Dimensions of data quality

Data Quality
M&E Capacity Strengthening Workshop,
19 and 20 September 2011
Arif Rashid, TOPS
Data Quality
Project Implementation
Data Management System
Project activities are implemented in
the field. These activities are designed
to produce results that are quantifiable.
An information system represents these
activities by collecting the results that were
produced and mapping them to a recording
Data Quality: How well the DMS represents the fact
True picture
of the field
Slide # 1
Why Data Quality?
Program is “evidence-based”
Data quality  Data use
Slide # 2
Conceptual Framework of Data Quality?
Dimensions of Data Quality
Quality Data
Intermediate aggregation levels
(e.g. districts/ regions, etc.)
Service delivery points
Data management and reporting
M&E Unit in the Country Office
Accuracy, Completeness, Reliability, Timeliness,
Confidentiality, Precision, Integrity
Functional components of Data Management
Systems Needed to Ensure Data Quality
M&E Structures, Roles and Responsibilities
Indicator definitions and reporting
Data collection and reporting forms/tools
Data management processes
Data quality mechanisms
M&E capacity and system feedback
Slide # 3
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
• 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
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.
Slide # 5
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.
Slide # 6
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.
Slide # 7
Data quality Assessments
Slide # 8
Data quality Assessments
• Two dimensions of assessments:
1. Assessment of data management and reporting
2. Follow-up verification of reported data for key
indicators (spot checks of actual figures)
Slide # 9
Systems assessment tools
M&E structures,
functions and
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
Are there operational indicator definitions meeting relevant
standards that are systematically followed by all service points?
Has the project clearly documented what is reported to who, and
how and when reporting is required?
Data collection and
reporting forms/tools
Are there standard data-collection and reporting forms that are
systematically used?
Are data recorded with sufficient precision/detail to measure
relevant indicators?
Are source documents kept and made available in accordance
with a written policy?
Slide # 10
Systems assessment tools
Data management
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?
Slide # 11
Schematic of follow-up verification
Slide # 12
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 # 13
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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