Vendor Data Management With Use-Case-Driven TTL Caching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Large organizations face inefficiencies in managing and leveraging vendor data due to diverse data formats and formats across business units, leading to suboptimal storage and utilization of vendor information.
Innovation Solution
Implementing an intelligent database caching system with configurable time-to-live (TTL) values and machine learning to optimize data staleness based on use cases, reducing the need for real-time data retrieval and minimizing costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If vendor data is stored and accessed from multiple diverse computing systems and applications, then data accessibility and versatility are improved, but data storage complexity and management difficulty increase
Solution Approach 1:
The system segments vendor data management into distinct functional components: data ingestion module, data normalization module, data storage module, and data retrieval module. Each component handles a specific aspect of the data lifecycle, reducing overall system complexity while maintaining accessibility across diverse systems.
Solution Approach 2:
The patent introduces a centralized vendor data management system that acts as an intermediary between diverse computing systems and the vendor data repository. This intermediary system standardizes data formats and provides unified access protocols, thereby reducing management complexity while preserving data accessibility across different platforms.
2Loss of time
If real-time vendor data retrieval is implemented across all business units, then data relevance and decision-making speed are improved, but system resource consumption and operational costs increase
Solution Approach 1:
The system pre-processes and normalizes vendor data upon ingestion, organizing it into standardized formats and structures in advance. This preliminary action eliminates the need for complex real-time data processing and transformation, enabling fast retrieval without excessive resource consumption during actual data access operations.
Solution Approach 2:
The patent implements periodic data synchronization and caching mechanisms where vendor data is retrieved and cached at intervals rather than continuously in real-time. This periodic approach maintains data relevance for most business operations while significantly reducing system resource consumption compared to continuous real-time retrieval.
3Reliability
If diverse data formats from different vendors are maintained as-is, then vendor data authenticity and original formatting are preserved, but data integration difficulty and processing complexity increase
Solution Approach 1:
The system extracts the essential vendor data elements from diverse source formats and separates them from their original formatting. The core data is normalized into a standardized structure while original format information is preserved separately as metadata, allowing data integration without loss of authenticity information.
Solution Approach 2:
The patent applies different data handling approaches to different parts of the vendor data structure: critical data elements are normalized into a standard format for easy integration, while vendor-specific formatting requirements are preserved in localized sections or as metadata. This selective approach maintains data authenticity where needed while reducing overall integration complexity.
Data Source
AI summary
An electronic online system is configured to receive, at the electronic online system, an expression of a use case; determine, using a machine-learning technique with the expression of the use case as input, a data source and a time-to-live (TTL) value to satisfy the use case; and configure a data cache to store data received from the data source with the TTL value.