Data Quality Assurance: An Impetus in
Improving Partner(s) Data Management and Reporting
NOPE BIANNUAL CONFERENCE
18-20th ...
Presentation Outline
• Background
• Objectives
• Methods
• Results/Discussions
• Lessons
• Recommendation
• Conclusion
2
Background
• Healthy Outcome through Prevention Education (HOPE)
Program seeks to improve HIV and AIDS Knowledge,
Attitude...
Background cont..
• The Program has developed a set of standardized data
collection tools for data capture by partners
• L...
Objectives of presentation
• Explore partners’ capacity gaps with regard to data
quality and management.
• Determine areas...
Methods
• A set of RDQAs was conducted between May and
September 2013 on four partners using a standardized
tool.
• Interv...
7
Results
• RDQA showed that data capture and management was a
key challenge
• Lack of a centralized depository for data c...
8
Results cont..
• Database developed in Access and excel guided by the
reporting requirements
• Information captured is d...
9
Lessons learnt
• Management support is a pillar in achieving set
objective/actions for quality improvement
• Databases a...
Recommendations
• Support and continuous training for partners to enhance
their M & E capacity and data quality
• Engageme...
Conclusions
• Systematic way of storing and managing data, contributes
to quality and timely reporting quality.
• Ensuring...
12
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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

  1. 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 1
  2. 2. Presentation Outline • Background • Objectives • Methods • Results/Discussions • Lessons • Recommendation • Conclusion 2
  3. 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 3
  4. 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. 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. 5
  6. 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. 6
  7. 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. 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. 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. 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 10
  11. 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. 12 Thank you

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