Introduction

In the modern digital economy, data has become one of the most valuable assets for organizations. Effective data management ensures accuracy, reliability, accessibility, and security of information—supporting strategic decision-making and driving business value. However, without proper governance and structure, organizations face risks of poor data quality, compliance issues, and inefficiencies.

The Certified Data Management Professional training program, designed by Global Horizon Training Center, provides participants with a solid foundation in data management principles, frameworks, and best practices aligned with global standards. The program integrates practical tools, real-world case studies, and interactive workshops to prepare professionals for managing data as a critical enterprise asset.

Objectives

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

• Understand the principles and scope of enterprise data management.

• Apply global frameworks (such as DAMA-DMBOK) to structure and govern organizational data.

• Ensure data quality, consistency, and compliance with industry regulations.

• Manage metadata, master data, and reference data effectively.

• Develop strategies for data security, privacy, and lifecycle management.

• Support organizational decision-making through reliable data governance practices.

Target Audience

This program is designed for:

• Data management and governance professionals.

• Database administrators and data architects.

• IT managers and business analysts.

• Compliance, risk, and information security professionals.

• Project managers handling data-driven initiatives.

• Anyone pursuing certification in data management.

Outlines

Day 1: Foundations of Data Management

• Introduction to data as an enterprise asset.

• Overview of global frameworks: DAMA-DMBOK and ISO standards.

• Core principles of data governance.

• Data lifecycle and its importance in business operations.

Day 2: Data Governance and Data Quality

• Building a data governance framework.

• Roles and responsibilities (data owners, stewards, custodians).

• Principles of data quality: accuracy, completeness, consistency, timeliness.

• Data quality assessment tools and techniques.

• Case study: Implementing data governance in a multinational organization.

Day 3: Master Data, Metadata, and Reference Data Management

• Understanding master data and its role in business processes.

• Metadata management: definitions, uses, and standards.

• Reference data: controlling and maintaining critical business values.

• Tools and technologies supporting data management.

• Group activity: Designing a master data model for a sample organization.

Day 4: Data Security, Privacy, and Compliance

• Data security principles and regulatory landscape (GDPR, HIPAA, local laws).

• Privacy by design and secure data handling.

• Risk management in data access and sharing.

• Building policies for data classification and protection.

• Role-play: Responding to a data breach scenario.

Day 5: Implementing and Sustaining Data Management Practices

• Developing a data management strategy aligned with organizational goals.

• Building a roadmap for data governance maturity.

• Measuring success: KPIs and data management performance metrics.

• Continuous improvement and adoption of emerging technologies (AI, big data, cloud).

• Final workshop: Creating a data management improvement plan for participants’ organizations.