Juhi Bhandari
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    5 min read·Project·Project

    GlobalTech L&D Optimization

    Used predictive analytics to identify equity gaps and completion risk drivers for L&D ROI.

    Team: GlobalTechTools: Tableau, Excel
    5-8pt
    Completion Lift
    3
    Clusters Mapped
    Built
    Risk Model

    TL;DR

    • Problem: A global company's L&D function was spending budget without knowing which divisions needed help most.
    • Decision: Used predictive analytics to identify equity gaps and build an ROI framework.
    • Result: Identified high-risk divisions where targeted intervention could lift completion by 5-8 points.

    Context

    GlobalTech needed to transform L&D from a cost center into a measurable business driver.

    Problem

    Training completion varied wildly across divisions. No visibility into why, no framework for where to invest next.

    What I Did

    Built Tableau heatmaps and treemaps to visualize completion clusters by department and location. Ran scatter analysis to identify required training load as the primary completion risk driver. Designed a five metric executive dashboard including completion rate, load-adjusted completion index, and training to outcome lift.

    Results

    Identified high risk divisions where reducing required load by 10 to 15 percent could lift completion by 5 to 8 points within one quarter.

    Learnings

    Data equity gaps are a product problem. If training is not reaching the right people, the system is broken, not the people.

    Artifacts

    Tableau heatmaps, treemaps, scatter analysis, five metric executive dashboard, load-adjusted completion index.