Target Audience: Data analysts, researchers, development practitioners, policy makers, and anyone working with data in South Asian contexts
Prerequisites: Basic familiarity with data collection and analysis helpful but not required
Materials Needed: Laptops/tablets for data exploration, notebooks, sticky notes, flip chart paper
By the end of this workshop, participants will be able to:
Government Report: "91% of Indian households have access to improved sanitation facilities (2021 Census)."
Feminist Analysis: "While 91% may have access, time-use data shows women still walk average 30 minutes daily for water in 23% of 'improved' households. Menstrual hygiene facilities absent in 67% of rural schools. Disabled women face additional barriers in 45% of community toilets."
Same data, different questions, different insights.
Who collects data? Who analyzes it? Who gets to decide what counts as "data"?
Use data to challenge existing hierarchies rather than reinforcing them.
Value experiential knowledge alongside statistical evidence.
Move beyond male/female, urban/rural to understand complex identities.
Multiple perspectives lead to better understanding than single "objective" view.
Numbers don't exist in vacuum - historical, cultural, political context matters.
Acknowledge who does the work of data collection and cleaning - often invisible.
| Data Domain | Who's Typically Counted | Who's Often Missing | Why It Matters |
|---|---|---|---|
| Economic Surveys | Formal sector workers, heads of household | Informal workers, women's unpaid labor, care work | Underestimates women's economic contribution |
| Health Studies | Cisgender adults, urban populations | LGBTQIA+ people, children, migrant workers | Health interventions miss vulnerable groups |
| Education Data | Enrolled students in formal schools | Out-of-school children, disability, caste discrimination | Policies don't address systemic exclusion |
| Violence Statistics | Reported crimes, police data | Domestic violence, caste violence, police violence | Underrepresents actual prevalence and patterns |
Small Groups (3-4 people):
Step 1 (5 min): Choose one dataset you use in your work (surveys, admin data, monitoring data)
Step 2 (7 min): Map the power dynamics:
Step 3 (3 min): Identify one way this power dynamic might bias your understanding
Report Back: Each group shares one insight about hidden power structures in their data
Intersectionality: People experience multiple, overlapping forms of discrimination and advantage. Our data analysis must capture this complexity, not just analyze single categories in isolation.
Aggregate Data: "Maternal mortality rate is 164/100,000 births"
Intersectional Analysis Reveals:
Insight: Caste, geography, age, and ability intersect to create vastly different maternal health risks. Generic "maternal health" interventions may miss those at highest risk.
Individual Reflection (5 min): Look at a dataset you know well. Pick one categorical variable (gender, occupation, religion, etc.)
Ask These Questions:
Pair Discussion (5 min): Share your analysis with a partner
Large Group (2 min): Quick insights about hidden assumptions in data categories
Remember: Feminist data practice isn't about rejecting quantitative methods - it's about using them more thoughtfully and equitably.
Before You Start Analysis:
During Analysis:
When Presenting Results:
Data is never neutral. Every dataset reflects the power structures, assumptions, and priorities of those who created it. Feminist data practice means interrogating these structures and using data to advance justice rather than perpetuate inequality.
Essential Reading:
South Asian Context:
Practical Tools:
Next Steps in ImpactMojo:
This handout is part of the ImpactMojo 101 Knowledge Series
Licensed under CC BY-NC-ND 4.0 • Free to use with attribution • www.impactmojo.in
For the complete Data Feminism 101 course with datasets, analysis templates, and intersectional frameworks, visit the ImpactMojo platform.
