Cross-Sectional Workforce Scaling for Biased Profile Data
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Solution Overview
Problem
Raw online occupational profile data does not accurately represent a company's workforce due to inconsistencies in representation likelihoods based on roles and regions, leading to inaccurate analyses when datasets are incomplete.
Innovation Solution
Cross-sectional scaling techniques are applied to occupational profile data to adjust for missing data, using likelihoods based on occupation and location biases, employing neural networks and reference data to stratify and aggregate data accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If simple aggregation of available online occupational profile data is used, then data collection is easy and quick, but the representation accuracy of the company's workforce deteriorates due to inconsistent representation likelihoods
Solution Approach 1:
The patent introduces scaling factors that transform the raw occupational profile data into adjusted representations. These scaling factors are derived from reference data and change the parameters of the dataset to account for representation biases, thereby improving accuracy while maintaining the efficiency of using existing online data sources.
Solution Approach 2:
The patent uses reference data as an intermediary layer between the raw occupational profile data and the final workforce representation. This intermediary reference data provides the scaling factors needed to adjust for biases, allowing the system to maintain productivity while improving measurement precision through a mediating computational layer.
2Measurement precision
If cross-sectional scaling with likelihood adjustments is applied, then workforce representation accuracy is improved, but computational complexity increases due to neural networks and reference data processing
Solution Approach 1:
The patent performs preliminary actions by pre-computing scaling factors from reference data before applying them to the occupational profile data. This preliminary processing of reference data allows the main data processing to be simpler and more efficient, reducing the apparent complexity while maintaining high accuracy through pre-prepared adjustment parameters.
Solution Approach 2:
The patent segments the data processing into distinct modules: collecting occupational profile data, obtaining reference data, computing scaling factors, and applying adjustments. This segmentation of the complex process into manageable stages reduces overall system complexity by making each component independent and well-defined.
3Measurement precision
If occupation and location biases are adjusted for, then demographic representation accuracy is improved, but data processing time increases due to multiple scaling adjustments
Solution Approach 1:
The patent merges the adjustment for occupation biases and location biases into a unified scaling process. By combining multiple adjustment factors into a single integrated scaling operation, the system improves demographic representation accuracy while minimizing the time loss that would result from sequential separate adjustments.
Data Source
AI summary
Cross-sectional scaling of electronically available occupational profile data to represent a workforce of individuals at one or more companies is described. Raw online occupational profile data does not accurately represent a company's workforce. Individuals in certain roles have a higher or lower likelihood of being represented electronically, and individuals in certain regions also have higher or lower likelihoods of being represented. Thus, simply aggregating available online data does not produce an accurate representation of a company's workforce. To overcome this and/or other problems, cross-sectional scaling techniques are used to output an accurate representation of the workforce at one or more companies (e.g., for one or more occupations at a given company) based on the likelihoods that individuals have electronically available occupational profile data (which are determined based on determined occupational groups and regions where the one or more companies operate).