A DataKit™ is a work-ready set of data, software, and innovation questions, curated by DataKind, in a domain of social good. As you engage with a DataKit, you will apply your skills for social impact while deepening your understanding of problems common in the space. All learnings, ideas, and insights resulting from this DataKit will be aggregated and used by DataKind to expand our impact in the financial inclusion and economic opportunity sector. Specifically, we aim to leverage your insights and prototype solutions to expand on our product offerings, supporting stakeholders to expand their reach and impact. Learnings will be shared throughout the DataKit and beyond.
Over the past two years, DataKind has engaged in work exploring the potential impact of data science and AI in strengthening financial inclusion and economic opportunity. From our partnership with the International Monetary Fund to tackle big questions on climate and gender to launching three products supporting financial inclusion last fall, DataKind is ensuring stakeholders have access to high-quality, up-to-date, and relevant data and data-driven insights to support strategic decision-making and service delivery. Help us deliver insights, understanding, and solutions to address the housing crisis in the United States.
The social sector and its stakeholders face significant challenges when financial inclusion and economic opportunity data are missing or inaccessible. Without comprehensive, high-quality, and timely data, organizations struggle to identify gaps in access to financial services, assess the impact of economic policies, and design interventions that effectively reach underserved communities. Limited data availability—especially disaggregated data by gender, geography, and socioeconomic status—prevents stakeholders from making informed, data-driven decisions, leading to inefficiencies and missed opportunities for meaningful impact.
With this in mind, DataKind aims to grow the breadth and depth of our knowledge in the financial inclusion and economic opportunity sector to support a global suite of stakeholders. Through this DataKit, we’re looking to explore international economic opportunity data and insights to better understand opportunities to leverage publicly available data to support local decision-making and impact.
Ready to go? Head to the Challenge to dive in. It comes with further details, some initial questions, and guidance for how to get started.
Join us to deliver insights, understanding, and solutions to explore and address global economic opportunity. As a volunteer, you can contribute to challenges in technical or nontechnical ways, as often as you’d like–there’s something for everyone. Here are some avenues to consider:
We’re working to capture the most comprehensive picture of economic opportunity. There is a lot of data and information out there -- help us find and evaluate data sources to expand access to key information.
Draw connections between datasets to help create a community-level understanding of financial and economic needs in selected countries. Visualize those relationships in interpretable ways so that we can answer key questions.
For this challenge, we’ve provided links to some initial datasets to explore by country, but we encourage you to do additional research to identify more sources, as relevant and applicable. We are also open to exploring new countries not yet listed, so feel free to offer suggestions!
Every bit of information you contribute is a chance for DataKind to better understand financial inclusion and economic opportunity, and provide data-driven insights to stakeholders in the sector. We want to see it all as everything will help–not just right answers, but wrong answers and works in progress too.
We ask that you share your work regularly on Slack or in the GitHub Discussions. When you share, please define the dataset(s) you found or used, how you did the work, and what you think the key takeaways are.
Please add your work to the appropriate sub-folder in this repository, and engage with the discussions.