Data Quality Assurance: An Impetus in Improving Partner(s) Data Management and Reporting
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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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Presentation Outline • Background• Objectives• Methods • Results/Discussions• Lessons • Recommendation • Conclusion
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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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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
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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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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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
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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
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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
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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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
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Thank you
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