Experience and recommendations from 10 years of implementation research by mWater and partners.

1. Building nationalWASH
monitoring systems that work
Experience and recommendations from 10 years of implementation
research by mWater and partners
by John Feighery, PhD | john@mWater.co | mWater

2. “Water, like many other things we pretend to know and control, leaks
from and undermines the stories we tell.”
-Nikhil Anand1
1. Anand, N. Hydraulic City: Water and the Infrastructures of Citizenship in Mumbai; Duke University Press: Durham ; London, 2017.

3. Why listen to us?
mWater is one of the largest
monitoring platforms in the sector:
• The free mWater mobile data collection
and visualization platform has more than
120,000 users in over 184 countries
• mWater partners include leading
organizations in WASH monitoring who
develop new approaches and
technologies to share with the sector
Our partners and investors include:
National monitoring systems
implemented with mWater include:
• Haiti – national utility monitoring system
and national water sector MIS
• Ethiopia – water asset management
system in Afar and Somali Regions
• Guinnea Bissau – national borehole and
CLTS monitoring systems (with UNICEF)
• Malawi – national water point mapping
and asset management system
• Papua New Guinea – national WASH
monitoring system (with WaterAid)
• Timor-Leste (East Timor) – RapidWASH
monitoring system (with WaterAid)

4. BetterWASH data is urgently needed
• “In almost all utilities studied [who successfully completed a
turnaround in performance], the first actions in their business plans
were improving human resources and Management Information
Systems.”1
• 79% of countries surveyed in the 2019 UN-Water GLAAS report have
government-led processes for monitoring progress toward national
targets but only 10% have sufficient human resources for
monitoring.2
• Only 10 of the 21 countries in Eastern and Southern Africa have
comprehensive annual reporting processes forWASH.3
1. Soppe, G.; Janson, N.; Piantini, S. Water Utility Turnaround Framework; World Bank, Washington, DC, 2018. https://doi.org/10.1596/30863.
2. World Health Organization. National Systems to Support Drinking-Water, Sanitation and Hygiene: Global Status Report 2019. UN-Water Global Analysis and
Assessment of Sanitation and Drinking-Water (GLAAS) 2019 Report; World Health Organization: Geneva, 2019.
3. Itad; mWater. SDG6 +5 Review: Understanding monitoring for SDG6 across Eastern and Southern Africa, Summary Report, 2021.

5. Yet most initiatives end in failure…why?
Tanzania1,2 Water Point Mapper
(WaterAid / Geodata)
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
1. Welle, K. Water Point Mapping in East Africa; WaterAid.
2. Verplanke, J.; Georgiadou, Y. Wicked Water Points: The Quest for an Error Free National Water Point Database. IJGI 2017, 6 (8), 244.
3. Miller, A.; Nhlema, M.; Kumwenda, S.; Mbalame, E.; Uka, Z.; Feighery, J.; Kalin, R. Evolving Water Point Mapping to Strategic Decision Making in Rural Malawi.
WEDC, Loughborough University 2019.
4. Welle, K. WaterAid Learning for Advocacy and Good Practice: WaterAid Water Point Mapping in Malawi and Tanzania; WaterAid, 2005.
Maji MIS
(World Bank)
Water Point Mapping
Project (MoWI / Geodata)
Taarifa
(World Bank)
Payment by
Results (DFID)
Malawi1,3,4 Water Point
Mapper
(WaterAid)
2003
Akvo FLOW
(Akvo, Water for People)
Monitoring & Evaluation Tool
(EWB Canada)
CJF Water Asset
Management System
(Scottish Gov’t)
Two examples from East Africa:

6. Adapted from Vitasovic, Z. C.; Olsson, G.; Liner, B.; Sweeney, M.; Abkian, V. Utility Analysis and Integration Model. Journal -
American Water Works Association 2015, 107 (8), 64–71. https://doi.org/10.5942/jawwa.2015.107.0117.
What is going on here?
Technology is the visible part of the
monitoring system, so stakeholders and
donors tend to focus too much on it.
The real work of building national
monitoring systems depends on the
capabilities and motivations of people
and the processes they use to get work
done in their organizations.

7. Common causes of monitoring failures
• Technology-driven - viewing the system as an IT project, to the exclusion of human resources,
processes, finance, incentives, and organizational culture
• Lack of government ownership – often due to donor-driven, one-off projects
• Lack of alignment – development partners not aligned to government-led approach, promoting
alternative systems that consume time and resources of stakeholders
• Focus on ‘best practices’ instead of performance – transplanting of ‘best practices’ and agenda
conformity (isomorphic mimicry1) rather than improved sector performance
• Too much emphasis on the building of the system and the initial baseline – rather than the
use of data through improved processes and human resources
• ‘Cocooning’1 or creating pilot projects – ensuring success in a narrow geographic or
administrative area, relying on financial resources or human capital that does not scale (e.g.
district-wide approaches, pilots, innovation funds, small water enterprises)
1. Andrews, M., Pritchett, L., Woolcock, M. Building State Capability: Evidence, Analysis, Action; Oxford, 2017.

8. Strategies for successful monitoring
Factors that lead to success:
Define objectives and success criteria with a
diverse group of stakeholders
Take the time to work out necessary changes
to organizational processes and human
resources (the “good struggle”)
Identify quick wins and priority needs to
include in first iteration of the system
Plan ahead for multiple iteration cycles and
fund the team to test, learn, and make
improvements over time
Address the entire data value chain
Build a data-driven institutional culture
Risks to avoid:
✘Development partners and donors creating
and operating parallel systems (“Can they be
interlinked?” No! -> $$$)
✘Premature load bearing – asking the system
to do too much too soon
✘Expecting technology to solve long-standing
management or resource issues
✘Building a bespoke software application that
requires dedicated expertise just to maintain
and keep up-to-date
✘Launching the system as a ‘pilot’, requiring
no real institutional buy-in

9. Addressing the entire data value chain1
1. Modified from the work of Curry E. (2016) The Big Data Value Chain: Definitions, Concepts, and Theoretical Approaches. In: Cavanillas J., Curry E., Wahlster W. (eds) New Horizons for a Data-Driven
Economy. Springer, Cham. https://doi.org/10.1007/978-3-319-21569-3_3
Generate
•Records and surveys
•Transactions
•Service calls and maintenance
Collect
•Spreadsheets
•Paper forms
•Phone / SMS / Mobile apps
Process
•Cleaning and verification
•Storage
•Updating
Analyze
•Aggregation
•Calculations
•Visualization
Distribute
•Reports
•Websites / dashboards
•Alerts and notifications
Use
•Incentives & accountability
•Learn / pivot
•Collaborate
People:
• Operations / Maintenance
• Health extension workers
• Customer service
• Community-based orgs / NGOs
Processes:
• Standard operating procedures
• Fee collection
• Status reports
• Household visits / surveys
Bottlenecks
Incentives
Gaps
People:
• M&E, IT, data specialists
• Engineers and analysts
• Customer service, billing, accounting
• District or regional water authorities
Processes:
• Analysis / reporting templates
• Management information systems
• Asset and inventory management
• Geospatial analysis / GIS
People:
• Utility and water resource managers
• Ministries and regional authorities
• Regulators
• Local government authorities
• Frontline workers (who also generate)
Processes:
• Standing meetings
• Sector performance reviews
• Online data portals and dashboards
• Annual reports
• Capital improvement plans
• Budgeting and contracting
Try to identify:

10. Building a data-driven culture
• Training and capacity building are only one piece of
the puzzle
• An organization lives within an ecosystem ( -> ) and
relies on its agents to carry out its strategy
• Becoming data-driven involves changing the way
that the organization makes decisions:
The DECIDE approach to Decision-making:1
• Define the problem
• Establish the criteria
• Consider the alternatives
• Identify the best alternative
• Develop and implement a plan of action
• Evaluate and monitor the solution
1. Anderson, C. Creating a Data-Driven Organization: Practical Advice from the Trenches; O’Reilly: Sebastopol, CA, 2015.
2. Andrews, M., Pritchett, L., Woolcock, M. Building State Capability: Evidence, Analysis, Action; Oxford, 2017.
3. Isomorphic mimicry, as defined by Andrews et al. (2), is a “technique of successful failure” characterized by replicating the systems or processes of successful governments in a way that
confuses form with function, leading to a situation where “looks like” substitutes for “does.”
How open is
the system?
Closed Open
How is novelty
evaluated?
Agenda
conformity
Enhanced
functionality
The Organizational Ecosystem:2
Organization
Strategies for
legitimation
Isomorphic
mimicry3
Demonstrated
success
Agents
Leadership
strategies
Organizational
Perpetuation
Value creation
Frontline
worker
strategies
Self-interest
Performance
oriented

11. “A great deal of strategy work is trying to figure out what is going on. Not just deciding what to
do, but the more fundamental problem of comprehending the situation.” (Richard P. Rumelt1)
1. Rumelt, R. Good Strategy / Bad Strategy; Random House: New York, 2011.

12. Key steps in designing a sustainable sector
monitoring system
1. Define the objectives and key stakeholders
• Why do we need this data? How will it be used to improve services?Who
will collect data?Why? How often?Who will act on this data? Do they
have the resources to act on it?
• Will it include representative data (sample surveys), operational data
(management information systems), and/or planning data (capital
investment plan)?
2. Start with organizational processes
• Convene working sessions with representative stakeholders from all
levels (ministry staff, regional or district water authorities, utilities, local
government, operators, mechanics…)
• Identify existing information management processes and gaps
• Define goals for the monitoring system and how processes need to be
updated or improved to meet the data needs
3. Identify human capacity needed
• Update position descriptions / terms of reference to include new data
responsibilities
• Identify ‘super-users’ in the organization who can be trained to a high
level to train and support others
• Identify resources: transportation, IT, communication
4. Choose a proven and sustainable technology
• Only consider software and technology that has been field-proven in
similar settings
• Do not build bespoke software in the name of local ownership – these
systems are owned by consultants, not the government
• Be aware that open source is not the same as free and it is not inherently
more sustainable; technical expertise is still required
5. Build a minimum viable product (MVP), then iterate
• An MVP is a version of a new product that allows the organization to
collect the maximum amount of validated learning with the minimum
effort1
• Get the most critical functions running quickly – with ongoing updating
and response – before adding complexity
• Regularly collect user feedback and incorporate it into subsequent
releases of the product
6. Operationalize the system
• Build up training capacity at appropriate levels in the organization
• Create or update StandardOperating Procedures (SOPs)
• Conduct regular (weekly or monthly) management reviews of
performance against targets, with actions assigned for follow-up
1. Ries, E. The Lean Startup: How Today’s Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses; 2001.

13. Join us!
mWater is an organization that democratizes data with a
free platform and knowledge, enabling data-driven
management to improve water and sanitation services.
The free and open access mWater data management
platform can help governments and organizations with all
the steps in the data value chain.
mWater also develops powerful tools for water and
sanitation asset management, accounting, operations and
planning.
Learn more at www.mWater.co