Certified Data Management Professional - CDMP

Apply data governance as the authority to monitor and enforce data management, ensuring policies, standards, and compliance while fostering data stewardship, governance programs, and change management to protect data value.

Drive data governance to reduce risks and improve processes, anchored in regulatory compliance with HIPAA, GDPR, and CcpA, and by enabling advanced analytics and data science.

Map the data governance organization, detailing the CDO-led governance and CIO-led data management verticals. Define the data governance council, steering committee, office, and roles like data stewards, owners, and analysts.

Explore centralized, replicated, and federated data governance operating frameworks, understanding how centralized policy standardizes across regions, replicated increases local autonomy, and federated coordinates with business units while preserving autonomy.

Establish an enterprise data governance framework with a formal strategy, readiness assessment, operating model, and defined policies, standards, and governance bodies.

Underwrite and embed data governance by aligning data management projects with policies, standards and objectives, defining business cases with ROI, and monitoring data quality, security, privacy and metadata compliance.

Explore the structure, functions, and responsibilities of data architecture, from defining data requirements and master blueprints to guiding data integration and aligning data strategy with business goals.

Explore how enterprise business, data, application, and technology architecture collaborate—from business needs and data models to data flows and integration APIs—delivering aligned systems and infrastructure.

Recognize that the data life cycle flows across platforms and is stored in different places, forming the enterprise data model and the data flow design.

Explore the enterprise data model and enterprise conceptual model, its physical and logical levels, normalization, subject areas, and horizontal and vertical mappings to improve governance, consistency, and interoperability.

Track data flows from tendering to invoicing, including RFPs, proposals, evaluations, and purchase orders, by designing a data flow matrix that captures CRUD operations on core entities.

Define data models as structured blueprints guided by business rules that organize data across relational and NoSQL databases, with an iterative process of discovering requirements and communicating a precise model.

Explore data modeling components: entities, relationships, attributes, and domains, and learn how entities as nouns capture business objects, define value domains, and enforce clarity, accuracy, and completeness.

Explore how attributes define, identify, and measure an entity with the employee example, and how completeness interacts with primary and candidate keys to uniquely identify instances and support alternate keys.

Explore data modeling components by examining attributes and identifiers, including simple key, compound key, composite key, and surrogate key, with natural keys or business keys such as social security number.

Explore how relationships are represented and classified, covering arity and cardinality with an invoice example. Learn unary relationships and the hierarchy and network structures in data modeling, including foreign keys.

Explains the arity of relationships—unary, binary, and ternary—with examples from invoice and invoice detail, and from courses, foundational courses, and prerequisites, including that a course cannot be its own prerequisite.

Explore data modeling relationships by counting cardinality, identifying one-to-many and many-to-many patterns, and distinguishing independent (parent) versus dependent (child) entities with primary and foreign keys.

Demonstrate binary relations by linking invoices to invoice details through a foreign key, populate data, and illustrate unary hierarchy and network relationships, then convert unary recursive into binary via prerequisites.

Learn how first normal form removes repeating groups, enforces atomic values, and uses a primary key to uniquely identify records, illustrated by procurement data.

Explore the second normal form, where non-key attributes depend on the primary key and a surrogate procurement id replaces composite keys, enabling product and supplier splits with foreign keys.

Define the CDM as a high-level, entity-focused conceptual data model that clarifies relationships and business requirements to guide logical and physical modeling.

Explore how the logical data model bridges conceptual and physical designs by detailing attributes, normalization, keys, and constraints to align business requirements with a db-agnostic blueprint.

Discover common database terms, architectures, and building blocks—instances, nodes, and clusters—and how schemas and database abstraction enable security, access control, and scalable, high-availability data management in distributed setups.

Centralized architectures are simple but risk outages; distributed architectures offer high availability with multi-node processing, using MapReduce with Hadoop, and databases like MongoDB and Cassandra for scalable performance.

Explore blockchain database architectures as distributed, federated systems that securely manage decentralized financial transactions, and examine blocks, hashes, Merkle roots, and consensus across nodes.

Examine how databases balance data integrity and scalability with acid and base processing, detailing atomicity, consistency, isolation, durability, and eventual consistency.

Data security uses policies and procedures to control access, authorization, authentication, and auditing of data, addressing regulatory, government, proprietary, and contractual privacy needs while classifying risks and applying security techniques.

Explore cyber threats, insider and physical threats, and malware categories like adware, spyware, Trojan horse, viruses, and worms, including phishing, social engineering, platform intrusion, SQL injection, and ransomware.

Explore phishing and social engineering as forms of deception, and examine platform intrusion and SQL injection attacks, with defenses like updates, firewalls, and parameterized queries and least privilege access.

Examine insider threats and how users with privileges can misuse access, including privilege elevation, shared and service accounts, with mitigations like least privilege, time-bound access, machine restrictions, and intrusion prevention.

Explore common data and network security terms such as backdoors, bots, cookies, firewalls, the network perimeter, DMZs, and VPNs, and understand how these elements defend or expose systems to threats.

Explore data integration and interoperability, moving and consolidating data across stores and applications. Understand migration, hubs and data marts, sharing, governance, and architecture that enable operational intelligence and decision support.

Capture deltas from inserts, updates, and deletes with change data capture (CDC) to drive data integration, and compare data-based, log-based, and external object approaches for real-time capability and overhead.

Explore the DII architecture and data integration concepts, including application coupling, loose coupling via APIs and ESB, orchestration, process controls, data federation and virtualization, and SOA for cloud-based integration.

Explore real-time complex event processing to detect patterns and anomalies across IoT, sensors, and data, and learn how data federation, virtualization, and data as a service enable seamless, scalable integration.

Learn how document and content management controls the life cycle management of unstructured and semi-structured data, enabling fast retrieval, secure access, and regulatory compliance.

The lecture defines content management and its life cycle, contrasting active and static content, and explains how indexing, taxonomies, and metadata support enterprise content management across websites and portals.

Define metadata as data about data and show how it enhances searchability for unstructured content. Model content by converting concepts into content types and attributes at product and component levels.

Explore taxonomies as controlled vocabularies that reduce ambiguity and ease search and retrieval across hierarchical taxonomy, poly hierarchy, facet taxonomy, and network taxonomy, with ontologies linking concepts in semantic web.

Discover how inconsistent department data, including names, region codes, emails, and cost centers, drives misreporting, and learn how reference and master data management align and govern data across the enterprise.

Explore how reference and master data management reconcile and integrate data for enterprise-wide sharing through stewardship, semantic consistency, and guiding principles: shared data ownership, quality, stewardship, control, change, and authority.

Master data management coordinates people, process, and technology to maintain the most up-to-date, unambiguous master data across the enterprise, governing data quality, integration, and access.

Plan and execute data acquisition within a single domain, selecting sources, applying data cleansing, validation, standardization, and enrichment to enable accurate entity resolution in MDM.

Understand the entity resolution process in master data management, including reference extraction, preparation, resolution, identity management, and relationship analysis and affiliation management, and how matching links records across systems.