Abstract Data Analysis System for GRC Insights
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Solution Overview
Problem
Existing business intelligence (BI) systems require extensive customization and expertise, making them costly and difficult to update or maintain, especially with the increasing volume and variety of data, which limits their adoption and effectiveness in providing deep insights.
Innovation Solution
An abstractly implemented data analysis system (AI-DAS) that uses a metadata layer to abstract underlying technologies, allowing users to create BI applications without coding and enabling plug-and-play extensibility, reusing existing analytics tools through APIs, and employing natural language processing to generate insights from aggregated data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing BI systems use customized hard coding and expert system integrators, then they can perform data analytics, but they require extensive technical knowledge and high implementation costs
Solution Approach 1:
The patent introduces an intermediary layer (visual interface and automated ETL processes) between the user and the complex data analytics system. This intermediary abstracts the technical complexity, allowing users to perform data analytics through visual drag-and-drop operations rather than requiring programming expertise or expert system integrators.
Solution Approach 2:
The system enables self-service data analytics by providing automated data extraction, transformation, and loading capabilities that work without requiring expert intervention. The visual interface allows business users to independently configure and execute analytics workflows, eliminating the need for expensive expert system integrators.
2Adaptability or versatility
If existing BI systems are heavily customized, then they can meet specific business needs, but they are difficult and costly to update or maintain
Solution Approach 1:
The patent implements a dynamic system where business rules and analytics configurations can be easily modified through the visual interface. Users can update and reconfigure analytics workflows by simply dragging and dropping new components or modifying existing connections, without requiring system re-coding or expert intervention, thus enabling easy adaptation to changing business needs.
Solution Approach 2:
The system segments the BI functionality into modular, independently configurable components that can be updated individually. Each analytics workflow, data source connection, and transformation rule is a separate module that can be modified, added, or removed without affecting the entire system, making updates and maintenance straightforward.
3Productivity
If new technologies like big data or cloud computing are adopted, then data storage and analysis capabilities improve, but the level of expertise required increases and implementation costs rise
Solution Approach 1:
The patent introduces an intermediary visual interface layer that abstracts the complexity of big data and cloud computing technologies. This interface allows users to leverage advanced data storage and analysis capabilities without needing to understand or configure the underlying complex technologies, effectively hiding the technical complexity while maintaining high productivity.
Solution Approach 2:
The system provides a universal visual interface that works across multiple underlying technologies (big data platforms, cloud computing environments, traditional databases). This multi-functional interface allows the same user-friendly operations to access diverse advanced technologies, eliminating the need for users to have specialized expertise in each specific technology stack.
Data Source
AI summary
Methods and apparatuses for organizations to monitor, analyze and respond to unstructured and structured data that is related to their Governance, Risk, and Compliance (GRC) programs. Embodiments of the invention generated mapped Risk Control Matrices (RCMs) and/or insights for improving the GRC process from unstructured and structured data. Natural language processing is employed to process the aggregated data from various data sources to create attributes and contributors. The attributes and weighted contributors are processed to form mapped RCMs and/or GRC-related insights.