The problem: Impact data isn’t built to scale

Over the past 15 (1) years, the impact investing industry has made significant progress in acquiring and standardising impact data. Yet approaches to organising, characterising, and linking impact data with investment intentions remain fragmented. This fragmentation increases costs, hinders communication within and across organisations, and ultimately reduces the impact potential of capital. The increase in technical avenues for exploring datasets underscores the urgency of bridging this gap.

More than data: Interoperable databases

Impact data includes a wide range of information relevant for measuring and managing impact performance. A systematic approach to organising and managing impact data across investments and portfolios is essential to understand impact results, compare performance across contexts, and build a credible evidence base that can support learning, benchmarking, and accountability.

Why data convergence is now feasible — and necessary

The impact investing ecosystem has matured through decades (2) of field-building efforts and now shares a common foundation of terminology. At the heart of this foundation is the Theory of Change (ToC) framing. Achieving data convergence is an indispensable next step for the industry to move beyond narrative-driven impact claims. Momentum is slowly emerging through initiatives that provide guidance on practically embedding the ToC framing, setting uniform representations of impact, and utilising the Five (3) Dimensions of Impact framing to contextualise impacts.

A shared structure for impact data

A reference structure for impact data is a necessary precursor to systematising impact measurement and management. A data structure does not define what to measure; it defines how impact data is organised so diverse metrics and frameworks can interoperate. Incorporating the ToC logic directly into the design of databases creates a shared language for representing real-world impact pathways. A structured data model operates across three (4) levels: conceptual, logical, and physical.

What changes if this is adopted?

For individual investment organisations, adopting a structured ToC-based data model can streamline internal information management processes, increase the decision relevance of data, and enhance stakeholder communication. For the ecosystem, broad adoption can lead to greater systemic alignment and establish a common language. Structured datasets can strengthen trust in impact investing, contribute to grow the evidence base, and help scale capital towards effective solutions.

What it will take to succeed

Several factors are critical to successful data convergence. Approaches must feature low barriers to entry and clearly demonstrate value. Individual organisations should benefit directly at multiple levels, with scalable implementation allowing them to adopt the model at varying levels of sophistication. Finally, supportive ecosystem conditions, including collaboration and shared standards, can accelerate convergence.

Moving from concept to practice

To advance data convergence, the industry can pilot and refine structured ToC-based impact data models through real-world applications. Additional recommended actions include developing supporting guidance, governance structures, and training materials. The industry must also test advanced analytics using structured impact datasets and foster collaboration among investors, data providers, and technology developers.