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Data Model Jump-Start Training


Clients often ask: “Are there any standard ways to model our common data structures? Surely we are not the first company to model standard constructs like customer relationships, sales transactions or purchase orders!”


Experience has shown that more than 60% of a data model (corporate or logical) or data warehouse design consists of common constructs that are applicable to most enterprises. This means that most data modeling or data warehouse design efforts spend as much as 60% of their time and budget creating constructs that have been built many, many times before! Doesn’t it make sense, then, to have a source for these common constructs? Get a head start on your data model or multi-dimensional modeling efforts and stop “reinventing the wheel” each time you develop a new system!


Additionally, how many times have you taken a course with simplified examples and then had difficulty in applying them in a complex real world environment? Why not learn through real world examples illustrating effective and ineffective ways of modeling?

Course Description

This interactive seminar starts with data modeling fundamentals and then walks you through the process of using common or “Universal Data Models” for data modeling You will leave not only with the concepts behind these models, but also with actual models customized for your organization. We will examine many data modeling, data warehouse, and enterprise data administration pitfalls and learn how to avoid them. These models, hints, and techniques could save your organization a great deal of time and money by avoiding unnecessary redevelopment costs!

This dramatically different seminar teaches data modeling by example. Other seminars focus on the techniques and methodologies behind data modeling. We will provide an overall data modeling fundamentals section on the beginning of the first day, and then we will mainly teach by allowing the student to see real life examples of models. The participants can then use these to help create data models for their organization —you will build on the examples presented, to develop your own data models that you can use immediately to jump-start your own efforts.


This interactive tutorial will provide participants with a powerful toolkit of “Universal Data Models” that can be the starting point for quality and integrated data architectures, databases and data warehouses.

This seminar will provide:

  • A data modeling fundamentals section – a fast track approach to learning data modeling techniques. This includes defining entities, relationships, sub-entities, types versus instances, cardinality, optionality, unique identifiers, attributes, recursions, exclusive arcs, normalization

  • A broad range of common, re-usable, “Universal Data Models” allowing you to jump start and quality assure your efforts. This course includes many structures that can be used to track CRM information, supply side processing, sales transactions and analysis, and much more. For example, it will include constructs for modeling people, organizations, relationships, contacts, products (goods and/or services), pricing of agreements and products, orders and sales transactions, shipments, invoices, work efforts, accounting, budgeting, web visits, server hits and additional e-commerce data structures. The content of the course will vary, depending on each enterprises needs, and will be customized to cover pertinent constructs for the enterprise taking the course.

  • Common pitfalls in data modeling and data warehousing modeling and “War stories” illustrating consequences of incorrect modeling

  • Explanations of alternative data warehouse and multidimensional approaches and how to convert logical data models into a data warehouse design. An explanation of common data warehouse design

  • approaches and architectures along with pros and cons of each approach will be provided. Also included will be an eight step design process for converting data models into data warehouse designs taking into consideration historical time factors, granularity considerations, and various common denormalizations used in data warehousing.

  • Overview of Data Warehousing, Master Data Management, Metadata, and Enterprise Data Governance and how data modeling and Universal Data Models and Patterns apply to these areas.

  • Optionally, a multiple choice exam for the participants/students to test their understanding. The data models provided in this seminar are robust and have been successfully implemented in a great variety of organizations.

Who Should Attend

Data modelers, data warehouse designers, data analysts, data administrators, database designers, database administrators, database consultants and any other information systems professionals who need to be involved in data warehouse designs, data models, database designs, and data integration issues.

Course Outline and Schedule
Day 1:

1.Introduction to Advanced Data Modeling, Patterns, Universal Data Models, and Enterprise Data Management

  • Overview of data management and data modeling

  • What is needed for advanced data modeling

  • What are Universal Patterns?

  • What are Universal Data Models (UDM)? What are the benefits?

  • Examples of how other organizations have used UDM and Patterns

  • Data warehousing, master data management, metadata management, data quality, enterprise data governance,

The following sections include modeling techniques, pitfalls, “war stories” of incorrect modeling, and “best practice” techniques. Each section also includes a workshop in which you customize models for your organization.

2. Advanced-Data Modeling Concepts


  • Types of data models to use, their purpose and nature

  • Abstract versus specific modeling

  • Variations on data modeling standards

  • Modeling subtypes

  • Modeling business rules

  • Definitions for entities and attributes

  • Modeling systems information

  • Surrogate keys versus natural keys

  • Modeling historical information

  • Who makes the final modeling decision?

  • Guidelines for converting logical to physical

Day 2:

3. Universal Patterns – Powerful, advanced, essential concepts in data modeling

  • Roles

    • Declarative roles

    • Exercise: Applying roles

    • Contextual roles

    • Exercise: Applying roles

  • Statuses

    • Statuses modeled specifically

    • Statuses modeled abstractly

    • Exercise: Applying recursions

  • Categorizations

    • Categorizations modeled specifically

    • Categorizations modeled abstractly

    • Exercise: Applying recursions

  • Hierarchies/recursions

    • Recursions modeled specifically

    • Recursions modeled abstractly

    • Exercise: Applying recursions

  • Type versus instance

    • Pattern for types versus instances

    • Applying type versus instance pattern

  • Rules

    • Pattern for modeling business rules

    • Applying business rules pattern

    • Case Study Exercise; Identifying and picking appropriate patterns

Day 3:

4. People and Organization Data Models

  • Parties

    • Person models

    • Organizations models

  • Roles

  • Relationships between parties

  • Addresses and other contact management information

  • Facilities

  • Data modeling exercise – applying the models

5. Product Data Models


  • Product definition

  • Part definition

  • Features

  • Product and part components, substitutions, and obsolescence

  • Inventory and inventory item configurations

  • Product pricing and costing

  • Deployments

  • Data modeling exercise – applying the models

6. Orders and Agreements Data Models


  • Order header and items

  • Order header information such as status and terms

  • Orders and adjustments

  • Order dependencies (such as backorders or related purchase orders)

  • Order relationships such as bill to, ship to, taken by, placing party, etc.

  • Agreements and/or contractual terms

  • Data modeling exercise – applying the models

7. Delivery/shipments Data Models


  • Shipment header and detail information

  • Shipment method such as types of vehicles used in shipments

  • Order and shipment associations

Day 4:

8. Work Effort Data Models


  • Work efforts and project management

  • Work tasks and scheduling resources

  • Time entries

  • Process plans

  • Work Efforts Applied to projects, maintenance, productions runs other types of work

  • Data modeling exercise – applying the models

9. Invoicing, Payments, and General Ledger to Track Revenue and Costs


  • Invoice header and detail

  • Invoice and shipment association

  • Billing accounts and other invoicing information

  • Payments

  • General Ledger Accounts

  • Budgeting

  • Data modeling exercise – applying the models to your organization

10. Human resources models

  • Employment

  • Position

  • Reporting structures

  • Authorizations and responsibilities

  • Other human resources models

  • Data modeling exercise – Applying the models

11. Applying the UDM in various industries


  • How to extend the patterns and UDMs for use in various industries

  • Telecommunications

  • Manufacturing

  • Insurance

  • Health Care

  • Financial Services

  • Other industries

Day 5: Enterprise Data Management Topics

12. Data Warehousing


  • Data warehouse architecture considerations

  • Practical Consideration for Using and Implementing patterns and universal data models

  • in data warehousing

  • Transformation steps to converting the UDM to a data warehouse design

  • Workshop Exercise Enhancing an Existing Data Warehouse Design

  • Data mart method—a practical approach to designing data marts

  • Examples of data mart designs using universal data models

  • Workshop exercise - Creating a data mart design using a universal data model

13. Master Data Management


  • What is master data management

  • Master data management strategies

  • Applying advance data models, patterns, universal data models to master data

  • management solutions

  • Workshop exercise – Developing a master data management solution

14. Metadata Management


  • What is meta data?

  • Why is it important?

  • Critical success factors

  • Best practices

    • Within data management

    • Meta data strategies

    • Meta data requirements

    • Meta data architectures

    • Industry standards

    • ROI

  • Workshop exercise

15. Enterprise Data Governance


  • What is enterprise data governance

  • How to really make integration happen!

  • What is needed for data integration and governance

  • Strategies for data governance

  • Human dynamics for data governance and data integration

    • Understand motivations

    • Have a clear, compelling, common vision

    • Integration requires trust

    • Gaining Involvement

    • Appreciate perspectives versus being right

  • Workshop Exercises

16. Data Modeling for Emerging Trends


  • UDMs and modeling for big data

  • UDMs and modeling for data science

  • UDMs and modeling for other emerging trends

17. Course conclusion


  • Summary of concepts and learnings

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