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Approach to Challenge 4 - Global WASH Data Portal

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Presentation by working group on how to approach building a Global WASH Data Portal

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Approach to Challenge 4 - Global WASH Data Portal

  1. 1. Challenge #4 Global WASH Data Portal Emergency Data Science York University 5 December, 2018 Toronto, Canada
  2. 2. Recap of the Challenge  Track 16 UNHCR WASH indicators: changing over time, geo-spatial  Missing Data - 70% urban WASH information (out of camp)  Interoperability with development data (SDG #6)  Make resource allocation decisions  Exportability / compatibility with other platforms  General information access for broad set of users
  3. 3.  Incomplete data (HR changes, out of camp)  Lack of unified source of data (methodologies)  Validating existing data (trust and quality / Gov data?)  Decision-making based on bad data (limited data / good enough?)  Siloed data, unknown data (data from other sectors logistics / health)  Dynamic nature of the problem space (fluid environment) Problems Identified
  4. 4.  Take advantage of data that already exists  Highlight gaps in existing data  Improve decision making (prioritise needs)  Be accessible to all humanitarian actors Refined Goals Anti Goals  Fix the cluster data problem (data quality / unifying data collection)
  5. 5. We believe people who are allocating resources have a problem getting and validating the information they need to make decisions. We believe we can help by building the portal with the following information, to compare both in-camp and out-of-camp data. Problem Statement
  6. 6. Who are our users Decision makers (people who have money) ● Good data to efficiently allocate resources, good data, prevent worst case scenarios ● Behaviors: decisions by committee What is the least amount of information needed to make meaningful/impactful decisions?
  7. 7. What is the least amount of information needed to make meaningful/impactful decisions? JMP data: joint monitoring program Latrines Water Soap By site, over time
  8. 8. In camp
  9. 9. Out of camp
  10. 10. Overlap Between In and Out of Camp
  11. 11. MVP: In Camp ● Demographic data ● UNHCR WASH Data ● By site and over time Out of camp (sub-municipal) ● Demographic data ● WASH indicators ● By geography From JMP (2 year data collection cycles)
  12. 12. We will know we are right when: ● # Visitors to the site, repeat visitors ● Positive user feedback
  13. 13. Tech Stack
  14. 14. Next Steps  Technical scoping  Produce a business case for the MVP  Fund  Trial  Refine  Rollout

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