Data to Action - Sparking sanitation improvement in Lusaka with Data

client: WSUP


Data to Action

Around 70% of Lusaka’s population live in peri-urban areas and have limited access to well managed sanitation and water services. “Data to Action” was a research and design project exploring how data could be used to improve sanitation services in these settlements.


Using documentation to structure pit emptying services

Centred on converting an existing offline database into a user-friendly digital tool, we tested data-collection activities to ensure this process would be implementable across the city of Lusaka.


Identifying the target audience

Our first task was to study the data and understand who could benefit the most from it. Was it to support local authorities in making strategic decisions? To help service operators provide more efficient services? To assist emergency service in identifying areas at risk of cholera outbreaks? During the first collective workshops, we identified that it was critical to keep the data relevant in a smooth and effortless way. And for that, we needed the collaboration of the pit emptier services.


Putting the local community first

Our research interactions totalled over 24 hours of interviews with 18 different individuals. We explored the challenges stakeholders face, gained insight into their individual experiences within the service supply chain and sought solutions through data use and service alterations.

Collectively stakeholders identified the start of the data collection process as the most valuable place for us to focus our efforts.

— Javier Soto Morras, Neeeu Co-Founder

Try it out

By working through both a public service provider and a business, respectively, we were able to test with the future expansion of the FSM sector in mind.

Making it digital

The digital database was an essential part of the project. A simple and clear map-based database that would allow users to easily view and digitalise the data logged during the pit emptying operations. Intuitive to use, we implemented a visual representation of the main variables in both map and graph form.

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