Learning Impact Measurement From Metrics to Data

The SIRI practicum is a valuable opportunity to offer faculty-supported consulting experience in sustainable investment. My team is supporting an intergovernmental organization working in blended and development finance, catalyzing capital for investment in low-capacity or higher-risk contexts that traditional financing struggles to reach. Our client is also strengthening how it measures and communicates impact across projects, aiming to build a more consistent indicator and evidence approach. For me, it has been a rare opportunity to step in a public-sector-facing finance ecosystem where profitability isn’t the only thing that counts as success.

The biggest adjustment for me so far has been learning to work without a fixed guidance. Coming from an economics and private-sector finance background, I was used to understanding tasks through numbers and executing within a well-established process: the goal and deliverable were defined, and the path forward was relatively linear. But here, I initially got stuck on unfamiliar language (e.g., catalytic impact), and it was not immediately clear how to define the problem and design a workplan. Over time, I realized that this field requires a more qualitative and deconstructive mindset: what “impact” means in a blended finance theory of change, which assumptions are being tested, and how that “impact lens” interacts with other lenses and constraints that project teams face. In practice, we need to think about what data is desirable vs. required, feasible vs. viable, and real vs. ideal, and whose priorities should guide those trade-offs at different stages?

Another smaller but concrete example of my learning was how I redefined what “digital innovation” looks like in impact work. Our work scope mentions using digital tools (e.g., mobile data, AI, blockchain) to improve the salience, feasibility, and effectiveness of development impact indicators. I have to admit that I initially assumed this meant something highly technical or “fancy” - the kind of framing I was used to seeing in venture capital investment memos. But as I dive deeper, I realized it points to the heart of impact measurement in the real world - the data collection problem. As my faculty advisor put it, impact measurement ultimately comes back to two foundational questions: (1) Why are these metrics? and (2) Who is measuring them? - this second question highlights the data collection challenge. After looking at leading practitioners such as Ulula and 60 Decibels (they collect beneficiary and stakeholder feedback at scale through lightweight surveys and structured listening tools), I realized the strongest solutions are often not glamorous, but operationally simple, scalable, and carefully designed for real constraints. What matters is not novelty, but whether a tool lowers the cost of collecting credible feedback and makes it easier for often-unheard stakeholders to be counted.

Going forward, I hope to gain a clearer understanding of how impact measurement functions in blended finance, and how to develop a practical, project-team facing toolkit that translates an existing indicator catalogue into actionable implementation guidance. Also, I want to strengthen the skills of building trust and alignment within a team, managing workstreams under ambiguity, and learning how to communicate with and serve a client.