If you are seeking expert tutelage for data modelling tools and techniques, you need look no further. Regardless of your level of expertise, as a data analyst, data modeler, data architect, database designer, database application developer, database administrator, business analysts, or systems designers, this book will serve as an invaluable resource in your effort to build reliable and effective data models. Beginning with the basics, this book provides a thorough grounding in theory before guiding the reader through the various stages of applied data modelling and database design. Later chapters delve into advanced topics and enterprise data modelling, covering business rules, data warehousing, data migration, and more. This new and expanded edition updates existing content where current practice dictates and adds new content on Modelling XML, Master and Reference Data, Mapping Between Models, Data Migration, and other areas of intense interest to the data modelling community. NEW TO THIS EDITION • Enhanced contextual treatment of data modeling by providing more examples of data models and their quality in examining where the benefits derive. • NEW chapter on Master and Reference Data Management • NEW chapter of Data Migration • NEW chapter on modeling XML messages • NEW chapter on Mapping Between Data Models The perfect balance of theory and practice giving you both the foundation and the tools to develop high quality data models. Perfect reference for the reflective practitioner providing clear and accessible guidance to data modeling techniques. An invaluable resource containing vast amounts of useful and well illustrated information to those involved in data modeling, from the novice to the expert.

This essential guide focuses on data quality and why the data model is so important, plus includes essential material on developing a real model, and covers organization of the modeling task and managing compromises.

"If a system is to support an enterprise's business information requirements, a necessary part of the design process is effective review of the design by appropriate business stakeholders. For such review to be effective, the design documentation provided to those stakeholders ('the business model') must be understandable, be complete, i.e. depict all information in which business stakeholders are interested, and not depict any information in which business stakeholders have no interest ('noise') which distracts or confuses reviewers, reducing review effectiveness. Logical data models do not meet these criteria, yet most conceptual data models are degenerate logical data models including much noise. This presentation details what should be included in and excluded from a data model to be reviewed by business stakeholders."--Resource description page.

"Most data models are static, in that they represent the properties of, and relationships between, business entities at a point in time. However, for a system to properly function over time, its data model must be designed to support data update in response to changes in the real world. Dynamic Data Modeling covers not only static data structures but update policies, by considering issues such as what real-world changes must be captured in the database? What are the requirements for preserving a record of the historic state of the attributes and relationships of any entity? Why must changes in attributes and changes in relationships be dealt with differently? Do we also need to record changes in our state of knowledge of the real world? What aspects of the time dimension need to be taken into account? This presentation provides an overview of the Dynamic Data Modeling toolkit, with which experienced data modellers can effectively support projects delivering BI or operational data resources with a significant time-variant component."--Resource description page.