, with a system that maps data from systems of record into these domains, categorizes it based on types, and removes redundancies, ensuring data integrity and accessibility for target systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is stored in multiple systems of record with different views, then each business group can access its preferred data format, but data redundancy and inconsistency increase
Solution Approach 1:
The patent introduces an intermediary layer (data mapping framework and standardized data model) between the systems of record and target systems. This intermediary translates and harmonizes data from different source formats into a unified standardized structure, allowing multiple business groups to access data in their preferred formats without creating redundancy in the source systems.
Solution Approach 2:
The patent segments the data management architecture into distinct layers: source systems of record, a mapping transformation layer with standardized data domains, and target consumption systems. This segmentation allows each layer to operate independently with its own data format, eliminating the need for all systems to maintain identical data copies.
2Reliability
If data is standardized across all systems, then data consistency and quality improve, but flexibility in accessing data in different formats decreases
Solution Approach 1:
The patent applies local quality by maintaining standardized data formats at the source system level while allowing target systems to request and receive data in their specific preferred formats. The mapping framework enables each target system to have customized data presentations without compromising the standardized quality control at the data origin.
Solution Approach 2:
The patent implements dynamic data mapping where the transformation from standardized source data to target-specific formats is flexible and adaptable. The mapping framework can dynamically adjust transformations based on target system requirements while maintaining consistent source data standards.
3Measurement precision
If manual data reconciliation is performed across business groups, then data accuracy can be verified, but time consumption and operational complexity increase
Solution Approach 1:
The patent replaces manual mechanical reconciliation processes with an automated computational mapping framework. The system automatically compares, validates, and reconciles data across systems using predefined mapping rules and validation logic, eliminating the need for manual verification while maintaining high data accuracy.
Solution Approach 2:
The patent incorporates feedback mechanisms where the mapping framework continuously monitors data quality metrics and reconciliation outcomes. This feedback loop automatically adjusts mapping rules and validates data transformations, ensuring ongoing data accuracy without requiring repeated manual interventions.
4Reliability
If multiple authorized data sources are established for different domains, then data security and access control improve, but system complexity increases
Solution Approach 1:
The patent creates a universal mapping framework that handles multiple data domains, security requirements, and target systems through a single standardized architecture. This multi-functional framework manages authorized data sources across different domains (e.g., customer, product, transaction) using consistent mapping and access control mechanisms, reducing overall system complexity despite the diversity of requirements.
Data Source
AI summary
Embodiments of the invention are directed to systems, methods, and computer program products for mapping data to an authorized data source. The system is configured to receive data from one or more systems of record, wherein the data comprises one or more fields; determine one or more domains associated with the data, wherein the one or more domains comprise at least a transaction domain, a reference and master data domain, a derived domain, and a discovery domain; determine one or more data types associated with each of the one or more domains; categorize the data into at least one of the one or more domains and the one or more data types, wherein categorizing the data further comprises reconciling the data and removing data redundancies; and store the categorized data as an authorized data source capable of being accessed by one or more target systems.