Data Management Foundation discusses what Data Management is, along with drivers and issues concerning organizations. It presents the intended purpose and scope of the DAMA International Data Management Body of Knowledge (DMBoK®), as well as gives a full overview of the disciplines within. Other topics included in this course are changes in the DMBoK and relationships of the DMBoK to other frameworks such as TOGAF and COBIT.

It also looks at why Data Governance is central to successful Data Management, roles and responsibilities, drivers and issues, reference models, organizational structures, and principles. It also discusses the role of the Data Governance Office and how to get started with Data Governance. Lastly, it covers the topic of Data Ethics, the information lifecycle, the importance of data for organizations, how Data Governance and Data Quality are linked, benefits and implications, and so much more.

Data Architecture and Lifecycle Management dives deep into a discussion around Data Architecture, Enterprise Architecture, and Lifecycle Management. It covers types of architectures, proactive planning around the data lifecycles from inception and acquisition though provisioning, exploitation, and destruction. It also discusses the data value chain, differences between lifecycles and systems development, and alignment with business models.

Data Quality Management discusses the many different parts of Data Quality and why validity is often confused with quality. It discusses the dimensions of Data Quality as well as the policies, procedures, metrics, technology, and resources for ensuring Data Quality. It looks at a specific reference model, as well as root cause analysis, tools that helps support Data Quality Management, creation and monitoring of Data Quality measures, and various myths and pitfalls around Data Quality Management.

Master and Reference Data Management looks at the many components necessary to leverage reference and master data. It discusses their differences, identification of resources, different Master Data Management (MDM) architectures, maturity assessments, and aligning with business processes. It also discusses different MDM solutions and approaches, as well as the relationships between MDM, Data Quality, and Data Governance.

The course covers Data Warehousing and Business Intelligence within the DAMA Body of Knowledge (DMBoK®) in depth. What is data warehousing and Business Intelligence (BI) Management? Why are they important Data Management disciplines? What types of models are there? This course covers these questions, along with various characteristics of data warehouse and BI platforms, dimensions and hierarchies, dimensional modeling, E/R modeling, implementation of warehouses and marts, data visualization, and more. It also addresses the fundamentals of Big Data.

Data Integration and Interoperability looks at applying Data I&I practices and solutions across the enterprise, as well as its core concepts and how it is used to support BI, analytics, MDM, and other operational efficiency efforts. It discusses data interchange, hub distributions, SOA, ETL, ELT, CDC, data replication, and where it all fits within Data Governance and expanded Data Management activities.

Metadata Management is defined in the DMBoK as “the planning, implementation, and control activities to enable access to high quality, integrated metadata.” This course covers all the key areas around metadata and Metadata Management. That includes an in-depth discussion around definitions, usage, and implementation of metadata, as well as practical examples. It also explores why organizations need metadata, the differences between data and metadata, types of metadata, architectures, lineage, benefits, and the relationship between Data Governance and Metadata Management.

Data Modeling explores numerous essential features of contemporary Data Modeling, including the System Development Lifecycle (SDLC), modeling styles, analysis and solution design, detailed data designs, Design Quality Management, types of data models, and data implementation, among many others.

Data Storage and Operations is defined in the DMBoK as “the design, implementation, and support of stored data to maximize value.” This course covers the essential components of effective Data Storage and Operations Management, including its main activities, Lifecycle Management, Data Technology Management, tools and operations, roles and responsibilities, database environments, availability, recoverability, architectures, and pitfalls.

Some of the key goals of Data Security Management include enabling appropriate access to organizational data assets and prevention of inappropriate usage. It is also an essential component in regulatory compliance, ensuring privacy and confidentiality of enterprise data assets. This course takes an in-depth look at the guiding principles, drivers, requirements, standards, categories of control, risk assessment, permissions, monitoring, and many other essential components of Data Security Management.

Effective Document and Content Management is an important discipline within the Data Management landscape. It helps to safeguard and ensure the availability of semi-structured and unstructured data assets, enables efficient retrieval of such assets from various enterprise platforms and systems, and aids in compliance and audit practices. Such practices also help to ensure business continuity through retention, recovery, and conversion practices, along with lowering operating costs. This course covers a range of key topics around Document and Content Management.