Data is a critical asset for any program, organization, or business. Poor-quality data degrades knowledge, leading to negative consequences in understanding the world, situations, systems, or events, and poor decision-making that negatively affects all aspects of life (health, social, economics, etc.).

Various factors influence data quality, and shortcomings in these factors will contribute to poor-quality data. As M&E practitioners, we must ensure that the data collected is of the highest possible quality–whether in routine monitoring or evaluations–so that follow-on actions and decisions based on the data are true and correct.

This three-module course will introduce participants to core concepts of data quality and how to structure data management systems for collecting and reporting data, as well as how to assess the quality of data collected and reported.

At the end of this course, participants should understand:

• Why data quality is important

• The concepts and terminologies used in data quality

•  The elements of strong data management systems to enhance data quality

• The components, tools, and processes of a Data Quality Assessment/Audit

This course is intended for:

• Researchers and evaluators

• Program Managers

• M&E Managers

• Any persons working with data.

Read more about each of the modules below. Classes will take place online via Zoom. Certificates of completion will be provided at the end of the course to all participants who successfully complete the three modules.

Module 1: Introduction to Data Quality

June 3, 9 a.m.-12 p.m. EDT 

This module presents the rationale for enhancing data quality and introduces core data quality terminology, criteria, and common data quality issues and risks.

Module 2: Data Management Systems

June 5, 9 a.m.-12 p.m. EDT

The second module focuses on the data management systems (DMS) that produce and report data. We will discuss how to structure the DMS for routine reporting and research/evaluation to enhance data quality. We will look at data management concepts and best practices and introduce approaches to correcting errors in M&E data sets.

Module 3: Data Quality Assessments/Audits (DQAs)

June 10, 9 a.m.-12 p.m. EDT