Last week, Databoom’s Founder, Kim Longfield, led a session at the 2021 Global Digital Development Summit (GDD Summit) about leveraging the power of upfront thinking to ensure digital solutions enable organizations to monitor, evaluate, and learn (MEL) from their work. The GDD Summit, organized by BAO Systems, Cooper/Smith, and Jhpiego, is an annual technology conference for global health and development implementers to collaborate and share best practices on how digital solutions can improve impact.
At Databoom, the two most common mistakes we see organizations make when creating a MEL system involve a lack of upfront thinking: 1) measurement is disconnected from strategy and 2) there’s a rush to build digital data solutions, including dashboards, without defining the problem that’s being solved. As a result, organizations can’t show impact because they haven’t defined the change they want to make in the world. A lack of upfront thinking means that the “learning” in a MEL system is nearly impossible.
The solution? We break MEL systems into three parts: 1) the design, 2) the build, and 3) use and learning. Then we use tailored tools to map our route.
During the design phase, critical questions for organizations are: What’s the problem we’re addressing? What’s our role in the solution? And how will we know when we’re making progress? Theories of change and logic models are tools used to describe how change will be achieved from beginning to end. Use cases are tools that identify, from a user’s point of view, how a MEL system should respond to requests.
During the build, organizations should ask: What data sources do we have? How do they fit together? And, what’s missing? For reporting against donor deliverables, logframes (or results frameworks) are tools that align project design with measurable outcomes.
The value of upfront thinking is that measurement is connected to strategy so digital data solutions can enable decision-making. Then programmatic objectives and impact are more likely to be achieved. Most importantly, the “learning” in MEL is maximized.
Watch Kim’s full session for further insights here.
Check out our open-source guidelines and templates for logic models and logframes here.
