Objectives:
  • Be knowledgeable about some of the different 
   data quality assessment tools that help identify 
   data issues and measure the quality of data.
  • Identify the appropriate data quality tool(s) to 
   apply in different contexts.
  • Understand how best to identify and select 
   indicators for data quality assessment.
  • Understand how to define, calculate, and 
   interpret data quality metrics.
   Existing Data Quality Assessment 
   Tools
   SS to EMU: Desk review examines population data 
   quality across four dimensions: 
    • Completeness 
    • Internal consistency 
    • External comparisons 
    • External consistency 
   RDQA: Facility/district data quality assessment 
   examines routine service delivery data:
    • Accuracy between data sources and reports
    • Data completeness in data sources and available reporting tools
    • Data reporting timeliness against defined deadlines
    • Data management
   What is the integrated approach 
   for FP data quality assessment?
   • Combines important features and processes from the 
    RDQA and the SS to EMU tools to solve the practical 
    challenges faced by FP programs. 
   • SS to EMU at national and subnational levels:
     o Identifies indicators or data elements with quality 
      problems. 
     o Informs where data quality problems are located. 
     o Determines whether problems are limited to specific 
      regions and/or certain FP methods.
   • RDQA at subnational and health facility levels:
     o Assesses the strengths and weaknesses of the underlying 
      data management and reporting system. 
     o Verifies the quality of reported data against data recorded 
      in the primary source documents.
   What challenges will be addressed 
   by the integrated approach?
    • Top-down approach using the SS to EMU tool to improve 
     targeting and/or prioritization of when and where a RDQA may 
     be most useful.
    • Conduct RDQA(s) at a limited number of facilities (selected 
     using purposive sampling) to understand the drivers of the data 
     quality issues identified through the top-down approach.
    • Integrate feedback on these drivers to national-level 
     stakeholders through national-level review meetings.
    • Improve national-level routine review systems/FP HMIS 
     dashboards by identifying elements of the RDQA, such as data 
     verification and cross-checks that can be integrated into:
      o Ongoing routine supervision visits 
      o Data monitoring meetings 
      o District/region periodic coordination meetings
  What will the integrated approach 
  improve?
   • Understandability/interpretation by users— 
    indicator trends and data quality can be:
     o Processed 
     o Explained
   • Actionability by users—use information about 
    data quality to:
     o Implement actionable steps that will either maintain 
       data quality, or 
     o Improve data quality