Collective CRM Insight Generation With Privacy-Preserving Data Aggregation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing customer relationship management (CRM) systems isolate data within enterprises, preventing the generation of business insights from overlapping and related CRM data across multiple entities due to privacy concerns and sensitive information handling.
Innovation Solution
A system that aggregates CRM data from multiple entities while sanitizing and anonymizing sensitive information, using machine learning models to generate collective insights by segmenting and training on relevant factors, and storing data in a common repository for deeper business analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If CRM data is isolated within each enterprise's computing infrastructure, then sensitive data privacy is protected, but business insights cannot be generated from collective CRM data across multiple entities
Solution Approach 1:
The patent introduces a data intermediary layer (data lake, data warehouse, or data marketplace) that sits between isolated enterprise CRMs and analysis systems. This intermediary receives data from multiple enterprises, applies privacy-preserving transformations (anonymization, aggregation, differential privacy), and provides processed data to insight generation systems. The intermediary enables collective analysis while maintaining data isolation and privacy protection.
Solution Approach 2:
The patent creates copies of CRM data in centralized repositories (data lakes, warehouses, or marketplaces) rather than requiring access to original enterprise systems. These copies undergo privacy-preserving processing and can be shared across enterprises. The copying approach allows multiple enterprises to contribute data without exposing their actual data stores, enabling collective insights while maintaining data sovereignty and privacy.
2Loss of information
If sensitive CRM data is transmitted outside self-hosted environments, then collective insights can be generated, but data security and privacy risks increase
Solution Approach 1:
The patent applies privacy-preserving transformations (anonymization, aggregation, differential privacy, pseudonymization) to CRM data before transmission to centralized repositories or external systems. By preprocessing data to remove or protect sensitive information in advance, the system enables collective analysis while minimizing security risks during data transmission and storage.
Solution Approach 2:
The patent transforms the harmful aspect of data sensitivity into a benefit by using the very sensitivity that prevents sharing as the basis for privacy-preserving techniques. Differential privacy, for example, adds controlled noise to data that protects individual privacy while preserving aggregate insights. The system converts the constraint of sensitivity into the mechanism for enabling safe data sharing.
3Reliability
If enterprise-specific CRMs are used, then data security is maintained, but data overlap and relationships across entities cannot be utilized
Solution Approach 1:
The patent segments the data architecture into distinct layers: enterprise-specific CRM systems that maintain secure data storage, intermediary data repositories (data lakes, warehouses, marketplaces) that enable sharing, and analysis systems that generate insights. This segmentation allows each layer to fulfill its specific function while contributing to the overall system, enabling both security and collaborative insight generation.
Solution Approach 2:
The patent creates universal data repositories (data lakes, warehouses, or marketplaces) that can receive and process data from multiple enterprise CRMs with different structures and formats. These universal repositories apply standardized privacy-preserving transformations and provide unified access to aggregated data, enabling insight generation across diverse enterprise sources while maintaining compatibility with individual enterprise systems.
Data Source
AI summary
Techniques for enterprise data aggregation and collective insights generation are disclosed, including: receiving multiple sets of enterprise-specific customer relationship management (CRM) data, respectively, from enterprise-specific CRMs; aggregating the sets of enterprise-specific CRM data, to obtain collective CRM data; receiving a request to generate a collective insight that is applicable to a particular subset of the collective CRM data; responsive to the request, selecting a particular machine learning model from multiple machine learning models, wherein each machine learning model is configured to generate collective insights for a respective subset of the collective CRM data; generating the collective insight that is applicable to the particular subset of the collective CRM data, using the particular machine learning model.