DAMA Data Quality Specialist

Introduction:

Data quality is a strategic organizational capability that enables trusted information, effective governance, regulatory compliance, and informed decision making across the enterprise. Establishing high quality data requires integrated governance structures, standardized quality dimensions, measurement methodologies, stewardship responsibilities, and continuous oversight throughout the data lifecycle. This training program explores the principles, frameworks, governance models, and organizational practices of enterprise data quality management. It provides an institutional perspective on how data quality supports enterprise data governance, operational excellence, business intelligence, and digital transformation.

Program Objectives:

By the end of this program, participants will be able to:

• Analyze enterprise data quality principles and DAMA governance frameworks.

• Evaluate organizational approaches for measuring, monitoring, and managing data quality.

• Assess data quality dimensions, controls, and stewardship structures.

• Examine enterprise practices for managing data quality risks and compliance.

• Explore integrated data quality governance approaches that support organizational objectives.

Target Audience:

• Data Quality Professionals.

• Data Governance Managers.

• Data Stewards and Data Custodians.

• Business Intelligence and Analytics Professionals.

• Information Management and Digital Transformation Professionals.

Program Outline:

Unit 1:

Enterprise Data Quality Foundations:

• Enterprise data quality principles and strategic objectives.

• DAMA-DMBOK (DAMA Data Management Body of Knowledge) data quality framework.

• Data quality dimensions and fitness-for-purpose concepts.

• Organizational roles for data ownership, stewardship, and accountability.

• Enterprise data governance and quality integration.

Unit 2:

Data Quality Management Frameworks:

• Data quality planning and management methodologies.

• Data quality assessment and measurement models.

• Data quality monitoring and reporting frameworks.

• Data quality controls and lifecycle management.

• Enterprise approaches to data quality issue classification and governance.

Unit 3:

Organizational Data Quality Excellence:

• Enterprise data quality maturity frameworks.

• Data quality risk and regulatory compliance considerations.

• Integration of data quality with master data and metadata management.

• Organizational performance and value realization through trusted data.

• Future directions in enterprise data quality management and governance.