Earning Code Classification for Normalized Payroll Data Analysis
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Solution Overview
Problem
Existing payroll data in human capital management is not normalized, leading to inefficiencies in evaluating and refining business operations, as earning codes are not systematically classified and utilized.
Innovation Solution
A computer-implemented method using statistical machine learning to classify and normalize employee transaction data into human resources-related attributes, enabling the presentation of a user interface to adjust organizational operating procedures based on these codes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If payroll data is collected and stored without normalization, then data collection is simple and quick, but the data cannot be systematically analyzed and evaluated
Solution Approach 1:
The system performs preliminary classification and normalization of payroll data during the data collection phase, assigning earning codes and HR attributes to transactions as they are entered. This preliminary action ensures data is ready for analysis without requiring complex post-processing, thus preventing information loss while avoiding excessive processing complexity later.
Solution Approach 2:
The patent introduces earning codes and HR attributes as intermediary elements that mediate between raw payroll transactions and analytical queries. These intermediaries standardize the data structure, enabling systematic analysis without requiring direct complex processing of raw heterogeneous data, thus resolving the contradiction between information usability and processing complexity.
2Loss of information
If earning codes are systematically classified using machine learning, then data analysis capability is improved, but processing time and computational resources increase
Solution Approach 1:
The machine learning model performs classification and assigns earning codes during the initial data processing phase rather than during analysis. This preliminary classification ensures data is pre-evaluated and structured, enabling fast analytical queries without requiring complex processing during actual analysis, thus improving data evaluability while minimizing processing time impact.
Solution Approach 2:
The system uses unsupervised machine learning algorithms that automatically classify payroll transactions and assign earning codes without requiring manual intervention or iterative analysis. This self-service classification occurs in the background during data ingestion, improving data evaluability while avoiding additional processing time during user interactions.
3Manufacturing precision
If payroll data is normalized into standardized codes, then data consistency and analysis accuracy are improved, but data processing complexity increases
Solution Approach 1:
The patent introduces earning codes and HR attributes as intermediary standardization layers that sit between raw payroll data and analytical processes. These intermediaries provide a consistent normalized structure without requiring complex transformation logic in every system component, thus improving data normalization accuracy while distributing processing complexity across the architecture rather than concentrating it.
Solution Approach 2:
The system replaces manual data normalization processes with automated machine learning-based classification. This substitution achieves high normalization accuracy through algorithmic pattern recognition while reducing the operational complexity burden on users, as the system automatically handles the classification task that would otherwise require complex manual procedures.
4Adaptability or versatility
If heterogeneous payroll data from multiple sources is aggregated, then comprehensive analysis is enabled, but data integration complexity increases
Solution Approach 1:
The system implements a universal earning code framework that can accommodate multiple data sources and payroll systems. By mapping diverse payroll transactions from different sources to a common set of earning codes and HR attributes, the system achieves broad data source compatibility without requiring source-specific integration logic for each system, thus enabling comprehensive analysis while managing integration complexity through standardization.
Data Source
AI summary
Managing and applying human resources data comprising aggregating employee transaction data for an organization. A number of human resources-related attributes are evaluated across heterogeneous transaction data. The employee transaction data is classified via statistical machine learning into a number of normalized codes according to the human resources-related attributes, a user interface is presented to adjust a number of organizational operating procedures according to the normalized codes.