Ever have a bad data day? If you're a business user, architect, analyst, designer or developer, then you've probably had some bad data days. It comes with the territory. Overcoming these problems is much easier if you have an in-depth understanding of the

Data Modeling Made Simple: A Practical Guide for Business & IT Professionals

Hoberman, Steve

Business & Money

Technics Pubns Llc

If you think enterprise data and geospatial data describe Star Trek episodes, you could use the DAMA Dictionary of Data Management. This glossary contains over 800 terms defining a common data management vocabulary for IT professionals, data stewards and business leaders. It is in pdf format embedded with links for easy navigation between terms, and delivered to you on CD-ROM. An index is included with the dictionary, which organizes the terms by topic. Topics include:  Finance & Accounting  Business  Marketing & Customer Relationship Management  Planning  Project Management  Knowledge Management  Process Management  Roles  Information Technology  Standards Organizations  Data  Data Management  DAMA & Professional Development  Data Governance and Stewardship  Architecture  Data Modeling  Normalization  Related Modeling and Analysis  Databases & Database Design  Structured Query Language (SQL)  Object-Orientation  Software Development  Artificial Intelligence  XML Development  Parallel Database Processing  Database Administration  Geospatial Data  Data Security Management  Data Movement & Integration  Data Warehousing & Business Intelligence  Analytics & Data Mining  Multi-dimensional / OLAP  Reference & Master Data Management  Meta Data Management  Data Quality Management  Document, Record & Content Management  Semantic Modeling   Press release: The Data Management Association International (DAMA) Releases DAMA Dictionary of Data Management Industry Takes an Environment-Friendly Stand on Standard DefinitionsDAMA Dictionary of Data Management availability was announced at the DAMA International Symposium & Wilshire Meta Data Conference in San Diego March 16-20, 2008.Over 800 terms defining a common data management vocabulary for IT professionals, data stewards and business leaders.Over 40 topics including finance and accounting, knowledge management, architecture, data modeling, XML, and analytics.Authored by DAMA International, published by Technics Publications, LLC, and edited by Mark Mosley, this dictionary will promote a standard set of data management terms within the field of Information Technology (IT). Mosley says "As the premiere organization for data management professionals, DAMA seeks to lead the data management profession to maturity. One of the hallmarks of a mature profession is a common vocabulary with clearly understood definitions.  DAMA offers the Dictionary to a field in great need of clarity in its terminology and semantics. We hope the Dictionary will be a useful tool for data management professionals, IT colleagues, managers, data stewards and business leaders. All these people share responsibility for data management, so it's very important for all parties to speak a common language." Deborah Henderson, President of DAMA International Foundation and VP Education and Research for DAMA International, believes the CD-ROM format will not only be easier to use than the traditional book format, it is also more compact and environmentally responsible. "Publishing this work on CD-ROM saves over 4000 pounds of paper and conservatively 700 pounds of Greenhouse gases."

The DAMA Dictionary of Data Management

Computers & Technology

Mark Mosley

Programming

This is the training manual for the Data Modeling Master Class that Steve Hoberman teaches onsite and through public classes. This text can be purchased prior to attending the Master Class, the latest course schedule and detailed description can be found on Steve Hoberman`s website.
 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. 
 Steve is an excellent presenter, keeping his audience engaged while covering in-depth relational and dimensional modeling techniques. His entertaining style combined with his many real world examples and exercises, held our interest throughout the course. S. DeCandia, Pfizer Global Manufacturing 
 In my long professional career, I have participated in many training seminars but I have never encountered a class in which the subject had been so thoroughly considered and presented in such a clear and engaging manor. As a fairly new, but full time data modeler, I expect to use the things that I learned in this class every day. My only regret is that the class had to end. G. Schmid, Travelers Insurance
 This was the most comprehensive, informative, energetic, interesting and just plain FUN class I have ever taken on the subject of Data Modeling. G. Werner, Long Island Railroad
 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. 
 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. 
 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. 
 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. 
 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. 
 Other training modules include: <ul>
<li>Following acceptable modeling principles </li>
<li>Determining the optimal use of generic concepts </li>
<li>Applying consistent naming standards </li>
<li>Arranging the model for maximum understanding </li>
<li>Writing clear, correct, and consistent definitions </li>
<li>Matching the model with the enterprise </li>
<li>Comparing the meta data with the data </li>
</ul>

Data Modeling Master Class Training Manual (Take It With You)

Databases & Big Data

Written by over 120 data management practitioners, the DAMA Guide to the Data Management Body of Knowledge (DAMA-DMBOK) is the most impressive compilation of data management principals and best practices, ever assembled. It provides data management and IT professionals, executives, knowledge workers, educators, and researchers with a framework to manage their data and mature their information infrastructure.     This print edition is also available in electronic PDF format on a CD (see ISBN 9780977140084).  The equivalent of the PMBOK or the BABOK, the DAMA-DMBOK provides information on:    Data Governance   Data Architecture Management   Data Development  Database Operations Management  Data Security Management   Reference & Master Data Management  Data Warehousing & Business Intelligence Management  Document & Content Management   Meta Data Management   Data Quality Management  Professional Development    As an authoritative introduction to data management, the goals of the DAMA-DMBOK Guide are:    To build consensus for a generally applicable view of data management functions.   To provide standard definitions for commonly used data management functions, deliverables, roles, and other terminology.   To document guiding principles for data management.   To present a vendor-neutral overview to commonly accepted good practices, widely adopted methods and techniques, and significant alternative approaches.   To clarify the scope and boundaries of data management.   To act as a reference which guides readers to additional resources for further understanding.

The Dama Guide to the Data Management Body of Knowledge (Take It With You)

DAMA International

Data Modeling Made Simple with ER/Studio Data Architect will provide the business or IT professional with a practical working knowledge of data modeling concepts and best practices, along with how to apply these principles with ER/Studio. You'll build many ER/Studio data models along the way, applying best practices to master these ten objectives: 
<ol>
<li>You will know why a data model is needed and which ER/Studio models are the most appropriate for each situation </li>
<li>You will be able to read a data model of any size and complexity with the same confidence as reading a book </li>
<li>You will know how to apply all the key features of ER/Studio </li>
<li>You will be able to build relational and dimensional conceptual, logical, and physical data models in ER/Studio </li>
<li>You will be able to apply techniques such as indexing, transforms, and forward engineering to turn a logical data model into an efficient physical design </li>
<li>You will improve data model quality and impact analysis results by leveraging ER/Studio's lineage functionality and compare/merge utility </li>
<li>You will achieve enterprise architecture through ER/Studio's repository and portal functionality </li>
<li>You will be able to apply ER/Studio's data dictionary features </li>
<li>You will learn ways of sharing the data model through reporting and through exporting the model in a variety of formats </li>
<li>You will leverage ER/Studio's naming functionality to improve naming consistency</li>
</ol> This book contains four sections: Section I introduces data modeling and the ER/Studio landscape. Learn why data modeling is so critical to software development and even more importantly, why data modeling is so critical to understanding the business. You will also learn about the ER/Studio environment. By the end of this section, you will have created and saved your first data model in ER/Studio and be ready to start modeling in Section II! Section II explains all of the symbols and text on a data model, including entities, attributes, relationships, domains, and keys. By the time you finish this section, you will be able to 'read' a data model of any size or complexity, and create a complete data model in ER/Studio. Section III explores the three different levels of models: conceptual, logical, and physical. A conceptual data model (CDM) represents a business need within a defined scope. The logical data model (LDM) represents a detailed business solution, capturing the business requirements without complicating the model with implementation concerns such as software and hardware. The physical data model (PDM) represents a detailed technical solution. The PDM is the logical data model compromised often to improve performance or usability. The PDM makes up for deficiencies in our technology. By the end of this section you will be able to create conceptual, logical, and physical data models in ER/Studio. Section IV discusses additional features of ER/Studio. These features include data dictionary, data lineage, automating tasks, repository and portal, exporting and reporting, naming standards, and compare and merge functionality.

Data Modeling Made Simple: With Er/Studio Data Architect

Software