1. Data Quality Assurance: An Impetus in
Improving Partner(s) Data Management and Reporting
NOPE BIANNUAL CONFERENCE
18-20th June 2014
NAIROBI
Presented by Lily Murei
Global Communities
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2. Presentation Outline
• Background
• Objectives
• Methods
• Results/Discussions
• Lessons
• Recommendation
• Conclusion
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3. Background
• Healthy Outcome through Prevention Education (HOPE)
Program seeks to improve HIV and AIDS Knowledge,
Attitudes and Practices (KAP) among primary and
secondary-aged students through peer, school, and
community-based interventions
• Implementation of the program is guided by a performance
monitoring plan that stipulates when and what data is to be
collected for reporting.
• Program performance is guided by data collected and
submitted by implementing partners
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4. Background cont..
• The Program has developed a set of standardized data
collection tools for data capture by partners
• Like most organizations, data collection is largely through
standardized participant signing sheet
• Data capture, management and verification is manual
which affects reporting timeliness and quality
• Routine data quality assessments is in built into program
monitoring and evaluation
• A mechanism to track and ensure quality assurance of
reporting data 4

5. Objectives of presentation
• Explore partners’ capacity gaps with regard to data
quality and management.
• Determine areas requiring technical support in data
quality and management.
• Explore ways of strengthening implementing partners’
staff capacity in data quality and management.
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6. Methods
• A set of RDQAs was conducted between May and
September 2013 on four partners using a standardized
tool.
• Interviews were done with partner program and
monitoring staff and management.
• Action plans for identified gaps were developed indicating
when and how to address gaps.
• Debriefing meetings held with management for ownership
and support of implementation of action plans.
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7. 7
Results
• RDQA showed that data capture and management was a
key challenge
• Lack of a centralized depository for data captured through
participants signing sheets for beneficiaries reached
• Summarizing information from the hard copy signed
sheets for reporting was cumbersome and led to errors and
late reporting
• Technical support was provided to implementing partners’
program and M &E staff

8. 8
Results cont..
• Database developed in Access and excel guided by the
reporting requirements
• Information captured is disaggregated by as per program
reporting (by type of activity age, sex, class, school)
• Database generate summaries for reporting
• Reduction of double counting or missing out
• Verification of data entered is easier- data clerks entered
data, verification is done by M&E staff
• Generation of reports is timely and is quality assured

9. 9
Lessons learnt
• Management support is a pillar in achieving set
objective/actions for quality improvement
• Databases act as a reference point, provide a wealth of
information for not only reporting but also for data
analysis and timely decision making
• Participatory approaches in M &E adds value to
organization social capital promotes stronger ownership
& commitment
• Signing sheet is a useful M &E data source, beyond it
being an evidence of participation

10. Recommendations
• Support and continuous training for partners to enhance
their M & E capacity and data quality
• Engagement with senior management in program M &E
activities to ensure sustainability of best practices
• Consistent data quality assurance is needed through onsite
monitoring and evaluation technical support/mentoring.
• Enhance partner onsite technical support/capacity
development as it is a better experiential learning
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11. Conclusions
• Systematic way of storing and managing data, contributes
to quality and timely reporting quality.
• Ensuring necessary commitments, resources, preparation
and skills for routine data assessments are key success
factors
• Building partnerships and sense of local ownership for not
only project performance but supporting partners to build
a knowledge base from data they generate and ;
• Provide evidence based programming and institutional
memory of impact 11

12. 12
Thank you