CDMP-RMD

The CDMP-RMD (Certified Data Management Professional - Risk Management & Data Governance) exam is designed to assess an individual's knowledge and understanding of data management risks and best practices for data governance.

Here are the main points covered in the exam:

1. Data Management Risk:

• Risk identification and assessment: Understanding different types of data management risks (e.g., data loss, breach, integrity, quality, regulatory non-compliance) and methodologies for identifying and assessing their impact.
• Risk analysis and mitigation: Evaluating the likelihood and impact of identified risks, developing strategies and controls to mitigate them, and prioritizing risk responses based on their severity.
• Risk management frameworks: Knowledge of common risk management frameworks like COSO, ISO 31000, and NIST Cybersecurity Framework and how to apply them to data management.

2. Data Governance:

• Data governance principles and practices: Understanding the key principles of data governance (e.g., accountability, data quality, access control, compliance) and how they apply to various data management activities.
• Data governance framework development: The ability to design and implement a comprehensive data governance framework that aligns with business objectives and regulatory requirements.
• Data policies and standards: Understanding the purpose and implementation of data policies, standards, and procedures for data management, including data classification, retention, and access control.
• Data ownership and accountability: Defining roles and responsibilities for data management activities, including data ownership, stewardship, and governance.

3. Data Security and Privacy:

• Data protection laws and regulations: Understanding relevant data privacy laws (e.g., GDPR, CCPA, HIPAA) and their implications for data management practices.
• Data security controls: Knowing different types of security controls used to protect data (e.g., encryption, access control, authentication, data masking) and how to implement and maintain them.
• Data breach response and recovery: Understanding the steps involved in responding to a data breach, including incident reporting, containment, and recovery.

4. Data Quality Management:

• Data quality principles and metrics: Understanding data quality principles and key metrics for measuring data quality (e.g., accuracy, completeness, consistency, timeliness).
• Data quality improvement techniques: Knowing how to identify and address data quality issues through data cleansing, validation, and other techniques.
• Data quality governance: Establishing governance processes for ensuring and maintaining data quality throughout the data lifecycle.

5. Data Management Life Cycle:

• Data management processes: Understanding the key stages of the data management life cycle, including data acquisition, storage, processing, distribution, and archiving.
• Data lifecycle management: Developing policies and procedures for managing data throughout its lifecycle, including data retention, disposal, and migration.

6. Data Management Technology:

• Data management tools and technologies: Understanding common data management tools (e.g., data warehousing, data integration, data quality tools, data governance platforms) and their capabilities.
• Cloud data management: Understanding cloud data management concepts, including data security, governance, and compliance in cloud environments.

The exam covers a wide range of topics related to data management risks and governance. It is important to have a strong understanding of data management principles, practices, and technology to succeed.

Remember, preparing for the exam involves reviewing relevant materials, studying the exam objectives, taking practice tests, and seeking additional training if needed.