Insight Engine Recommendations for Role-Based Dashboard Creation
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Solution Overview
Problem
Users face challenges in identifying and addressing insights from large volumes of dispersed data across multiple platforms, as they often lack a cohesive view of trends and relevant information tailored to their roles, leading to inefficiencies in data consumption and formatting.
Innovation Solution
An insight engine generates user-directed insights based on usage tracking information, including user-based parameters, followed by a recommendation engine providing tailored recommendations and dashboard profiles or components to address the identified insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually gather and analyze data from multiple platforms, then they can identify insights, but it becomes time-consuming and costly
Solution Approach 1:
The system performs self-service by automatically gathering usage tracking information from multiple platforms and generating user-directed insights without requiring manual user intervention. The insight engine autonomously processes data from various sources, analyzes usage patterns, and produces tailored insights specific to each user's role and context, eliminating the time-consuming manual data collection and analysis process while maintaining high insight identification accuracy
Solution Approach 2:
The patent replaces the mechanical manual process of data gathering and analysis with an automated computational system. The insight engine uses algorithms and machine learning models to substitute human analysts, automatically processing usage tracking information from multiple platforms and generating insights through computational analysis rather than manual examination, thereby reducing time loss while preserving measurement precision
2Ease of operation
If users manually format data into dashboards, then they can present insights, but it increases cost and complexity
Solution Approach 1:
The recommendation engine provides self-service by automatically generating tailored dashboard recommendations based on the user-directed insights and usage tracking information. Instead of requiring users to manually create dashboards, the system autonomously determines which dashboard components and visualizations would be most useful for each user based on their role, preferences, and the identified insights, thereby simplifying the presentation process while reducing operational complexity
Solution Approach 2:
The recommendation engine acts as an intermediary between the insight generation process and the user. It translates complex usage tracking information and insights into simplified dashboard recommendations that are easy for users to understand and implement. This intermediary layer automatically handles the complex task of data formatting and visualization selection, presenting only the most relevant dashboard options to users without requiring them to navigate complex dashboard creation tools
3Quantity of substance
If data is dispersed across multiple platforms, then comprehensive information is available, but cohesive viewing becomes difficult
Solution Approach 1:
The insight engine merges data from multiple dispersed platforms by collecting usage tracking information from various sources and consolidating it into a unified analysis framework. It combines data across platforms while maintaining the context and relationships between different data sources, enabling cohesive viewing of trends and patterns that span multiple platforms without losing the comprehensive information available in the dispersed data
Solution Approach 2:
The system segments the dispersed data from multiple platforms into organized categories and dimensions based on user roles and insights. It divides the comprehensive data set into meaningful segments that can be cohesively viewed and analyzed, presenting information in structured formats that maintain the relationships between different data sources while making trends and patterns visible across the segmented information
4Adaptability or versatility
If insights are not tailored to user roles, then general observations can be made, but role-relevant insights are missed
Solution Approach 1:
The insight engine applies local quality by tailoring insights to specific user roles and contexts. It customizes the analysis and presentation of insights based on each user's role, preferences, and organizational context, ensuring that the insights are locally optimized for each user's specific needs rather than providing generic observations. This role-based customization enhances both the adaptability of insights to different users and the measurement precision of insight relevance to each user's responsibilities
Data Source
AI summary
Systems and methods for providing autonomous user-directed insights and recommendations are provided herein. For example, a system includes a non-transitory computer-readable medium and a processor communicatively coupled to the non-transitory computer-readable medium. The processor is configured to execute processor-executable instructions to determine, by an insight engine, first usage tracking information associated with a first client device and generate, by the insight engine, a user-directed insight based on the first usage tracking information associated with the first client device. The user-directed insight includes a natural language insight. The processor is also configured to execute processor-executable instructions to generate, by a recommendation engine, recommendations based on the user-directed insight and the first usage tracking information, where each of the recommendations includes a recommendation response and one of a recommendation for a dashboard profile corresponding to the user-directed insight or a recommendation for creating a dashboard corresponding to the user-directed insight.