Data Quality, Quality Measurement Dimensions, Quality Management Functions, Quality Scorecard, Best Practices and more
What you'll learn
Understand fundamentals of Data Quality Management
Importance of Data Quality and impact on businesses
Real Examples : Highlighting cost of poor quality data
Develop skills to measure, monitor, communicate, and manage Data Quality effectively
Gain knowledge on data profiling, cleansing, and using a Data Quality Scorecard
Talk with confidence when it comes to Data Quality Management
Requirements
No Prior Data Knowledge Required
Interest in Learning About Data Quality
Curiosity to Understand How Data Quality Impacts Business
Description
"Data Quality Management: A Primer" is your essential guide to the principles and practices of effective Data Quality Management. Designed for beginners, this course provides a comprehensive introduction to the key concepts and strategies needed to ensure high Data Quality.In this course, we'll explore:Understanding Data Quality & Measurement Dimensions: Delve into the fundamental dimensions of Data Quality such as accuracy, completeness, consistency, and timeliness. Learn how to evaluate and improve Data Quality in various contexts.Implementing Effective Data Quality Processes: Understand processes for data profiling, cleansing, validation, and monitoring. Learn how to establish and maintain processes that ensure data remains reliable and valuable.Utilizing the Data Quality Scorecard: Understand how to use a Data Quality Scorecard to provide a clear snapshot of data quality, track progress, and identify areas needing improvement. Learn the importance of Data Quality and the significant cost implications of poor data quality on organizational performance.Exploring the Impact of Data Quality on Business Decisions: Examine how high-quality data supports better decision-making and operational efficiency, and understand the broader impact of data quality on business success. We will also go through real examples highlighting the cost of poor data quality. Recognizing Common Data Quality Challenges: Identify common pitfalls and challenges in data quality management and more.
Overview
Section 1: Introduction
Lecture 1 Who is this course for?
Lecture 2 Prerequisite for taking up this course
Lecture 3 Course Structure
Section 2: Introduction to Data Management & Data Quality Management
Lecture 4 Data Management - Big Picture
Lecture 5 What is Data Quality Management?
Lecture 6 What is Data Quality?
Lecture 7 Importance of Data Quality
Lecture 8 Cost of Poor Data Quality : Real Examples
Lecture 9 Summary
Section 3: Data Quality Measurement Dimensions
Lecture 10 What is Data Quality Measurement Dimensions?
Lecture 11 Accuracy
Lecture 12 Completeness
Lecture 13 Consistency
Lecture 14 Timeliness
Lecture 15 Validity
Lecture 16 Uniqueness
Lecture 17 Summary
Section 4: Data Quality Scorecard
Lecture 18 Introduction to Data Quality Scorecard
Lecture 19 Structure of Scorecard
Lecture 20 Best Practices
Lecture 21 Summary
Section 5: Data Quality Management Functions
Lecture 22 Introduction
Lecture 23 Data Profiling Introduction
Lecture 24 Data Profiling Process
Lecture 25 Data Quality Monitoring & Testing
Lecture 26 Data Cleansing
Lecture 27 Data Standardisation
Lecture 28 Root Cause Analysis
Lecture 29 Change Management
Lecture 30 Data Stewardship
Lecture 31 Summary
Section 6: Data Quality Tools and Mistakes to Avoid
Lecture 32 Common Mistakes : Data Quality Management
Lecture 33 Data Quality Tools
Lecture 34 Data Quality Tools : Example
Section 7: Wrap up
Lecture 35 Thank you
Data Pipeline Developers,Data Pipeline Testers,Business Analysts,Data Project/Product Managers,Anyone interested to learn about Data Quality Management
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