Nimble Elephant,

"Get it done well and get it done fast" are twin, apparently opposing, demands. Data architects are increasingly expected to deliver quality data models in challenging time-frames, and agile developers are increasingly expected to ensure that their solutions can be easily integrated with the data assets of the overall organisation. If you need to deliver quality solutions despite exacting schedules, this book will help by describing proven techniques that leverage the libraries of published data model patterns to rapidly assemble extensible and robust designs. The three sections in the book provide guidelines for applying the lessons to your own situation, so that you can apply the techniques and patterns immediately to your current assignments. The first section -- "Foundations for Data Agility" addresses some perceived aspects of friction between "data" and "agile" practitioners. As a starting point for resolving the differences, pattern levels of granularity are classified, and their interdependencies exposed. A context of various types of models is established (e.g. conceptual / logical / physical, and industry / enterprise / project), and you will learn how to customise patterns within specific model types. The second section -- "Steps Towards Data Agility" shares guidelines on generalising and specialising, with cautions on the dangers of going too far. Creativity in using patterns beyond their intended purpose is encouraged. The short-term "You Aint Gonna Need It" (YAGNI) philosophy of agile practitioners, and the longer-term strategic perspectives of architects, are compared and evaluated. Consideration is given to the potential of enterprise views contributing to project-specific models. Other topics include industry models, iterative modelling, creation of patterns when none exist, and patterns for rules-in-data. The section ends with a perspective on the modeller's possible role in agile projects, followed by a case study. The final section -- "A Bridge to the Land of Object Orientation" provides a pathway for re-skilling traditional data modellers who want to expand their options by actively engaging with the ranks of object-oriented developers.