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More and more companies initiate data quality programs and form data stewardship groups every year. The starting point for any such program must be data quality assessment. Yet in absence of a comprehensive methodology, measuring data quality remains an elusive concept. It proves to be easier to produce hundreds or thousands of data error reports than to make any sense of them.
This online training course gives comprehensive treatment to the process and practical challenges of data quality assessment. It starts with systematic treatment of various data quality rules and proceeds to the results analysis and building aggregated data quality scorecard. Special attention is paid to the architecture and functionality of the data quality metadata warehouse.
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You will learn:
- The what, why, when, and how of data quality assessment
- How to identify and use data quality rules for assessment
- How to ensure completeness of data quality assessment
- How to construct and use a data quality scorecard
- How to collect, manage, maintain, warehouse and use data quality metadata
This course is geared towards:
- Data quality practitioners
- Data stewards
- IT and business analysts and everyone else involved in data quality management
DQ-05 Data Quality Assessment
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DQ-05-00 About the Course (9 min)
DQ-05-01 Introduction (53 min)
- Why Assess Data Quality
- Business Value of Data Quality Assessment
- Types of Data Errors
- Data Quality Assessment Approaches
- How Rule-Driven Approach Works
- Project Planning
- Project Steps
DQ-05-02 Data Quality Rules Overview (63 min)
- Attribute Domain Constraints
- Relational Integrity Constraints
- Complex Data Relationships
DQ-05-03 Rules for Historical Data (56 min)
- Historical Data Overview
- Timeline Constraints
- Value Pattern Rules
- Rules for Event Histories
- Rules for State-Dependent Objects
DQ-05-04 Finding Data Errors (76 min)
- Discovering Data Quality Rules
- Implementing Data Quality Rules
- Building Rule Catalog
- Building Error Catalog
- Fine-Tuning Data Quality Rules
DQ-05-05 Building Data Quality Scorecard (46 min)
- School Report Card Example
- A First Look at DQ Scorecard
- Introduction to Aggregate Scorecards
- Recurrent Data Quality Assessment
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Click –here- to download a more detailed outline of this course.
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This exam tests knowledge and understanding of processes and practical challenges of data quality assessment.
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You will be tested in these areas:
- Data quality assessment objectives, value, and approaches
- Data quality assessment project steps
- Various categories of data quality rules, including attribute domain constraints, relational integrity constraints, rules for historical data, rules for state-dependent objects, and general attribute dependencies
- Approaches to discovery, implementation, and fine-tuning of the data quality rules
- The structure of rule catalog and error catalog
- Types of aggregate scores and techniques for score tabulation
- The architecture and applications of the data quality scorecard
- Approaches to recurrent data quality assessment
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Additional Information
Number of Questions: 25
Time Limit: 50 Minutes
Passing Score: 70%
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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.
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Course Review 
Course Review from 9/5/2023 