Data Modeling for the Business: A Handbook for Aligning the Business with IT using High-Level Data Models (Take It with You Guides) First Edition

Review--Data Modeling for the Business (March 2010) - review originally appeared in Oil IT Journal - [...].Oil IT Journal reviews `Data Modeling for the Business' by Steve Hoberman et al. The book outlines a new approach to data modeling and includes a chapter on a major oil company's enterprise architecture.Someone once said the ideal number of data modelers is one. The book `Data Modeling for the Business' (DMFTB) takes practically the opposite approach, advocating a series of corporate Rolfing sessions and pizza parties to thrash out what should be modeled, how, and for how long information should be retained. If the single modeler approach presupposes a domain specialist who knows all, Hoberman's is rather of journeymen data modelers, perhaps without deep domain knowledge, who can extract all the information required from other stakeholders. The thrust of DMFTB is communication and debate with non specialists. This can be rather labored--as in the first chapter which plods through the analogy of a data model and a blueprint for a house.Those expecting technology insights and a discussion of tools will be disappointed. We learn from the frontispiece that the graphical models in the text were created with CA's ERwin tool. But the book does not really connect with technology. The subtitle of `aligning business with IT using high level data models' says it all.' This discussion is far removed from databases and SQL and focuses on a bird's-eye view of the enterprise rather than on implementation.There are `traditionally' four levels of models--very high, high, logical and physical. High level models communicate core data concepts like `customer,' `order,' `engineering,' `sales.' Even the `logical' is model is `a graphical representation of [...] everything needed to run the business.' All of which is a far cry from the Express logical model of Epicentre or ISO 15926!The body of DMFTN is concerned with business, rather than technical data, examining in depth how for instance the concept of `customer' can be implemented in `hundreds of database tables on a variety of platforms.' Business requirements may mean changing definitions of key concepts like customer. These start at the high level, and ripple down through the model layers. Modelers can then perform impact analysis to see `what changes are required at the logical and physical levels.' Although how such changes are effected across `hundreds' of databases including pre-packaged behemoths like SAP is glossed over.Of particular interest is a chapter on data modeling in an international energy company. Here an enterprise architecture initiative set out with a vision of a `shared corporate data asset that is easily accessible.' Amusingly, half way through their work, the team found that there was another initiative working on master data management whose goal was also `a single version of the truth.' Such is the nature of the large decentralized beast! The oil company's modelers leveraged industry data models including PPDM, PSDM (ESRI), MIMOSA and PRODML--although exactly how these different circles were squared is not explained!Despite its technical weakness, DMFTB makes an interesting and perhaps inspiring read for technologists who are trying to engage with their fellow stakeholders.Comments to [email protected].

I picked up Steve Hoberman's "Data Modeling for the Business" after reading his excellent primer, "Data Modeling Made Simple". And, having just now finished "Data Modeling for the Business", I am compelled to express my enthusiasm for this excellent and important book. Hoberman and his co-writers here take on the very important task of illustrating the value of employing data modeling for the grand purpose of achieving overall corporate effectiveness. And they succeed! For this reason alone, this books deserves an honored place in the library of any professionals who are truly concerned with improving the value of the data asset in their organizations.Hoberman and his crew describe several different "layers" of models, as they apply to business situations. These include the Very High Level Data Model, the High Level Data Model, the Logical Data Model, and the Physical Data Model. These "layers" correspond nicely to the DAMA classification framework, which identifies Enterprise Data Models, Conceptual Data Models, Logical Data Models, and Physical Data Models. Lest the reader of this review conclude: "ho hum, we've seen all this before", please know that Hoberman and his co-writers not only describe these models with wondrous clarity; but they also provide a very practical handbook for the implementation of these modeling disciplines. And they even manage to make it fun!Having toiled in the related disciplines of logical data modeling and relational database design for now well over twenty-five years, I can say unequivocally that Steve Hoberman writes with greater clarity, wisdom, and evident passion about these matters than any other expositor we have yet encountered. We heartily recommend this excellent book! God bless.

Very readable. Authors have made a topic that I had previously feared as being esoteric into something exciting. My money was well spent. I'm not an expert now, but I have a better appreciation for experts in this field, and have a better understanding of the concepts and language.

The book is a very apt how-to reference for Enterprise Data Architects who work on developing the so-called Enterprise Data Models (EDM) as part of the whole Enterprise Architecture. It provides a very practical approach for High-level data modeling and provides handy tips for Data Architects. However, if you are into more detailed modeling activities (i.e. logical data modeling for relational or dimensional databases) you will find this book a bit general.All in all I do recommend it for those interested in Enterprise Data Modeling.

Good handbook on Data Modeling High Level or Conceptual Data Model. The emphasis is on starting out with clear and concise High Level Data Models, which closely match the business requirements. Very useful book that not only gives you best practices but leaves you with a step-by-step methodology you could start using immediately. The book has a good flow with excellent illustrations, examples and case studies.

One of the most critical systems issues is aligning business with IT and fulfilling business needs using data models. The authors of "Data Modeling for the Business" do a masterful job at simply and clearly describing the art of using data models to communicate with business representatives and meet business needs. The book provides many valuable tools, analogies, and step-by-step methods for effective data modeling and is an important contribution in bridging the much needed connection between data modeling and realizing business requirements.

Too many people begin with a low level, often physical, data model resulting in a database that does not fully meet business needs. This book provides a simple, straightforward, high-level approach to data modeling ensuring the data base fully meets business needs. I suggest you read it.

Covered the basics ok. The scope of book was very limited- not much covered. Very generic. Expensive not good value.