Indicator Relationship Mapping for Lead-Time Metric Prediction
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Solution Overview
Problem
Conventional indicator identification systems fail to address the relation among numerous indicators and their lead times in impacting a goal, limiting the ability to effectively monitor progress towards a target metric over time.
Innovation Solution
A data processing system using a machine learning model to identify a closed group of indicators, comprising a source indicator predictive of both an intermediate indicator and a target metric, allowing for finer-grained insight into organizational data and outcomes over an extended period.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional indicator identification systems are used, then the system structure remains simple, but the ability to identify relationships among numerous indicators and their lead times is insufficient
Solution Approach 1:
The patent segments the indicator identification process into distinct functional modules: data acquisition module, relationship analysis module, lead time calculation module, and visualization module. This segmentation allows the system to handle complex indicator relationships through specialized sub-components, resolving the contradiction by organizing complexity into manageable segments that preserve relationship information.
Solution Approach 2:
The patent introduces an intermediary processing layer that analyzes relationships among indicators and calculates lead times. This intermediary module acts as a mediator between raw indicator data and final insights, enabling the system to capture and process relationship information without requiring complete system redesign.
2Duration of action of moving object
If conventional target metric identification systems are used, then the monitoring period is limited, but the system complexity remains low
Solution Approach 1:
The patent applies preliminary action by pre-calculating lead times for different indicator relationships and pre-organizing indicator hierarchies before monitoring begins. This allows the system to extend monitoring periods effectively, as the computational heavy lifting is performed in advance, enabling long-term monitoring without proportional increases in real-time system complexity.
Solution Approach 2:
The patent extends monitoring from a single time point to multiple time dimensions by incorporating lead time calculations and intermediate indicators. This dimensional expansion allows the system to monitor progress over extended periods by tracking indicators at different stages of their influence on target metrics, resolving the contradiction between monitoring duration and system complexity.
3Measurement precision
If conventional systems are used, then the insight granularity is coarse, but the computational requirements are low
Solution Approach 1:
The patent applies local quality by focusing computational resources on analyzing specific indicator relationships and lead times that are most relevant to the target metric. Rather than uniformly processing all possible indicator combinations, the system identifies and deeply analyzes local relationships that provide the most valuable insights, achieving fine-grained insight precision while managing computational requirements through selective analysis.
Data Source
AI summary
One aspect of a method for data processing includes identifying target time series data for a target metric and candidate time series data for a plurality of indicators predictive of the target metric; training a machine learning model to predict the target time series data based on the candidate time series data; computing first through third predictivity values based on the machine learning model, wherein the first predictivity value indicates that a source indicator from the plurality of indicators is predictive of the target metric, the second predictivity value indicates that an intermediate indicator from the plurality of indicators is predictive of the target metric, and the third predictivity value indicates that the source indicator is predictive of the intermediate indicator; and displaying a portion of the candidate time series data corresponding to the intermediate indicator and the source indicator based on the first through third predictivity values.