Nonprofits collect a lot of stories, observations, and open-ended feedback—but turning all that into decisions is hard. In this TechSoup Connect Canada session, Ooloi Labs co-founders Yashna Jhamb and Akshay Roongta of Ooloi Labs showed practical ways to capture, structure, and analyze qualitative data at scale (including a demo of Dots, formerly the Open Knowledge Framework). Here are seven takeaways you can put to work right away.
- Start with intent, not a tool
Define the question before collecting anything. Are you scanning for early warnings, documenting best practices, tracking behaviour change, or informing policy? A clear inquiry keeps teams from hoarding anecdotes and helps you design the right prompts, forms, and workflows. - Structure the chaos (without flattening it)
Unstructured ≠ unusable. Use lightweight templates that nudge richer detail (context, actors, what changed), and attach metadata (who, where, when, program, stakeholder) the moment stories are captured. Those “knowledge units” make later synthesis, filtering, and cross-linking possible. - Let stories speak to numbers
Quantitative data tells you what happened; qualitative explains why. Pairing the two reveals blind spots—like a “successful” distribution program that still depresses attendance because the real barrier wasn’t measured. Use qual to detect signals, then validate with quant (and vice-versa). - Treat taxonomy as a shared language, not a fixed truth
Codebooks work best when they’re contextual, living documents. Start with a small, shared vocabulary that your team understands; group related tags into themes; and expect to refine as new contexts and terms emerge. Consistency beats completeness. - Use AI where it helps—mainly for deductive coding and retrieval
AI can summarize, cluster, and speed up deductive tagging (when themes are pre-defined). For inductive discovery and culturally nuanced interpretation, humans still lead. Think of AI as an accelerant for pattern-finding and “pattern asking” (querying your corpus), not an arbiter of meaning. - Start small with rituals, not big platforms
Before you buy anything, pilot a weekly story ritual. Give frontline staff a short template (3–5 prompts) and an owner who nudges contributions. Over 6–8 weeks you’ll build a habit, a corpus, and clarity on the tags and fields you actually need.
- Prompts that work:
- What happened? Who was involved?
- What surprised you or challenged your assumptions?
- What changed for the person/community? How do we know?
- What should we try next?
- What happened? Who was involved?
- Design for downstream use: filters, reports, and decisions
Plan from the end: who needs to act, and what view do they need? Set up saved filters (by geography, demographic, program), annotation explorers to read evidence in context, and purpose-built reports for stakeholders. Example: a custom brief that visualizes how religious beliefs shape sanitation choices—complete with quotes, counts, and maps—so program and policy teams can act.
More About Ooloi Labs and Dots
Dots, a platform we’ve built to help teams like yours structure and analyze qualitative and mixed-methods data to ensure qualitative data informs program and policy decisions — securely, efficiently, and at scale. Data could be stories from the ground, community insights, longitudinal studies, or implementation monitoring, Dots helps turn unstructured data into usable insights.
Core features include:
- Collect– Standardize and streamline qualitative data capture
- Organise – Tag, clean, and structure large volumes of narrative data
- Analyse – Surface themes using AI and share via dashboards or custom reports
Dots also supports secure access controls, no-code workflows, and integrations with existing tools.
Teams use dots to:
- Standardize and organize qualitative data using customizable templates and tagging
- Discover themes and insights through both manual and AI-supported analysis
- Query and visualize data using dashboards and natural language chat
- Bridge qualitative and quantitative data to inform decision-making
Use Cases:
- Use Case 1: Research Teams: Dots enables streamlined collection, tagging, and synthesis across large-scale qualitative or mixed-method studies. You can read more about the case study with Gender Equality Design Labs & GivingTuesday’s Data Commons.
- Use Case 2: MEL teams use Dots to track program effectiveness by synthesizing data from WhatsApp, field notes, surveys, and reports. You can read about some case studies with Nandi Foundation and Make a Difference.
Try Dots: Request for a trial ; Follow us here for latest updates on the tool.
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