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Challenge #4
Global WASH Data Portal
Emergency Data Science
York University
5 December, 2018
Toronto, Canada
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
 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
 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)
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
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?
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
In camp
Out of camp
Overlap Between In
and Out of Camp
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)
We will know we are right when:
● # Visitors to the site, repeat visitors
● Positive user feedback
Tech Stack
Next Steps
 Technical scoping
 Produce a business case for the MVP
 Fund
 Trial
 Refine
 Rollout

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Approach to Challenge 4 - Global WASH Data Portal

  • 1. Challenge #4 Global WASH Data Portal Emergency Data Science York University 5 December, 2018 Toronto, Canada
  • 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.  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.  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. 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. 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. 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
  • 10. Overlap Between In and Out of Camp
  • 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. We will know we are right when: ● # Visitors to the site, repeat visitors ● Positive user feedback
  • 14. Next Steps  Technical scoping  Produce a business case for the MVP  Fund  Trial  Refine  Rollout