Action Corrections System Using Dual Model Segmentation
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Solution Overview
Problem
Service providers face challenges in quantitatively measuring their performance across multiple entities and determining actionable improvements, as existing systems lack the ability to effectively combine generic and proprietary data while maintaining confidentiality and providing accurate predictive models.
Innovation Solution
An action corrections system that receives generic and proprietary data, cleanses and joins entities, extracts predictive variables, builds descriptive and action models, and generates potential improvements, allowing for personalized performance predictions and actionable corrections without compromising proprietary data confidentiality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If service providers use existing systems to measure performance across multiple entities, then they can obtain some performance data, but the measurement precision and ability to determine actionable improvements is insufficient
Solution Approach 1:
The system segments performance measurement into two distinct models: a descriptive model that analyzes historical performance data to understand past behavior, and an action correction model that identifies specific actionable improvements. This segmentation allows each model to specialize in its function, improving overall measurement precision while capturing actionable information that a single undifferentiated system would miss.
Solution Approach 2:
The system introduces an intermediary layer between raw performance data and actionable insights. The descriptive model acts as an intermediary that processes and structures historical data, while the action correction model serves as another intermediary that translates this processed information into specific improvement recommendations. This intermediary layering prevents loss of actionable information by systematically transforming data through multiple processing stages.
2Measurement precision
If the system combines generic and proprietary data to build predictive models, then measurement precision improves, but the complexity of data processing and model building increases
Solution Approach 1:
The system divides the complex task of performance analysis into two separate modeling components: a descriptive model handling historical data patterns and an action correction model handling predictive improvements. This segmentation reduces system complexity by allowing each model to focus on specific aspects of performance analysis rather than attempting to handle all functions in a single monolithic system.
Solution Approach 2:
The system performs preliminary data cleansing, entity joining, and variable extraction before model building. By preparing and structuring the combined generic and proprietary data in advance through these preliminary actions, the system reduces the complexity of the actual model training process and improves measurement precision with cleaner, pre-processed input data.
3Measurement precision
If the system processes and analyzes extensive data to provide actionable corrections, then the quality of performance measurement improves, but the time and computational resources required increase
Solution Approach 1:
The system performs data cleansing, entity joining, and variable extraction as preliminary actions before the actual model building and analysis. By preparing the data in advance and storing it in a structured format, the system reduces the time required for actual performance analysis and correction generation, while still maintaining high measurement precision through thorough data processing.
Solution Approach 2:
The system extracts only the most relevant variables and features from the extensive data through the variable extraction process. By taking out and focusing on the most critical data elements rather than processing every piece of information equally, the system improves actionable correction accuracy while reducing overall processing time and computational resource requirements.
Data Source
AI summary
An action corrections system includes an interface and a processor. The interface is configured to receive generic data and proprietary data. The processor is configured to determine a generic data entity; match the generic data entity to a proprietary data entity to form a joined entity; determine a response variable from the proprietary data; determine a need for action corrections in regards to the joined entity based at least in part on the response variable; and determine one or more action corrections in regards to the joined entity.