Guided BI Pages for Cross-Domain Data Discovery
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Solution Overview
Problem
Business intelligence systems require custom programming and maintenance by professional systems analysts, limiting user control over data discovery, and tablet-based solutions lack effective data linking across domains.
Innovation Solution
A method and system for automated discovery of relevant data dimensions using a columnar array organized by a set of common dimensions across multiple domains, generating guided navigation pages with actionable elements, and utilizing machine learning to identify dynamic dimensions based on user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If custom programming and maintenance by professional systems analysts is used to build business intelligence systems, then system functionality and data discovery capabilities are improved, but user control over data discovery is limited and system complexity increases
Solution Approach 1:
The system enables end users to dynamically create and customize business intelligence solutions without requiring professional systems analysts. Users can directly control data discovery through intuitive interfaces, selecting dimensions, filters, and visualizations autonomously, thereby eliminating the need for custom programming while maintaining full user control over data exploration
Solution Approach 2:
The patent replaces the mechanical system of custom programming and manual system configuration with an automated, software-driven platform. The system uses algorithmic processing and pre-configured data models to enable dynamic business intelligence creation, substituting the need for professional analysts' manual intervention with automated computational processes that users can access through simple interfaces
2Reliability
If dynamic linking of data is limited to diving vertically within a data domain, then data consistency within the domain is maintained, but users cannot readily view related data in other data domains without cumbersome regeneration
Solution Approach 1:
The system implements a universal data model where a single set of core dimensions serves multiple data domains simultaneously. This multi-functional approach allows users to access and analyze related data across different domains (e.g., sales, marketing, operations) through a unified interface, eliminating the need for domain-specific regeneration processes while maintaining data consistency through centralized dimension management
Solution Approach 2:
The patent merges previously separate domain-specific data structures into a unified multi-domain model. By combining core dimensions that are common across domains with domain-specific attributes, the system enables seamless cross-domain data exploration. Users can navigate between domains using the same dimensional framework, eliminating cumbersome regeneration and enabling efficient horizontal data linking while preserving vertical data consistency within each domain
3Reliability
If traditional business intelligence systems are used, then comprehensive data analysis is possible, but tablet-based devices cannot effectively utilize these systems due to lack of optimized solutions
Solution Approach 1:
The system implements dynamic, responsive interfaces specifically optimized for tablet devices. The user interface automatically adapts to touch-based interaction patterns, featuring large touch-friendly controls, swipe-based navigation, and mobile-optimized visualizations. This dynamic adaptation enables comprehensive data analysis capabilities to be fully accessible on tablet devices, transforming static desktop BI systems into flexible mobile platforms that maintain analytical power while providing tablet-optimized user experiences
Data Source
AI summary
Configuring and automatically populating a columnar, multi-domain business intelligence data structure can support the continuation of a business workflow. Configuring includes ingesting data from a plurality of disparate sources, formatting it according to a standardized schema, and aligning it with workflow-derived data dimensions. This process includes parsing the source data, harmonizing data formats, normalizing strings based on locale-specific language or industry-specific terminology, identifying relevant dimensions, mapping each dimension to a respective column, and populating the data structure accordingly. The resulting alignment ensures that data is organized by dimension in a highly efficient and automated manner to facilitate multi-domain business intelligence operations.