A multitude of problems is likely to arise when developing data
models. With dozens of attributes and millions of rows, data
modelers are always in danger of inconsistency and inaccuracy. The
development of the data model itself could result in difficulties
presenting accurate data. The need to improve data models begins
with getting it right in the first place.
Using real-world examples, "Developing High Quality Data Models"
walks the reader through identifying a number of data modeling
principles and analysis techniques that enable the development of
data models that both meet business requirements and have a
consistent basis. The reader is presented with a variety of generic
data model patterns that both exemplify the principles and
techniques discussed and build upon one another to give a powerful
and integrated generic data model. This model has wide
applicability across many disciplines in government and industry,
including but not limited to energy exploration, healthcare,
telecommunications, transportation, military defense,
transportation, and more.
* Uses a number of common data model patterns to explain how to
develop data models over a wide scope in a way that is consistent
and of high quality
*Offers generic data model templates that are reusable in many
applications and are fundamental for developing more specific
templates
*Develops ideas for creating consistent approaches to high
quality data models
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