How do impact investors resolve the "measurement paradox" when social outcomes in developing countries lack reliable data?

Impact investing in developing nations struggles with poor data availability—e.g., tracking job quality at a local small business or long-term environmental benefits . This creates a paradox: investors need to prove impact but lack tools to measure it accurately. How are firms addressing this? Do they use AI to analyze proxy data (e.g., mobile payment activity as a job indicator), or partner with local NGOs to collect ground-level metrics? I want to explore hybrid measurement models that balance scalability with accuracy, and how they justify investments to stakeholders when hard data is scarce.