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Data Management Fundamentals (DAMA)

  • This 4-day course addresses all the information management disciplines as defined in the DAMA body of knowledge (DMBoK). Taught by a highly experienced Enterprise Architect, Dr. Steve Else, 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.
     
    This course is designed for 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.
     
    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.
     
    Students who register for this course will have the option to purchase the course with or without the exam voucher. To purchase the course without the exam voucher use discount code DAMA300 at checkout. ​ 
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.
A 100% refund, minus a 5% processing fee, will be given to students who drop or withdraw from the EA Principals, Inc. class no later than the 21st day prior to the announced scheduled start date. No refund will be given within 21 days of the published start date. Students missing a portion of a class, due to emergencies or unforeseen circumstances, will be able to attend the next class on the same topic for the days missed, without additional fee. Students need not re-register; however, they must notify EA Principals, Inc. (by email or telephone), so the registration fee for the makeup class can be waived and class logistics provided.
 
Note: This refund policy does not apply if using special discount codes that have “no refund” specified in the usage instructions.

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