Front-End Demonstration Data Interpretation for Enterprise Systems
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Solution Overview
Problem
Evaluating business information enterprise systems using pre-defined fictional data is inefficient and costly, as it does not reflect the specific data types of the organization, making it difficult for users to assess the system's suitability.
Innovation Solution
A system and method that allows demonstration data to be directly entered and interpreted via a front-end application, eliminating the need for a staging engine, enabling flexible and efficient data processing and reporting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If pre-defined fictional data is used for system demonstration, then the system can be demonstrated with standardized data, but the demonstration does not reflect the organization's specific data types, reducing evaluation accuracy
Solution Approach 1:
The patent creates a copy of the organization's actual data structure and format within the demonstration environment. The system allows importing or replicating real data schemas, sample data formats, and organizational hierarchies into the demonstration instance, enabling accurate evaluation while maintaining standardized demonstration processes
Solution Approach 2:
The system enables dynamic configuration of demonstration data parameters to match the organization's specific requirements. Users can modify data types, formats, granularity levels, and organizational structures in the demonstration environment without changing the core system architecture, allowing both standardized demonstration and customized evaluation
2Reliability
If a staging engine is used to load demonstration data, then data can be processed through the complete system workflow, but the process is time-consuming and costly
Solution Approach 1:
The system performs preliminary data validation, transformation rule definition, and processing pathway configuration before actual data loading occurs. By pre-configuring the staging engine with organization-specific transformation rules and validation criteria, the actual data loading process is accelerated while maintaining complete and reliable data processing
Solution Approach 2:
The data loading process is divided into separate, independently configurable stages: data import, validation, transformation, and loading. Each stage can be executed selectively or in parallel based on requirements, reducing overall processing time while ensuring data integrity through staged validation and error handling
3Device complexity
If pre-defined fictional data is used, then the demonstration setup is simplified, but the system cannot be properly evaluated for specific organizational needs
Solution Approach 1:
The demonstration system transitions from static pre-defined fictional data to dynamic configurable data that can adapt to organizational requirements. Users can dynamically adjust data characteristics, organizational structures, and evaluation criteria without requiring complex reconfiguration of the underlying system architecture
Solution Approach 2:
The system creates a universal demonstration framework that can serve multiple purposes: standardized system demonstration, customized organizational evaluation, and flexible data format testing. By incorporating configurable data templates and transformation capabilities, a single demonstration instance can fulfill multiple evaluation needs
Data Source
AI summary
According to some embodiments, demonstration data is received via a front-end application associated with a business information enterprise system. The demonstration data may then be interpreted in accordance with at least one rule to generate business data. A query may be received at a back-end application associated with the business information enterprise system. At least a portion of the business data may then be presented in accordance with the received query.