This document discusses ensuring data quality for funders. It notes that funders can lapse in terms of data quality and consistency, and provides some tools to address this, such as data quality audits. For small funders on limited budgets, it recommends deciding on desired outcomes, using data for site/participant selection, requiring capacity building, and auditing data quality. The document provides various examples of reporting data issues and suggests investing in data quality will lead to better quality data.

1. Khulisa Management Services (Pty) Ltd
Funders (of all shapes and sizes)
should have rules and tools to
ensure data quality
Jennifer Bisgard
March 14, 2013
Accurately Measuring Progress

2. Panel Questions…
• Where do funders tend to lapse in terms
of data quality and consistency?
• What are some of the tools that funders
can employ to ensure quality and
consistency of reported data?
• How can small funders promote data
quality on a limited budget?
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3. Is this acceptable?
• NGO X delivered services to 236 people
in the village…
• 6 buildings were built…
• 82 people were fed...
• School X improved its matric pass rate
from 50% to 80%
• 32 teachers attended a workshop…
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4. What do the numbers mean?
• but 25 people received
delivered services to 256 …
services 10 times, and 6 more came once
• 6 buildings built… but 4 are unused and 2 are
vandalised
• 82 people fed... On over-boiled cabbage and
Koeksusters
• but only 16
matric pass rate improved from 50% to 80%…
passed in both years (difference 32 took exam in
2011 and only 20 in 2012)
• Attended a workshop… but played games on their cell
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5. What data do we get?
Name Pre-test Post-test
Jabu 34 36
Siza 23 28
Thumi 27 25
Lebo 16 21
John 25 29
Elly 28 22
Zandi 38 33
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6. Why invest in data quality?
No Investment
in Quality Poor Quality
Data Sent
The Data are
Not Valued The Data are
No Good
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7. Invest in data quality!
Investment
in Quality Quality
Data Sent
The Data are
Valued The Data are
Good
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8. Data Quality Audit Framework
DATA MANAGEMENT
DATA QUALITY SYSTEM
SYSTEM AUDITABLE SYSTEM
Data Quality Processes /
Consists of Data Verification of the entire Data
Procedures to achieve Data
Management Processes / Management System
Quality Criteria
Procedures
Source
Risk Risk
Collection
Reduction Verification
Validity
Collation Reliability
Integrity Audit
Precision Trails
Analysis Timeliness
Completeness
Reporting
Usage
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9. What can smaller funders do?
• Decide on the desired outcome
• Use data to select sites/participants
• Invest in monitoring requirements
• Require capacity building
• Demand Mastery rather than attendance
• Assess performance against standards
• Audit data quality
• Conduct evaluations
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10. Resources
• South African Monitoring and Evaluation
Association (SAMEA) www.samea.org.za
• African Evaluation Association www.afrea.org
• Better Evaluation
http://betterevaluation.org/
• My M&E http://www.mymande.org/
• Impact Reporting & Investment Standards GIIN
IRIS http://iris.thegiin.org/
• “Indicators as Interventions”
www.rockefellerfoundation.org/
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11. Thank you
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