Towards a Governance Structure for the Data-Driven Prioritization of Humanitarian Aid

Abstract

The incidence of natural disasters worldwide is increasing. As a result, there is a growing number of people in need, whereas limited resources to help these people are available. It is therefore important to effectively and efficiently prioritize the most vulnerable people in the preparedness phase, and the most affected people in the response phase of humanitarian action. Data-driven models have the potential to help in doing so. However, to be able to apply these models in a country, a certain level of data preparedness is required. Therefore, there is a need to understand how to facilitate, stimulate and coordinate data-sharing between humanitarian actors. To realize this, a generic governance structure is needed that can facilitate the achievement of this level of data preparedness on a large scale. This thesis aims to develop this generic governance structure using a data ecosystem perspective, by providing insight in the success criteria for establishing a ‘humanitarian data ecosystem’. As no scientific literature exists on this topic, as a first step, a general framework with data ecosystem governance success criteria is developed by means of a systematic literature review. Subsequently, the applicability of this framework in the humanitarian sector is assessed through a case study on the ‘Community Risk Assessment and Prioritization toolbox developed by 510 Global, the data team of the Netherlands Red Cross. It is found that the data ecosystem approach provides a suitable framework for assessing the criteria to be addressed when aiming to establish a successful humanitarian data ecosystem. Moreover, the empirical findings resulting from the case study led to the creation of a generic governance structure to be used when determining on the approach to adopt when rolling out the Community Risk Assessment and Prioritization toolbox in a new country.