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Dimensions of Data Quality Dimensions of Data Quality

Dimensions of Data Quality - PowerPoint Presentation

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Dimensions of Data Quality - PPT Presentation

MampE Capacity Strengthening Workshop Addis Ababa 4 to 8 June 2012 Arif Rashid TOPS Project Implementation Project activities are implemented in the field These activities are designed to produce results that are quantifiable ID: 265343

quality data slide amp data quality amp slide system management reporting assessment dimensions systems collection staff dqa information ensure

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Slide1

Dimensions of Data Quality

M&E Capacity

Strengthening Workshop,

Addis Ababa

4 to 8 June 2012

Arif Rashid, TOPSSlide2

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 fact

True picture of the field

Data Management System

Data Quality

?

Slide # 1Slide3

Why Data Quality?

Program is “evidence-based”

Data quality

 Data use

Accountability

Slide # 2Slide4

Conceptual Framework of Data Quality

Service delivery points

Intermediate aggregation levels

(e.g. districts/ regions, etc.)

M&E Unit in the Country Office

Data management and reporting system

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

Dimensions of Data Quality

Validity, Reliability, Timeliness, Precision, Integrity

Quality Data

Slide # 3Slide5

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 # 4Slide6

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 # 5Slide7

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 timeData Quality Assessment –Review of project M&E system to ensure that quality of data captured by the M&E system is acceptable.

Slide # 6Slide8

What’s a Data Quality Assessment (DQA)?

Slide # 7

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.Slide9

Data quality Assessments

Local Govt.

Managers

Technicians

Field staff

Partners

Headquarters

Project participants

Slide # 8Slide10

Components of DQA (1/2)

Assess four main dimensions of

data collection process:Design

Organizational structure Implementation practicesFollow-up verification of reported data

Slide # 9Slide11

Components of DQA (2/2)

Systems assessment

of data

m

anagement 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 errorsSlide # 10Slide12

M&E 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

5Are 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 # 11Slide13

M&E Systems Assessment Tools

Data management

processes

8

Does clear documentation of collection, aggregation and manipulation steps exist?

9

Are data quality challenges identified and are mechanisms in place for addressing them?

10

Are there clearly defined and followed procedures to identify and reconcile discrepancies in reports?

11

Are there clearly defined and followed procedures to periodically verify source data?

M&E capacity and system feedback

12Do M&E staff have clear understanding about the roles and how data collection and analysis fits into the overall program quality?

13

Do M&E staff have clear understanding with the PMP, IPTT and M&E Plan?

14

Do M&E staff have required

skills in data collection,

aggregation, analysis, interpretation and reporting

?

15

Are

there clearly defined feedback mechanism to improve data and system quality?

Slide # 12Slide14

Schematic of follow-up verification

Slide # 13Slide15

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 # 14Slide16

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 # 15Slide17

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 # 16Slide18

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.