Recently the SMARTerWASH project (2014-2016) was closed. This project was a huge joint effort of IRC, Community Water and Sanitation Agency (CWSA), Akvo, and SkyFox Limited to improve the rural water sector’s monitoring system in Ghana. While working on the end-report it was good to look back and reflect on the successes of the project and the challenges faced and ahead. “So What for Lunch?” presentation for IRC staff

1. Supporting water sanitation
and hygiene services for life
Marieke Adank
So what for lunch, 2 May 2017
Data collection, data use
and (sub-)systems
building: the
SMARTerWASH
experience

2. SMARTerWASH
3,5-year project (March 2013 -December 2016)
Partners: IRC, CWSA, Akvo FLOW, SkyFox
Purpose: To scale up and consolidate WASH sector monitoring in rural
communities and small towns in Ghana.
Major components:
− ICT development
− Monitoring (baseline data collection) at scale

3. 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Publication of
monitoring
framework
NLLAP
meeting on
indicators
Triple-S
ICT development
Regional posters and factsheets
National Forum
Development of indicator framework
Piloting of indicator
and data collection
tools in 3 districts
Data
collection
16 districts
SMARTerWASH
Data
collection
131
districts
DiMES
development
SkyFox SMS dev
FLOW dev

4. The successes: ICT development
DiMES, Akvo FLOW, SkyFox further developed
Interoperability between ICT systems

5. Akvo FLOW
(dashboard and
app)
- Surveys
- Collect data
Skyfox
Database with:
- Facilities
- Spare parts
- orders
DiMES
The elements

6. Akvo FLOW
(dashboard and
app)
- Surveys
- Collect data
Skyfox
Database with:
- Facilities
- Spare parts
- orders
SMS/ USSD /
Call centre
DiMES
Current database
Reports
Excell
Data cleaning
Community
CWSA
management
CWSA
Regions
Districts
SkyFox data
- functionality DIMES database:
- Population data
- Facility data / asset
data
FLOW data
• Functionality
• Service level
• SP Performance
• SA performance
Manual entry
Other
stakeholders
• Coverage figures
• Functionality
• Service level
• SP Performance
• SA performance

7. The successes: data collection at scale
• Leveraging funding from different projects
• Data from 131 districts was collected,
processed and analysed
• Capacity built for data collection, processing
and analysis
• Regional and district factsheets, regional
posters, digital atlas produced

8. The successes: data collection at scale
23,001 handpumps
938 piped schemes
Almost 15,000 Water and
Sanitation Management
Teams
131 Service authorities
Data collected by Local government, in collaboration
with CWSA:
• Functionality
• Level of service (reliability,
distance, crowding, quality,
quantity), assessed against
national norms and standards
Performance on:
• Governance
• Operations
• Financial management
Assessed against indicator
benchmarks, based on national
guidelines
Performance assessed against
indicator benchmarks

9. Baseline results: Comparing handpump service
levels between regions
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Brong
Ahafo
Central Northern Upper
East
Upper
West
Western
Proportionofhandpumps
Per region
I: Not functional or not used
II: Sub-standard service level - 2 or
less of 5 service level norms met
II: Sub-standard service level - 3 of
5 service level norms met
II: Sub-standard service level - 4 of
5 service level norms met
III: Basic service level
6%
25
%
30
%
14
%
26
%
Overall

10. Baseline results: Comparing performance of small
community (rural) and small town Water and Sanitation
Management Teams
Indicator
group Indicator – Benchmark
Small
communit
y WSMT
Small
town
WSMT
Governance
WSMT composition - In line with guidelines and members have
been trained
6% 40%
Operational team (PS only) - At least half filled by qualified staff NA 33%
Financial and operational records - Kept up-to-date 10% 31%
Political interference - no political interference in composition of
WSMT
97% 85%
Operations
Spare parts - Available within 3 days 54%
74%Area mechanic (HP) / technical services (PS) - Available within 3
days
68%
Routine maintenance - Done at least annually (HP) / according to
maintenance schedule (PS)
44% 21%
Water quality testing - Done by certified institute on regular basis
(HP) / annual basis (PS)
6% 25%
Financial
manageme
nt
Revenue/expenditure - Positive balance 14% 85%
Bank account - available and accounts up to date (HP) / 3 bank
accounts and accounts up-to-date (PS)
10% 11%
Tariff - Tariff set 22% 80%
Facility management plan (HP only) - Facility management plan in
place
22% NA

11. Baseline results: Comparing performance of small
community (rural) between districts

12. Baseline results: Comparing performance of service
authorities between districts

13. Lesson learnt:
Baseline data collection at scale is doable (with
project funding)
Costs of data collection (Per diems, transport, tools etc only):
− About 6422 Euro per district (for on average 176 handpumps, 7 piped
schemes, 115 WSMTs, 1 service authority)
However, main challenge: ensuring ongoing monitoring
(incentives, financial and institutional frameworks and mechanisms for
ongoing monitoring)
− About 3235 Euro per district for subsequent monitoring rounds (so
about 28 Euro per WSMT per year)

14. Lessons learnt:
Is data used and does it lead to better
services?
Mostly anecdotal evidence of data use at district level, e.g. data
used to:
− Inform District Water and Sanitation Plans (DWSP) in 11 districts in the
Upper West, Upper East, Western, Brong-Ahafo and Northern Regions
− Inform repairs and rehabilitation of over 600 boreholes with hand pumps
restoring water services to an estimated 180,000 people; ( e.g. rehabilitation
of 18 boreholes, construction of 75 boreholes with handpumps and four
mechanised boreholes in Hilton districts)
− Stimulate DA to form or reconstitute WSMTs (e.g. reconstitution of 203
WSMTs in Hilton districts and 24 in Unicef districts).

15. Does it lead to better services?
Answer based on data over 5 year period from 2 districts: “No”
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2012 2013 2014 2015 2016
Handpumpfunctionality
East Gonja
Sunyani West
Just having the data is not sufficient.
Need for human, financial and logistical capacity and motivation and incentives
for data use.
This goes beyond the monitoring (sub-) system and possibly even beyond the
WASH sector

16. Lessons learnt related to systems building
• Building (sub-)systems takes time;
• In order to improve services, there is a need to look
beyond the monitoring (sub-) system alone.
Way forward
• Baseline data collection in remaining 85 districts
• Ensure use of data at all levels
• Data analysis on whole dataset, including cross analysis (schemes
and management), which can inform national level planning and
policy making
• Development and formalisation of institutional and financial
mechanisms for continuous monitoring
………Other suggested lessons learnt?
………..Other suggestions for the way forward?