Data Quality Management : A Primer

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