Introduction to Data Quality
Explore the basics of data quality management. Learn the key concepts, dimensions, and techniques for monitoring and improving data quality.Start Course for Free
2 Hours13 Videos37 Exercises
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Explore the Basics of Data QualityData 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 DimensionsYou’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 ManagementBy 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.
Defining Data Quality TermsFree
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 quality50 xpValue of data quality50 xpData quality questions100 xpData quality terms and concepts50 xpData quality definition in context50 xpBonus data quality dimensions50 xpData quality dimension practice100 xpImportance of consistency50 xpData quality roles and responsibilities50 xpThe Data Governance Team50 xpWhich data quality role fits50 xpWhich role is responsible100 xp
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.Data quality rules using dimensions50 xpData quality rules50 xpData quality scenarios100 xpData profiles50 xpData quality rules based on a data profile50 xpData profile conclusions100 xpMetadata and data quality50 xpIdentifying metadata50 xpOrder of data lineage100 xpData quality issues triage50 xpUsing data lineage and metadata50 xpExecuting data quality processes100 xp
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.Detective Data Quality Rules50 xpIdentifying detective rules50 xpRemediating detected issues100 xpPreventative Data Quality Rules50 xpWhen to use a preventative rule50 xpDetective versus preventative data quality rules100 xpAnomaly detection50 xpWhen to use anomaly detection50 xpTriaging anomalies100 xpData quality thresholds50 xpThreshold selection50 xpMatching threshold to criticality100 xpWrap-Up50 xp
Chrissy BloomSee More
Head of Enterprise Data Strategy & Governance at National Cooperative Bank
Chrissy holds a Masters in Business Analytics, is a Certified Data Management Professional (CDMP), and has over a decade of experience in data governance. She is passionate about increasing the business value of data by focusing on the intersection of data quality, data governance, and the strategic use of data.
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