Linking impact data: How a data ontology can ease impact data collection and analysis

Understanding the impact of impact investments is a challenge for asset managers. At Common Approach, we believe that an impact data ontology is a crucial part of the digital infrastructure that will be needed to improve impact measurement in the years ahead.

Since its early days, the field of impact measurement and management has matured considerably. We now have well-established standards like the Global Reporting Initiative, GIIN’s IRIS+ System, Impact Frontier’s Impact Norms, the Impact Management Platform, and SDG Impact Standards, and impact measurement and management practices are taking root in a wide variety of organizations.

However, with those developments have come new challenges. As the partners of the Impact Management Platform have illustrated in their systems map, the various standards serve different purposes for different users. An emerging challenge is how to accommodate that heterogeneity. Further, the growing adoption of impact measurement practices is rapidly proliferating the amount of data available. Another emerging challenge is to accommodate more complex and more voluminous impact data in ways that illuminate connections and insights, including across disparate standards and metrics.

To further advance impact management, the asset managers doing impact investing need better ways to store, exchange and analyze the emerging volume and complexity of impact data. This will deliver value across the impact ecosystem through efficiencies, coordination, and relevant fit-for-purpose measures and standards. Effective digital infrastructure will be crucial to meeting these emerging challenges and further advancing impact measurement and management.

An impact data ontology will improve impact measurement by facilitating interoperability among standards, generating efficiencies in impact measurement and management, and elevating transparency.

Let’s explore a crucial piece of digital infrastructure: impact data ontology

Data ontology is a computer science term for the conceptual scaffolding that maps how data is organized. It connects taxonomies together. A shared impact data ontology has the potential to ease data collection, make different standards interoperable, and deepen the analytic power of the data collected by asset managers doing impact investing.

Part 1: “What is an impact data ontology?” defines what a data ontology is and distinguishes it from vocabularies (sometimes called glossaries) and taxonomies.

The impact investing field has vocabularies and taxonomies. Impact Norms 5 dimensions of impact are a vocabulary. The IRIS+ impact taxonomy is, as the name states, a taxonomy. The SDGs (goals, targets and indicators) are a taxonomy.

An impact data ontology would connect these taxonomies: linking stakeholder characteristics to SDGs and IRIS metrics. These connections, well structured in data, will provide more context around and more insights into the data that is already being collected. The ontology increases insight through better connections rather than even more data collection.

The proliferation of impact data requires a new, more sophisticated understanding of data itself—its flows between actors; context-sensitivity; and the ways it is or could be informing decisions. Taxonomies provide labels and categories. Data ontology examines the meaningful relationships in information systems. We need both, but the former is not enough any longer. An impact data ontology would facilitate linkages among many standards.

Key takeaway: Ontologies are better at capturing context. Taxonomy identifies hierarchical relationships within a category; Ontologies allow multi-dimensional relationships, connecting many vocabularies and taxonomies.

Part 2: “The problems that an impact data ontology can solve if widely adopted”explores how a shared impact data ontology could help impact investors with their impact measurement. It describes the benefits that impact investors and their investees will experience if an impact data ontology is widely adopted. We focus on three:

• Reducing the tedium and repetition of collecting and sharing impact data.
• Allowing investors to reconcile different impact measurement standards used by investees without imposing metrics on them
• Providing a method for funds of funds to gain insights into underlying assets, enabling aggregate impact data across many funds

Key takeaway: Investors and investees have been building data exchange solutions for those two-party (investor-investee) relationships, but the resulting data exchange creates a lot of inefficiencies in the field. An impact data ontology creates the digital infrastructure that optimizes data infrastructure for the impact investing field

Part 3: “The Common Impact Data Standard: an impact data ontology” describes an example of an impact data ontology: the Common Impact Data Standard. It briefly explains why and how the Common Impact Data Standard was developed and who contributed. It outlines the particulars of this specific data ontology and provides examples of how the Common Impact Data Standard has been used in practice.

Key takeaway: The Common Impact Data Standard is the leading impact data ontology (the only one used by more than one software), and investors can start using it by using aligned software.

Part 4: “Realizing the potential” looks at the path from today to the widespread adoption of an impact data ontology. It specifies what benefits are available to asset managers today, with the technologies and updates that already exist, and which benefits will not be realized until more widespread adoption. We revisit proliferation challenges, linking ontology to overcoming these issues and others and how we could get there.

Key takeaway: there are things you can do today to be ready for a Common Impact Data Standard—and we hope you do—but the full benefits are not realized until we have widespread adoption.

Our goal for this series is to introduce the idea of “data ontologies” to impact investors.

While, of course, we would like the impact investing field to use the Common Impact Data Standard, our motivation is to advance the conversation about digital infrastructure and standards from vocabularies (sometimes called glossaries) and taxonomies to ontologies.

We hope that the next time you are in a conversation about digital infrastructure for impact investing, and those around you are talking about taxonomies and vocabularies, you will ask, “How might a data ontology help to integrate these efforts?” We hope this series of documents answers that question.