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Data Management Fundamentals E-learning

E-Learning: E-Learning | Duration: 12 months | £849
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As data becomes the strategic fuel powering modern organisations, effective data management is essential for driving value and maintaining control. This course provides a solid foundation in data management principles, helping professionals understand how to treat data as a business asset.

Key topics include the role of data in decision-making, the challenges of balancing governance with innovation, and best practices for managing data across the organisation. The course is based on the internationally recognised Data Management Body of Knowledge (DMBOK) developed by DAMA, the leading authority in the field.

This certification training course is designed to prepare learners for the Certified Data Management Professional (CDMP) exam. The training includes comprehensive theory, practical exercises, and targeted practice questions. It is ideal for anyone looking to deepen their understanding of data management or take the next step in their data career.

Candidates should be able to demonstrate a knowledge and understanding in the following topics:

  • The core principles and strategic importance of data management within an organization.
  • The DAMA-DMBOK framework to assess and structure data management practices.
  • The ethical considerations and support organizational change in data initiatives.
  • The importance of evaluating data management maturity across different functional areas.
  • The design and implementation of effective data models aligned with business needs.
  • The use of entity-relationship (ER) modeling techniques to represent complex data relationships.
  • The application of top-down and bottom-up approaches in data modeling and design.
  • The normalization of data structures to improve relational database efficiency and integrity.
  • The development and implementation strategies to ensure data quality, accuracy, and consistency.
  • Data governance frameworks to meet compliance and quality standards.
  • Navigation of regulatory requirements related to data privacy and protection.
  • Implementation and management of database systems for optimal data storage and retrieval.
  • Integration and harmonization of data from multiple sources for greater interoperability.
  • Utilization of big data technologies and analytics to drive informed decision-making.
  • Management of metadata to improve data discovery, understanding, and reuse.
  • Govern master and reference data to ensure consistency across systems.
  • Protect sensitive data through effective security measures, access control, and privacy policies.
  • Leverage business intelligence tools to extract and communicate actionable insights.

Candidates should be able to demonstrate a knowledge and understanding in the following topics:

  • The core principles and strategic importance of data management within an organization.
  • The DAMA-DMBOK framework to assess and structure data management practices.
  • The ethical considerations and support organizational change in data initiatives.
  • The importance of evaluating data management maturity across different functional areas.
  • The design and implementation of effective data models aligned with business needs.
  • The use of entity-relationship (ER) modeling techniques to represent complex data relationships.
  • The application of top-down and bottom-up approaches in data modeling and design.
  • The normalization of data structures to improve relational database efficiency and integrity.
  • The development and implementation strategies to ensure data quality, accuracy, and consistency.
  • Data governance frameworks to meet compliance and quality standards.
  • Navigation of regulatory requirements related to data privacy and protection.
  • Implementation and management of database systems for optimal data storage and retrieval.
  • Integration and harmonization of data from multiple sources for greater interoperability.
  • Utilization of big data technologies and analytics to drive informed decision-making.
  • Management of metadata to improve data discovery, understanding, and reuse.
  • Govern master and reference data to ensure consistency across systems.
  • Protect sensitive data through effective security measures, access control, and privacy policies.
  • Leverage business intelligence tools to extract and communicate actionable insights.
  • Module 1: Introduction to Data Management
  • Module 2: Data Governance
  • Module 3: Data Architecture
  • Module 4: Data Modeling and Design
  • Module 5: Data Storage and Operations
  • Module 6: Data Security
  • Module 7: Data Integration and Interoperability
  • Module 8: Document and Content Management
  • Module 9: Reference and Master Data
  • Module 10: Data Warehousing and Business Intelligence
  • Module 11: Metadata (Management)
  • Module 12: Data Quality (Management)
  • Module 13: Data (Handling) Ethics
  • Module 14: Big Data and Data Science
  • Module 15: Data Management maturity assessment
  • Module 16: Data Management organization and Role Expectations
  • Module 17: Data Management and Organizational Change Management
  • Module 18: Trial Exam

 

Exam Information

  • 100 Multiple-choice questions
  • 60% pass mark
  • 90 minutes exam duration
  • Closed book

There are no mandatory prerequisites.

  • Overview

    Candidates should be able to demonstrate a knowledge and understanding in the following topics:

    • The core principles and strategic importance of data management within an organization.
    • The DAMA-DMBOK framework to assess and structure data management practices.
    • The ethical considerations and support organizational change in data initiatives.
    • The importance of evaluating data management maturity across different functional areas.
    • The design and implementation of effective data models aligned with business needs.
    • The use of entity-relationship (ER) modeling techniques to represent complex data relationships.
    • The application of top-down and bottom-up approaches in data modeling and design.
    • The normalization of data structures to improve relational database efficiency and integrity.
    • The development and implementation strategies to ensure data quality, accuracy, and consistency.
    • Data governance frameworks to meet compliance and quality standards.
    • Navigation of regulatory requirements related to data privacy and protection.
    • Implementation and management of database systems for optimal data storage and retrieval.
    • Integration and harmonization of data from multiple sources for greater interoperability.
    • Utilization of big data technologies and analytics to drive informed decision-making.
    • Management of metadata to improve data discovery, understanding, and reuse.
    • Govern master and reference data to ensure consistency across systems.
    • Protect sensitive data through effective security measures, access control, and privacy policies.
    • Leverage business intelligence tools to extract and communicate actionable insights.
  • Learning outcomes

    Candidates should be able to demonstrate a knowledge and understanding in the following topics:

    • The core principles and strategic importance of data management within an organization.
    • The DAMA-DMBOK framework to assess and structure data management practices.
    • The ethical considerations and support organizational change in data initiatives.
    • The importance of evaluating data management maturity across different functional areas.
    • The design and implementation of effective data models aligned with business needs.
    • The use of entity-relationship (ER) modeling techniques to represent complex data relationships.
    • The application of top-down and bottom-up approaches in data modeling and design.
    • The normalization of data structures to improve relational database efficiency and integrity.
    • The development and implementation strategies to ensure data quality, accuracy, and consistency.
    • Data governance frameworks to meet compliance and quality standards.
    • Navigation of regulatory requirements related to data privacy and protection.
    • Implementation and management of database systems for optimal data storage and retrieval.
    • Integration and harmonization of data from multiple sources for greater interoperability.
    • Utilization of big data technologies and analytics to drive informed decision-making.
    • Management of metadata to improve data discovery, understanding, and reuse.
    • Govern master and reference data to ensure consistency across systems.
    • Protect sensitive data through effective security measures, access control, and privacy policies.
    • Leverage business intelligence tools to extract and communicate actionable insights.
  • Course outlines
    • Module 1: Introduction to Data Management
    • Module 2: Data Governance
    • Module 3: Data Architecture
    • Module 4: Data Modeling and Design
    • Module 5: Data Storage and Operations
    • Module 6: Data Security
    • Module 7: Data Integration and Interoperability
    • Module 8: Document and Content Management
    • Module 9: Reference and Master Data
    • Module 10: Data Warehousing and Business Intelligence
    • Module 11: Metadata (Management)
    • Module 12: Data Quality (Management)
    • Module 13: Data (Handling) Ethics
    • Module 14: Big Data and Data Science
    • Module 15: Data Management maturity assessment
    • Module 16: Data Management organization and Role Expectations
    • Module 17: Data Management and Organizational Change Management
    • Module 18: Trial Exam

     

    Exam Information

    • 100 Multiple-choice questions
    • 60% pass mark
    • 90 minutes exam duration
    • Closed book
  • Prequisites

    There are no mandatory prerequisites.

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