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.

Importance of data quality

Value of data quality

Data quality questions

Data quality terms and concepts

Data quality definition in context

Bonus data quality dimensions

Data quality dimension practice

Importance of consistency

Data quality roles and responsibilities

The Data Governance Team

Which data quality role fits

Which role is responsible

Defining Data Quality Terms

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.

Data quality rules using dimensions

Data quality rules

Data quality scenarios

Data profiles

Data quality rules based on a data profile

Data profile conclusions

Metadata and data quality

Identifying metadata

Order of data lineage

Data quality issues triage

Using data lineage and metadata

Executing data quality processes

Data Quality Processes and Components

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.

Detective Data Quality Rules

Identifying detective rules

Remediation process for detected issues

Remediating detected issues

Preventative Data Quality Rules

When to use a preventative rule

Options

Detective Data Quality Rule

Preventative Data Quality Rule

Detective versus preventative data quality rules

Anomaly detection

When to use anomaly detection

Triaging anomalies

Data quality thresholds

Threshold selection

Level 1 - Warning

Level 2 - Critical issue alert

Level 3 - Critical issue prevent

Matching threshold to criticality

Wrap-Up

Data Quality Rules In Action

Cheat Sheet - Data Quality Dimensions

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. <br><br>
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. <br><br>
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.

<h2>Explore the Basics of Data Quality</h2>
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. 
<h2>Learn About Data Quality Dimensions</h2>
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. <br><br> 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. 
<h2>Master the Basics of Data Quality Management</h2>
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.

Explore the basics of data quality management. Learn the key concepts, dimensions, and techniques for monitoring and improving data quality.

Introduction to Data Quality Course | Data Quality Management Fundamentals

Introduction to Data Quality

Data Governance Fundamentals

All records must have a customer account type populated. If 10%+ of records are not populated, let the data producer know. 

All customer types in the dataset1 must match the customer types in the dataset2. If 10%+ records are inconsistent, let the data producer know.

All records must have a customer account open date in the past. If errors are found, alert the data producer and report on the remediation SLA.

All records with an open status must have an open date within 3 days. If error found, alert the data producer and report on the remediation SLA.

All customer records must have a current balance populated. If 10% or more records fail, do not load the data and alert the data producer.

Matching threshold to criticality

Introduction to Data Quality

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