DM 401 – Enterprise Data Model Development

Duration: 3 – 5 days

This course is designed to teach students how to locate, define, and represent the data needed by the enterprise. This interactive course provides a combination of lecture and small workshop exercises using a continuing case study that allows students to grasp and practice the concepts of data modeling on an enterprise scale. It blends basic data modeling activities and concepts with the enterprise view of business data needs. The course can be presented using a particular modeling tool or taught independently of any modeling tool.

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Description

Enterprise data modeling is the set of activities that enables an organization to identify, categorize, and define their data assets. An enterprise data model provides the structure around which most data-oriented decisions can be made, including the evaluation of packaged applications, the re-engineering of legacy systems, and the development of a data warehouse or other decision support environment.

This course is workshop-focused since design concepts are best learned through practice. The workshops are oriented to solving problems in participants’ current projects. These exercises allow participants to learn how the concepts are applied and to develop skills in data modeling from conceptual to logical to physical.

Additionally, the organization’s chosen data modeling tool can be incorporated into the exercises, and training in tool usage can be added to this course.

Objectives

Analyze an enterprise, identifying data components and relationships among them
Develop the various levels of data models used within an organization
Determine how to actively engage business people in data modeling
Model data for the enterprise and represent it via entity-relationship diagrams
Understand the uses of data models in the enterprise for many initiatives

Seminar Content

Information as an Enterprise Asset
- Business value of managing data and information
- Definitions
Enterprise Information Management – Introduction
- What is EIM?
- EIM framework
- EIM goals
- EIM guiding principles
- Issues and challenges of implementing an EIM program
- Key attributes to a successful EIM program
- The EIM organization structure
Overview of an Enterprise Information Management Program
- Defining EIM requirements
- Identifying data and metadata sources
- Approaches to EIM implementation – roadmap approach
Introduction to Enterprise Data Modeling
- Basis for Enterprise Data Modeling – industry best practices and acceptance
- Development of models as part of an enterprise’s business value proposition
- Technical aspects of enterprise data model development
- Success factors for enterprise data modeling
- Challenges and issues in enterprise data modeling
Staffing the enterprise data model effort
- Credibility
- Representation – business and technical
- Expertise within and outside the team
- Logistics
Scoping the enterprise data model effort
- Time commitments and durations
- Time-boxing for effective delivery
Recording the work
- Tool requirements
- Logistics
- Notations, conventions, and standards
Scoping the enterprise
- The organization chart
- Generic functions
- Unique functions
- Enterprise interfaces
- Determining “shared data”
Input to the modeling effort
- Porter’s value chain model
- “Universal” / open-source data models, COTS data models
- Existing internal data models
Model construction techniques and guidelines
Modeling concepts and types review
- Names and definitions
- Leveling
- Delineation
- Decomposition
- Achieving consensus
Subject Area Data Modeling
- Identifying and defining subject areas
- Subject area relationships
- Conceptual data modeling
- Identifying and defining conceptual entities
- Conceptual entity relationships
- Conceptual entity attributes
Logical Data Modeling Overview
- Relationship types
- E-R diagrams and how to read them
- Keys in enterprise data modeling
- Normalization for the enterprise
- Patterns in data modeling
- Addressing missing data and data redundancy
- Sub-Entity modeling
- Identifier integrity
- Recursive relationships
- Modeling entity types and subtypes
- Generic (metadata) entity types
How to use the models: How to not be shelf-ware
- Presenting for validation and acceptance
- Dissemination and use
- Data ownership and governance
- Business reference: taxonomy, intranet portal, etc.
- Integration with other components of the IT architecture
- Relationships to more detailed data models
- Implementation for initiatives: data warehousing / business intelligence, re-engineering, enterprise data integration, enterprise resource planning, etc.
- Maintenance
Integrating enterprise data modeling into an EIM program
Special Topics in Enterprise Data Modeling
- Model metadata
- Supporting multiple business views
- Data model walkthroughs
- Reconciling the data model
- Techniques for effective workshops
- Gaining and sustaining management commitment and involvement
Conclusion
- Workshop Summary, Additional Exercises, and Reference Materials

About the Course Designer

This training was designed by David Marco, PhD, an internationally recognized authority on data and AI governance, to help teams succeed in real organizational conditions. The curriculum equips participants with practical judgment, shared language, and decision clarity that hold under scale, risk, and executive accountability.

DM 401 – Enterprise Data Model Development

Free Consultation

Description

Objectives

Seminar Content

About the Course Designer

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