Who Should Attend &
Course FAQs

No. No previous logical data modeling, E/R diagramming, database design, programming, analytics, or AI experience is required.

The course introduces data-oriented concepts, business vocabulary, E/R diagramming constructs, modeling rules, and foundational patterns systematically.

Experienced practitioners also benefit by strengthening established techniques with complex data patterns, decision-level analytical requirements, enterprise reference models, semantic grounding, and modern AI-readiness considerations.

A logical data model provides a detailed, technology-independent representation of the business concepts, attributes, relationships, and data-oriented rules that an organization needs to manage.

The three levels serve different purposes:

• Conceptual data model: Defines the major business concepts and high-level relationships within a business domain.
• Logical data model: Defines entities, attributes, relationships, cardinality, dependencies, normalization, and detailed business meaning without committing to a specific technology.
• Physical data model: Translates the logical design into database-specific tables, columns, keys, indexes, data types, and implementation structures.

The course focuses on the logical level while explaining how conceptual models progress into logical models and how logical models inform physical implementation.

Yes. The course teaches a business-oriented framework for creating clear, accurate, and scalable Entity Relationship diagrams.

Participants learn to model:

• Entity types, attributes, and metadata
• Cardinality, dependency, and foundational relationships
• Domain and associative relationships
• Repeating groups and transactional patterns
• Super-type and sub-type structures
• Recursive hierarchies and networks
• Role-based associations
• Time-dependent data structures

The emphasis is on understanding the business meaning and rules represented by each modeling construct, not simply drawing diagrams.

Yes. Participants learn to distinguish the structures needed to support operational transactions from those needed to support reporting, business intelligence, analytics, and strategic decision-making.

The analytical portion of the course includes:

• Defining tactical and strategic business questions
• Clarifying reporting and analytical requirements
• Identifying facts and dimensions
• Applying dimensional modeling techniques
• Defining decision-level data requirements
• Determining whether required data is reliable and reachable at the moment of decision

This combined perspective helps organizations create data structures that support both day-to-day operations and meaningful enterprise analysis.

The logical data model provides the structural foundation that helps AI systems interpret business concepts, relationships, terminology, and data-oriented rules consistently.

AI-generated applications and AI agents act on the definitions and relationships they encounter. When those structures are incomplete or inconsistent, structural ambiguity can become semantic drift, faulty assumptions, and reasoning defects.

The course introduces the progression from data to meaning to reasoning:

• Logical data model: Establishes structure
• Semantic model: Establishes shared meaning
• Ontology: Formalizes concepts and relationships for machine reasoning

The course explains this progression and how logical data models support semantic grounding, retrieval, metadata context, and constraints on agent reasoning. Ontologies and ontological modeling are addressed in greater depth in Inteq's Advanced Data Modeling course.

You will be able to translate real-world business concepts, terminology, relationships, and rules into clear, professional logical data models using a repeatable, business-oriented method.

• Identify and define business entities, attributes, and metadata
• Model cardinality, dependency, and business relationships
• Create clear and accurate E/R diagrams
• Apply baseline and complex data modeling patterns
• Distinguish transactional and analytical data requirements
• Normalize and generalize data structures
• Develop scalable enterprise reference models
• Transform conceptual models into logical and physical designs
• Improve semantic consistency across business and technology teams
• Establish a stronger data foundation for applications, analytics, AI-assisted development, and AI agents

Logical Data Modeling is the "ground the work" stage of Inteq's business transformation curriculum. It establishes the business concepts, relationships, definitions, and data-oriented rules that provide a shared structural foundation for enterprise solutions.

It complements Inteq's Business Systems Analysis, Business Process Modeling & Analysis, and Business Process Reengineering courses by defining the information structures needed to support processes, requirements, applications, analytics, and transformation.

Logical Data Modeling also provides the prerequisite foundation for Inteq's Advanced Data Modeling and Querying Complex Data Patterns courses.

The course includes professional development credentials, completion recognition, and the complete set of course resources.

• 1.4 CEUs
• 14 IIBA PDUs or CDUs
• Personalized Logical Data Modeling digital badge
• Certificate of completion
• Comprehensive course manual
• Models, frameworks, patterns, and methods used throughout the course

Inteq is an IIBA Endorsed Education Provider.

Two delivery formats are available: self-paced Anytime eLearning™ and live Team Training.

Anytime eLearning™: Self-paced access from any device, with 90-day course access. Tuition is $695.

Team Training: Live onsite or virtual training tailored to your organization's objectives. Team Training can also combine content from multiple Inteq courses into three, four, or five-day hybrid programs. Contact Inteq for scope and pricing.

Yes. Inteq is an approved GSA prime contractor, and Inteq training and consulting are available to U.S. Government organizations through applicable GSA Professional Services Schedule offerings.