Unified Engagement Data Model for Enterprise Process Adaptation
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Solution Overview
Problem
Conventional enterprise-related software and computing processes are inadequate in accurately managing disparate enterprise activities, leading to risks such as attrition, cancellations, and loss of customers due to a lack of effective monitoring and response to engagement data.
Innovation Solution
An engagement management engine is configured to generate and implement a data aggregation model that monitors, analyzes, and responds to engagement data, facilitating the modification of enterprise processes to optimize revenue and mitigate risks by aggregating and analyzing data from multiple sources, including financial, project, and supply chain data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional enterprise software processes are used to manage disparate enterprise activities, then enterprise operations can be executed, but monitoring and response to engagement data is insufficient leading to customer loss and financial risk
Solution Approach 1:
The patent combines multiple previously separate enterprise systems (CRM, ERP, supply chain management, project management) into a unified data aggregation model. This merging enables comprehensive engagement data collection and real-time monitoring across all enterprise functions, eliminating the information loss that occurred when systems operated in silos.
Solution Approach 2:
The patent creates a universal data aggregation model that serves multiple functions simultaneously: monitoring engagement data, analyzing financial health, predicting customer attrition, and triggering automated responses. This multi-functional system replaces multiple specialized systems while providing more comprehensive oversight.
2Adaptability or versatility
If traditional SaaS models are implemented with multiple ancillary services, then enterprise offerings are enhanced, but operational complexity and management burden increase
Solution Approach 1:
The unified data aggregation model serves as a universal platform that handles multiple service types (SaaS, professional services, implementation services, training) through a single system architecture. This eliminates the need for separate management systems for each service type, reducing operational complexity while maintaining versatility.
Solution Approach 2:
The patent segments the complex operational data into distinct, manageable categories (engagement data, financial data, project data, supply chain data) while maintaining their relationships through the unified model. This segmentation makes the complex system more manageable and easier to monitor.
3Loss of information
If comprehensive engagement data collection is implemented across multiple data sources, then a 360-degree view of operations is achieved, but data aggregation complexity increases
Solution Approach 1:
The patent merges data from multiple sources (CRM, ERP, supply chain, project management systems) into a single unified data aggregation model. This consolidation provides a complete 360-degree operational view while managing complexity through a single integrated structure rather than multiple separate data systems.
4Productivity
If real-time monitoring and automated response to engagement data variations is implemented, then enterprise processes can be optimized, but system complexity and computational requirements increase
Solution Approach 1:
The patent implements a feedback mechanism where the data aggregation model continuously monitors engagement data, compares it against established thresholds, and automatically triggers responses when variations are detected. This feedback loop enables real-time process optimization without requiring complex manual intervention systems.
Solution Approach 2:
The system performs self-service by automatically detecting engagement data variations and triggering appropriate responses without human intervention. This automation optimizes enterprise processes in real-time while reducing the complexity of manual monitoring and decision-making systems.
Data Source
AI summary
Various embodiments relate generally to data science and data analysis, computer software and systems, and computing architectures and data models configured to facilitate management and performance of enterprise functions, and, more specifically, to an enterprise computing and data processing platform configured to identify and aggregate engagement data for managing enterprise data and work flows, and, in response to data values of aggregated engagement data, the enterprise computing and data processing platform is further configured to generate a command, for example, to modify automatically an enterprise data flow or work flow. In some examples, a method may include analyzing a pool of data including project, billing, and supply chain data to generate an engagement dataset including attributes based on aggregated subsets of project, billing, and supply chain data, and calculating updated values for the engagement dataset automatically.