1. Data Quality Considerations
M&E Capacity Strengthening Workshop, Maputo
19 and 20 September 2011
Arif Rashid, TOPS

2. Data Quality ? Project Implementation Data Management System
Project activities are implemented in the field. These activities are designed to produce results that are quantifiable.
Data Management System
An information system represents these activities by collecting the results that were produced and mapping them to a recording system.
Data Quality: How well the DMS represents the fact ?
True picture of the field
Data Management System
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3. Why Data Quality? Program is “evidence-based” Data quality  Data use
Accountability
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4. Conceptual Framework of Data Quality?
Dimensions of Data Quality
Accuracy, Completeness, Reliability, Timeliness, Confidentiality, Precision, Integrity
Quality Data
Data management and reporting system
M&E Unit in the Country Office
Functional components of Data Management Systems Needed to Ensure Data Quality
M&E Structures, Roles and Responsibilities
Indicator definitions and reporting guidelines
Data collection and reporting forms/tools
Data management processes
Data quality mechanisms
M&E capacity and system feedback
Intermediate aggregation levels
(e.g. districts/ regions, etc.)
Service delivery points
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5. Dimensions of data quality
Accuracy/Validity
Accurate data are considered correct. Accurate data minimize error (e.g., recording or interviewer bias, transcription error, sampling error) to a point of being negligible.
Reliability
Data generated by a project’s information system are based on protocols and procedures. The data are objectively verifiable. The data are reliable because they are measured and collected consistently.
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6. Dimensions of data quality
Precision
The data have sufficient detail information. For example, an indicator requires the number of individuals who received training on integrated pest management by sex. An information system lacks precision if it is not designed to record the sex of the individual who received training.
Completeness
Completeness means that an information system from which the results are derived is appropriately inclusive: it represents the complete list of eligible persons or units and not just a fraction of the list.
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7. Dimensions of data quality
Timeliness
Data are timely when they are up-to-date (current), and when the information is available on time.
Integrity
Data have integrity when the system used to generate them are protected from deliberate bias or manipulation for political or personal reasons.
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8. Dimensions of data quality
Confidentiality
Confidentiality means that the respondents are assured that their data will be maintained according to national and/or international standards for data. This means that personal data are not disclosed inappropriately, and that data in hard copy and electronic form are treated with appropriate levels of security (e.g. kept in locked cabinets and in password protected files.
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9. Data quality Assessments
Project participants
Managers
Technicians
Field staff
Local Govt.
Partners
Headquarters
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10. Data quality Assessments
Two dimensions of assessments:
Assessment of data management and reporting systems
Follow-up verification of reported data for key indicators (spot checks of actual figures)
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11. Systems assessment tools
M&E structures, functions and capabilities 1
Are key M&E and data-management staff identified with clearly assigned responsibilities? 2
Have the majority of key M&E and data management staff received the required training?
Indicator definitions and reporting guidelines 3
Are there operational indicator definitions meeting relevant standards that are systematically followed by all service points? 4
Has the project clearly documented what is reported to who, and how and when reporting is required?
Data collection and reporting forms/tools 5
Are there standard data-collection and reporting forms that are systematically used? 6
Are data recorded with sufficient precision/detail to measure relevant indicators? 7
Are source documents kept and made available in accordance with a written policy?
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12. Systems assessment tools
Data management
processes
Does clear documentation of collection, aggregation and manipulation steps exist?
Are data quality challenges identified and are mechanisms in place for addressing them?
Are there clearly defined and followed procedures to identify and reconcile discrepancies in reports?
Are there clearly defined and followed procedures to periodically verify source data?
M&E capacity and system feedback
Do M&E staff have clear understanding about the roles and how data collection and analysis fits into the overall program quality?
Do M&E staff have clear understanding with the PMP, IPTT and M&E Plan?
Do M&E staff have required skills in data collection, aggregation, analysis, interpretation and reporting ?
Are there clearly defined feedback mechanism to improve data and system quality?
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13. Schematic of follow-up verification
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14. M&E system design for data quality
Appropriate design of M&E system is necessary to comply with both aspects of DQA
Ensure that all dimensions of data quality are incorporated into M&E design
Ensure that all processes and data management operations are implemented and fully documented (ensure a comprehensive paper trail to facilitate follow-up verification)
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15. This presentation was made possible by the generous support of the American people through the United States Agency for International Development (USAID). The contents are the responsibility of Save the Children and do not necessarily reflect the views of USAID or the United States Government.