Multi-tenant Data Analysis System with Collective Intelligence Recommendations
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Solution Overview
Problem
Conventional data analysis and visualization tools often fail to bridge the gap between data analysts and business experts, leading to communication gaps, incorrect reports, and delayed decision-making due to the need for specialized statistical and visualization expertise, resulting in inadequate support for timely business decisions.
Innovation Solution
A multi-tenant data analysis system that recommends user actions to business experts based on collective intelligence from past user interactions, providing a user interface with widgets for taking recommended actions, and determining recommendation scores based on user actions, expertise, and context, allowing users to control the analysis process while leveraging collective intelligence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional data analysis tools are used, then analysis depth and accuracy are improved, but the complexity of operation increases requiring specialized expertise
Solution Approach 1:
The patent introduces an intermediary layer between the business expert and the complex data analysis tools. This intermediary automatically generates SQL queries and visualization configurations based on natural language inputs from business experts, eliminating the need for them to learn complex query languages or visualization techniques while still accessing powerful analysis capabilities.
Solution Approach 2:
The system enables business experts to perform their own data analysis without relying on data analysts. By providing automated query generation and visualization tools that respond to natural language inputs, the system allows business experts to independently explore their data, generate reports, and make decisions without needing specialized technical expertise.
2Measurement precision
If data analysts perform the analysis, then analytical expertise is utilized, but the time delay increases
Solution Approach 1:
The system empowers business experts to perform their own data analysis independently without waiting for data analysts. By providing automated tools that translate business questions into SQL queries and visualizations, the system eliminates the time delay inherent in the traditional workflow where business experts must request and wait for analyst support.
Solution Approach 2:
The system pre-configures templates and automated query generation capabilities that allow business experts to immediately analyze their data as questions arise. Rather than waiting for analysts to become available, the system has the analytical infrastructure ready and waiting, automatically executing queries and generating visualizations in real-time based on business expert inputs.
3Productivity
If business experts use the tools directly, then decision-making speed is improved, but the risk of incorrect analysis increases without specialized knowledge
Solution Approach 1:
The system introduces an intelligent intermediary that acts as a guardrail between business experts and the data analysis process. This intermediary automatically generates syntactically correct SQL queries, validates the logic of analysis requests, and ensures proper data interpretation, thereby preventing common errors that arise from lack of technical expertise while still enabling business experts to perform their own analysis.
4Adaptability or versatility
If conventional tools are used, then comprehensive analysis capabilities are provided, but the communication gap between analysts and experts persists
Solution Approach 1:
The system eliminates the need for communication between business experts and data analysts by enabling experts to perform their own analysis independently. Business experts can directly input their analytical needs in natural language, and the system automatically generates and executes the appropriate queries and visualizations, completely removing the communication gap and information loss that occurs in traditional workflows.
Data Source
AI summary
A multi-tenant system stores data for customers. The multi-tenant system presents user interfaces allowing users associated with the customers to perform analysis of data stored for the customer. The multi-tenant system determines recommendations for subsequent user actions that can be performed by a user in a context. The context includes a report being analyzed, a type of visualization of the report, one or more interactions performed by the user with the report, and so on. The multi-tenant system presents one or more widgets based on the recommendations that allow the user to perform the recommended action. The multi-tenant system may determine a recommendation based on past interactions of a subset of users of the multi-tenant system, for example, users associated with a type of industry, users having a particular role in an organization, or a level of experience of the user with analysis of data.