Master Data Automation with User-Centric Derivation Rules
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current master data maintenance tools face inefficiencies due to complex technical data models and incorrect or incomplete data operations, leading to loss of effectiveness and efficiency.
Innovation Solution
A computer-implemented method using a model-driven approach to automate master data management by generating enriched data through derivation scenarios and rules, allowing for secure and computationally efficient data updates, enabling users to adjust automation rules without IT support and optimizing data processing with machine learning techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If complex technical data models with multiple fields and derivation logic are used to support various master data requirements, then data completeness and accuracy are improved, but system complexity and difficulty of operation increase
Solution Approach 1:
The system enables users to independently adjust automation rules and derivation logic without requiring IT department support. Users can directly modify data models, add derivation rules, and configure field relationships through a user-friendly interface, making the complex system self-serviceable and reducing operational barriers despite its underlying complexity
Solution Approach 2:
The patent introduces an intermediary layer between the complex data model and the user interface. This intermediary provides abstraction mechanisms that hide the complexity of derivation logic and data relationships from users, presenting simplified views and automated processes while maintaining the sophisticated underlying data model for accuracy
2Reliability
If manual user inputs and secondary inputs are required for master data maintenance, then user control and data verification are improved, but processing time and productivity are reduced
Solution Approach 1:
The system performs preliminary actions by automatically deriving field values from source data before user submission. Derivation rules pre-calculate and populate dependent fields based on input data, reducing the need for manual user inputs and secondary verifications while maintaining data quality through automated validation
Solution Approach 2:
The system implements feedback mechanisms where derivation rules automatically adjust and refine data based on predefined logic. The system provides real-time feedback on data completeness and accuracy, automatically correcting errors and suggesting improvements, thereby maintaining reliability while reducing manual intervention requirements
3Adaptability or versatility
If derivation logic is customized based on data type and operation purpose, then data specificity and applicability are improved, but system complexity and maintenance difficulty increase
Solution Approach 1:
The system implements dynamic derivation logic that automatically adapts to different data types and operation purposes. Rather than requiring static, hard-coded rules for each scenario, the system dynamically selects and applies appropriate derivation logic based on the data being processed, making the system versatile while keeping maintenance simple through centralized rule management
Solution Approach 2:
The patent creates a universal derivation engine that handles multiple data types and operation purposes through a single flexible framework. This universal system uses configurable templates and parameters to adapt to different scenarios without requiring separate complex logic for each case, improving versatility while simplifying maintenance through standardized structures
Data Source
AI summary
In some implementations, generating enriched data includes actions of receiving a user input comprising object data. A communication with an analytics library is triggered to determine, by using an identification algorithm, a derivation scenario corresponding to the object data, the derivation scenario being stored in a relational database and comprising derivation rules. Modeled information is determined based on the derivation rules. Enriched data is generated based on the object data and by using the modeled information and provided for display.