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Data Modeling for the Business (English Edition)
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Data Modeling for the Business (English Edition) [Format Kindle]

Steve Hoberman , Donna Burbank , Chris Bradley

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Descriptions du produit

Présentation de l'éditeur

Did you ever try getting Businesspeople and IT to agree on the project scope for a new application? Or try getting Marketing and Sales to agree on the target audience? Or try bringing new team members up to speed on the hundreds of tables in your data warehouse - without them dozing off?

Whether you are a businessperson or an IT professional, you can be the hero in each of these and hundreds of other scenarios by building a High-Level Data Model. The High-Level Data Model is a simplified view of our complex environment. It can be a powerful communication tool of the key concepts within our application development projects, business intelligence and master data management programs, and all enterprise and industry initiatives.

Learn about the High-Level Data Model and master the techniques for building one, including a comprehensive ten-step approach and hands-on exercises to help you practice topics on your own. In this book, we review data modeling basics and explain why the core concepts stored in a high-level data model can have significant business impact on an organization. We explain the technical notation used for a data model and walk through some simple examples of building a high-level data model. We also describe how data models relate to other key initiatives you may have heard of or may be implementing in your organization.

This book contains best practices for implementing a high-level data model, along with some easy-to-use templates and guidelines for a step-by-step approach. Each step will be illustrated using many examples based on actual projects we have worked on. One example spans an entire chapter and will allow you to practice building a high-level data model from beginning to end, and then compare your results to ours.
Building a high-level data model following the ten step approach you will read about is a great way to ensure you will retain the new skills you learn in this book.

As is the case in many disciplines, using the right tool for the right job is critical to the overall success of your high-level data model implementation. To help you in your tool selection process, there are several chapters dedicated to discussing what to look for in a high-level data modeling tool and a framework for choosing a data modeling tool, in general.

This book concludes with a real-world case study that shows how an international energy company successfully used a high-level data model to streamline their information management practices and increase communication throughout the organization - between both businesspeople and IT.

Data modeling is one of the under-exploited, and potentially very valuable, business capabilities that are often hidden away in an organizations Information Technology department. Data Modeling for the Business highlights both the resulting damage to business value, and the opportunities to make things better. As an easy-to follow and comprehensive guide on the why and how of data modeling, it also reminds us that a successful strategy for exploiting IT depends at least as much on the information as the technology.
Chris Potts, Corporate IT Strategist and Author of fruITion: Creating the Ultimate Corporate Strategy for Information Technology

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.
Len Silverston, author of The Data Model Resource Book series

Biographie de l'auteur

Steve Hoberman is a world-recognized innovator and thought-leader in the field of data modeling. He has worked as a business intelligence and data management practitioner and trainer since 1990. He is the author of Data Modelers Workbench and Data Modeling Made Simple, the founder of the Design Challenges group and the inventor of the Data Model Scorecard.

Détails sur le produit

  • Format : Format Kindle
  • Taille du fichier : 4365 KB
  • Nombre de pages de l'édition imprimée : 236 pages
  • Editeur : Technics Publications, LLC; Édition : First (24 juin 2012)
  • Vendu par : Amazon Media EU S.à r.l.
  • Langue : Anglais
  • ASIN: B008EN4VWI
  • Synthèse vocale : Activée
  • X-Ray :
  • Classement des meilleures ventes d'Amazon: n°174.688 dans la Boutique Kindle (Voir le Top 100 dans la Boutique Kindle)
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9 internautes sur 9 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 A necessary start to any modeling journey 12 mai 2009
Par D. Meador - Publié sur
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.
10 internautes sur 11 ont trouvé ce commentaire utile 
3.0 étoiles sur 5 Review from Oil IT Journal -[...] 22 avril 2010
Par MCNAUGHTON NEIL - Publié sur
Format:Broché|Achat vérifié
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
6 internautes sur 7 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 Much needed book to bridge business and data modeling 1 avril 2009
Par L. Silverston - Publié sur
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.
5 internautes sur 6 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 A practical book you must read 28 mars 2009
Par Michael Brackett - Publié sur
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.
1 internautes sur 1 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 Simply Great! 5 mars 2011
Par Michael Tozer - Publié sur
Format:Broché|Achat vérifié
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.
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