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Data Semantics Management...Vol 1 & 2
The Data Semantics Management book is divided into two volumes. Volume 1 addresses Rationale, Requirements, and Architecture. Volume 2 addresses Deployment.
These volumes justify why accomplishing data semantics management is so critical to the overall success of data interoperability and shared data.
The approach embraced by these two volumes is founded on the idea of top-down and centralized architecture, engineering, policies, and procedures, but bottom-up, distributed accomplishment. If attempted only top-down, the outcomes will mirror familiar centralized czar-like failures of the past. If accomplished only bottom-up, the outcomes will be the vast forests of semantic stove pipes.
The volumes also identify and describe the two error classes that prevent data interoperability and sharing. Each chapter addresses how these two classes are addressed.
Each chapter concludes with an extensive set of questions and exercises.
Volume 1, Rationale, Requirements & Architecture provides:
* A Justification for the Shared Data Environments Essential to Business Success
* A Critical Semantic Foundation for Reliable and Repeatable Shared Data
* Lessons Learned from Multi-Hundred Million Dollar Failures
* A Detailed Framework for Shared Data Environments
* A Strategy to Address Names, the Achilles Heel of Shared Data
* A Step-by-Step Approach for Automatic Name and Definition Construction
* An Approach to Deal with the Hidden Shared Data Killer - Value Domains
* A Focused Look at the Six Classes of Business Facts
* And Much, Much More!
Volume 2, Development provides:
* Approaches to Build Data Element Foundation Blocks for Shared Data
* Methods to Construct Data Model Templates to Develop Shared Databases
* Processes to Engineer Database with Data Elements and Data Model Templates
* Techniques to Create Data, Process, and State Objects for Project Teams
* Forward/Reverse Engineering Strategies to Integrate Legacy Data Models
* Strategies for Client Server, XML, and Service Oriented Architectures
* Work Plans to Deploy and Maintain Shared Data Environments
* Reviews of How Shared Data Errors Are Eliminated or Severely Reduced
* And Much, Much More!
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