PDM-067: Data Trust (Aka Data Quality)

Course Instructor:

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Novice Level:
1 Hour
Intermediate Level:
8 - 16 Hours
No Prerequisites
Contact for Private Training
Course Option: 
Classroom, Instructor-Led Webinar

Target Audience:
Every data creator, user or manager should be aware of how data trust is established or broken. Software or data system developers should understand the impact that design decisions can have on data trust, reliability, and integrity. Managers and executives should understand the importance of ensuring that data is stewarded as a trusted strategic asset that “belongs” to no single user group.

Course Description:
This course describes the foundations of data distrust, data attenuation and dissonance. It lays out the methods by which trust can be maintained as data quality is measured and validated. Students will have a thorough understanding of the advantages and disadvantages of data design decisions. Methods to detect and remediate data problems are covered.

Learning Outcomes:
  • Understand the central role played by data in integrating people, process, and technology
  • Discuss the basis for trusted data
  • Define data dissonance and data attenuation and describe the problems that arise
  • Illustrate the difference between proving that data is correct and demonstrating that data can be trusted (probably)
  • Describe the importance of data governance to establishing data trust
  • Describe and illustrate nine commonly used quality dimensions
  • Define the difference between accuracy and precision
  • Show how rules can be developed and used to test data
  • Show the relationships between concepts, principles, data rules and data tests
  • Explain challenges in assessing NULL, inferred or missing data
  • Illustrate how rule collections can be used to manage a rules library
  • Explain and illustrate how trust can be maintained, helping data users avoid retesting data that has been verified
  • Discuss methods for version control in data systems, and the circumstances in which each method is useful
  • Discuss how the order in which a data store is created can impact data completeness and quality
  • Illustrate some useful examples of data verification

Concept checks throughout the course.
Final Exam - Students must achieve 70% or higher on the exam to obtain a completion award.
Completion Award:
PPDM Association Certificate of Completion, Badge, PDU for CPDA.

Special Requirements:
None required.