Introduction to Data Quality

Course Description

Explore the Basics of Data Quality

Data quality is a fundamental concept critical to understand if you work with data. Data quality concepts and processes span industries and can be applied by any person who produces or consumes data. This course covers the basics, including data quality dimensions, roles and responsibilities, and types of data quality rules. You’ll gain an understanding of the data quality process and be prepared to start monitoring your own data’s quality.

Learn About Data Quality Dimensions

You’ll start by learning the definition of data quality and why it is so important to consider in business decision-making. Once you understand the importance, you will learn about six foundational data quality dimensions. You will use these dimensions to define detective and preventative data quality rules.

You will also learn the basics of anomaly detection, a more advanced way to monitor data quality. You will put these concepts together by applying the data quality process. You will learn which role is responsible for specific data quality tasks and the order in which these tasks should be completed.

Master the Basics of Data Quality Management

By the end of this course, you will understand how to monitor, identify, and resolve data quality issues. You will look at your data through a more critical lens and think about potential data quality issues before using it. Ultimately, you will be able to make better decisions and have more trust in your data by applying the basic data quality techniques covered in this course.

What You’ll Learn

Defining Data Quality Terms

Chapter 1 introduces basic data quality terms, including data quality dimensions and data quality roles and responsibilities. You will also learn the importance and value of data quality in a business context.

Data Quality Rules In Action

In chapter 3, you’ll learn about the different types of data quality rules and the concept of data quality alert thresholds. You’ll finish the chapter with an exercise that puts dimensions, data quality rules, data quality processes, and data quality alerts together.

Data Quality Processes and Components

You’ll start chapter 2 by identifying data quality rules for each data quality dimension using data profiles. You’ll also learn about metadata and data lineage before exploring the overall data quality process for triaging and remediating issues.