Data for Decision-Making

Water and Sanitation in Low-Resource Settings

BACKGROUND

Sustainable access to safe and equitable water, sanitation, and hygiene (WASH) is a basic human need that remains unmet in numerous locations. Nations around the world strive to close this gap, at present under the banner of the United Nations Sustainable Development Goal 6. Achieving universal, adequate, accessible, and equitable WASH coverage requires the concerted efforts of professionals from national and local governments, development agencies, civil society organizations, private companies, and research institutions, as well as citizens and community organizers.

A further imperative to advance WASH development emerged during a global crisis, the COVID-19 pandemic. The accompanying global shutdown, which limited traditional, field-based WASH data collection and monitoring, has focused increasing attention on emerging data sources and analytics that could be better leveraged to support WASH improvement efforts. Over the long run, actions to consolidate WASH information resources, reduce one-time use of datasets, and leverage a broader range of data sources (including those previously considered unrelated) will have powerful implications. Accompanying advances in artificial intelligence (AI) analysis methods will also increase capabilities for learning and responding to critical WASH needs.

Objective

The goal of this report is to coalesce knowledge about how WASH stakeholders view emerging trends in data science. It represents a planning effort to align data science advances with the most potent WASH needs and demands. Analyzing how various professionals contribute to or could use data science illustrates points of potential engagement that could lead to clearer partnerships and reduce duplicative or ineffective efforts. In cases of severe data paucity, data science activities could be prioritized to offer a baseline for movement toward better-informed decision-making.

APPROACH

More than 65 decision-makers were invited to participate in this research, representing a broad cross-section of WASH organizations. Researchers administered semi-structured interview questions during phone or video calls between March and June 2020. The interview guide included both general questions for all interviewees and specific questions regarding predetermined data science “use case” hypotheses, tailored as applicable to the decision-makers’ professional organizations or roles. Common information needs to be reported across decision-makers and their organizations were then clustered by topic. Researchers pooled information from multiple interviews as well as related literature to assess and define the characteristics of nine specific data science use cases spanning water, sanitation, communities, programming, finance, and health.

Satellite Image of United Kingdom Map

Data for Decision-Making – USE CASES

1. Forecasting groundwater quality and quantity— Groundwater supplies are critical to meeting water demand, yet data on their quantity and quality remain hard to come by. Platforms that encourage data access and sharing across political boundaries would help to predict and forestall water supply shortcomings.

2. Reducing non-revenue water (NRW) — Treated water is lost at a high rate in many locations due to both natural and social causes, reducing compensation to water suppliers and straining environmental resources. Addressing this issue through technologies such as remote sensing and telemetry sensors could enhance water service efficiency.

3. Coordinating fecal sludge emptying — Pit latrine and septic tank emptying often takes place ad hoc, leaving pits overflowing, homeowners frustrated, and service providers without work. Coordinating these services using a central application and sensor-equipped vacuum trucks could better align the needs of workers, customers, and regulators.

4. Understanding sanitation costs — Sanitation planning at a city level often introduces excess complexity and ignores the hidden costs of fecal sludge treatment and disposal. Considering the entire sanitation value chain, newer costing applications could use local pricing information to optimize a blend of appropriate options.

5. Anticipating waterborne disease outbreaks — Retrospective disease surveillance leaves little response time for public health managers to plan or modify prevention and mitigation efforts. Risk mapping and forecasting tools might use algorithms to put decision-makers a step ahead.

6. Interpolating household data — Achievement of global WASH goals relies on household-level access, but descriptive household data are time-consuming to gather and not uniformly available. Advanced data interpolation techniques could be applied to use fewer survey points to generate high-resolution maps and summary statistics.

7. Understanding local contexts through community classification — Tailoring WASH interventions to local community context is both critical to successful programming and notoriously challenging at large scales. Leveraging and combining existing data offers a powerful means to better customize intervention planning.

8. Targeting the poor and vulnerable — Using a single indicator such as annual gross income to qualify household for WASH subsidies may extensively misjudge poverty levels and creditworthiness. Brief, multi-question “smart” surveys offer a pathway to more accurately target financial support using alternative wealth indicators.

9. Evaluating impacts — WASH monitoring and evaluation often falls prey to negative evaluation data at or near the end of projects, when it is too late to respond. Improved, real-time processing of interim or passive data could yield valuable insights to guide investments and clarify success factors.

RECOMMENDATION

1. Anticipate frequent, albeit often indirect, data use for decision-making. 
2. Normalize sharing to improve the cost-effectiveness of data production. 
3. Use advances in automated data recording and analytics to vastly assist, but not replace, human decision-making efforts.
4. Expand capabilities for reframing the timing of evaluation.
5. Embrace the crucial role of data science in WASH development.