Current Public Seminars
"IRM courses are always taught by top professionals. In this case it was Steve Hoberman for the Data Modelling Masterclass. Even though I am a seasoned data modeler I walked out of the course with new techniques and concepts to aid in my daily work activities. Steve Hoberman is indeed an international thought leader in this area and I recommend his classes highly."
Angelo R Bobak, Director, Business Intelligence, Siemens IT Solutions and Services, Inc.
"Excellent speaker. Used various examples. Good stuff."
Adebukonla Onabadejo, NSPIS Custody System Analyst, National Policing Improvement Agency
"Steve was enthusiastic, very knowledgeable, humourous and a great instructor."
Jacqueline Tomlinson, Data Analyst, Pension Protection Fund
2-Day Seminar
Data Modelling Masterclass
Sharpen Your Data Modelling Skills!
| Register On-line: 8-9 March 2012, London 13-14 September 2012, London |
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Click
Here To Download The 2012 .PDF Brochure
- Overview
- Learning Objectives
- Seminar and Workshop Outline
- Audience
- Speaker Biography
- Testimonials
- Seminar Fee
- Multiple Seminar Booking Discount and Group Booking Discount
- Hotel Venue and Accommodation
Click here for an in-house quote request or for further information regarding in-house training.
Overview
Do you already know data
modelling basics and want more? Take the Data Modelling Masterclass! This
course starts off with an overview of the Data Model Scorecard®, ten
categories for validating data model quality. Each of these categories are then
discussed, with an emphasis on advanced techniques and guidelines within data modelling
and requirements elicitation. You will know not just how to build a data model,
but also how to build a data model well. Three case studies and many exercises reinforce
the material and enable you to apply these techniques in your current projects.
We will complete a full workshop where we get to practice many of the
techniques learned in this course, including building subject area, logical,
and physical data models.
- Apply requirements elicitation techniques including interviewing and prototyping
- Validate any data model through the Data Model Scorecard®
- Practice finding structural soundness issues and standards violations
- Build relational and dimensional subject area, logical, and physical data models
- Recognize situations where abstraction would be most valuable and situations where abstraction would be most dangerous
- Use a series of templates for scoping and validating requirements, and for data profiling
- Express how to write clear, complete, and correct definitions
- Describe the two reasons an enterprise data modelling project can fail, and the factors that must be in place for the enterprise data model to succeed
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 modeller 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 modelling 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 metadata with the data
Reviewing 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 briefly explained in
this section.
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. You will be able to answer the
following questions by the end of this section:
- What is the Requirements Lifecycle?
- What are the most useful ways of eliciting requirements?
- What are the proper ways to phrase an interview question?
- When is brainstorming an effective way to capture requirements?
- What are three creative prototyping techniques for the non-techie?
- 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 Interview Template and Family Tree to validate requirements?
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. You
will be able to answer the following questions by the end of this section:
- Why is the line between data and metadata starting to blur?
- What techniques can you use to avoid scope creep?
- How do you play “Metadata Bingo”?
- What type of metadata is most abused?
- How can you use the Grain Matrix to scope requirements?
Following acceptable modelling principles
We will focus on
techniques for building sound designs. You will be able to answer the following
questions by the end of this section:
- What tools exist to automate checking model structure?
- What are circular relationships and why are they evil?
- What are the most common structural violations on a data model?
- Can an alternate key ever be empty?
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.
You will be able to answer the following questions by the end of this section:
- 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 metadata entities?
- How do different modelling notations handle subtyping?
- What are some common modelling patterns?
Applying consistent naming standards
We will focus on
techniques for applying correct and consistent naming standards. You will be
able to answer the following questions by the end of this section:
- 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
We will focus on
techniques for arranging the entities, data elements, and relationships to maximize
readability. You will be able to answer the following questions by the end of
this section:
- 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
We will focus on
techniques for writing useable definitions. You will be able to answer the
following questions by the end of this section:
- How do you play Definition Bingo?
- 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 their definitions?
Matching the model with the enterprise
We will focus on
techniques for ensuring the data model complements the “big picture”. You will
be able to answer the following questions by the end of this section:
- What is an enterprise data model and why have one?
- What are the secrets to achieving a successful enterprise data model?
- What are industry data models and how can they be leveraged?
Comparing the metadata 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? You will be able to answer the following questions by the end of
this section:
- How can the Data Quality Validation Template help us with catching data surprises early?
- What are the some of the challenges in conducting an early data quality assessment?
- How can I quickly identify potential data quality issues using the data model?
Audience
This course has as
prerequisite Data Modelling
Fundamentals or at a minimum an understanding of data modelling concepts.
Typical delegates include:
- Data Modeller
- Data Architect
- Data Analyst
- Data Manager
- Business Analyst
- Enterprise Architect
- Information Architect
- Solutions Architect
- Applications Architect
- IT Consultant
- Project Manager
- Programme Manager
- Developer
- Senior Designer
- Data Administrator
- Database Administrator
- Data Quality Manager
- Data Steward
Special Features
- Attendees receive a copy of Steve Hoberman's classic book, Data Modeling for the Business..
IIBA Accreditation
This course has been endorsed by The International Institute of Business Analysts. As such, this course has been approved as being aligned to the Business Analysis Body of Knowledge (BABOK) and hence are recommended training for business analysts who wish to sit the exam to become Certified Business Analysis Professionals (CBAP). For further information on how to register for the CBAP examination please refer to certification at www.theiiba.org.
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Steve Hoberman is the most requested data modelling instructor in the world. Introduced at over 50 international conferences as everything from a “data modelling guru” to “data modelling rock star”, Steve balances the formality and precision of data modelling with the realities of building software systems with severe time, budget, and people constraints. In his consulting and teaching, he focuses on templates, tools, and guidelines to reap the benefits of data modelling with minimal investment. He taught his first data modelling class in 1992 and has educated more than 10,000 people about data modelling and business intelligence techniques since then, spanning every continent except Africa and Antarctica. Steve is known for his entertaining, interactive teaching and lecture style (watch out for flying candy!), and is the author of five books on data modelling, including the bestseller Data Modelling Made Simple. He is the founder of the Design Challenges group, inventor of the Data Model Scorecard®, and CEO of Technics Publications. |
Seminar Fee
£1,095 + VAT (£219) = £1,314
OPTION: If both Data Modelling Masterclass and Data Modelling Fundamentals seminars are booked together, the fee is £1,970 plus VAT (£394) = £2,364.
Hotel Venue and Accommodation
8-9 March 2012
Venue: The DoubleTree Hilton
DoubleTree by Hilton London-West End
Southampton Row
WC1B 4BH London
Tel: +44 (0)20 7242 2828
Fax: +44 (0)20 7831 9170
Email: info@dtlondonwestend.com
http://www.crimsonhotels.com/doubletreelondon/
13-14 September 2012
Venue: TBA London
London Accommodation: IRM UK in association with JP Events Ltd has arranged special discounted rates at all venues and at other hotels nearby the venue. Please visit the JP Events website for further information.
E-mail: info@jpetem.com Tel +44 (0)84 5680 1138 Fax +44 (0)84 5680 1139.
In-House Training
If you require a quote for running
this course in-house, please contact us with the following details:
- Subject matter and/or speaker required
- Estimated number of delegates
- Location (town, country)
- Number of days required (if different from the public course)
- Preferred date
Please contact:
Jeanette Hall
E-mail: jeanette.hall@irmuk.co.uk
Telephone: +44 (0)20 8866 8366
Fax: +44 (0)1923 828 770
Speaker: Steve Hoberman
Click here if you would like to receive a copy of all Steve Hoberman’s articles that have appeared in our free monthly e-newsletter. To subscribe to this e-newsletter click here.
| Endorsed by |
DAMA International UK Chapter |
Endorsed by |
 |
Data Modelling Masterclass is an endorsed course by the IIBA |
Data Management Series
Defining and Executing Your Information Strategy
Information Process Quality Improvement
New Technologies and Architectures for Data Warehousing and Business Intelligence
Data Virtualization for Agile Business Intelligence Systems
Multiple Seminar Booking Discount
Attend more than one of our seminars and you will be entitled to the following discounts:
- 2nd course 10%
- 3rd course 15%
- 4th course 20%
- 5th+ course 25%
Group Booking Discount
20% discount for 5 or more registrations made at the same time.
We regret that this offer cannot be used in conjunction with the Multiple Seminar Discount or any other discount.
Special Feature Data Modeling For the Business Related Books Data Modeling Made Simple Data Modeler's Workbench Data Modeling Made Simple With CA ERwin r8 Available on Amazon US |
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