Data Management Fundamentals (DAMA)

DATA/INTRODAMA

Who should attend?

  • This 5-day course addresses all the information management disciplines as defined in the DAMA body of knowledge (DMBoK). Taught by an industry recognized DAMA DMBoK (2.0) author and CDMP(Fellow) this course provides a solid foundation across all of the disciplines across the complete Information management spectrum. By attending the course, delegates will get a firm grounding of the core Information Management concepts and illustrate their practical application with real examples of how Information architecture is applied.
Audience :
  • Practitioners involved in Information management, data governance, master data management and data quality initiatives including: information managers, information architects, data architects, enterprise architects data managers, data governance managers, data quality managers, information quality practitioners, business analysts, executives, technology leaders, business technology partners.

Level : Discovery

Course Content

  • INTRODUCTION TO DATA MANAGEMENT

      • 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 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.
  • 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 MODELING

      • 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 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 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.
  • METADATA MANAGEMENT

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

      • 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.
      • 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.
  • DATA OPERATIONS MANAGEMENT

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

Learning Objectives

  • Upon completion of the course, participants will be able to:
  • understand the need for and the application of information management disciplines for 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.

Ways & Means

  • Daily lecture and exercises and case studies.