Years ago, I watched a project team maintain two monitoring systems for the same program. One existed for the funder. The other existed for the people actually doing the work. Guess which one the team relied on to make program decisions?
At the time, I was supporting a democracy assistance organization in Tunisia ahead of the country's first elections after the 2011 overthrow of its authoritarian government. The official monitoring, evaluation, and learning (MEL) system was in place and built to produce data required for funders’ quarterly reports. But the implementation team knew the real story of their program was more nuanced than anything this system was designed to capture. Together, we built a parallel system that produced data that the program team used extensively to inform ongoing adjustments to their program strategy. It left me with a question: why did the more useful system need to be the parallel one?
Over two decades of providing MEL support to grantees and funders around the world, I’ve noticed a common assumption: grantees are frustrated with monitoring and reporting because MEL itself is burdensome, diverting time and resources from their real work. That assumption has gained momentum in the trust-based philanthropy movement, which rightly seeks to reduce unnecessary reporting requirements and ease administrative burdens. But I think this misses something really important. The team in Tunisia wasn't frustrated with MEL. They were frustrated with a system they had designed primarily to meet funder expectations, rather than to answer the questions they most needed to understand to confirm progress and surface honest signals of where things were falling short. When they had a system that supported better decisions, they didn't resist it. They relied on it.
Across different contexts in my career, I've seen organizations engage deeply with MEL when it was designed to help them think more clearly, make better decisions, and understand the change they were creating. Three design principles, in particular, consistently made the difference.
Theory of Change
One of the most valuable MEL exercises I've facilitated wasn’t about indicators or reporting. It began by asking grantees to revisit their theories of change.
For several years, I provided MEL support to a large portfolio of grantees funded by a family foundation working to strengthen democratic institutions in the Global South. As a first step in a MEL workshop, we revisited the theories of change grantees included in their proposals. Many had been written by grant writers, sometimes from offices in another country, and bore little resemblance to the programs they were meant to guide.
For most organizations in the portfolio, it was the first time the program team had set aside dedicated time to discuss the change they hoped to contribute to, the pathways that would lead there, and how their activities connected to meaningful outcomes. The conversations surfaced consensus and tensions that might otherwise have stayed hidden until implementation, trading guesswork about what funders wanted for clarity about what the organization was actually trying to accomplish.
I also had grantees review one another’s theories of change and explored synergies in service of a larger portfolio goal. The exercise shifted how they saw themselves in the work. Not as a single organization delivering discrete activities, but as part of a larger effort toward change.
The theory of change work was valuable on its own, but it also laid the foundation for monitoring systems aligned to what grantees actually hoped to achieve. Done well, and with the right people in the room, a theory of change is more than a box to check during the proposal process. It facilitates deep thinking about the strategy ahead and clarifies the information teams need to know whether it's actually working.
Right-Sized Monitoring Systems
I once supported a global maternal health organization that had developed a comprehensive monitoring plan during the proposal stage, but now needed help putting it into practice. During our first workshop, we reviewed the plan together. Then I asked the team to set it aside and consider a question: Thinking about the work ahead, what's the most important information you need on hand to know whether your strategy is on track? Each participant wrote down five learning priorities. We posted them on the wall, grouped similar ideas together, framed them into learning questions, and voted on the top five.
That exercise exposed a disconnect. The monitoring plan hadn’t been designed to provide evidence for any of the priority learning questions the team had just identified. It had been designed to strengthen the proposal. Its indicators were lofty but difficult to measure, and they offered little insight into the questions the team cared about most. With the funder's permission, we redesigned the monitoring system and included indicators and data collection methods that could surface the evidence the team needed.
More indicators don’t necessarily produce more learning. A handful of well-chosen indicators, aligned to real learning needs and tracked consistently, is much more valuable than a sprawling system no one has the resources or motivation to implement.
Storytelling
A few years ago, I worked with an organization in Jordan focused on women's economic empowerment. During one conversation, a team member described spending months persuading a transportation company and a factory to work together.
She spent significant time building trust between the two organizations. One meeting led to other meetings, until the transportation company agreed to re-route a bus service so workers could reliably reach available factory jobs. The team had countless stories that showcased key accomplishments that supported their programs but didn't know how to share them. They were eager to talk about success, but held back, worried the stories were "too anecdotal."
That conversation reinforced another lesson I’ve learned: not all meaningful evidence fits neatly into a spreadsheet. Some of the richest evidence of a program's impact emerges through stories: the nuance, the unexpected turns, and the lived experience that numbers alone can't capture. A well-told story puts a human face on results in a way numbers can’t manage on their own.
Together, we developed a simple framework for surfacing, documenting, and sharing these stories with the same intentionality they applied to quantitative data. They began including them in funder reports, newsletters, and social media, and they also became an important source of evidence for learning.
Few organizations lack stories worth telling. More often, they lack the confidence or systems to treat those stories as an important part of their MEL approach, something that stands alongside the data and is credible on its own terms.
The Purpose of MEL
I often think back to the team in Tunisia that quietly maintained two monitoring systems. The official system produced reports while the unofficial one produced learning. They didn't need less MEL. They needed MEL designed to be useful.
That’s the distinction I hope we keep in mind. The goal isn’t simply to reduce the burden of MEL. It’s to ensure the time and resources organizations invest in MEL helps them adapt, improve, and create greater impact.
