29 Camilla Nestor: FINclusion Lab creates single datasets and databases where previously siloes existed. For example, the data—which primarily includes access point location and demand-side data like population density, cellular coverage, poverty rates and the like—is usually found in project documents (PDFs), or separate online locations managed by regulators, or even individual Excel files from financial institutions. Bringing it all together in one place allows users—often regulators, financial institutions or others—to conduct analyses across different types of data including service points (geo- coded data), credit/deposit usage and demographics. It also allows users to visualize the data across geographies anddrilldowntomorespecificlocales.Becausewepublish this data in a highly interactive format, users can explore the data based on their specific questions or interests. For example, a user can explore a particular district or type of financial service provider, or pick a reference period to view trends. Militzer: Doyouhaveanyexamplesofhowtheplatform is currently being used to improve financial access and health in emerging markets? Nestor: Regulators and policy makers, for example, are able to identify geographic areas where there are either no access points or very few access points. They can also examine the socio-economic data in these same areas, which may indicate whether there is an opportunity to create incentives-based policies for financial service providers to expand. Similarly, these same actors can explore the interactive data to understand market concentration of financial service access points and identify opportunity areas. Additionally, bringing this data together allows stakeholders to monitor and analyze progress on financial inclusion targets. Militzer: Where does the platform’s data come from, and how do you determine what data sets to include? Nestor: This can be the difficult part. Typically, in a particular country, our team at MIX gathers a group of local stakeholders, including funders, financial service providers, regulators, associations and others. We work with them to understand the priority use cases and, based on those use cases, determine exactly what data are needed. For example, if a priority is to understand access to finance for smallholder farmers, it may be especially important to capture data on agricultural markets in that country. The same group of stakeholders also helps our team to identify the available data sets, which often take different formats. These conversations with local stakeholders are key to creating useful information tools. In the end, these same actors (and others like them) are also the users. Militzer: Why did the MIX decide to aggregate existing data, rather than generating new data? Are there any downsides to this approach? Nestor: The short answer: Creating new data is expensive! There have been a number of experiments with surveying to gather data, for example, collecting data using enumerators or surveying all the agents in a particular country. But this approach is very expensive to undertake and, even if there is funding available for the initial collection, it is difficult to find support to maintain and update those datasets on a continuous basis, even annually. Instead, we prefer existing data sources. In particular, we see regulators as the preferred source of data because those collection costs are already baked into the system, so to speak. If we can prove the value with those data sources, we are then also more likely to see them improve their data because they are using the data in meaningful analysis. MIX CEO Camilla Nestor.