Student Profile: The target audience for this course is anyone interested in learning about Data Modeling. Analysts, architects, business users, developers, managers and modelers have all been successful in this class.
Duration: 4 Days
Description: The Master Class is a complete course on data modeling, containing 4 days of practical techniques for producing solid relational and dimensional data models. After learning modeling concepts and terms, you will apply a best practices approach to building and validating data models through the Data Model Scorecard. You will learn not just how to build a data model, but also how to build a data model well. Challenging exercises and workshops will reinforce the material and enable you to apply these techniques in your current projects.
Prerequisites: This course assumes no prior data modeling knowledge and, therefore, there are no prequisites.
Objectives: After taking this course, you will;
Know when a data model is needed, and which type of data model is most effective for each situation
Be able to clearly explain core data modeling terminology
Be able to read a data model of any size and complexity with the same confidence as reading a book
Be able to validate any data model with the Data Model Scorecard
Be able to build a fully normalized relational data model as well as an easily navigatable dimensional model
Be able to apply techniques to turn a logical data model into an efficient physical design
Know when to use abstraction, and when it should never be used
Be able to leverage a series of templates for capturing and validating requirements
Be able to write clear, complete, and correct definitions
Be able to explain the critical factors that must be in place for a successful enterprise data model
Topics Covered:
Modeling Basics
What is a data model and how can a piece of paper with boxes and lines be such a valuable wayfinding tool to our organizations?
How does a data model improve communications during the analysis process and after the model is complete?
What two situations can degrade a data model's precision?
What are five key skills every data modeler should posses?
What do a data model and a camera have in common?
What are entities, data elements, domains, and relationships?
Why subtype and what are the four subtypes?
What are the different types of keys on a model?
What is cardinality and how are the relationships on a data model read?
What is recursion and why is it such an emotional topic?
Why is the line between data and meta data starting to blur?
What is the difference between Structured, Semi-Structured, and Unstructured Data?
Overview to the Data Model Scorecard
Understanding subject area, logical, and physical data models
Ensuring the model captures the requirements
Validating model scope
Following acceptable modeling principles
Determining the optimal use of generic concepts
Applying consistent naming standards
Arranging the model for maximum understanding
Writing clear, correct and consistent definitions
Matching the model with the enterprise
Comparing the meta data with the data
Understanding subject area, logical, and physical data models
How do relational and dimensional models differ?
What are the three types of subject area models and how are they built?
What is normalization and how do you apply it?
What are some dimensional modeling dos and donts?
What is the difference between a star schema and a snowflake?
Where should denormalization be performed on your models?
What are the five ways of denormalizing?
What is the difference between aggregation and summarization?
What are views, indexing, and partioning and how can they be leveraged to improve performance?
Ensuring the model captures the requirements
What does optionality reveal on a data model?
How can you validate that a data model captures the requirements without showing the data model?
How can you leverage the Family Tree, Grain Matrix, and Interview templates?
What are the perceived and actual benefits of surrogate keys?
What really is a Slowly Changing Dimension?
Validating model scope
What techniques can you use to avoid scope creep?
What type of meta data is most abused?
What is a meta data checklist?
Following acceptable modeling principles
What tools exist to automate checking model structure?
What are circular relationships and why are they evil?
Can an alternate key ever be empty?
Determining the optimal use of generic concepts
Why are what if scenarios so important to document?
What three questions must be asked prior to abstracting?
Why are Roles so important to Business Intelligence projects?
What are meta data entities?
What are some modeling components that can be reused across models?
Applying consistent naming standards
Explain name structure and give examples
Explain term and give examples
Explain syntax and give examples
Learn why class words are so important
Arranging the model for maximum understanding
How do you improve model readability at a model level?
How do you improve model readability at an entity level?
How do you improve model readability at a data element level?
How do you improve model readability at a relationship level?
Writing clear, correct, and consistent definitions
Why are definitions so much more important now than they were in the past?
What are some techniques for writing a good definition?
How do you validate a definition?
Which types of data elements require sample values in a definition?
Matching the model with the enterprise
What is an enterprise data model and why have one?
What are the secrets to building a successful enterprise data model?
What are industry data models and how can they be leveraged?
What are the three approaches to building an enterprise data model?
Comparing the meta data with the data
How can you catch data surprises early?
What are the some of the challenges in early detection?
How can the Data Quality Validation Template help us with catching data surprises?
Certification Applicability: This offering is an endorsed course by the IIBA V1.6 of the BABOKฎ as registered under Steve Hoberman & Associates. Earn 24 Continuing Development Units (CDU) through the IIBA, and 24 Professional Development Units (PDUs) through the Project Management Institute!
IIBA and BABOK are trademarks owned by the International Institute of Business Analysis
Cost: $2,700 | 
