Workforce Data Standardization & LMS Optimization

Improving training accuracy across 48 locations through workforce data cleanup, standardized job classifications, and system alignment.

Project Overview

This project focused on improving the accuracy and consistency of workforce data used across onboarding, human resources, reporting, and learning management systems.

A comprehensive review of 964 employee records was completed to identify legacy classifications, consolidate job-title variations, and establish a dependable workforce structure across 48 locations.

The Business Challenge

Years of legacy employee records, title variations, and changing onboarding practices had created inconsistencies within workforce reporting and training-assignment processes.

Employee information was automatically synchronized between onboarding, HR, and learning management systems. This meant that inconsistent job classifications could create problems throughout the entire technology environment.

  • Employees could receive incorrect role-based training.
  • Workforce reports could group similar positions differently.
  • Legacy records could distort location and role comparisons.
  • Administrators had to spend additional time validating data.
  • System integrations became less reliable as variations increased.

Project Scale

964

Employee records reviewed

48

Locations standardized

~20

Core job classifications established

The Solution

A comprehensive workforce-data review was conducted to identify legacy classification issues and align employee records to a standardized organizational structure.

Dozens of title variations were consolidated into approximately 20 core operational job classifications. Similar roles were grouped according to their actual responsibilities, reporting needs, and training requirements.

The resulting structure created a consistent framework that could be used across onboarding platforms, HR systems, workforce reports, and the organization’s learning management environment.

Standardization Process

1. Record Review

Reviewed employee records to identify inconsistent titles, outdated classifications, duplicate naming conventions, and legacy onboarding data.

2. Data Transformation

Used structured data-cleanup methods to organize title variations and identify repeat classification patterns.

3. Role Mapping

Mapped legacy titles to a smaller set of standardized classifications based on operational responsibilities and training needs.

4. System Alignment

Structured the final classifications to support consistent data flow between onboarding, HR, reporting, and LMS platforms.

System Alignment

Standardizing the source data improved the reliability of every connected system that depended on employee job classifications.

  • Onboarding records could be assigned to a consistent role structure.
  • HR data could support cleaner workforce reporting and comparisons.
  • LMS assignments could use standardized roles to deliver the appropriate training.
  • Administrators could validate records against one dependable classification framework.

Results & Impact

  • Standardized workforce classifications across 48 locations
  • Consolidated legacy job titles into a consistent reporting structure
  • Improved role-based training-assignment accuracy
  • Increased confidence in workforce reporting data
  • Reduced administrative effort associated with data validation and cleanup
  • Strengthened integration reliability between HR, onboarding, and LMS platforms

Project Scope

  • Workforce data auditing
  • Legacy title identification
  • Job classification mapping
  • Data cleanup and transformation
  • LMS assignment optimization
  • Cross-system data alignment
  • Reporting structure improvement

Key Skills

Power Query Excel Workforce Analytics LMS Administration Data Governance Reporting Optimization Process Improvement Systems Analysis

Business Value

The standardized classification structure created a more reliable foundation for workforce reporting, onboarding, and employee development. Instead of repeatedly correcting downstream errors, the organization could address data quality at its source and allow connected systems to operate from the same consistent framework.

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