Data Govermane – Data Management Fundamentals

• INTRODUCTION TO DAMA

• • What is data management and why is it critical.
  • What are the different disciplines of data management?
  • DAMA & the DMBoK 2.0, and its relationship with other frameworks (TOGAF/COBIT…).
  • Overview of available professional certifications focusing on DAMA CDMP.

• DATA GOVERNANCE

• • What is Data Governance and why it is important. A typical data governance reference model.
  • The main data governance roles: owner, steward, custodian.
  • The role of the Data Governance Office (DGO) and its relationship with the PMO.
  • What is the difference between Data Governance and IT Governance, and does it matter?
  • Overview of the Data Management implications of a selection of other regulations.
  • The key steps that organizations can take to prepare for compliance with current and future regulations.
  • How to get started with data governance and sustaining and building data governance.

• DATA LIFECYCLE MANAGEMENT

• • Proactive planning for the management of data across its lifecycle.
  • Differences between data life cycle and a Systems Development Lifecycle (SDLC).
  • Data governance touch points throughout the data lifecycle.

• METADATA MANAGEMENT

• • What is metadata and why it is important?
  • Types of metadata, their uses and their sources.
  • Metadata and business glossaries. What is the connection?
  • How metadata provides the essential glue for data governance and metadata standards.

• DG MINI PROJECT

• • Starting the Data Governance Program, what you must get in place early. How to produce a realistic business case for DG linked to business objectives?

• DOCUMENT RECORDS & CONTENT MANAGEMENT

• • Why document and records management is important.
  • Taxonomy vs. ontology… what’s the difference.
  • Legal and regulatory considerations impacting records and content management.

• DATA MODELING BASICS

• • Types of data models, their use and how they interrelate.
  • The development and exploitation of data models, ranging from enterprise, through conceptual to logical, physical and dimensional.
  • Maturity assessment to consider the way in which models are utilized in the enterprise and their integration in the System Development Life Cycle (SDLC).
  • Data modeling and big data.
  • Why data modeling plays a critical part in data governance and BP case study.

• DATA QUALITY MANAGEMENT

• • The different facets of data quality, and why validity is often confused with quality.
  • The policies, procedures, metrics, technology and resources for ensuring data quality.
  • A data quality reference model and how to apply it.
  • Why data quality management and data governance are interconnected and case studies.

• DATA OPERATIONS MANAGEMENT

• • Core roles and considerations for data operations.
  • Good data operations practices.

• DATA RISK & SECURITY

• • Identification of threats and the adoption of defenses to prevent unauthorized access, use or loss of data and particularly abuse of personal data.
  • Identification of risks (not just security) to data and its use.
  • Data management considerations for different regulations, e.g. GDPR, BCBS239.
  • The role of data governance in data security management.

• MASTER & REFERENCE DATA MANAGEMENT

• • The differences between reference and master data.
  • Identification and management of master data across the enterprise.
  • 4 generic MDM architectures and their suitability in different cases.
  • How to incrementally implement MDM to align with business priorities.
  • Statoil (Equinor) case study.

• DATA WAREHOUSING, BUSINESS INTELLIGENCE & DATA ANALYTICS

• • What is data warehousing and business intelligence and why do we need it.
  • The major data warehouse architectures (Inmon & Kimball).
  • Introduction to dimensional data modeling.
  • Why master data management fails without adequate data governance.
  • Data analytics and machine learning and data visualization.

• DATA INTEGRATION & INTEROPERABILITY

• • What are the business (and technology) issues that data integration is seeking to address?
  • Data integration and data interoperability – What’s the difference?
  • Different styles of data integration and interoperability, their applicability and implications.
  • The approaches and guidelines for provision of data integration and access.

• DAMA CERTIFICATION-FIRST LEVEL

• • Students will have the opportunity to sit the CDMP Data Quality specialist exam at the end of this course to attain DAMA Certified Data Quality Professional designation and a credit towards attainment of a full CDMP at Practitioner or Master Level.

Upon completion of the course, participants will be able to:

• different categories of challenges,
• appreciate concepts including lifecycle management, normalization, dimensional modeling and data virtualization and appreciate why they are important,
• understand the critical roles of master data management and data governance and how to effectively apply them,
• understand the different facets (dimensions) of data quality and explore a workable data quality framework,
• describe the major considerations for successful data governance and how it can be introduced in bite-sized pieces,
• understand the different types of data models and their applicability,
• attend the DAMA certification exam.