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Determine key components of a Data Governance
Data governance is the process by which an organization ensures that the data it collects is accurate, complete, and consistent. Having a data governance program in place ensures that your business has a reliable source of data for making informed decisions about your business.
Data governance helps you ensure compliance with regulations such as GDPR and HIPAA, as well as helps to avoid fines by maintaining records that are accurate and up-to-date.
Here are some key components of a data governance program:
Data Quality Management: This component focuses on ensuring that all data collected is accurate and complete. You can use tools like data profiling to find errors in your database, then fix them using tools like data cleansing or deduplication. Our DAMA DG-1220 exam dumps will save your time and money.
Data Security Management: This component focuses on protecting your company against security breaches caused by hackers or unauthorized access to sensitive information. You can do this by implementing strong password policies, limiting access to certain parts of your network, encrypting sensitive information, and regularly monitoring for potential threats.
Data Lifecycle Management: This component focuses on keeping track of all the changes made to your database so that you have an audit trail in case there's ever an issue with it later down the line (for example, if someone accidentally deleted some important records).
DAMA DG-1220 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Roles, Responsibilities and Organization | - Data Governance Roles (Steward, Owner, Custodian) - Governance Operating Models |
| Topic 2: Data Quality and Metrics | - Governance Metrics and KPIs - Data Quality Principles |
| Topic 3: Data Governance Framework and Principles | - Key Concepts and Definitions - Purpose and Scope of Data Governance |
| Topic 4: Policies, Standards and Compliance | - Policy and Standard Development - Regulatory Requirements (e.g., GDPR, Data Protection) |
| Topic 5: Integration with Other Data Disciplines | - Lifecycle and Architecture Alignment - Relationship to Data Management Disciplines |






