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Dimensions of Data Quality

Dimensions of Data Quality. M&E Capacity Strengthening Workshop, Addis Ababa 4 to 8 June 2012 Arif Rashid, TOPS. Data Quality. Project Implementation Project activities are implemented in the field. These activities are designed to produce results that are quantifiable.

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Dimensions of Data Quality

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  1. Dimensions of Data Quality M&E Capacity Strengthening Workshop, Addis Ababa 4 to 8 June 2012 Arif Rashid, TOPS

  2. Data Quality Project Implementation 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: Howwell the DMS represents the fact ? True picture of the field Data Management System Slide # 1

  3. Why Data Quality? • Program is “evidence-based” • Data quality  Data use • Accountability Slide # 2

  4. Conceptual Framework of Data Quality Quality Data Data management and reporting system M&E Unit in the Country Office Intermediate aggregation levels (e.g. districts/ regions, etc.) Service delivery points Slide # 3

  5. Dimensions of data quality • Validity • Valid or accurate data are considered correct. Valid 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. Slide # 4

  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. • 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. Slide # 5

  7. Data Quality: Assurance and Assessment • Data Quality Assurance - A process for defining the appropriate dimensions and criteria of data quality, and procedures to ensure that data quality criteria are met over time • Data Quality Assessment –Review of project M&E system to ensure that quality of data captured by the M&E system is acceptable. Slide # 6

  8. What’s a Data Quality Assessment (DQA)? • A data quality assessment is a periodic review that: • Helps Food for Peace and the implementing partner determine and document “How good are the data?” • Provides an opportunity for capacity-building of implementing partners. • DQAs are required of all USAID data that are reported to the federal government. It is a requirement by the US Government. Slide # 7

  9. Data quality Assessments Project participants Managers Technicians Field staff Local Govt. Partners Headquarters Slide # 8

  10. Components of DQA (1/2) • Assess four main dimensions of data collection process: • Design • Organizational structure • Implementation practices • Follow-up verification of reported data Slide # 9

  11. Components of DQA (2/2) • Systems assessment of data management and reporting • Are systems and practices in place to collect, aggregate, analyze the appropriate information? • Are these systems and practices being followed? • Verification of reported data for key indicators • Spot checks to find non-sampling errors Slide # 10

  12. M&E Systems Assessment Tools Slide # 11

  13. M&E Systems Assessment Tools Slide # 12

  14. Schematic of follow-up verification Slide # 13

  15. Practical DQA Tips • Build assessment into normal work processes • Use software checks and edits of data on computer systems • Get feedback from users of the data • Compare the data with data from other sources • Obtain verification by independent parties Slide # 14

  16. DQA realities! • The general principle is that performance data should be as complete, accurate and consistent as management needs and resources permit. Consequently, DQAs are not intended to be overly burdensome or time intensive Slide # 15

  17. 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) Slide # 16

  18. 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.

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