Data Quality Based On Dama

Master the key principles of Data Quality and learn how to profile, cleanse, monitor, and govern your data effectively

What you'll learn

Understand what data quality is and why it is essential for effective data management

Identify the key dimensions of data quality and how to measure them with practical metrics

Learn how to design and implement data quality rules, policies, and standards

Discover the main roles and responsibilities in a successful data quality program

Apply profiling, cleansing, and monitoring techniques to ensure reliable data

Integrate data quality into operational processes to increase efficiency and trust

Explore real use cases where data quality directly impacts business performance

Develop a sustainable and scalable approach to long-term data quality management

Requirements

Familiarity with data-related terminology can be helpful, but no prior knowledge is required

Description

Do you want to ensure that your organization’s data is reliable, consistent, and fit for decision-making? Are you looking to detect and fix data issues before they impact operations or strategy? Then this course, “Data Quality in Data Governance Based on DAMA”, is exactly what you need.In this course, you will explore the core principles of Data Quality and learn how to apply them within the DAMA-DMBOK framework. You’ll understand how to profile, cleanse, monitor, and standardize data across your systems to ensure accuracy, completeness, consistency, and timeliness.We’ll cover key topics such as defining quality dimensions, creating validation rules and standards, implementing continuous monitoring processes, and addressing real-world data quality challenges. You’ll also learn how to embed Data Quality into broader governance initiatives and how to align people, processes, and tools for sustainable improvement.This course combines theory, practical guidance, and hands-on case studies to help you tackle the most common Data Quality issues across industries.Whether you're a data professional, a business analyst, or simply someone who works with data and wants to improve its reliability, this course offers a structured, flexible, and practical path to mastering Data Quality.Enroll now and learn how to turn poor data into powerful insights, driving efficiency, trust, and value across your organization!

Overview

Section 1: Introduction to Data Quality

Lecture 1 Definition of Data Quality

Lecture 2 Importance of Data Quality in Organizations

Lecture 3 Relationship Between Data Quality and Data Governance

Section 2: Business Drivers for Data Quality

Lecture 4 Factors Driving the Need to Manage Data Quality

Lecture 5 Impact of Data Quality on Operations and Decision-Making

Lecture 6 Examples of Common Problems Caused by Poor Data Quality

Section 3: Principles and Objectives of Data Quality Management

Lecture 7 Key Principles to Ensure Data Quality

Lecture 8 Accuracy: Data That Accurately Reflects Reality

Lecture 9 Integrity: Completeness of Required Data

Lecture 10 Consistency: Uniformity of Data Within Its Context

Lecture 11 Timeliness: Data Available When Needed

Lecture 12 Uniqueness: Elimination of Duplicates and Redundancies

Section 4: Key Activities in Data Quality Management

Lecture 13 Data Profiling and Quality Analysis

Lecture 14 Defining Business Rules and Quality Standards

Lecture 15 Continuous Monitoring and Measurement of Data Quality

Lecture 16 Data Cleansing and Error Correction

Lecture 17 Designing Operational Procedures for Data Quality

Section 5: Tools and Techniques for Data Quality

Lecture 18 Tools for Data Profiling and Analysis

Lecture 19 Solutions for Data Transformation and Validation

Lecture 20 Monitoring Techniques and Quality Reporting

Section 6: Governance in Data Quality Management

Lecture 21 Importance of Governance to Ensure Data Quality

Lecture 22 Roles and Responsibilities in Data Quality Management

Lecture 23 Policies and Standards for Data Quality

Section 7: Best Practices for Implementing a Data Quality Program

Lecture 24 Key Success Factors for Effective Data Quality Management

Lecture 25 Common Challenges and Lessons Learned From Data Quality Projects

Lecture 26 Strategies for Sustainable and Scalable Processes

Section 8: Practical Use Cases in Data Quality Management

Lecture 27 Case 1: Duplicate Resolution in a CRM System

Lecture 28 Case 2: Implementing Quality Monitoring in a Data Warehouse

Lecture 29 Case 3: Data Profiling and Cleansing in Legacy System Integration

Lecture 30 Case 4: Defining Business Rules to Ensure Master Data Consistency

Anyone who wants to understand how to ensure high-quality data to support reliable and efficient decision-making,Professionals who want to apply the DAMA-DMBOK framework to design and implement data quality initiatives,Individuals with no prior experience in data who want to learn the fundamentals of data quality and its business impact,Managers and team leaders seeking to improve data reliability and governance across their teams and departments,Students and recent graduates interested in building a solid foundation in one of the most in-demand areas of data management,Data analysts, developers, or IT professionals who want to strengthen their skills in profiling, cleansing, and monitoring data,Business consultants looking to incorporate data quality best practices into their projects and client solutions,Entrepreneurs or professionals from any sector aiming to improve their operations through better data practices