Prepackaged Analytics Module for Quote-to-Cash Data Privacy
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Solution Overview
Problem
Current quote-to-cash automation systems lack integration with predictive and prescriptive analytics, requiring human data scientists and access to sensitive sales data, making them costly and inefficient for medium to large enterprises.
Innovation Solution
Prepackaged analytics modules are created using machine-learning techniques without manual intervention, leveraging a pre-defined transactional data model native to the quote-to-cash application, allowing for customer-specific instances to be automatically retrained and validated without exposing sensitive data, ensuring seamless integration and data privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If custom predictive analytics solutions are built using traditional approaches with human data scientists, then analytical insights can be generated, but the cost and time requirements increase significantly
Solution Approach 1:
The system enables organizations to build and deploy predictive analytics models without human data scientists by automating the entire model development process. The prepackaged modules self-configure using metadata definitions and automatically train on customer data, eliminating the need for manual model building while maintaining high analytical quality
Solution Approach 2:
Prepackaged analytics modules are created in advance with predefined data models and metadata definitions. These modules are prepared and validated before deployment to customer environments, so that when deployed, they can be quickly customized and trained without requiring time-consuming manual model building
2Reliability
If custom predictive analytics solutions are built, then analytical insights can be generated, but costs increase due to data scientist requirements
Solution Approach 1:
The system replaces expensive human data scientists with automated self-service modules that perform model building, training, and validation. The prepackaged modules automatically configure themselves using metadata and customer data, eliminating the need for specialized human expertise while maintaining model quality
Solution Approach 2:
Prepackaged analytics modules are created as reusable templates that can be copied and deployed across multiple customers. Once a module is developed and validated, it can be instantiated for numerous customers without requiring additional model building effort, significantly reducing per-customer costs
3Productivity
If third-party analytics platforms are used, then predictive models can be deployed, but customer data privacy is compromised due to data access requirements
Solution Approach 1:
Metadata definitions serve as an intermediary layer between the prepackaged analytics modules and customer data. The modules interact with data through standardized metadata schemas rather than direct data access, allowing model training while maintaining data privacy and security controls within the customer's environment
4Measurement precision
If prepackaged analytics modules are automatically retrained with customer data, then model accuracy improves, but data security challenges arise
Solution Approach 1:
Metadata definitions act as an intermediary that enables the module to understand and train on customer data without requiring direct human access to the data itself. The automated retraining process uses metadata schemas to guide data extraction and model training, maintaining both accuracy and security
Solution Approach 2:
Only the necessary metadata definitions and aggregated statistical patterns are extracted from customer data for model training, rather than extracting or exposing the actual sensitive customer data. This allows model improvement while keeping the core customer data secure within the customer's environment
Data Source
AI summary
The present disclosure describes a system, method, and computer program for creating and deploying prepackaged, predictive and prescriptive analytics modules for use with a quote-to-cash application. When deployed, the prepackaged analytics modules execute seamlessly, from the user's perspective, with the quote-to-cash application to provide predictions, recommendations, or other data-driven insights at one or more places in the quote-to-cash process. The modules are created by leveraging a pre-defined transactional data model that is native to the quote-to-cash application. A separate instance of the module is created for each customer that deploys a module. A customer's instance of the module is automatically retrained with the customer's own quote-to-cash data during the deployment phase by retrieving customer data matching metadata definitions predefined by the quote-to-cash application. Each of the prepackaged analytics modules is configured to work with at least one custom input attribute of a select data type and data distribution.