Data Quality ConsiderationsM&E Capacity Strengthening Workshop, Maputo19 and 20 September 2011

Arif Rashid, TOPS

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: How well the DMS represents the factData Quality: How well the DMS represents the fact

True picture

of the field

True picture

of the field

Data Management

System

Data Management

System

Data Quality

?

Slide # 1

Why Data Quality?

• Program is “evidence-based”

• Data quality Data use

• Accountability

Slide # 2

Conceptual Framework of Data Quality?

Service delivery points

Intermediate aggregation levels(e.g. districts/ regions, etc.)

M&E Unit in the Country Office

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Functional components of Data Management Systems Needed to Ensure Data QualityM&E Structures, Roles and Responsibilities

Indicator definitions and reporting guidelinesData collection and reporting forms/tools

Data management processes

Data quality mechanisms

M&E capacity and system feedback

Dimensions of Data Quality

Accuracy, Completeness, Reliability, Timeliness, Confidentiality, Precision, Integrity

Quality Data

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 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

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

systems2. Follow-up verification of reported data for key

indicators (spot checks of actual figures)

Slide # 9

Systems assessment toolsM&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?

Slide # 10

Systems assessment toolsData 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?

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.