A few years ago, during a workshop with seasoned evaluators, a colleague dropped a term that has stayed with me ever since: zombie practices.
Zombie practices are the routines in monitoring, evaluation, and learning (MEL) that we carry out almost sleepwalking. We rarely pause to reflect on them; we simply repeat them because "that’s how things are done."
You likely recognize these patterns:
These practices persist because they are reinforced from both sides. Granting institutions often view thick reports as proof of thoroughness, while evaluators—seeking to demonstrate professional rigor and methodological credibility—feel compelled to deliver them even when no one asks for them. We have collectively built a culture where we mistake the size of a deliverable for the quality of our evidence.
The real danger of these zombie practices is that they actively stand in the way of the impact we want to achieve. Yet there’s a way out.
To break free from these sleepwalking habits, evaluation must become a practice that actually gives more energy than it takes. And to do that, we have to approach one of the field's most critical untapped opportunities: narrative and qualitative data.
Everyone in the social impact sector knows that the true story of change lives in human relationships, mindset shifts, and contextual breakthroughs. Yet qualitative data is frequently treated as a secondary byproduct—relegated to decorative quote boxes in donor reports or buried in appendices because it feels too slow, messy, or subjective to aggregate.
This leaves us with an urgent challenge: How can we make narrative and qualitative data quickly, easily, and strategically accessible—for project teams, leadership, and the wider public—without stripping away context or flattening the story?
How do we move qualitative data away from isolated anecdotes and turn it into a dynamic sensing radar that informs strategic decisions while the work is actively happening?
This is where the Signal Tracker comes in. Over the past few years, we have been testing and evolving a lightweight method that bridges this gap: combining short micro-narratives with participant-led tagging and live visual mapping. It shifts evaluation from a backward-looking audit into a living sensing practice.
This article lays out how the Signal Tracker works—and how you can design one for your own organization or network across four practical steps.
To move towards real-time sensing and set up your own Signal Tracker, this blog article guides you through four clear phases:
To make this framework tangible, we will draw on two live use cases throughout this guide:
Before building forms or choosing software, you must answer two fundamental questions:
Many M&E data collection tools fail not because the technology is poor, but because they treat data collection as an administrative imposition rather than a thoughtful human experience. Step 1 is about establishing the strategic intent and structural foundation before a single data point is collected. To do so, there are three key aspects to consider that we are going to explore further:
Start by narrowing your lens. A Signal Tracker is not designed to catch every routine task; it is designed to catch significant micro-moments. Are you sensing for:
A central feature of the Signal Tracker is that it takes very little time to respond to. This leans towards mapping the natural touchpoints and rhythms that already exist in your context—whether they occur weekly, monthly, or around specific milestones:
Keep the entry options flexible. Depending on your participants, offer multiple pathways to share—such as short text prompts, voice notes, or quick facilitated debriefs.
This is the single most critical element of experience design: Data collection must give back to the person providing it.
If submitting a signal feels like sending data into a black hole for external compliance, participation will quickly drop. In a regenerative approach, the act of reflecting and logging a signal should offer personal or collective value to the storyteller.
Let’s have a quick look on how we set this up in our two examples:
By anchoring Step 1 in clear strategic intent, natural rhythms, and mutual value, data collection ceases to feel like an extractive task and becomes a generative habit.
At the heart of the Signal Tracker is a simple design choice: combining short qualitative micro-narratives with self-signification (participant-led tagging).
Self-signification does not replace established qualitative methods; in fact, it works hand-in-hand with approaches like Grounded Theory, Most Significant Change or Story Harvesting, where evaluators and participants analyze open narratives to surface emergent patterns. What self-signification adds is an immediate layer of participant authorship. By inviting storytellers to tag the meaning of their own reflections, we preserve their authentic voice while creating structured data that is fast to aggregate, cluster, and compare in real time.
A micro-narrative is a brief, open-ended description of a real-world experience, insight, or moment of reflection. Keeping the initial prompt simple—such as "What is a recent insight, shift, or observation from your practice?"—allows emergent dynamics to surface naturally without forcing responses into predefined boxes.
Immediately after sharing their narrative, participants answer 2 to 4 lightweight tagging questions. By selecting the categories, enabling conditions, or strategic themes that best fit their experience, participants achieve two key outcomes:
Once micro-narratives and self-signified tags are collected, the next priority is making that data immediately visible. The goal of this phase is to establish an automated data flow so that incoming entries instantly feed into a live visualization without requiring manual data transfer or long processing delays.
In our practice, we use a lightweight, accessible setup:
While this is our current tech stack, the exact tools can be adapted to your ecosystem. Whether you use Airtable, Notion, Typeform, Power BI, or Kumu, the underlying principle remains the same: automate the connection between capture and visualization so data becomes scannable the moment it is shared.
The true value of the Signal Tracker unfolds when the visual map supports collective sense-making, strategic adaptation, and relational accountability with those sharing their experiences.
The invitation is to bring project teams, community members, leadership, or network partners together into regular sense-making circles—whether during monthly team check-ins, end-of-cohort sessions, or strategic learning spaces.
Together, the group explores the visual landscape to invite deeper inquiry:
A core practice of a regenerative approach is maintaining relational accountability with participants. When storytellers see how their micro-narratives directly contribute to strategic reflections, programmatic shifts, or new learning focus areas, data sharing becomes a mutually enriching relationship rather than a one-way extraction.
Moving towards real-time qualitative sensing doesn't require massive software overhauls or complex, extractive processes.
Whether you are seeking to evolve internal MEL practices, equip a learning cohort, or map emergent trends across a global network, we would love to support your journey. We partner with organizations and networks to:
