Employee Performance Prediction via Career Signpost Correlation
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Solution Overview
Problem
Workforce Engagement (WFE) software databases store siloed data that lacks the ability to uncover relationships between different information items, making it difficult to facilitate personnel or organizational decisions, and existing analytics tools do not effectively predict or prescribe measures for employee performance.
Innovation Solution
A system and method that acquire historical career record data from WFE databases, identify correlations between historical and present data points, predict future performance, and provide prescriptive actions by correlating key performance indicators (KPIs) with historical signposts, using algorithms like PCA and K-Means, to transmit predicted performance points and reasons to data consumers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If historical career record data is analyzed to predict future performance, then prediction accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent segments the career record data into distinct components: historical data points, signposts (milestone events), and performance metrics. This segmentation allows the system to process and analyze specific temporal patterns and causal relationships independently, reducing overall processing complexity while maintaining prediction accuracy through focused analysis of relevant segments.
Solution Approach 2:
The system performs preliminary actions by pre-identifying and storing correlations between historical signposts and performance outcomes before making predictions. The correlation engine pre-processes historical data to establish relationships between signposts and KPIs, so that when predicting future performance, the system can directly query pre-computed correlations rather than performing complex real-time analysis.
2Loss of information
If correlations between historical signposts and performance data are established, then attribution capability is improved, but computational resources required increase
Solution Approach 1:
The patent extracts and isolates specific causal relationships between signposts and performance outcomes from the broader historical data. The correlation engine identifies and extracts only the relevant associations between signposts and KPIs, storing them as discrete correlation records. This extraction process reduces computational resources by focusing only on meaningful relationships rather than analyzing all possible data combinations.
Solution Approach 2:
The system creates a simplified representation of historical relationships by copying correlation patterns into a structured format that can be efficiently queried. Instead of storing and processing the entire complex historical dataset for each analysis, the system copies and stores only the essential correlation relationships, enabling rapid attribution analysis with reduced computational resource requirements.
3Speed
If real-time monitoring of present career record data is implemented, then prediction responsiveness is improved, but system complexity increases
Solution Approach 1:
The patent introduces a correlation engine as an intermediary component that bridges real-time data monitoring and prediction functions. This intermediary pre-establishes and maintains correlation relationships between signposts and performance metrics, allowing the system to rapidly query pre-computed relationships when new data arrives, thereby achieving responsive predictions without the complexity of real-time complex analysis.
Solution Approach 2:
The system performs preliminary actions by pre-identifying and storing correlations between historical signposts and performance outcomes before making predictions. The correlation engine pre-processes historical data to establish relationships between signposts and KPIs, so that when predicting future performance, the system can directly query pre-computed correlations rather than performing complex real-time analysis.
Data Source
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AI summary
A system and method for attributing the performance of an organization employee or team to events in the employees' career record and predicting future performance. The system acquires historical career record data comprising data of an employee or team of employees, including key performance indexes (KPIs) of the employee/team; finds at least one signpost-an individual data point or group of data points in the career record data having a comparatively high correlation with one of the KPIs of the employee/team or with increases/decreases of the KPI; monitors the career record for new occurrences of the signposts; predicts the KPI or whether the KPI will increase/decrease as a function of the occurrence of the signpost, and transmits the predicted KPI or increase/decrease thereof and its attribution to the occurrence of the signpost to a data consumer. In some embodiments, the system provides prescriptive measures for improving future performance.