For years, Trickle Up has been using data to guide programs, measure progress, and ensure accountability. But today we’re sharpening our approach to become even more data-led—not just collecting information, but using it in real time to further adapt, improve, and scale what works. This isn’t a new direction; it’s a deepening of our commitment to evidence-based impact.
And we’re not alone. Across the globe, organizations implementing economic inclusion programs to create opportunities for the poorest (like BRAC’s Ultra-Poor Graduation Program and Village Enterprise) are demonstrating how data can drive smarter decisions, faster learning, and more sustainable change.
Data Matters Now More Than Ever
In the past, NGOs often measured success by counting activities: how many trainings were held, how many participants attended, how many reports were submitted. But real transformation—the kind that lifts families out of extreme poverty—requires knowing what works, for whom, and under what conditions.
Being data-led means asking better questions:
- Are we collecting the right data, not just the easiest to measure?
- Are program teams, not just M&E specialists, using data to make decisions?
- Is data shaping strategy, or just filling donor reports?
For example, in Trickle Up’s FUERTE project in Mexico, we followed 6,051 Indigenous women through our graduation program. Early data showed encouraging signs of financial inclusion and decision-making agency, but highlighted regional variation in how women were engaging with household finances and coaching support.
Because we monitored this data in real time, we didn’t wait for year-end reports—we adjusted coaching strategies and savings group facilitation on the fly, providing more targeted sessions around confidence-building and resource planning. By the end of the program cycle, 97% of participants were saving regularly, and 79% reported greater agency in household decision-making.
Just as important, we learned from the complexities of measuring sensitive outcomes like food security. While standard surveys like the Latin American and Caribbean Food Security Scale (ELCSA) offered a baseline, our teams found that qualitative methods, like focus groups and community reflections, were often more reliable in understanding dietary habits and nutritional challenges in Indigenous communities.
These outcomes and insights were possible because we didn’t just collect data—we used it, questioned it, and acted on it.
Building a Stronger Data Culture
At Trickle Up, becoming more data-led isn’t just about technology, it’s also about people, processes, and purpose. We’re assessing how our MIS and M&E systems can better “talk” to each other, ensuring data flows seamlessly from the field to decision-makers. We’re prioritizing accuracy, completeness, and relevance, because better data beats more data.
From field staff to senior leadership, we’re investing in training so everyone can use and trust the data they work with. We’re also exploring AI and machine learning to uncover patterns— like predicting dropout risks or identifying high-impact interventions—while always ensuring that these tools are developed and applied with strong ethical safeguards. This includes actively working to detect and mitigate bias, protecting individual privacy, and ensuring that decisions informed by data are transparent, fair, and accountable, especially when they affect vulnerable populations. And we’re reinforcing quarterly and monthly feedback loops so data gets translated into action, not just stored in reports.
Just as importantly, we’re committed to sharing what we learn. Trickle Up is dedicated to contributing to global public goods that advance inclusive, data-driven development such as the State of Economic Inclusion Report 2024. We’ll also be sharing our learnings through practical implementation tools, case studies, and blogs—all designed to strengthen the broader ecosystem for economic inclusion by making proven approaches more accessible and actionable.
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