Foundations of Inclusion Data Storytelling

This unit introduces foundational methods for measuring diversity, inclusion, and well-being and teaches how to convert people data into trustworthy, actionable stories. Students will learn quantitative and qualitative collection techniques, the role of AI and text analytics, visualization best practices, and strategies for influencing change while avoiding common pitfalls.

Learning Objectives

• Analyze quantitative, qualitative, and passive data sources for measuring diversity, inclusion, and well-being, identifying strengths, limitations, and potential biases in measurement approaches
• Apply text analytics and AI-assisted techniques to synthesize qualitative data (focus groups, interviews, notes) into thematic insights while documenting ethical considerations and data quality constraints
• Demonstrate the Three Cs framework (clear, credible, concise) by producing a people-data narrative that uses appropriate visualization dos and don’ts and tailored recommendations for target stakeholders
• Evaluate trust and change-management risks related to implementing inclusion recommendations and propose a prioritized, actionable plan that addresses common pitfalls and stakeholder influence strategies