Interview Questions for Ewan Oglethorpe, Data Friendly Space's Executive Director
Data Friendly Space (DFS), a U.S.-based NGO established in 2018, has been making waves in the international humanitarian community. With over 85 staff members working across more than 10 countries as of 2021, DFS has been bridging the gap between the level of technology in the humanitarian sector and that of the private technology sector.
DFS's flagship tool, DEEP, has been instrumental in mitigating the complexities of humanitarian crisis response, particularly during the COVID-19 pandemic. Prior to DEEP, humanitarian analysts relied on laborious methods such as juggling spreadsheets and post-its to sort through large amounts of qualitative data.
DEEP, a standard web application stack written in Python and Javascript, has transformed this landscape. Key technologies including Django, GraphQL, ReactJS, and D3.js power DEEP, making it a tool that provides actionable insights from qualitative data such as news articles, reports, and social media posts.
DEEP's open-source nature, with the code available on GitHub and contributors welcome, has been a boon for the humanitarian community. It has allowed for the systematic extraction of information from secondary data sources, helping to create a well-catalogued library for qualitative information that can be easily accessed.
Moreover, DEEP utilizes natural language processing solutions to help analysts use the tool for more routine tasks. This automation of repetitive tasks has been a game-changer, allowing the humanitarian community to focus more on critical missions rather than data management.
DEEP has provided analysis support and developed new features of the platform for COVID response efforts in countries like Colombia and Bangladesh. It has also been instrumental in extracting and housing legacy data in an indexed and centralized location, making it more actionable.
DFS's work doesn't stop at DEEP. The organisation also develops systems for semi-automated "human in the loop" information retrieval and extraction. This approach aims to help organisations with limited IT capacity or budget to improve their data infrastructure and make their data more readily usable.
While specific initiatives taken by DFS to improve digital capacity in the global aid community, focusing on field-level digital literacy and data infrastructure, could not be found in the provided results, it is generally known that such efforts often involve training programs, workshops, the development of simple, context-adapted digital tools, data infrastructure improvements, and collaborative approaches.
Despite this gap in information, it is clear that DFS is committed to reducing the time spent on managing data, allowing organisations to focus on their critical missions. By creating human-friendly data systems, taming the flood of qualitative data, and automating repetitive tasks using tools like DEEP, DFS is truly making a difference in the humanitarian sector.
- The Data Friendly Space (DFS), leveraging artificial intelligence and data-and-cloud-computing technology, is developing systems for semi-automated "human in the loop" information retrieval and extraction, aiming to help organizations with limited IT capacity or budget to improve their data infrastructure.
- To make DEEP, the flagship tool of DFS, more effective, DFS utilizes natural language processing solutions, automating repetitive tasks and enabling analysts to focus on critical missions instead of data management.
- DEEP, an open-source application, allows for the systematic extraction of information from secondary data sources, creating a well-catalogued library for qualitative information that can be easily accessed, thus promoting the use of open data in the humanitarian sector.
- In the COVID-19 pandemic response, DFS's work, notably DEEP's provision of analysis support and new platform features, has been instrumental in Colombia and Bangladesh, demonstrating the power of innovation in the humanitarian sector, particularly in data infrastructure and artificial intelligence.
