Cross-Customer Analytics via Anonymized Data Aggregation
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Solution Overview
Problem
Existing systems face challenges in aggregating and comparing industry-specific data across organizations without revealing sensitive information, as data is stored in diverse models and technologies, making it difficult to create meaningful industry benchmarks and peer groups.
Innovation Solution
A method and system for automatically determining cross-customer metrics by aggregating data from customer-specific stores without storing customer-specific identifiers, using incremental data tracking, parameter-based peer group creation, and standardizing dimensions to compute comparable metrics across customers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data from multiple customer organizations is aggregated in a common store for cross-customer analytics, then the ability to compare performance across peers is improved, but data privacy and security are compromised due to exposure of sensitive customer-specific information
Solution Approach 1:
The patent extracts and removes customer-specific identifiers and sensitive information from the aggregated data in the common store. Only anonymized, aggregated metrics are retained, allowing cross-customer comparison while eliminating the ability to trace data back to individual customers, thus resolving the contradiction between analytics capability and data privacy.
Solution Approach 2:
The patent introduces an intermediary layer of aggregation and anonymization between the customer-specific data sources and the cross-customer analytics interface. This intermediary process transforms raw customer data into protected aggregated metrics, enabling peer comparison without direct exposure of sensitive information.
2Measurement precision
If customer-specific identifiers and data points are stored in the common store to enable detailed analytics, then the precision of cross-customer metrics is improved, but the complexity of data management and security protocols increases
Solution Approach 1:
The patent extracts only the necessary aggregated metrics from customer-specific data while removing customer identifiers. This extraction approach maintains measurement precision for comparative analytics while significantly reducing data management complexity by eliminating the need to store and protect granular customer-specific information in the common store.
3Adaptability or versatility
If data is aggregated from diverse data models and technologies across organizations, then the versatility of cross-industry comparison is improved, but the difficulty of data integration and standardization increases
Solution Approach 1:
The patent transforms diverse data from different customer organizations into a standardized set of aggregated parameters and metrics in the common store. By changing the data representation from diverse source formats to unified aggregated metrics, the system enables versatile cross-industry comparison while simplifying data integration through parameter standardization.
4Speed
If incremental data tracking is implemented to update cross-customer metrics efficiently, then the speed of analytics updates is improved, but the complexity of data synchronization mechanisms increases
Solution Approach 1:
The patent implements preliminary action by pre-aggregating customer-specific data and computing baseline metrics before they are needed for cross-customer comparison. This preliminary processing allows incremental updates to be applied efficiently when new data arrives, improving update speed while managing synchronization complexity through advance preparation.
Data Source
AI summary
A method for automatically determining one or more cross customer metrics from a cross customer store for cross customer analytics is provided. The method includes the steps of: (i) automatically standardizing dimensions associated with the one or more customers to create one or more cross customer metrics; (ii) automatically determining incremental data from one or more customer specific stores by tracking (a) a time stamp of a last pull time (T) for customer data stored in the one or more customer specific stores, and (b) a number of items involved (I); (iii) automatically updating the one or more cross customer metrics in the cross customer store with the incremental data; and (iv) automatically comparing the one or more cross customer metrics of a customer with one or more peer groups created at run time based on customer selected values for one or more parameters.