When sick-leave days spike 62% in one department, your leadership wants answers—not guesses. You'll learn why absenteeism spikes are symptoms that require systematic diagnosis, not single-cause explanations. Using fishbone diagrams in Miro, you'll classify potential causes into categories (People, Process, Policy, Environment, Tools, Management) that prevent tunnel vision and reveal hidden drivers. You'll use ChatGPT to help draft a 150-word reflection explaining why this methodology works—then verify the AI's output matches your actual analysis before sharing with stakeholders. Through realistic role plays where you diagnose a colleague's absenteeism mystery and present your findings to leadership, you'll practice the systematic problem-solving that turns HR data into actionable insights. Designed for HR professionals, people analytics practitioners, and anyone who needs to explain workforce problems without guessing. No analytics background required; access to free Miro and ChatGPT recommended.

Solve HR Problems with Root-Cause Analysis

Solve HR Problems with Root-Cause Analysis
This course is part of People Data Foundations: Metrics & Visualization Specialization


Instructors: Coursera Support
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Beginner level
Recommended experience
2 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
Identify root causes of absenteeism spikes by applying the fishbone diagram methodology.
Classify potential causes into structured categories.
Document analysis using AI-assisted reflection that explains why systematic root-cause analysis outperforms intuitive problem-solving.
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Assessments
2 assignments
Taught in English
Recently updated!
April 2026
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This course is part of the People Data Foundations: Metrics & Visualization Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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