Embedded BI Bidirectional Flow With Permission-Based Data Removal
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Solution Overview
Problem
Current systems lack a method for bidirectional data flow between a parent application and an embedded business intelligence (BI) tool, relying on manual steps that introduce errors and inefficiencies.
Innovation Solution
A system and method for bidirectional data flow between a business intelligence tool and a SaaS-hosted report generation tool, involving data import, visualization generation, approval based on access permissions, and data removal, all within a containerized environment in a virtual private cloud.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual data flow methods are used between parent application and BI tool, then implementation simplicity is maintained, but error rate increases and productivity decreases
Solution Approach 1:
The patent introduces an intermediary data exchange mechanism with standardized formats and automated validation that mediates between the parent application and BI tool. This intermediary layer automatically transforms and validates data, eliminating manual copy-paste operations while ensuring data integrity and reducing errors.
Solution Approach 2:
The patent replaces manual mechanical operations (screen capture, manual import, copy-paste) with automated electronic data exchange processes. The system uses automated data extraction, transformation, and loading mechanisms that eliminate human intervention in the data flow process, thereby reducing errors and improving efficiency.
2Productivity
If automated bidirectional data flow is implemented, then productivity improves, but system complexity increases
Solution Approach 1:
The patent segments the data flow system into distinct modular components: data extraction module, transformation module, validation module, and loading module. Each component handles a specific aspect of the data flow, making the overall complex system manageable through clear separation of concerns and independent deployment of each segment.
Solution Approach 2:
The patent implements universal data exchange standards and formats that enable the system to handle multiple types of data flows between different applications and BI tools through a single standardized interface. This multi-functional approach reduces complexity by avoiding the need for separate custom integrations for each data flow scenario.
3Loss of information
If data is continuously synchronized between tools, then data freshness is improved, but data security risks increase
Solution Approach 1:
The patent implements preliminary validation and security checks before data is transferred between systems. Data is validated against predefined schemas and security policies are applied before the actual data flow occurs, preventing unauthorized or malformed data from being synchronized while maintaining data freshness through automated scheduled updates.
Solution Approach 2:
The patent incorporates feedback mechanisms that monitor data flow operations and automatically adjust synchronization behavior based on security conditions. The system provides feedback loops that track data changes, validate transformations, and control the timing and manner of data synchronization, balancing freshness requirements with security constraints.
Data Source
AI summary
A method of bidirectional data flow includes: receiving analytics data at a repository of the business intelligence tool, wherein the analytics data is imported in a repository-compatible format; generating visualizations with the business intelligence tool using the analytics data; importing the generated visualizations from the business intelligence tool to a SaaS-hosted report generation tool; locating the imported visualizations within a report generated using the SaaS-hosted report generation tool, wherein the imported visualizations are located using one or more predefined templates; receiving approval for the located imported visualizations, wherein the received approval is based on one or more access permissions associated with a user based on access permissions stored in an identity and access module; and removing the analytics data from the repository of the business intelligence tool based on the received approval for the located imported visualizations, wherein the business intelligence tool is hosted in a containerized environment.