Graham Witt

Data Modeling for Quality Graham Witt

This book is for all data modelers, data architects, and database designers―be they novices who want to learn what's involved in data modeling, or experienced modelers who want to brush up their skills.




A novice will not only gain an overview of data modeling, they will also learn how to follow the data modeling process, including the activities required for each step. The experienced practitioner will discover (or rediscover) techniques to ensure that data models accurately reflect business requirements. This book describes rigorous yet easily implemented approaches to:

modeling of business information requirements for review by business stakeholders before development of the logical data modelnormalizing data, based on simple questions rather than the formal definitions which many modelers find intimidatingnaming and defining concepts and attributesmodeling of time-variant datadocumenting business rules governing both the real world and datadata modeling in an Agile projectmanaging data model change in any type of project transforming a business information model to a logical data model against which developers can codeimplementing the logical data model in a traditional relational DBMS, an SQL:2003-compliant DBMS, an object-relational DBMS, or in XML.




Part 1 describes business information models in-depth, including:

the importance of modeling business information requirements before embarking on a logical data modelbusiness concepts (entity classes)attributes of business conceptsattribute classes as an alternative to DBMS data typesrelationships between business conceptstime-variant datageneralization and specialization of business conceptsnaming and defining the components of the business information modelbusiness rules governing data, including a distinction between real-world rules and data rules.




Part 2 journeys from requirements to a working data resource, covering:

sourcing data requirementsdeveloping the business information modelcommunicating it to business stakeholders for review, both as diagrams and verballymanaging data model changetransforming the business information model into a logical data model of stored data for implementation in a relational or object-relational DBMSattribute value representation and data constraints (important but often overlooked)modeling data vault, dimensional and XML data.