The Complete Guide to Data Management DMBOK2 Revised CDMP

Learn the fundamentals of data ethics, including what it means to handle data responsibly and why ethical data practices are essential for protecting individuals, maintaining trust, and ensuring organizational success.

Discover how ethical data handling builds stakeholder trust, enhances reputation, and reduces risks. Understand why leadership and organization-wide commitment are critical for ethical behavior.

Explore the ethical foundations for managing data, including the Belmont Report, the EDPS Four Pillars, and the concept of ethical data lifecycles that prioritize privacy, fairness, and transparency.

Gain insight into how global privacy laws like GDPR, PIPEDA, and FTC Guidelines translate ethical principles into legal requirements and how organizations must comply with these standards.

Examine real-world examples of unethical data practices—such as misleading visualizations, biased sampling, and poor integration—and learn how to recognize and avoid them.

Learn how to assess current data practices, define ethical standards, and build a culture of accountability through governance, training, and communication.

Understand how to evaluate the broader societal impact of data projects and implement a risk model that supports responsible analytics, protects vulnerable groups, and empowers ethical decision-making.

Learn the foundational definition of Data Governance and understand why it's critical for modern organizations. We'll explore how governance functions guide data practices, and what business and technical drivers—like compliance, risk reduction, and analytics—necessitate strong governance.

This lecture explains the core goals and guiding principles of Data Governance, such as sustainability, embeddedness, and measurement. You’ll also understand key concepts like the data-centric organization, oversight vs. execution, and the separation between Data Governance and IT governance.

Dive into how Data Governance is structured through councils, committees, and stewardship roles. Learn about different types of Data Stewards—business, technical, executive—and their responsibilities in maintaining data quality and consistency.

Understand how policies codify governance principles and explore the concept of data asset valuation. This lecture also introduces Generally Accepted Information Principles (GAIP) for measuring and managing the value and risks of data.

Learn how to plan and define the scope of Data Governance within an enterprise. This includes readiness assessments, business alignment, and developing organizational touchpoints to embed governance across the organization.

Explore how to define a Data Governance strategy and build an operating model that aligns with your organization’s structure, culture, and regulatory environment. We’ll also cover how to craft effective governance policies and principles.

Discover how to support and underwrite data management projects, while embedding change management practices. Learn how to gain stakeholder buy-in, promote awareness, and drive behavior change throughout the organization.

This lecture outlines how to manage issues and ensure regulatory compliance. You’ll learn escalation paths, issue tracking, and how Data Governance supports audit readiness and risk reduction.

Learn how to roll out a Data Governance program with clear implementation roadmaps and foundational activities like glossary creation and architectural coordination.

This session covers how to sponsor data standards and procedures, develop business glossaries, and integrate governance with Data and Enterprise Architecture efforts.

Understand how to assign value to data assets and embed governance practices into day-to-day operations. This ensures that governance is sustained across the business lifecycle.

Explore the tools—like scorecards, glossary solutions, and workflow systems—that support Data Governance. This lecture also provides practical guidelines for incremental rollout and cultural adaptation.

Discover how to use metrics to demonstrate the value, effectiveness, and sustainability of your Data Governance program. Learn what KPIs to track and how to measure cultural and operational change.

Defines Data Architecture and its role in managing data as a strategic enterprise asset. Explores the components, goals, and value proposition of Data Architecture within the overall data management framework.

Covers the key business motivations for Data Architecture, such as aligning business and IT strategy, and explains the outcomes (e.g., data value chains, blueprints, reusable designs) that effective architecture delivers.

Introduces core architecture domains (business, data, application, technology) and explains how frameworks like Zachman and TOGAF structure enterprise architecture efforts.

Details the modeling layers—conceptual, logical, physical—and covers subject area modeling, bottom-up vs. top-down approaches, and how data flows and lineage are documented across systems.

Explains how Data Architecture is implemented in practice, including staffing models, methods for influencing project scope, and architecture’s role in agile and waterfall development lifecycles.

Presents tools used for modeling, documentation, and asset management. Covers best practices for rolling out architecture artifacts, maintaining clarity, and ensuring usability.

Focuses on architecture’s alignment with governance, how to assess readiness and manage risk, and how to shift organizational culture to support sustainable architectural practices.

Explores how to measure the effectiveness of Data Architecture efforts—using metrics tied to reuse, project enablement, compliance, and business value realization.

Define data modeling and its role in data management. Explore why data models are essential for understanding, documenting, and managing data requirements across organizations.

Examine how data models align with business goals, facilitate communication, reduce costs, and support initiatives like MDM and data governance.

Understand key terminology and the purpose of modeling. Learn about different types of data (category, resource, event, transactional) and why modeling them matters.

Explore entities, relationships, and cardinality. Learn how they are represented graphically and why they are foundational to all models.

Dive into how attributes describe entities, how keys identify records, and how domains enforce data quality through constraints and rules.

Introduce the six main modeling schemes—Relational, Dimensional, Object-Oriented, Fact-Based, Time-Based, and NoSQL—and their use cases and notations.

Examine the three levels of modeling detail and how each serves different stakeholders, from business to technical users.

Discuss how models are built from scratch (forward) or reverse-engineered from existing databases. Introduce common techniques and iteration cycles.

Overview of tools used by data modelers (modeling software, lineage tools, profiling tools) and common techniques like naming conventions and model patterns.

Learn the best practices in naming, design, and data structure optimization using the PRISM framework—Performance, Reusability, Integrity, Security, and Maintainability.

Understand the standards, versioning, quality assurance, and governance needed to maintain data models as living documentation.

Review how to evaluate model quality using the Data Model Scorecard®, and what constitutes a good model from both business and technical perspectives.

This lesson introduces what Data Storage and Operations covers, including database support and database technology support, and explains why DBAs play a central role in this knowledge area.

This lesson explores why business continuity drives storage and operations, outlines core goals such as availability, integrity, and performance, and introduces practical guiding principles like automation, reuse, and standards with flexibility.

This lesson establishes essential database terminology—such as database, instance, schema, and nodes—so practitioners can communicate precisely and reduce operational risk.

This lesson compares centralized, distributed, and federated database architectures and explains how coupling increases cost and risk, along with strategies to manage it effectively.

This lesson explains ACID and BASE processing models and the CAP Theorem, helping practitioners understand consistency, availability, and partition-tolerance trade-offs in real-world systems.

This lesson reviews common storage media (disk, SAN, in-memory, columnar, SSD) and database environments across the SDLC, emphasizing cost, performance, and operational implications.

This lesson focuses on selecting, evaluating, standardizing, and operating database technologies, with attention to scalability, risk, automation, and long-term platform sustainability.

This lesson covers operational responsibilities such as continuity planning, performance management, test data handling, and data migration, highlighting repeatable and auditable practices.

This lesson surveys the tooling and techniques DBAs use—modeling, monitoring, management tools, scripting, and standards—to reduce manual work and improve operational reliability.

This lesson explains how to implement storage and operations practices responsibly using readiness assessments, metrics, asset tracking, audits, and governance to manage risk over time.

This lesson introduces the scope of data security and the main forces that shape security requirements, including stakeholder expectations, regulation, business risk, and operational access needs. It also explains why strong security is both a risk-reduction capability and a business enabler.

This lesson builds the core language of data security, including vulnerability, threat, risk, classification, integrity, access, authentication, authorization, entitlement, monitoring, and audit. It gives learners the vocabulary needed to discuss security requirements clearly across technical and business teams.

This lesson explains how encryption, obfuscation, and masking protect sensitive data in production and non-production environments. It also shows when to use persistent masking, dynamic masking, and different encryption approaches based on real operational needs.

This lesson covers practical security controls across the wider environment, including perimeter concepts, DMZs, VPNs, penetration testing, device security, and credential management. It connects data protection to the realities of networks, endpoints, remote access, and user identities.

This lesson examines confidentiality levels, regulated data, least privilege, and common security failure patterns such as excessive access, privilege abuse, service account misuse, SQL injection, and default passwords. It helps learners recognize where real-world exposure usually begins.

This lesson shows how organizations identify security requirements and translate them into enforceable policies and standards. It focuses on classification, regulatory obligations, role design, and the practical structure needed to turn security intent into operational rules.

This lesson explains how access decisions are implemented and sustained through entitlements, query-level controls, monitoring, and audit mechanisms. It highlights how organizations prove compliance, detect misuse, and manage access over time rather than treating security as a one-time setup.

This lesson reviews the major tools and techniques that support data security, including anti-virus software, web security, identity management, intrusion detection and prevention, firewalls, metadata tracking, masking, encryption, CRUD matrices, and document sanitization. It connects each tool to its practical role in protecting data.

This lesson closes the chapter with implementation guidance for culture, readiness, outsourcing, cloud environments, governance alignment, and metrics. It helps learners understand how to operationalize security as an enterprise discipline and measure whether controls are truly working.

Understand why document and content management matters, including key business drivers, goals, and foundational principles such as compliance, retrieval, and accountability.

Explore how content is structured and managed using metadata, content models, and delivery approaches to enable reuse, searchability, and integration.

Learn how controlled vocabularies, taxonomies, and term governance improve consistency, search accuracy, and enterprise-wide understanding of content.

Understand how classification structures, ontologies, and information architecture support effective search, navigation, and workflow automation.

Examine the lifecycle of documents and records, including data mapping and e-discovery processes for legal and compliance needs.

Learn how to design content and records lifecycle strategies, including classification, retention policies, and content handling standards.

Understand how to operationalize lifecycle management through architecture, capture processes, version control, and content delivery practices.

Explore the tools, governance structures, and metrics needed to manage content effectively and support compliance, discovery, and performance measurement.

Learn what DII covers, why it matters, and how it supports core enterprise needs such as migration, sharing, consolidation, and analytics. This lesson also introduces the main goals and guiding principles behind effective integration design.

Understand the basics of ETL and ELT, including how data is extracted, transformed, and loaded into target systems. This lesson also compares batch, near-real-time, asynchronous, and synchronous approaches so you can see the trade-offs clearly.

Explore common patterns for moving data, including change data capture, staging, replication, and archiving. You will also learn how canonical models and exchange standards help reduce complexity and improve consistency.

This lesson introduces point-to-point, hub-and-spoke, and publish-subscribe interaction models used in real integration environments. It also explains coupling, orchestration, control mechanisms, and enterprise architecture concepts such as ESB and SOA.

Learn how strong integration work starts with clear business requirements, data discovery, lineage, and profiling. This lesson also shows why business rules are essential for designing reliable and usable integration solutions.

This lesson walks through how to design integration architecture, model interfaces, map sources to targets, and build reusable data flows. It also covers migration, publication patterns, and the basics of complex event processing design.

See what it takes to run integration services in production, including monitoring, alerting, and metadata maintenance. This lesson also reviews the main tools used in DII, from ETL and virtualization to ESB, rules engines, and profiling tools.

This lesson explains how to implement DII in a sustainable way with the right sponsorship, governance, and business accountability. It closes with the key metrics used to measure availability, speed, cost, complexity, and reuse.

This lesson explains why organizations rely on shared data across systems and the risks of inconsistency. It shows how master and reference data improve quality, reduce cost, and enable reliable decision-making.

This lesson defines master data and reference data, explains how they differ, and shows how they work together to provide context for business operations and analytics.

This lesson introduces code sets, taxonomies, and hierarchies, and explains how they structure data for consistency, integration, and reporting across systems.

This lesson explains what MDM is, how it resolves identity and duplication issues, and how organizations create and maintain authoritative “golden records.”

This lesson walks through the lifecycle of master data—from creation and validation to distribution, maintenance, and retirement—highlighting the role of data stewardship.

This lesson explores key master data domains such as party, product, financial, and location, and shows how they connect to support business processes and analytics.

This lesson covers how MDM and RDM are implemented through architectures and integration patterns, including hubs, APIs, and centralized or federated models.

This lesson explains the governance structure behind MDM and RDM, including roles, policies, metrics, and tools that ensure data remains accurate and controlled.

This lesson outlines how to implement MDM and RDM programs, including building a business case, designing operating models, overcoming challenges, and driving continuous improvement.