Rethinking Data Collection: A Community-First Approach

How shifting from extractive to collaborative data practices can transform research outcomes

Rethinking Data Collection: A Community-First Approach

Traditional data collection often treats communities as sources of information rather than partners in knowledge creation. This extractive approach not only produces incomplete insights but also perpetuates the power imbalances that research should help address.

The Problem with Extractive Data Practices

When researchers parachute into communities, collect data, and leave to analyze it elsewhere, several problems emerge:

1. Context gets lost - Numbers without community interpretation miss crucial nuance
2. Questions reflect researcher priorities - Not necessarily what communities need to know
3. Benefits flow elsewhere - Academic careers advance while communities see little change
4. Trust erodes - Communities become research-weary after repeated extraction

A Collaborative Alternative

At Just Knowledge, we’re pioneering a different approach that puts communities at the center of every stage:

Co-Design Research Questions

Instead of imposing our research agenda, we work with communities to identify what they most need to understand about their own situations.

Shared Data Interpretation

Raw data means nothing without context. We analyze findings together with community partners, ensuring interpretations reflect lived experience.

Community Ownership

All data and findings belong to the communities who helped create them. They decide how and when to share insights.

Capacity Building

Every project includes training components so communities can continue the research independently.

Early Results

Our pilot projects have shown that this collaborative approach doesn’t just produce more ethical research—it produces better research:

• Higher response rates when communities understand and shape the research
• Richer insights from combining quantitative data with community knowledge
  
• More actionable findings because communities are involved in identifying solutions
• Sustained impact as communities continue using research tools independently

Tools for Change

We’re developing open-source tools that make this collaborative approach scalable:

# Example: Community data dashboard template
library(shiny)
library(plotly)

# Interactive visualization that communities can customize
create_community_dashboard <- function(data, community_priorities) {
  # Dashboard code that prioritizes community-defined metrics
}

Looking Forward

This is just the beginning. We’re working on:

• Training modules for collaborative research methods
• Open datasets with community consent and ownership
• Policy recommendations for ethical data governance
• Partnerships with organizations ready to embrace this approach

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Want to explore collaborative research for your community or organization? Get in touch to discuss how we can work together.