Reusable Analytics System for Custom Insights
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Solution Overview
Problem
Business intelligence tools require significant time and resources to customize and reuse graphical components and data structures for different analytical purposes, limiting efficiency in providing custom insights across various areas.
Innovation Solution
The development of a system that enables the reuse of data structures, data processing, and graphical components by creating and modifying models and model instances, allowing for the efficient provision of custom insights across different analytics areas, with features like data cleaning, mapping, and widget creation for multiple clients.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If graphical components and data structures are customized for different analytical purposes, then custom insights can be provided for specific areas, but significant time and resources are required
Solution Approach 1:
The system segments analytics into reusable components including data structures, processing logic, and graphical components. Each component can be independently created, stored, and reused across multiple analytics areas, eliminating the need to recreate entire analytics solutions from scratch.
Solution Approach 2:
The system enables copying of existing analytics models, data structures, and graphical components to new contexts. Users can replicate proven analytics solutions and modify them for different purposes, significantly reducing the time and resources needed compared to creating custom analytics from scratch.
2Adaptability or versatility
If graphical components are reused for different activities, then additional analysis becomes possible, but custom data sets and analysis customization are required
Solution Approach 1:
The system creates universal data structures and processing logic that can serve multiple analytical purposes. A single data structure can be used across different analytics models and graphical components, reducing the need for separate customizations for each analytical area.
Solution Approach 2:
The system performs preliminary actions by pre-defining data structures, processing logic, and graphical components that can be reused. This advance preparation eliminates the need for repeated customization efforts when reusing analytics across different areas.
3Measurement precision
If custom analytics are created for each area, then specific insights are provided, but the process requires significant resources and time
Solution Approach 1:
The system merges common data processing logic, data structures, and analytical methods into shared components that can be reused across multiple analytics areas. This consolidation maintains analytical accuracy while dramatically improving development efficiency by eliminating redundant work.
Data Source
AI summary
Methods, computer systems, and computer storage media are provided for enabling the reuse of data structures, data processing, data cleaning, data mapping, analytic concepts, analytic widgets, logic, and graphical components to efficiently provide custom insights to new areas. Additionally, the reuse of data structures, data processing, data cleaning, data mapping, analytic concepts, analytic widgets, logic, and graphical components created for a single model can also be reused components across new models, which enables new models to be efficiently created for a completely separate set of analytics. A model is inimitably created by creating, cleaning, and/or mapping a data concept and creating widgets that provide analytic capabilities for the model. These data concepts and widgets are specifically designed to be reused across models and model instances for a plurality of clients or users. Any data concepts and widgets of the model can also be modified to provide a customized solution.