Training analytics systems utilize layered metrics that measure participation, behavioral change, skill development, and business performance impact. These metrics can be categorized into descriptive, diagnostic, predictive, and prescriptive analytics layers.
Descriptive analytics answer “what happened?” Examples include course completions, attendance, learning hours, certification status, and assessment results. These metrics provide foundational visibility but do not explain root causes or outcomes.
Diagnostic analytics answer “why did it happen?” These metrics analyze correlation and causation across engagement patterns, training formats, role profiles, and performance data. Diagnostic metrics enable optimization of content, delivery, and user experience.
Predictive analytics answer “what is likely to happen?” Predictive models estimate future skill needs, readiness timelines, attrition risk, and workforce capability gaps based on historical learning data and performance signals. Predictive analytics support strategic workforce planning and transformation initiatives.
Prescriptive analytics answer “what should we do?” These models recommend training pathways, development interventions, mentoring, or role transitions to close skill gaps and improve performance outcomes.
Training analytics systems also track skill metrics. Skill progression data reveals whether employees are acquiring and applying new competencies. Skill application metrics can integrate with performance systems to measure real-world impact.
Business impact metrics quantify value in terms of productivity, time-to-competency, operational efficiency, safety outcomes, customer satisfaction, compliance adherence, and performance KPIs. These metrics elevate training to a strategic investment category.
Together, these layers provide a holistic understanding of how learning influences workforce capability and business results.
