Standalone Derived Objects for Cross-Dossier Analytics Reuse
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Solution Overview
Problem
Existing data analytics tools require significant redefinition for new projects, making them inefficient for reuse in different contexts, and changes to blends created at the dossier level do not propagate across dossiers, leading to broken links and security issues.
Innovation Solution
Creating standalone derived objects from datasets that can be used across multiple dossiers, allowing for the generation and propagation of analytics information, and enabling blends to be pre-populated or pre-created for new dossiers, ensuring consistent and secure data usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data analytics tools are customized for new projects, then analytics accuracy is improved, but time and effort required increases
Solution Approach 1:
The system pre-creates standardized blend objects with common analytics configurations that can be automatically applied to new dossiers. This preliminary preparation allows new projects to start with pre-configured analytics templates, reducing the time and effort required for customization while maintaining accuracy through proven analytics designs.
Solution Approach 2:
The system enables copying of derived objects and blends across multiple dossiers. Once a blend object is created and validated in one dossier, it can be copied to other dossiers, eliminating the need to recreate the same analytics configurations repeatedly. This copying mechanism preserves analytics accuracy while significantly reducing the time and effort required for new project setup.
2Adaptability or versatility
If blends are created at the dossier level, then dossier-specific customization is improved, but consistency and security across dossiers deteriorates
Solution Approach 1:
The system segments blend objects into two types: dossier-specific blends and standardized reusable blend objects. The standardized objects are created separately from individual dossiers and can be selectively applied across multiple dossiers. This segmentation allows each dossier to maintain its specific customization needs while simultaneously ensuring consistency and security through the use of centrally managed standardized blend objects.
Solution Approach 2:
The system introduces standardized blend objects as intermediaries between dossier-specific requirements and centralized control. These intermediary objects serve as templates that can be customized for specific dossiers while maintaining a standardized foundation, thus balancing adaptability with consistency and security across the entire system.
3Ease of operation
If derived objects are stored within dossiers, then dossier autonomy is improved, but reusability and efficiency deteriorates
Solution Approach 1:
The system creates derived objects with universal properties that enable them to function independently of any single dossier. These standalone derived objects can be referenced and used across multiple dossiers, providing both dossier autonomy (each dossier can use them independently) and high reusability (the same object serves multiple dossiers). This multi-functionality resolves the contradiction by making derived objects versatile enough to operate autonomously in any dossier context while being efficiently reusable.
Data Source
AI summary
A computer-implemented method for creating standalone objects may comprise: creating a dossier incorporating at least one dataset; creating a derived object using the at least one dataset; storing the derived object in memory, such that the derived object is a standalone object, independent of the dossier; and utilizing the derived object to generate and provide analytics information to a user via a display.