DAMA Specialist Exam: Data Quality
Master the Health of Your Data: Data Quality Management (DMBOK2RE Aligned)Data is only as valuable as it is trustworthy. High-quality data is the lifeblood of accurate analytics, reliable AI, and informed decision-making. This specialized course provides a deep dive into Data Quality Management, equipping you with the framework to define, measure, and improve data quality across your organization.From identifying critical data ddlms to implementing automated quality checks and remediation workflows, you will learn how to transition from "reactive cleaning" to "proactive prevention." Gain the specialized skills needed to eliminate "garbage-in, garbage-out" and become a champion for data integrity in the modern enterprise.
What You Will Master:
- Develop a governed approach to make data fit for purpose based on data consumers’ requirements.
- Define standards, requirements, and specifications for data quality controls as part of the data lifecycle.
- Define and implement processes to measure, monitor, and report on data quality levels.
- Identify and advocate for opportunities to improve the quality of data, through process and system improvements.
Who Should Attend?
Whether you’re starting your career or already leading data strategy, this course helps you grow.
- Aspiring Professionals | Build a strong foundation
- Mid-Career Experts | Validate your real-world experience
- Data Leaders | Establish credibility and leadership in the field
- Consultants & Trainers | Demonstrate your authority to clients
Upcoming Courses
Check our below Upcoming courses and Register
Course Outline
The DAMA Specialist Exam prep course for Data Quality Management, based on the DAMA Body of Knowledge (DMBOK), validates your expertise in data quality management across:
- The Foundations of Quality
- The DQ Definition: Understanding Data Quality as “fitness for use.”
- The Business Case: Quantifying the cost of “Bad Data” (rework, lost opportunity, and risk).
- Principles & Drivers: Aligning with the DAMA DQM goals and organizational strategy.
- Defining & Measuring Quality (The Dimensions)
- The DQ Dimensions: Deep dive into Accuracy, Completeness, Consistency, Timeliness, Validity, and Uniqueness.
- Defining Metadata Requirements: How metadata acts as the “yardstick” for quality.
- Profiling Data: Techniques for discovering patterns, anomalies, and hidden risks in your datasets.
- The Data Quality Lifecycle
- Plan: Identifying Critical Data Elements (CDEs) and defining Business Rules.
- Do: Implementing data entry controls and technical DQ checks.
- Check: Monitoring DQ through dashboards, scorecards, and service level agreements (SLAs).
- Act: Root cause analysis, data remediation, and continuous improvement loops.
- DQ Tools & Technology
- The DQ Toolset: An overview of profiling, parsing, standardization, and enrichment tools.
- Automation: Moving from manual spreadsheets to automated DQ firewalls.
- Integration: How DQ fits into the ETL/ELT pipeline and Data Governance workflows.
- Governance & Culture
- Roles & Responsibilities: The interaction between Data Stewards, Owners, and DQ Analysts.
- Data Quality Policy: Drafting the standards that the organization must follow.
- Sustaining a “Quality-First” Culture: Changing the mindset from “fixing data” to “fixing the process.”