November 17th-20th, 2009
Attend the Data Modeling Master Class in New York City

Learn not just how to build data models, but how to build data models well!

The Master Class is a complete course on data modeling, containing four 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.

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You will learn

  • Data modeling concepts and terminology
  • The value of the Data Model Scorecard™
  • Relational and Dimensional Subject Area, Logical, and Physical modeling techniques
  • The traits of a super model
  • How to tackle modeling challenges
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Prerequisite(s)

This course assumes no prior data modeling knowledge and, therefore, there are no pre-requisites. Analysts, business users, developers, managers and modelers have all been successful in this class.

Topics

Part 1: Modeling Basics

Assuming no prior knowledge of data modeling, we will begin this section with an entertaining exercise that will illustrate an important gap filled by data models. Next, we will explain data modeling concepts and terminology. We will also explore each component on a data model and practice reading business rules. We will answer the following questions:
  • 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 communication 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 possess?
  • 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 subtype types?
  • 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?

Part 2: Overview to the Data Model Scorecard™

The Scorecard is a set of ten categories for validating a data model. We will explore best practices from the perspectives of both the modeler and reviewer, and you will be provided with a template to use on your current projects. Each of the following categories heavily impacts the usefulness and longevity of the model. Our discussion of them will be accompanied by many examples.
  • 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

Part 3: Understanding subject area, logical, and physical data models

The subject area model captures a business need within a well-defined scope; the logical data model captures an application-independent business solution; and the physical data model captures the technical solution by focusing on factors such as performance and security. Each of these models will be explained in detail in this section. We will answer the following questions:
  • 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 do’s and don’ts?
  • 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 partitioning and how can they be leveraged to improve performance?

Part 4: Ensuring the model captures the requirements

We will focus on techniques such as the use of spreadsheets and business assertions to ensure the data model meets the business requirements. We will answer the following questions:
  • 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?

Part 5: Validating model scope

We will focus on techniques for validating that the scope of the requirements matches the scope of the model. If the scope of the model is greater than the requirements, we have a situation known as “scope creep.” If the model scope is less than the requirements, we will be leaving information out of the resulting application. We will answer the following questions:
  • What techniques can you use to avoid scope creep?
  • What type of meta data is most abused?
  • What is a meta data checklist?

Part 6: Following acceptable modeling principles

We will focus on techniques for building sound designs. We will answer the following questions:
  • What tools exist to automate checking model structure?
  • What are circular relationships and why are they evil?
  • Can an alternate key ever be empty?

Part 7: Determining the optimal use of generic concepts

We will focus on techniques for capturing the ideal use of generic concepts such as Party and Event. We will answer the following questions:
  • 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?

Part 8: Applying consistent naming standards

We will focus on techniques for applying correct and consistent naming standards. We will discuss the following:
  • Explain name structure and give examples
  • Explain term and give examples
  • Explain syntax and give examples
  • Learn why class words are so important

Part 9: Arranging the model for maximum understanding

We will focus on techniques for arranging the entities, data elements, and relationships to maximize readability. We will answer the following questions:
  • 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?

Part 10: Writing clear, correct, and consistent definitions

We will focus on techniques for writing useable definitions. We will answer the following questions:
  • 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?

Part 11: Matching the model with the enterprise

We will focus on techniques for ensuring the model complements the “big picture”. We will answer the following questions:
  • 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?

Part 12: Comparing the meta data with the data

We will focus on techniques for confirming the data elements and their rules match reality. Does the data element Customer Last Name really contain the customer’s last name, for example? We will answer the following questions:
  • 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?