Cross-tenant Agent Data Processing for Cloud Recruitment
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Solution Overview
Problem
Current cloud computing systems lack a comprehensive analytical metric to evaluate candidate agents' true asset value and facilitate fair comparisons across different cloud computing tenants, leading to inefficient recruitment processes and high costs due to mismatched agent roles.
Innovation Solution
A cloud computing system and method for agent data tracking and processing that utilizes a unique identifier for agents, accumulates and refines data from various sources, and calculates a Base Asset Value (BAV) score based on Key Performance Indicators (KPIs), enabling cross-tenant comparisons and objective assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If company-specific agent evaluation methods are used, then recruitment decisions can be made within a single organization, but fair comparison of agents across different cloud computing tenants is not possible
Solution Approach 1:
The patent creates a universal agent evaluation system that functions across multiple cloud computing tenants. The Base Asset Value (BAV) score is designed to be tenant-agnostic, allowing the same metric to evaluate agents from different organizations on a common scale. This enables hiring managers to compare candidates from various tenants fairly, as the BAV calculation methodology remains consistent regardless of which tenant the agent previously worked for.
Solution Approach 2:
The system transforms raw agent performance data into standardized percentile-based scores through parameter transformation. By converting diverse performance metrics into normalized percentile ranks, the system enables meaningful comparisons across different tenants and roles. This parameter change from raw values to percentiles ensures that agents can be objectively compared despite differences in organizational contexts.
2Loss of information
If comprehensive analytical performance reports are collected for all candidates, then accurate agent assessment is possible, but data accumulation and processing complexity increases
Solution Approach 1:
The system extracts only the essential performance indicators needed for agent evaluation from the comprehensive data set. Rather than processing all available candidate information, the system identifies and extracts key performance metrics that directly contribute to the BAV calculation. This extraction approach maintains assessment accuracy while reducing processing complexity by focusing on the most relevant data elements.
Solution Approach 2:
The patent segments the agent evaluation process into distinct components: data collection, data refinement, KPI determination, and BAV calculation. Each segment handles specific aspects of the evaluation, allowing the system to process comprehensive information in manageable stages. This segmentation reduces overall system complexity by breaking down the complex task of comprehensive agent assessment into smaller, more manageable processing steps.
3Productivity
If traditional recruitment assessment methods are used, then recruitment processes are simple to conduct, but recruitment time and costs increase due to lack of comprehensive analytical metrics
Solution Approach 1:
The system performs preliminary data accumulation and BAV calculation before the actual hiring decision is made. By pre-calculating the Base Asset Value scores and having comprehensive performance reports ready, the system eliminates the need for time-consuming assessments during the interview process. Hiring managers can quickly review pre-computed BAV scores and make informed decisions, significantly reducing recruitment time and associated costs.
Data Source
AI summary
A system is provided for a cloud computing environment that is adapted to perform data processing and tracking of agent data between cloud computing tenants. The system includes a processor and a computer readable medium operably coupled thereto, to perform operations which include determining a unique identifier (ID) for an agent of the cloud computing tenants, accumulating, over a time period, agent data for the agent, refining the agent data to curated data views for the agent data based on a plurality of aggregate reports for each of a plurality of KPIs in the agent data, determining a batch processing job for the refined agent data, calculating, using the batch processing job, a base asset value (BAV) score for the agent, and updating a profile for the agent associated with the unique ID based on the calculated BAV score.