Introduction:

Data quality is a fundamental discipline of enterprise data management, ensuring that organizational data is accurate, complete, consistent, timely, valid, and fit for business purposes. Effective data quality management integrates governance, quality frameworks, metadata, stewardship, measurement, monitoring, and continuous improvement to support informed decision-making and organizational performance. This training program explores data quality principles, frameworks, assessment methodologies, governance practices, and improvement techniques aligned with DAMA data management practices. It provides an organizational perspective on how structured data quality management enhances data reliability, regulatory compliance, operational efficiency, and business value.

Program Objectives:

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

• Analyze enterprise data quality frameworks and DAMA data quality principles.

• Evaluate data quality dimensions, governance structures, and stewardship responsibilities.

• Assess data quality measurement, monitoring, and root cause analysis methodologies.

• Examine data quality improvement, issue management, and control frameworks.

• Develop organizational approaches for sustaining enterprise data quality and continuous improvement.

Target Audience:

• Data Quality Specialists.

• Data Governance Professionals.

• Data Stewards and Data Owners.

• Business Intelligence and Data Analysts.

• Information Management Professionals.

Program Outline:

Unit 1:

Foundations of Data Quality Management:

• Enterprise data quality principles.

• DAMA data quality framework.

• Data quality dimensions.

• Data quality lifecycle.

• Roles and responsibilities in data quality management.

Unit 2:

Data Quality Governance and Assessment:

• Data governance and quality integration.

• Data ownership and stewardship.

• Data quality policies and standards.

• Data quality assessment methodologies.

• Data profiling and quality evaluation.

Unit 3:

Data Quality Measurement and Monitoring:

• Data quality metrics and scorecards.

• Data quality rules and validation frameworks.

• Continuous monitoring methodologies.

• Root cause analysis for data quality issues.

• Data quality reporting and dashboards.

Unit 4:

Data Quality Improvement and Controls:

• Data cleansing and standardization frameworks.

• Data quality issue management.

• Preventive and corrective quality controls.

• Master data quality considerations.

• Data quality risk management.

Unit 5:

Enterprise Data Quality Excellence:

• Continuous data quality improvement.

• Data quality maturity models.

• Integration with enterprise data governance.

• Regulatory compliance and audit readiness.

• Emerging trends in enterprise data quality management.