One of the pleasures of working on FINclusionLab has been getting my work translated into the languages of most of the countries we cover: French, Spanish, and Turkish. By happy coincidence these are also most of the languages that I either speak or am making a serious effort to learn. I am not fluent enough in any of them to do the translating myself, but I can at least understand the translations that I’m applying, and read domestic press coverage when it comes out. It’s not always easy to get key stakeholders to actually use the tools we’re making for them, so it was particularly nice to see Mexico’s national bank trumpeting the release of the financial inclusion dashboards we made:
La CNBV presentó los mapas interactivos para el análisis de la inclusión financiera en México
[Google’s translation is not bad, albeit even wordier than an already government-speak heavy press release]
My biggest project for the past few years has been an ongoing series of workbooks about access to financial services in Africa, Asia & Latin America. I do this work as a subcontractor to an NGO called MIX, whose CEO recently gave an interview concisely explaining why we do this work and what it’s useful for. This paragraph gets to the heart of it:
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 and drill down to more specific locales. Because we publish 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.
I’ve also been working behind the scenes on the infrastructure we use to conduct and share analyses, simplifying the toolchain and making updates & translations easier to apply. This included rebuilding a venerable Tableau template from the ground up, and here’s the first country workbook we’ve published in the new template: