Contextual Embedded Analytics System for Real-Time Decision Support
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Companies face difficulties in achieving higher quality of service, compliance with regulations, and business-specific KPIs due to vast data stored across multiple data sources and applications, making it challenging to obtain contextual analytics at the point of use and drive data-driven decisions.
Innovation Solution
A contextual embedded analytics system that analyzes user input and historical data to provide insightful recommendations, narrowing down relevant data and suppliers based on previous relationships and agreements, and offering frameworks for entities to create application-specific insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If vast data is stored across multiple data sources and applications, then data availability and storage capacity are improved, but data accessibility and ease of obtaining contextual analytics deteriorate
Solution Approach 1:
The system segments the vast data landscape into organized data domains and contexts, creating a structured framework that divides data accessibility into manageable segments. This allows users to access contextual analytics through defined domains rather than searching through all available data sources, resolving the contradiction between data quantity and accessibility.
Solution Approach 2:
The system introduces an intermediary layer (the contextual analytics engine) that sits between the vast stored data and the end users. This intermediary automatically processes, contextualizes, and delivers relevant analytics without requiring users to manually search through multiple data sources, thereby maintaining data availability while dramatically improving accessibility.
2Measurement precision
If manual data review is performed across extensive data sources, then data accuracy and completeness are improved, but time consumption and productivity deteriorate
Solution Approach 1:
The system performs preliminary actions by pre-processing, organizing, and contextualizing data in advance through automated pipelines and data domain definitions. When users need analytics, the work is already done, eliminating the need for manual review while maintaining accuracy. This resolves the contradiction by performing thorough data preparation beforehand rather than during the decision-making process.
Solution Approach 2:
The system replaces the mechanical process of manual data review with automated computational processes. Algorithms and AI models automatically analyze data accuracy, validate completeness, and generate contextual analytics, substituting human manual review with machine-based processing that is both faster and more consistent, thereby improving productivity without sacrificing precision.
3Ease of operation
If contextual analytics are embedded at the point of use, then ease of operation and decision-making are improved, but device complexity and system architecture deteriorate
Solution Approach 1:
The system creates a universal contextual analytics engine that serves multiple applications and data sources through a single unified platform. Rather than embedding separate analytics systems in each application, the universal engine provides contextual analytics across all points of use, reducing overall system complexity while maintaining ease of operation. This multi-functional approach resolves the contradiction by consolidating complexity into a single reusable component.
Data Source
AI summary
Systems and methods are provided for detecting input via a user interface on a computing device, determining that the input triggers a recommended action related to the input and analyzing historical data to extract relevant data for the recommended action. The systems and methods further provide for generating the recommended action based on the extracted relevant data and causing display of the recommended action on the user interface of the computing device.