data quality in remote monitoring (mona fetouh, unoios)

16
Data Quality in Remote Monitoring A comparative analysis of experiences in Somalia and Eastern Burma Mona Fetouh (Co-Author and Presenter), Christian Balslev-Olesen (Co-Author), and Volker Hüls (Co-Author)

Upload: alnap

Post on 30-Jun-2015

246 views

Category:

Documents


0 download

TRANSCRIPT

Page 1: Data quality in remote monitoring (Mona Fetouh, UNOIOS)

Data Quality in Remote Monitoring

A comparative analysis of experiences in Somalia and Eastern Burma

Mona Fetouh (Co-Author and Presenter), Christian Balslev-Olesen (Co-Author), and Volker Hüls (Co-Author)

Page 2: Data quality in remote monitoring (Mona Fetouh, UNOIOS)

Remote Monitoring Humanitarian space restricted in many situations; risks

have increased Both delivery and monitoring of programmes affected Remote monitoring and cross-border approaches

required in a number of countries in recent years Increased reliance on local actors Reduced ability to collect and verify information Often results in compromises on data quantity & quality Although remote management is increasingly common,

evidence on best practice is still emerging

Page 3: Data quality in remote monitoring (Mona Fetouh, UNOIOS)

Comparable Contexts Somalia

Classic remote management situation Difficulty of access due to insecurity, heavy reliance on

national civil society (NGOs, communities) for aid delivery (still largely managed from neighbouring Kenya)

Risk to external monitors; monitoring relies heavily on local partners – risk of bias

Eastern Burma Hard to impossible to access from capital due to

government restrictions and long-running conflicts between ethnic groups in the East and the government

Heavy reliance on civil society (LNGOs, communities) for aid delivery that is managed from neighbouring Thailand

Little to no access for INGO staff, including national staff Risk to external monitors, monitoring relies heavily on local

partners – risk of bias

Page 4: Data quality in remote monitoring (Mona Fetouh, UNOIOS)

Different Data Environments (1)

Somalia:Main concern is availability of data. Little opportunity to collect data regularly,

even for local partnersLocal partners have varied capacity to

produce quality data, severe education gap results in overall low staff capacity

Focus of strengthening objective monitoring was through investment in independent systems (“third party verification”).

Page 5: Data quality in remote monitoring (Mona Fetouh, UNOIOS)

Different Data Environments (2) Eastern Burma:

Main concern is management and quality of data Data are abundant, both monitoring data and surveys Local partner capacity is varied but good – strong

ethnic and professional exchange with Thai border area; better access to education

Information remains in technical silos; local NGOs are largely confined to ethnic areas; access opportunities are often limited to a particular sector.

Few mechanisms for independent verification/ triangulation of data.

Focus of strengthening objective monitoring was on the quality and verification of information

Access is improving due to ceasefire agreements

Page 6: Data quality in remote monitoring (Mona Fetouh, UNOIOS)

Comparative Analysis Similar contexts require different

approaches Presentation with details on

Third party verification in SomaliaQuality assurance of monitoring information in

Eastern Burma Experiences presented are based on of

work of UNICEF (Somalia) and International Rescue Committee and the Border Consortium (Thailand/Burma)

Page 7: Data quality in remote monitoring (Mona Fetouh, UNOIOS)

Somalia – Third Party Verification (1) First Level: Information from partners and

networks  Implementing Partner Reports are main source of

primary performance data Reviewed against:

Previous track record of partner / confidence level Specific concerns about partner performance Reporting complete and realistic? Specific issues flagged in or apparent from

requiring follow-up Comparison to occasional information from staff

contacts in the filed (email, telephone)

Page 8: Data quality in remote monitoring (Mona Fetouh, UNOIOS)

Somalia – Third Party Verification (2) Second Level: Third Party Verification

Flagged issues are scheduled for third party monitoring

3rd Party Systems use field monitors that are not affiliated with any implementing partner often outside of the aid business

And therefore can move more freely with less risk are not as qualified to judge details of implementation used mostly for verification of easily obtainable information

Third party monitors, are ‘blind’ tasked to avoid fabrication of reports

Were successfully tasked to track leakage of relief goods into markets, including quantities and pricing.

Page 9: Data quality in remote monitoring (Mona Fetouh, UNOIOS)

Somalia – Third Party Verification (3) Third Level: Follow up on concerns from Level 1 and 2 

Third party information is kept confidential and assessed for the risk level of a particular performance issue.

Depending on risk level, issues are taken up with the partner:

without revealing source e.g. dedicated open monitoring mission at next opportunity

Main reasons for staggering: First level flags issues, but is in itself not sufficient for

reliable data Second level is costly, and can be targeted to only flagged

issues Third level is costly and not timely, and should only be used

with knowledge of problems Low-level third party networks have worked well in

Somalia for other purposes, e.g. for food price monitoring

Page 10: Data quality in remote monitoring (Mona Fetouh, UNOIOS)

Eastern Burma – Strengthening Monitoring Quality (1)

Correlating data in geographical information systems  Main limitation to data correlation / triangulation is

sector-based systems (Health, Education, Relief information management systems)

Sector IMS are basis for cross-sectoral GIS solution Platform maps service delivery data of all sectors to

village location Allows analysis of performance data from all sectors

per location Allows sectors to engage in cross-monitoring and

data sharing

Page 11: Data quality in remote monitoring (Mona Fetouh, UNOIOS)

Eastern Burma – Strengthening Monitoring Quality (2) Regular surveys are expanded in scope

and feed into the information system Key strength of the Eastern Burma

programmes is history of conducting regular (sector) surveys.

All partners are now supporting the expansion of the scope and the coverage of these surveys.

Example: Annual Poverty Survey

Page 12: Data quality in remote monitoring (Mona Fetouh, UNOIOS)

Eastern Burma – Strengthening Monitoring Quality (3) Increased linkages between implementing

partners, and improved M&E capacityEfforts in recent years to connect local

organizations across sector and ethnic groupDiscussion on cross-monitoringComprehensive M&E training for local groups Improved community feedback/village

monitoring (also used by Oxfam in Somalia and Tearfund in Afghanistan)

Page 13: Data quality in remote monitoring (Mona Fetouh, UNOIOS)

Eastern Burma – Strengthening Monitoring Quality (4) Post-facto review of health centre

logbooks and patient filesSimple but innovative example:Can be done remotely and is not time

sensitive Reveals substantial information about quality

of support and services Initiated by the IRC, and now being expanded

to whole sectorVariations conceivable for other sectors

Page 14: Data quality in remote monitoring (Mona Fetouh, UNOIOS)

Eastern Burma – Strengthening Monitoring Quality (5) Increased use of photographic and video

evidence of implementationWhere possible, local NGOs use

photography video

to document their work. Use of video started with success by one local

NGOProvides better representation e.g. of

trainings and public awareness activities.

Page 15: Data quality in remote monitoring (Mona Fetouh, UNOIOS)

Lessons learned, and looking towards the future Similar contexts, different data environments Good examples of when similar contexts warrant

different approaches Both contexts are changing rapidly – more access in

Somalia, political reforms in Burma Improved monitoring systems instill long-term effects

to adapt to these changes—stronger information, increased local capacity

Changes in Somalia may make Eastern Burma lessons applicable in near future

Experience in Somalia valuable for similar situations elsewhere, e.g. Syria

Page 16: Data quality in remote monitoring (Mona Fetouh, UNOIOS)

Thank you!

For further questions:

Mona Fetouh - [email protected]

Volker Hüls - [email protected]

Christian Balslev Olesen - [email protected]