Data Quality Mastery: Frameworks, Metrics & Governance

Master data quality frameworks, metrics, and governance strategies to ensure reliable data for analytics and compliance

What you'll learn

Define and apply major data quality frameworks (ISO 8000, DAMA-DMBOK, Six Sigma) to structure quality management processes.

Assess data quality dimensions like accuracy, completeness, consistency, timeliness, validity, and uniqueness with practical examples.

Design SMART data quality metrics and KPIs to track quality performance and support data-driven decision-making.

Develop policies, standards, and governance structures with defined roles to enforce consistent data practices organization-wide.

Requirements

Basic knowledge of data analysis and business processes; familiarity with databases or spreadsheets; access to a computer with Excel, SQL or Python.

Description

Unlock the power of reliable data and propel your organization towards insightful decision making with our comprehensive Data Quality Management course. In today's data-driven world, poor data quality can lead to costly errors, compliance risks, and lost opportunities. This course dives deep into the core frameworks, metrics, and governance theories you need to implement effective data quality practices. Through a structured curriculum, you will build the expertise to assess, measure, and enhance data quality across any data domain.We begin by exploring foundational data quality concepts, including the key dimensions of accuracy, completeness, consistency, timeliness, validity, and uniqueness. You will learn how to apply leading frameworks such as ISO 8000, DAMA-DMBOK, and Six Sigma to establish a robust quality management system tailored to your organization's unique needs. Our step-by-step guidance on maturity models will help you assess current capabilities and define a roadmap for continuous improvement.Next, we delve into the art and science of data quality metrics. Discover how to design SMART (Specific, Measurable, Achievable, Relevant, Time-bound) metrics that align with business objectives and drive actionable insights. You will practice creating primary metrics like accuracy rates, completeness percentages, and timeliness scores, and learn data profiling techniques to uncover anomalies and set meaningful thresholds. With hands-on exercises, you will master the use of both open-source and commercial data quality assessment tools.Effective governance is the cornerstone of sustainable data quality. In this course, you will define and assign critical roles such as data stewards, owners, and custodians, and develop policies, standards, and procedures that ensure consistent data practices. Learn how to structure governance bodies, implement risk management strategies, and navigate regulatory requirements such as GDPR and CCPA. We also cover change management and stakeholder communication tactics to foster a data-driven culture.To bring theory into practice, we guide you through real-world case studies and best practices that highlight common challenges and success factors in data quality initiatives. You will design intuitive dashboards and scorecards to visualize data health, set up automated monitoring and alerts, and establish feedback loops for continuous improvement. By the end of the course, you will be equipped to lead data quality projects from assessment to implementation and sustain high-quality data operations.This course is ideal for data analysts, data governance professionals, data stewards, business analysts, and IT managers seeking to enhance their data management capabilities. Prior experience with data analysis, familiarity with databases or spreadsheets, and a basic understanding of business processes will help you get the most out of this training.Enroll today to transform your data into a strategic asset. You will gain the skills to evaluate data quality maturity, apply industry frameworks, design KPI-driven metrics, and establish governance structures that elevate your organization's data reliability. Join us and take the first step towards mastering data quality management!

Overview

Section 1: Introduction

Lecture 1 Hello and Course Overview

Section 2: Data Quality Fundamentals & Frameworks

Lecture 2 Understanding Data Quality

Lecture 3 Exploring Data Quality Dimensions

Lecture 4 Common Data Quality Challenges

Lecture 5 Maturity Models in Data Quality

Lecture 6 Overview of Data Quality Frameworks

Lecture 7 Building Blocks of a Data Quality Framework

Lecture 8 Choosing the Right Framework

Section 3: Data Quality Metrics & Measurement

Lecture 9 Introduction to Data Quality Metrics

Lecture 10 Designing Metrics with SMART Criteria

Lecture 11 Key Data Quality Metrics

Lecture 12 Data Profiling Techniques

Lecture 13 Data Quality Assessment Tools

Lecture 14 Setting Thresholds & KPIs

Lecture 15 Designing Data Quality Dashboards

Lecture 16 Continuous Monitoring & Improvement

Section 4: Data Governance & Organizational Theory

Lecture 17 Foundations of Data Governance

Lecture 18 Roles & Responsibilities

Lecture 19 Data Policies, Standards & Procedures

Lecture 20 Organizational Structures for Governance

Lecture 21 Change Management & Communication

Lecture 22 Best Practices & Case Studies

Section 5: Summary & Next Steps

Lecture 23 Summary and Next Steps

This course is ideal for data analysts, data stewards, data governance professionals, business analysts, and IT managers who want to develop expertise in data quality frameworks, metrics, and governance.