30 Militzer: How does MIX work with other players in the industry, to avoid duplicating efforts or siloing as you aggregate and share this data? Nestor: Any public domain data that MIX “normalizes” to ensure its reliability and standardization is available from the “About the Data” tab on the FINclusion Lab website (free registration is required to download the files). We also reuse existing datasets, such as those created from financial services providers’ service point censuses and through actors like the FSD Network in Africa. Sourcing reliable data—as you probably already know—is always challenging. We want to make sure that good, useful data is available to the financial inclusion community so decisions can be better informed. Militzer:Whataresomeofthebiggestchallengesyou’ve faced in operating the platform, and how have you overcome them? Nestor: Data availability for the full set of priority use cases—as determined by the initial group of local stakeholders —is always a challenge and one that cannot be easily overcome in the near term. Rather, we hope that by demonstrating the value of this analysis, it will spur various actors, including financial service providers and regulators, to act to bridge these data gaps. One challenge we’ve faced and overcome is that of geo- location. We geo-locate data by aggregating it at the most detailed geographic (or administrative) unit. In the context of the United States, a county is more detailed than a state, and a municipality is more detailed than a county. In many markets, those administrative units change with surprising frequency and datasets do not always line up, especially at the most granular level. In those cases, instead of tossing that data out, we map it to the next highest administrative level. Obviously, it reduces the granularity of the data but it increases the reliability. This is a good example where the underlying data has quality issues and we’ve developed a solution to accommodate those changes. Again, if actors see the insights that can be derived from this information, it could lead to improvements in that underlying data. But that is a long-term challenge. Militzer: Why are data aggregation and mapping so important—and how much more effective could “Big Data” be in global development, if we had better ways of synthesizing it? Nestor: You can imagine a scenario where you look at Google Maps and are able to see financial access points down to a very local level—including mobile money or banking agents, branches and other types—updated through crowdsourcing rather than through expensive surveys. And taking that further, you might be able to see real-time data on mobile money agent liquidity, what financial products are offered at which access points, cost data, reviews—everything a consumer would need to do educated shopping for financial services. This isn’t so far- fetched and has considerable implications for some of the major financial inclusion data collection efforts underway. Militzer: Relatedly, is Big Data all it’s cracked up to be in the financial inclusion space? Does it live up to the hype—or are any unexpected challenges/downsides diluting its impact? Nestor: Big data holds enormous potential to create better-designed products, improve the efficiency of delivery channels, and pull excluded consumers into the financial sector. It’s obviously a very exciting space. But of course, there are risks—particularly around data privacy. Depending on how digitally connected they are, people can generate hundreds of data points about their lives each day, raising real issues around who can access that data, and how it’s used. Even in mature markets with comparably strong privacy laws, you have situations that expose personal data about people—who never agreed, or “opted in” to having their data collected and sold. The breach of trust with consumers—in particular, low-income consumers who are often less likely to trust formal financial services—can be enormous. I hope we can apply these lessons to efforts in emerging markets to ensure we’re tapping the upside potential of big data to further financial inclusion without exposing households to risks they’re ill-equipped to handle. Militzer: Do you have any thoughts on the challenges facing donor-financed initiatives that serve a public good, but that often lack a clear revenue model that could allow them to exist as for-profit businesses? What is the path forward for these types of initiatives, when/ if their donors’ priorities shift?