 |
 |
 |
|
|
Data quality management is a broad and challenging field that goes well beyond finding and fixing errors in data. Data quality practitioners need a foundation of concepts, principles, and terminology that are common in quality management. Building on that foundation, they need to understand how quality management concepts are applied to data, how data quality is evaluated across multiple dimensions and criteria, and how quality is measured, monitored, and improved.
|
|
Data quality matters because data is used across many different operational, analytical, AI, and decision-making activities. When data quality is poor, the effects will ripple across reports, processes, decisions and actions, customer interactions, AI outcomes, and business outcomes. As organizations rely more heavily on analytics, automation, and AI, the need to manage data quality with care and discipline is increasingly important.
Data quality management requires a broad range of practical skills. Practitioners define quality rules and expectations, measure and monitor quality, detect and correct defects, analyze root causes, and prevent problems before they occur. They need to understand how quality is shaped by data design, business processes, system behavior, human behavior, and the ways data is created, transformed, and used.
This course provides a comprehensive view of data quality fundamentals. It will help data quality practitioners in every role to understand and apply the concepts, principles, and practices of data quality management.
You will learn:
- basic concepts, principles, and practices of quality management
- data quality management terminology
- four core dimensions of data quality and the criteria used to evaluate them
- causes and consequences of poor data quality
- how data quality rules are identified, defined, and applied
- how data quality is measured, monitored, and communicated
- how to build data quality into data processes, systems, and practices
- processes and practices of a data quality management program
This course is geared toward to:
- data quality practitioners of all types
- data stewards who define, monitor, and improve data quality
- data governance practitioners who need to guide data quality policies and practices
- data owners and data managers with data quality responsibilities
- business and technical professionals who collaborate with or support data quality management
- anyone who is getting started in the data quality field
- anyone who needs to speak the language of data quality and collaborate with data quality practitioners
Module 0: About the Course (2 min)
Module 1: Data Quality Concepts (32 min)
- What is Data Quality?
- Dimensions of Data Quality
- Module Summary
Module 2: Data Quality Context (52 min)
- Causes of Data Quality Defects
- People, Processes, and Data Quality
- Module Summary
Module 3: Exploring Data (41 min)
- Exploring Data
- Statistical Data Profiling
- Visual Data Profiling
- Module Summary
Module 4: Discovering Data Quality Rules (36 min)
- Discovering Data Quality Rules
- Discovering Correctness Rules
- Discovering Integrity Rules
- Discovering Usability Rules
- Discovering Objectivity Rules
- Module Summary
Module 5: Managing Data Quality (52 min)
|
This exam tests knowledge and understanding of basic concepts, principles, and terminology of quality management in general, and data quality management in particular.
|
You will be tested in these areas:
- Data quality concepts and definitions
- Data quality dimensions and criteria
- Common causes of data quality defects
- Data exploration and data quality rules discovery
- Data quality measurement and scorecards
- Data quality defect correction and preventions
- Data quality management programs
|
Additional Information
Number of Questions: 24
Time Limit: 48 Minutes
Passing Score: 70%
|
|
Once you pass the exam, you will receive a Certificate of Education documenting that you have demonstrated mastery of the topic. Course exams count towards eLC certification programs. Visit our Certification page for more information about our various programs.
We recommend that you take detailed notes and review the course material multiple times before taking this exam.
Click here to learn more about CIMP exams.
|
|
|
|
 |
|
 |
|