- Shopping Bag ( 0 items )
Offers the reader data models that are practical for jump-starting data development projects. Shows how to integrate databases and data warehouses across the enterprise, and much more. Text comes with a companion Web site with free downloads. The CD-ROM provides the SQL code for use with the text.
On many data modeling consulting engagements, clients have asked the same question: "Where can we find a book showing a standard way to model this structure? Surely, we are not the first company to model company and address information."
Many organizations develop their data models or data warehouse designs with very few outside reference materials. A large cost is associated with either hiring experienced consultants or using internal staff to develop this critical component of the system design. Often there is no objective reference material that the company can use to validate its data models or data warehouse designs or to seek alternate options for database structures.
Based on numerous experiences of using template or "universal data models" and customizing them for various enterprises, we have concluded that usually more than 50 percent of the data model (corporate or logical) consists of common constructs that are applicable to most organizations, another 25 percent of the model is industry specific (these models are covered in The Data Model Resource Book, Volume 2), and, on average, about 25 percent of the enterprise's data model is specific to that organization. This means that most data modeling efforts are recreating data modeling constructs that have already been created many times before in other organizations.
With this in mind, doesn't it make sense to have a source to use to get a head start on your data model so that you are not "reinventing the wheel" each time a company develops a new system? Organizations can save time and money by leveraging the use of common or universal database structures. Even if a company has data models from its previous systems development efforts, it is very helpful to be able to check the designs against an unbiased source in order to evaluate alternative options.
Although a large number of publications describe how to model data, very few compilations of data model examples exist in published form. This book provides both a starting point and a source for validating data models. It can help data modelers minimize design costs and develop more effective and integrated database designs.
Who Can Benefit from Reading This Book?
This book can assist many different systems development professionals: data administrators, data modelers, data analysts, database designers, data warehouse administrators, data warehouse designers, data stewards, corporate data integrators, or anyone who needs to analyze or integrate data structures. Systems professionals can use the database constructs contained in this book to increase their productivity and provide a checkpoint for quality designs.
The Need for Universal Data Models
Data modeling first gained recognition in Dr. Peter Chen's 1976 article, "EntityRelationship Modeling," which illustrated his newfound approach. Since then data modeling has become the standard approach used to design databases. By properly modeling an organization's data, the database designer can eliminate data redundancies, which are a key source of inaccurate information and ineffective systems.
Currently, data modeling is a well-known and accepted method for designing effective databases. Therefore, there is a great need to provide standard templates to enterprises (the term "enterprise" is used to describe the organizations for whom the models and systems axe being developed) so that they can refine and customize their data models instead of starting from scratch.
Although many standards exist for data modeling, there is a great need to take data modeling to the next step: providing accessibility to libraries of common data model examples in a convenient format. Many different organizations and industries should be able to use these libraries of data. models. Such universal data models can help save tremendous amounts of time and money spent in the systems development process.
A Holistic Approach to Systems
One of the greatest challenges to building effective systems is integration. Systems are often built separately to meet particular needs at different times within each enterprise. Enterprises need to build many systems: contact management systems, sales order systems, project management systems, accounting systems, budgeting systems, purchase order systems, and human resources systems, to name a few.
When systems are built separately, separate pools of information are created for each system. Many of these systems will use common information about organizations, people, geographic locations, or products. This means that each separate system will build and use its own source of information. A huge problem with this approach is that it is almost impossible to maintain accurate, upto-date information because the same type of information is stored redundantly across many systems. In large organizations, it is not uncommon to see information about customers, employees, organizations, products, and locations stored in dozens of separate systems. How is it possible to know which source of information is the most current or most accurate?
Another disadvantage of building separate systems with non-integrated data structures is that the enterprise (the organization for which the models and systems are being designed) does not have the benefit of viewing integrated information. Being able to see a complete profile for a person, organization, product, or inventory item is an enormous benefit. Imagine systems that are built so that each part of an organization knows what the other part is doing, where the customer service, sales, purchasing, and accounting departments of an organization have integrated information about the people, organizations, and products of the enterprise. This integration can make a big different in the service, sales, and performance of an enterprise.
Another way to approach systems development is from a perspective that an enterprise's systems are connected and, in fact, may be viewed as one interconnected system. From this perspective, there are tremendous benefits to building an enterprise-wide framework so that systems can work together more effectively. Part of this framework should include a corporate data model (i.e., an enterprise data model) that can assist the enterprise in maintaining one of its most valued assets: information. Because each system or application may use similar information about people, organizations, products, and geographic locations, a shared information architecture can be invaluable.
The IS (information systems) industry has recognized the need for integrated designs, prompting the many corporate data modeling and corporate data warehouse modeling efforts. Unfortunately, the IS track record for building and implementing corporate data models has been very poor. Enterprises have realized that it takes a tremendous amount of time and resources to build these models.
Enter CASE (Computer-Aided Systems Engineering) tools. These tools claimed tremendous productivity and time savings when used for corporatewide modeling efforts. While these tools help document the models, unfortunately they do not reduce the time needed to develop good corporate models.
Many enterprises have stopped building corporate data models because of their time constraints. They are looking at the track record of corporate data modeling and CASE efforts and choosing other alternatives.
Enter data warehousing. Finally, here is an approach to provide executives with the management information they need, without all the time and expense of corporate data modeling. Enterprises are now extracting the various pieces of information they need directly from their operational systems in order to build decision support systems.
The only problem with this approach is that the same problem exists! First of all, the information in the data warehouse may be extracted from several different, inconsistent sources. If there are multiple places where customer information is being held, which system represents the most accurate source of information?
According to data warehousing principles, the transformation routines are responsible for consolidating and cleansing the data. If different departments have different needs for various pieces of data, then each department may build its own extracts from the operational systems. One department may transform the information using one algorithm; a different department may use another algorithm. For example, if two departments are extracting sales analysis information, one department may use the order entry system as its source and another department may use the invoicing system as its source. A high-level manager may view information from both data warehouses and see inconsistent results, thus questioning the credibility of all the information. This type of scenario actually compounds the initial problem of many data sources by creating even more slices of data.
This is not to say that data warehousing is the wrong approach. It is an ingenious approach that can be used extremely effectively not only to create decision support systems but also to build a migration path to an integrated environment. The data warehouse transformation process helps to identify where there are data inconsistencies and data redundancies in the operational environment. It is imperative, though, to use this information to migrate to more integrated data structures.
The answer is still to build integrated data structures in order to provide good, accurate information. The only effective way to do this is to understand how the data within an enterprise and the relationships fit together and to be able to see the data in a holistic integrated manner. It is necessary to understand the nature of the data in order to build effective systems. Instead of saying that corporate data modeling or CASE is the wrong approach because it just takes too long, the IS community needs to find a way to make it work effectively. By building common, reusable data structures, the IS community can produce quicker results and move toward integrated structures in both the transaction processing and data warehouse environments...
Research in the last few years has supported what practitioners have known for a long time: rather than modeling from first principles, experienced data modelers re-use and adapt models and parts of models from their previous work. In fact, their "experience" may well reside more in their personal library of models-typically remembered rather than documented-than in greater facility with the basic techniques. The use of pre-existing templates also changes the nature of the dialog between the business experts and modelers: modelers will seek to discover which model or models from their repertoire may be appropriate to the situation, then to check the detail of those models. This is a fax more proactive role for modelers than that traditionally described, and recognizes that both parties can contribute ideas and content to the final model.
Of course, it takes time and exposure to a wide variety of business requirements for an individual to build up anything approaching a comprehensive library of models. Only specialist data modelers are likely to have this opportunity, and the reality is that much data modeling is performed by non-specialists.
The obvious step forward from this rather haphazard individual approach is for experienced modelers to develop and publish models for the most commonly encountered business requirements, so that solutions can be shared, reviewed and improved. Almost every commercial enterprise needs to keep data about customers, about staff, about sales. And almost every data modeler has spent time wrestling with these common-but by no means simple-situations, painfully aware that he or she is re-inventing the wheel, but without any confidence that any particular modeler has done a better job.
Such additions to data modeling's "body of knowledge" have been a long time coming. Books, papers, and educational material have continued to focus on the foundations of data modeling: modeling paradigms, diagramming conventions, and normalization. These are important topics, to be sure, but the absence of more developed material lends credence to the argument that data modeling does not deserve the status of a fully-fledged discipline.
Perhaps the reason for the gap in the literature is that the individuals best placed to recognize common situations and to develop models for them are data modeling practitioners-more particularly consultants who have had the opportunity to see a range of different business requirements. The models that they have developed over the years are a valuable professional resource, more profitably deployed on consulting assignments than as material for general publication. It also takes some courage to present one's own solutions for scrutiny by peers, all of whom will turn naturally to the problems for which they have personally developed the most elegant solutions!
I am therefore delighted that Len Silverston has chosen to publish a second and substantially expanded edition of The Data Modeling Resource Book. The first edition was essential reading for anyone charged with developing data models for business information systems, and was particularly notable for including contributions by specialists in particular data modeling domains. The second edition retains this feature, covers new business areas, and updates the original material. Len's willingness to continue to improve the material gives me hope that the core models will acquire a deserved status as standard starting points.
The second edition of The Data Modeling Resource Book is an excellent answer to the question "what is the second data modeling book I should purchase, once I've learned the basics?"-and every practitioner of data modeling should own at least two books on the subject!
Posted September 9, 2004
If you are looking to save time and money developing and implementing databases, look no further than this book. The Data Model Resource Book by Len Silverston is an excellent roadmap for various data models and has served as a valuable reference for me in numerous projects. I will continue to take advantage of this resource in the future. These data models are both practical and efficient and work in a real-world environment. Len has over 20 years of experience and knowledge that is passed along in this book. I highly recommend The Data Model Resource Book to anyone.Was this review helpful? Yes NoThank you for your feedback. Report this reviewThank you, this review has been flagged.