Field Guide for Data Quality Management

Data quality is a cornerstone of accountability in program reporting. In the international development sector, although we are often focused on reporting, ensuring the quality of the data that we report is critical for our partners, our donors, and our beneficiaries. In addition, Data Quality Management Plans and Routine Data Quality Assessments are both important elements of Pact’s Results and Measurement Standards. The intent of this manual is to provide guidance on how to ensure excellent data quality in all our programming. A slide set accompanying the module provides an opportunity to engage in practical exercises to test the skills outlined in this text.

How to Use This Manual

Chapters 1 through 4 of this manual will provide Pact Staff with a solid understanding of how to assess data quality and how to best conduct data management for data quality. The shaded boxes at the beginning of each chapter outline the key learning concepts and the exercises at the end of each chapter will help you begin formulating aspects of your project’s Data Quality Management Plan. In the annexes you will find:

Instructions on how to use the Excel-based Routine Data Quality Assessment (RDQA) Tool—to use when conducting RDQAs of your own data and M&E systems, as well as your partners’ data and M&E systems;

A Data Quality Management (DQM) Plan template to customize to your own program.

This manual was updated and revised in 2014 to reflect field experience with routine data quality assessments and Pact’s own internal expertise in improving data quality. The updated manual was revised by Lauren Serpe, Alison Koler, Reid Porter, Rachel Beck, and Jade Lamb. Copyediting was done by Karen Cure. With the exception of a new RDQA Tool, much of the original manual’s content remains, and I would like to thank Lynn McCoy, Rita Sonko, Hannah Kamau, Jacqueline Ndirangu, Titus Syengo, and Ana Coghlan for their contributions.