Dynamic Mapping Table for Data Mart Integration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Businesses face challenges in fully utilizing market research data due to frequent changes in data fields and types by market research companies, as they often lack awareness of these updates, leading to incomplete data analysis and insights.
Innovation Solution
A computer-implemented method that processes a data delivery package by identifying missing fields from a mapping table, creating supplemental mappings, and updating the table to include new fields, allowing for dynamic data mapping and storage in data marts, enabling the software application to analyze and utilize the latest market research data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a static mapping table is used to map data fields, then the system structure is simple and stable, but the system cannot adapt to new data fields added by market research companies
Solution Approach 1:
The mapping table is transformed from a static structure to a dynamic one that automatically updates itself. The system monitors data delivery packages, identifies new fields, generates corresponding mappings, and updates the mapping table without manual intervention, enabling continuous adaptation to changing data structures from market research companies
Solution Approach 2:
The system performs self-updating of the mapping table by automatically detecting new data fields in incoming packages, generating appropriate mappings, and inserting them into the mapping table. This self-service mechanism eliminates the need for manual configuration updates and allows the system to adapt autonomously to data format changes
2Reliability
If manual updates to mapping table are performed, then the mapping accuracy is maintained, but the time required to process data changes is increased
Solution Approach 1:
The system performs preliminary actions by pre-defining the mapping table structure and pre-establishing the framework for field mappings. When new data fields arrive, the system only needs to add new entries to this pre-established framework rather than redesigning the entire mapping structure, thus maintaining accuracy while reducing update time
Solution Approach 2:
The mapping table update process is automated through self-service mechanisms where the system automatically detects new fields, generates mappings, and updates the table without human intervention. This eliminates manual update operations and significantly reduces the time required to process data structure changes while maintaining mapping accuracy through systematic automation
3Adaptability or versatility
If the mapping table is updated frequently to accommodate new fields, then the system remains up-to-date with market research data, but the system stability may be compromised
Solution Approach 1:
The system maintains stability during updates through self-service automation, where predefined protocols and structured approaches guide the automatic addition of new mappings. The systematic nature of automated updates prevents chaotic modifications and ensures that changes are applied consistently and predictably, maintaining system stability even as data currency increases
Solution Approach 2:
The mapping table is designed with a pre-established framework and structure that guides how new fields should be integrated. This preliminary structuring ensures that updates follow consistent patterns and do not disrupt the overall system architecture, allowing frequent updates to maintain data currency while preserving system stability through structured evolution
Data Source
AI summary
Techniques are described for mapping supplemental data from a data delivery package into data marts that are accessible to a software application. In some embodiments, new mappings can be dynamically generated for a mapping table when the data delivery package includes fields which do not have a corresponding mapping in the mapping table. The new mappings can be generated at runtime while the data delivery package is being processed. Once generated, the mapping table can be used to store the supplemental data into the data marts.