Tenant Data Commingling for Custom Benchmark Calculations
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Solution Overview
Problem
In a software as a service platform, tenants face challenges in accessing and performing computations on pooled data sets for comparative purposes, as existing data segregation methods restrict access to only predefined computations, limiting users' ability to obtain results for custom calculations.
Innovation Solution
A system is implemented that allows tenants to opt-in for secure data commingling, enabling the creation and execution of user-defined calculations on a commingled data set, with a calculation warehouse for sharing and licensing user-defined calculations, using a processor and interface for receiving and performing these calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If tenant data is strictly segregated on different storage units to maintain security, then data security is improved, but the ability to perform custom computations on pooled data is worsened
Solution Approach 1:
The system divides data storage into segregated tenant-specific storage units for security, while creating a separate pooled data repository that combines data from multiple tenants. This segmentation allows simultaneous maintenance of security (through isolated storage units) and adaptability (through the pooled repository that enables custom computations).
Solution Approach 2:
The system introduces an intermediary pooled data repository that sits between the segregated tenant storage units and the computation interface. This intermediary layer enables custom computations on pooled data without requiring direct access to individual tenant storage units, thus maintaining security while enabling versatility.
2Productivity
If a third party performs computations on pooled data, then computation results are obtained, but the ability to perform different computations is limited
Solution Approach 1:
The system enables tenants to directly define and execute their own custom computations on pooled data through a self-service interface. Instead of relying on a third party to perform all computations, tenants can independently create, modify, and execute custom computation logic, thereby maintaining productivity while dramatically improving computation type flexibility.
3Adaptability or versatility
If user-defined calculations are enabled on commingled data, then calculation flexibility is improved, but data security risks are worsened
Solution Approach 1:
The pooled data repository acts as an intermediary layer that enables user-defined calculations on commingled data while maintaining security. The architecture ensures that users can perform flexible computations on the pooled data without gaining direct access to underlying tenant-specific storage units, thus enabling calculation flexibility while mitigating data security risks.
4Adaptability or versatility
If data is pooled for benchmarking, then comparison capabilities are improved, but access control is worsened
Solution Approach 1:
The system segments the data architecture into tenant-specific storage units and a pooled data repository. This segmentation enables improved data comparison capabilities through the pooled data while maintaining simplified access control through the isolated storage units. The segmented architecture naturally enforces access control without requiring complex permission management.
Data Source
AI summary
A system for using a user-defined benchmark calculation includes an interface and a processor. The interface is configured to receive an indication to use the user-defined benchmark calculation from a user associated with a tenant. The processor is configured to perform the user-defined benchmark calculation on commingled data stored in a commingled data storage unit to determine a user-defined benchmark calculation result, where the commingled data storage unit stores tenant data associated with the tenant and other tenant data associated with other tenants. The processor is configured also to provide the user-defined benchmark calculation result.