Data Quality Fundamentals
Data Quality Fundamentals Course Description
Data is a critical asset for any program, organization, or business. Poor-quality data degrades knowledge, leading to negative consequences in understanding of 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 data collected is of the highest possible quality–whether in routine monitoring or in 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 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.
Classes will take place online, via Adobe Connect or Zoom. Certificates of completion will be provided at the end of the course to all participants who successfully complete the three modules.
Please contact us with any questions at firstname.lastname@example.org.
Module 1: Introduction to Data Quality
This module presents the rationale for enhancing data quality, and introduces core data quality terminology and criteria, and common data quality issues and risks.
Module 2: Data Management Systems
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/Audit (DQAs)
The final module presents the methodology for data quality assessments/audits (DQAs) – whether internal or external – including how to verify the accuracy/precision of data and how to assess the data management system for structural risks that may compromise data quality.
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