Our
Most Requested Class:
Data Modeling Master Class
Learn not just how to build data models, but how to build data models well!
The Master Class is a complete course on requirements gathering and data modeling, containing four days of practical techniques for producing solid relational and dimensional data models. After learning the styles and steps in gathering and modeling requirements, you will apply a best practices approach to building and validating data models through the Data Model Scorecard®. You will know 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.
This course has recently received world recognition by the International Institute of Business Analysis (IIBA): The Data Modeling Master Class is an endorsed course by the IIBA V1.6 of the BABOK® 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!
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Course Objectives
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Prerequisite(s)
This course
assumes no prior data modeling knowledge and, therefore, there are no
pre-requisites. Analysts, architects 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 are the perceived
and actual benefits of surrogate keys?
- 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 metadata starting to blur?
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.
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 also practice building several data models and
answer the following questions:
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:
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:
We will focus on techniques for building sound designs. We will answer the
following questions:
We will focus on techniques for capturing the ideal use of generic concepts
such as Party and Event. We will answer the following questions:
We will focus on techniques for applying correct and consistent naming
standards. We will discuss the following:
We will focus on techniques for arranging the entities, data elements, and
relationships to maximize readability. We will answer the following
questions:
We will focus on techniques for writing useable definitions. We will answer
the following questions:
Part
3: Understanding subject area, logical, and physical data models
Part
4: Ensuring the model captures the requirements
Part
5: Validating model scope
Part 6: Following acceptable modeling principles
Part 7: Determining the optimal use of generic concepts
Part 8: Applying consistent naming standards
Part 9: Arranging the model for maximum understanding
Part 10: Writing clear, correct, and consistent definitions
Part 11: Matching the model with the enterprise
