Foundations of Inclusive Data Storytelling

This unit introduces methods and ethical practices for measuring diversity, inclusion, and well-being using both quantitative and qualitative people data. Students learn how passive data, text analytics, and AI influence insight generation and practice trustworthy, actionable data storytelling and change-management planning to prepare for applied projects in subsequent units.

Learning Objectives

• Analyze metrics and measurement approaches for diversity, inclusion, and well-being, comparing strengths and limitations of passive and active data sources and selecting appropriate indicators for given scenarios
• Apply qualitative methods and text-analytics techniques (e.g., focus groups, in-depth interviews, coding, basic NLP) to extract human-centered insights from unstructured people data
• Demonstrate ethical, privacy-aware use of AI and passive data by identifying potential bias and trust issues and proposing mitigation strategies consistent with professional standards
• Evaluate and construct people-centered data stories using the Three Cs framework and visualization dos and don’ts to generate clear recommendations and anticipate change-management pitfalls