Universal Data Mart for Automated Business Intelligence
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Solution Overview
Problem
Analyzing large datasets across different applications is challenging due to the difficulty in identifying patterns and manually managing diverse business logic requirements, leading to inefficiencies and errors in data preparation and integration.
Innovation Solution
A system and method for automatically grouping data based on characteristics using machine learning and text mining techniques, which involves a communication interface, storage medium, and processors to analyze and process data, identify patterns, and provide ranked information for universal access across multiple applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual data management and analysis methods are used across different applications, then flexibility in handling diverse business logic requirements is maintained, but time consumption and error rates increase significantly
Solution Approach 1:
The patent segments data management into distinct modular components including data extraction modules, transformation modules, loading modules, and pattern recognition modules. Each module handles specific tasks independently, allowing parallel processing and reducing overall time consumption while maintaining the ability to handle diverse business logic requirements through configurable module assemblies.
Solution Approach 2:
The patent creates a universal data management system that can handle multiple applications and diverse business logic requirements through a single integrated platform. The system uses universal data models, standardized interfaces, and configurable transformation rules that can be adapted to different applications without requiring separate manual management processes for each, thereby improving productivity while reducing time loss.
2Reliability
If manual management of diverse business logic requirements is performed, then adaptability to different application needs is maintained, but error rates increase due to manual intervention
Solution Approach 1:
The patent implements self-service mechanisms where the system automatically discovers data patterns, generates transformation rules, and configures analysis parameters without manual intervention. The pattern recognition modules automatically learn from data and adjust business logic rules, reducing errors associated with manual management while maintaining adaptability to different application requirements through automated rule generation and validation.
Solution Approach 2:
The patent incorporates feedback loops where system performance is continuously monitored and used to automatically adjust business logic rules and transformation parameters. Error rates are tracked and fed back to the configuration modules, which automatically refine rules to improve accuracy. This automated feedback mechanism reduces errors while managing complexity without requiring manual intervention for each adjustment.
3Productivity
If automated data processing systems are implemented, then productivity and accuracy improve, but system complexity increases
Solution Approach 1:
The patent divides the automated processing system into segmented, independent modules that can be developed, tested, and maintained separately. This modular architecture reduces overall system complexity by allowing each component to be understood and managed in isolation while maintaining high productivity through automated workflows that coordinate these modules efficiently.
Solution Approach 2:
The patent introduces intermediary layers including standardized data models, abstraction layers, and configuration interfaces that simplify the complexity of automated processing. These intermediaries provide uniform interfaces between diverse data sources and processing logic, reducing the perceived complexity for users while enabling high-speed automated processing in the background through coordinated module interactions.
Data Source
AI summary
A device and method are described for a universal analytical data mart and data structure for same. The analytical data mart (ADM) associated data structure is designed to allow data from disparate sources to be integrated, enabling streamlined business intelligence, reporting and ad hoc analysis. Conceptually, the ADM enables analytics and business intelligence from multiple frames of reference including people, such as parties and actors including individuals and organizations, places, such as addresses with geographic information at various levels of view, objects, such as insured properties, automobiles and machinery, and events, milestones which happen at points in time and provide analytical/business value.