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

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Towards a Governance Structure for the Data-Driven Prioritization of Humanitarian Aid a Data Ecosystem Approach Haak Developing a successful humanitarian data ecosystem Towards a Governance Structure for the Data-Driven Prioritization of Humanitarian Aid A data ecosystem approach Master Thesis By Elise Haak October 2017 Towards a Governance Structure for the Data- Driven Prioritization of Humanitarian Aid A data ecosystem approach Master thesis submitted to Delft University of Technology in partial fulfilment of the requirements for the degree of MASTER OF SCIENCE In Complex Systems Engineering and Management Faculty of Technology, Policy and Management By Elise Haak Student number: 4111672 To be defended in public on November 6th, 2017 Graduation committee: Chairperson : Prof. Dr. B.A. (Bartel) Van de Walle, Policy Analysis First Supervisor : Drs. J. (Jolien) Ubacht, ICT Second Supervisor : Dr. S. (Scott) Cunningham, Policy Analysis External Supervisor 1 : M. (Marc) van den Homberg, 510 Global External Supervisor 2 : S. (Stefania) Giodini, 510 Global PREFACE In front of you lies the result of my past months of researching the possibilities to enable the data-driven prioritization of humanitarian aid. It has been a great experience to apply the knowledge I gained during the CoSEM Master’s program to a research problem with such a societal relevance. I learned a lot about the very interesting and dynamic humanitarian sector, and the impact of the trend of the increasing availability of data on this sector and its operations. Also, I had a great time working with the extremely motivated and inspiring 510 Global team. I have been very lucky that while carrying out this Master’s thesis, I was supported by supervisors who were very helpful and very much involved with my research process and progress. I want to thank my graduation committee from the TU Delft: Jolien, Bartel and Scott. Jolien, thank you for supporting me from the beginning, for helping me to structure my ideas, reminding me to stay focused, and for always providing me with very extensive feedback. Bartel, thank you for sharing your humanitarian expertise with me, and for helping me to remain critical. And thank you, Scott, for your detailed comments and insights, and for showing me different perspectives on how to approach my research problem. I would also like to thank the 510 Global team, for the guidance and support I received from them in developing this thesis. Thank you, Marc, for your time, dedication and all the great insights I received from you, both on a practical and on an academic level. Your expertise and experience very much helped me to make this happen. Stefania, thank you for thinking along at points where I needed assistance, and for always being flexible and helpful. Maarten, thank you for giving me the opportunity to write my thesis at 510 Global and for helping me specify my research proposal, and thanks to Jannis for patiently answering all my questions. And lastly, thanks to the entire 510 Global team, and to everyone else who contributed in one or another way to this thesis. Elise Haak October 2017 i SUMMARY The incidence of natural disasters worldwide is increasing. As a result, there is a growing number of people in need, whereas a limited amount of resources to help these people is available. It is therefore important to effectively and efficiently prioritize the most vulnerable people prior to a disaster (preparedness phase), and the most affected people in the response phase of humanitarian action. The rapidly changing information environment that results in an increasing availability of data, offers the potential to make these prioritization processes both more effective and more efficient. A growing number of studies is dedicated to data-analytic models that can help in humanitarian decision-making processes when trying to respond to the consequences of a natural disaster. One of these models is the ‘Community Risk Assessment and Prioritization toolbox’ of 510 Global, the data team of the Netherlands Red Cross (NLRC). This is a data-driven solution that aims to better prepare vulnerable communities in the preparedness phase, and, once a disaster hits, to be able to provide timely and adequate humanitarian aid. The toolbox is under development for three different countries: Malawi, the Philippines and Nepal. If it can be taken to scale across the humanitarian system by being able to roll it out in many countries prone to natural disasters, it could contribute to significant time and cost savings in the provision of aid. Currently it is a very challenging, time-consuming and resource-intensive process to collect all the data necessary for the toolbox to function well. Therefore, to realize this scale up, it must become clear how to facilitate, stimulate and coordinate data-sharing between humanitarian actors on a large scale. 510 Global needs a generic, non-country-specific governance structure to determine which approach to adopt in countries where they want to roll out the toolbox. This led to the following research question of this thesis: WHAT DOES A GENERIC GOVERNANCE STRUCTURE THAT FACILITATES THE PROCESS OF SCALING UP THE COMMUNITY RISK ASSESSMENT AND PRIORITIZATION TOOLBOX LOOK LIKE? To answer this question, the theoretical outcomes of a systematic literature review were combined with the empirical findings that resulted from interviews with eight humanitarian data experts representing different countries or regions, with a wide knowledge on the inter- organizational data landscape in their country or region. The systematic literature review focused on ‘data ecosystems’, which are networks of actors between whom data-sharing needs to be facilitated. To come to a generic governance structure for scaling up the Community Risk Assessment and Prioritization toolbox, a ‘humanitarian data ecosystem’ must be established. So far, there is no scientific literature that addresses this concept. The outcome of the systematic literature review, which was validated with a group of open data researchers from the Delft University of Technology, was a framework of criteria to consider when developing a successful data ecosystem. Three different categories of criteria were distinguished: 1) Data supply, relating to the provision of data as open data, 2) Governance, being the framework of policies, processes and instruments to realize common goals in the interaction between entities (and facilitating data supply), and 3) User characteristics, including their needs and capacities. Translating this theoretical framework to the process of scaling up the Community Risk Assessment and Prioritization toolbox in an evaluation session with 510 Global team members led to the conclusion that the humanitarian data ecosystem around the toolbox is still very ii much in its infancy. As a result, some of the theoretical criteria only become relevant in a later stage of the humanitarian data ecosystem. Currently there is a strong focus on initial data collection. The governance of the humanitarian data ecosystem is very much underdeveloped. There is a lack of central governance that can stimulate the data ecosystem development, and can coordinate and align the different and fragmented individual humanitarian data initiatives. The empirical findings, which were structured based on the theoretical governance criteria found in literature, showed that to successfully govern the process of rolling out the Community Risk Assessment and Prioritization toolbox in a country and thereby contributing to its scale up, there are generically four different roles to be identified: 1. The initiator/coordinator: Leading agency that should initiate, coordinate and support the development of the model in a country. The most suitable organization to take up this role is a UN agency, due to their strong and structural position in a country, and their relatively advanced information management capacities. 2. The local lead: National body (government agency) responsible for managing and maintaining (e.g. including new features in and updating the data in) the toolbox, to ensure its sustainability and to create local buy-in. 3. Data providers: Organizations sharing data that can serve as an input for the toolbox. The identification of data providers is a time-consuming process, and data providers are not always willing to share their data, which is why it is important to make clear ‘what is in it for them’. 4. Toolbox users: Organizations that use the toolbox and incorporate the output in their operations. There is a wide range of potential users for the toolbox, mostly humanitarian and government agencies. The initiator/coordinator is responsible for the capacity-building of the other roles. For all roles, a shift in mentality and organizational culture is required to move towards more data-driven operations. To realize this and to incentivize parties to take up their role, it is very important for the initiator/coordinator to involve all relevant stakeholders from the start of the model development, to create ownership and have them realize the added value. 510 Global are advised to either try to directly approach a suitable initiator/coordinator using their current network, or adopt a stepwise approach, in which they start the toolbox development in a country with a less ideal initiator/coordinator and try to attract other parties as the development advances. The outcomes of this study are twofold. In the first place, the study provided a generic governance structure on how to approach the process of scaling up the Community Risk Assessment and Prioritization toolbox in a country, to contribute to the shift towards more evidence-based humanitarian decision-making (societal contribution). In the second place, the study resulted in the creation of a theoretical framework of data ecosystem success criteria, which was theoretically validated by open data experts, and empirically validated by applying it to the humanitarian context of this study (scientific contribution). Future research should focus on further validating the theoretical framework by applying it to other case studies. Other case studies can also provide insight in the evolvement of the maturity of other data ecosystems, which is something the immature humanitarian data ecosystem can learn from.
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