Posts byNick Dickinson

WASHNote is led by Nicolas Dickinson. He is an associate at IRC and has been supporting country-led monitoring and evaluation of water and sanitation services at national and district/municipal levels where government, civil society and private sector set the monitoring agenda and the questions to be answered. In addition, he has worked on providing training for decentralized data collection, data processing, and life-cycle costing. He evaluated mobile-enabled water, sanitation and energy services for the Mobiles for Development Utilities Innovation Fund as a Grant Panel member and supports the commissioning and procurement of technology for governments and the WASH sector. Along with his passion for the use of evidence, he worked on developing data exchange and technology standards (WPDx) and tools for service providers, governments, NGOs and financiers.

WASH Learning Companion

We are excited to be developing and testing together with IRCWASH WASH Systems Academy, an AI Learning Companion to help onboard and support WASH professionals to learn about WASH systems and the building blocks. IRCWASH and WASHNote signed a Memorandum of Understanding at the start of 2024 to make this a reality.

It is built from two components:

  1. The Knowledge Companion, that provides a validated set of information and a model of the conceptual framework to be used.
  2. The Learning Companion, a conversational agent that can chat with users via mobile apps such as Telegram, Whatsapp, Signal or Element.

The Learning Companion document  and data repository focuses on WASH systems and their building blocks, and the conceptual framework of the WASH Systems Academy courses (IRC building blocks).

These components are easily extensible and it is possible to switch conceptual frameworks (knowledge graphs) on the fly to be able to map, for example, results from one data source to another methodology. Ultimately, these will be used to help in monitoring and evaluation and to support the development of validated public datasets.

So far, together with a small team, we’ve built a knowledge graph of the key concepts used, a document repository that can be easily cited by the learning companion and a Telegram bot. Much of our effort has been used to build up our user journeys, validate with existing users and develop a evaluation framework for both improving conversations and evaluating the outcomes of the exchanges.

We’ve also researched how to run this AI product in an affordable manner so that it can scale and achieve an impact on WASH services, which is the ultimate goal. Some of this research will be published shortly.

In the coming weeks, we will be testing with users in Rwanda and Ethiopia and we look forward to sharing our findings. As the product matures, the relevant software components will be open-sourced and shared online. More importantly, we are building with open source and out of principle also contribute back our improvements to these original open-source projects so that the bigger community online can benefit.

Comparing water point based and household survey based water access estimates with publicly available data

Water access graph comparing JMP and WPDx access

I am excited to share with WPDx a preprint of a study comparing different ways of measuring access to water services. . Comment and download the pre-print here:

It builds on the work of the jmpwashdata data science library that I started last year to enable researchers to use JMP WASH data like never before .

The purpose of the study is to determine how water point level estimates for rural basic water service coverage from the Water Point Data Exchange (WPdx) compare to the household level estimates from the Joint Monitoring Programme of WHO/UNICEF (JMP) in multiple geographies. The study describes how these different estimates are produced and proposes the comparison of JMP basic minus services on premises to WPdx basic access. WPdx basic access estimates the population with 1km of an improved water point. Comparing between metrics and triangulating different measured results can be useful to validate conclusions and inform decision making. This study finds a relatively strong correlation and linear trend between these two estimates in four countries that suggests that using household surveys and water point inventories together can be useful to decision makers who may only have one or the other data sources or may want to validate the conclusions from one against another. The WPdx basic estimates allow a more granular geographical level of access estimates that can be useful to districts and enable national vulnerability assessments. This could strengthen the type of analysis provided in JMP inequality charts showing the differences between country regions. At the same time, further research is needed to validate these trends at these lower geographical levels. Rural water leaders, including national and local governments, development partners, service providers and civil society should continue to advocate for the publication of water point data and the validation of access estimates on the basis of publicly available information. This plays an important role in improving the quality of both public and private data sets and analyses used by researchers and decision makers.

Read more about the study in the WPDx blog.


This study would not have been possible without the contribution of open data on water points by data providers to WPdx. Members of the Water Point Data Exchange (WPdx) working group reviewed both the proposal and findings of this work. Katy Sill of WPdx first recognized the potential of the work, provided invaluable feedback, and responded quickly with explanations about how the WPdx algorithms work while investigating and delivering improvements to the tools when required to make this comparison possible.

Similarly, the National Statistics Offices (NSOs) and the Demographic and Health Surveys (DHS) Program of the United States Agency for International Development (USAID) made it possible to use household survey data from different countries. I would like to thank the Joint Monitoring Programme of WHO/UNICEF (JMP) team for sharing country, regional and global estimates of progress on drinking water, sanitation and hygiene (WASH) in households as well as the estimates for the sub-indicators required to generate those estimates, for providing clarifications about the JMP methodology, and for taking time to reflect on study findings.

This material is based upon work supported by USAID under award number 7200AA18CA00033.