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Presentations
- Presentation -- Elena Alfaro
- Presentation -- Robert Kirkpatrick
- Presentation -- Martin Mubangizi
- Presentation -- Heather Savory
- Presentation -- Derval Usher
Big data refers to the vast amount of information which is created as a result of people 's increasing use of digital tools such as -- mobile phones, social networks, internet search engines, online payment systems, web applications, satellites etc. Big data presents both opportunities and risks. One of the advantages big data presents for the sustainable development and humanitarian communities is that, when extracted, patterns from datasets can unveil real-time insights at a cheaper cost than other tools requiring several months of planning. Moreover, in countries torn by conflict and other crisis, big data could sometimes reveal information that would have not been available otherwise. There are also risks associated with big data. Data privacy is often cited as one of them. However, when harnessed safely and responsibly, big data can play a vital role in sustainable development and humanitarian action. Although several data innovation projects demonstrating the potential of big data for public good have been conducted, only a few have scaled beyond proof-of-concept.
This session will examine inspiring examples of data innovation in the development and humanitarian sectors that have been institutionalised. Participants will hear from speakers working on data innovation and artificial intelligence in the public sector to understand barriers and recommendations for what is needed next.
The session will discuss the enabling environment for operationalising big data for sustainable development and humanitarian action touching on data collaboratives/philanthropy, the role of NSOs, encryption, and legal frameworks.
The session will aim to answer the following questions:
- What has been the scale of the BD4SD projects to date?
- What has been the impact of BD4SD projects implemented?
- How are governments actually using these data tools?
- Why are NSOs/national statistics fundamental to the functionality of these systems? How do NSOs add value in these data ecosystems?
- What are the main challenges for scaling up BD4SD pilots?
- What are the main opportunities with scaling up BD4SD pilots?
- What needs to be done to scale-up BD4SD projects?