It is nice to connect with you as always. Several weeks ago I received the latest volume of your best selling Data Model Resource Book series, and it is a welcome addition to my resource collection. I would be remiss if I did not mention your co-author, Paul Agnew, before we get started. Thank you for taking a few minutes to discuss your latest book with me. Len Silverston LS : Mr. It is always my pleasure and honor to connect with you and to be able to share with the TDAN subscribers.

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It is nice to connect with you as always. Several weeks ago I received the latest volume of your best selling Data Model Resource Book series, and it is a welcome addition to my resource collection. I would be remiss if I did not mention your co-author, Paul Agnew, before we get started. Thank you for taking a few minutes to discuss your latest book with me. Len Silverston LS : Mr. It is always my pleasure and honor to connect with you and to be able to share with the TDAN subscribers.

Thank you for your many years of service to the data management community with this excellent publication. LS: The Data Model Resource Book series, Volumes 1, 2, and 3, provides reusable data model constructs for standard models, industry models, and underlying data model patterns.

The first volume provides standard Universal Data ModelsTM that are very common for all types of enterprises such as models for party, product, orders, invoicing, accounting and many other reusable constructs. The second volume provides industry Universal Data Models that extend the standard models to cover data modeling requirements for a variety of industries such as models for manufacturing, telecommunications, health care, insurance, financial services, professional services, travel and e-commerce.

Why volume 3? So this book provides standard ways of modeling very prevalent themes in data modeling. Another driving force was the need for a book showing how the same data modeling requirements may be modeled using either specific or more generalized styles of modeling and that there are pros and cons to each. Volume 3 shows variations on the Universal Patterns ranging from specific to more generalized versions of the same pattern.

We use the concept of levels, with a level 1 pattern being a very specific way to model something, a level 2 pattern being less specific, a level 3 pattern being more generalized and a level 4 pattern is even more generalized. A more specific level of the pattern is not better than a more generalized level of the pattern or vice versa; is just has different pros and cons.

Therefore, the modeler can decide from numerous intelligent choices and apply what makes most sense in the current situation. The more specific patterns are easier to understand and better for communicating, especially to nontechnical audiences, and the more generalized versions of the pattern offer more flexibility when implemented.

In volume 3 we also address the need to show and illustrate, with concrete examples, how these patterns can be used across a wide variety of efforts such as for prototyping, applications, enterprise data modeling efforts, master data management or various types of data warehousing efforts.

Finally, there was also a need to show how to socialize patterns so they can be adopted and in this volume we address human dynamics and principles that help with this critical area of data integration. RSS: You mentioned that this book focuses on assisting people to save time and improve the quality of any type of data modeling effort.

How does this book accomplish that? LS: In any type of data modeling effort, there is a need to model the same types of data, for example, the various types of roles that people and organizations play , statuses, hierarchies, recursions, classifications, contact information, and business rules.

In application development it is a very common practice to reuse routines, functions and services, instead of developing them yourself. Just like reusing a standard function or piece of code that is well tested, organizations can reuse models that have been well thought out and improved over many years.

Another way that it saves a great deal of time is by providing a way to model consistently. When data models are schizophrenic and the same type of data is modeled differently in different parts of the model, there are much higher maintenance costs for the model, the resulting database, as well as the routines that access the database. This happens because there is a great amount of overhead involved in recreating different models, databases and routines, when the same type of structure or function could have been reused.

Usually, modeling efforts do not even realize that the same type of thing is being modeled inconsistently in different parts of the model. For example, when modeling statuses, one part of the model may show statuses as attributes in an entity e.

I have been involved in many efforts where we have realized that we have modeled similar things differently at the end of the modeling effort and, at this point, it is often too late to correct it.

Thus it can help correct possible big mistakes that could cost a lot of time and money. The patterns can also help to not only develop data models quicker, but also help to integrate data by using very holistic, patterns that make it easier to integrate data by facilitating common types of data structures. RSS: That makes perfect sense to me. Explain to my readers how the patterns you use assist you reaching your goals and the goals of the people that read your books and bring you in to assist them in their data modeling efforts?

LS: The patterns assist in many ways for data modeling efforts. They assist by: Providing standard, mature patterns and templates that help to data model very common types of constructs in a consistent fashion, Showing various ways of modeling the same type of common data modeling scenarios and sharing the pros and cons of modeling them different ways. Providing a third-party source against which an enterprise can evaluate and check its data models so it can evaluate alternative options or see if it perhaps may have missed something.

RSS: Are these patterns used in volumes 1 and 2 or your book? LS: Volumes 1 and 2 use many of the patterns that are described in volume 3. We knew that there were reusable patterns and even before volume 3, we applied them to many of the standard and industry models in volumes 1 and 2.

After publishing hundreds of models in Volumes 1 and 2, we gained a lot of clarity regarding different options modeling these fundamental constructs, and we felt a need to publish these patterns in Volume 3. In fact, some readers have recommended that a new reader could even start with Volume 3. RSS: So it seems to me as though the three volumes would be a wise investment for anybody that undertakes or will undertake data modeling initiatives now and in the future.

How does the third volume extend what was already discussed in the first two volumes? LS: The third volume offers patterns which can be used as tools to extend the models in the first two books. They can also be used to develop models that are not in the first two volumes, and we have found that these patterns apply to just about any type of data modeling effort.

Volume 3 is very complementary but also is quite different than volumes 1 and 2. Volume 3 focuses on Universal Patterns that are more fundamental in nature and can be used as building blocks or templates for modeling efforts.

Volume 2 extends these standard data models to cover an additional third of the data modeling requirements that often occur for specific industries. In other industries outside of data modeling, the use of patterns is very common. For example, when carpenters build tables or chairs, there are underlying patterns that they reuse over and over, regardless of the type of table they build.

RSS: Can you summarize the pros and cons of having alternatives when delivering a data model rather than always following very specific or very general data modeling patterns? LS: There is an argument that says that if you only provide patterns for either a specific or more generalized style of modeling, then the models will be more consistent and easier to manage. When there are many alternative patterns offered, there is a risk that the data model using these patterns may be more varied and have inconsistent styles of modeling.

One possibility is that people using the volume 3 book that only want to use specific styles of modeling could choose to standardize on the level 1 or level 2 styles of pattern, which are more specific styles. Likewise, others that only want to use generalized styles of models could choose to standardize on the level 3 or level 4 styles of patterns the more generalized styles.

However, I think it is critical to use the pattern that bests the situation. Sometimes, it is appropriate to standardize on specific patterns, for example, when developing data models that are used to communicate data requirements to business representives. Sometimes it is appropriate to standardize on generalized patterns, for example, when developing data models that form the basis for a flexible database design, such as for a master data management system.

Often the modeler must decide how much flexibility is needed within different parts of a data model and then pick the appropriate level of generalization for that part of the model since there are tradeoffs that are discussed in the book for the different levels of the patterns provided.

Thus, it may be appropriate for some models to use both specific and generalized patterns, even within the same data model. We felt it was imperative that we offer various alternatives for modeling these patterns as well as label them by levels e. RSS: Len, over the past few year, I have seen you evolve not only as a data modeling and data management industry leader, but also as an individual that focuses on the human elements and aspects of data management.

Can you please explain the relationship between your Data Model Resource Books and this evolution of your areas of expertise? LS: After spending more many years developing data models, I came to a realization that while these reusable data model constructs can help organizations, there was something else that was so critical that needed to be addressed more thoroughly.

These universal principles have to do with the how people and organizations interact and the human dynamics involved in any data modeling or data integration effort. Examples of this include understanding the motivations that are at work, how the vision is developed, how trust is created or not created and how people manage conflict.

Many years ago, I became keenly interested and focused on what really leads to successful data integration, and I started compiling case studies, principles, strategies, techniques, and toolsets that can help people and organizations from an effective human dynamics perspective.

While I believe that effective data modeling is important, I also believe that human dynamics are an essential aspect to integrating data, systems and people, and we have continued to develop training, consulting, publications, and tools to help in this important area. Thank you for picking on me throughout the time I attended.

For example, an enterprise data model helps create common constructs that can be used consistently throughout the organization, thus helping each project to use similar semantics and models.

Another purpose of data modeling is that within a particular application, the data model can help with common semantics and a common understanding of the data for that application. So, a key need in data modeling is to get to a common understanding of the data; and in order to do this, there are many human factors and human dynamics involved. It is essential to be able to effectively communicate and to understand the various perspectives and motivations at hand.

I have found that a common issue on many data modeling efforts is that data modelers often disagree on the way that something should be modeled. In short, data modeling is largely about integrating data silos. These data silos, at their root, come from people silos, and it is so important for us to address the root of these issues. RSS: You are I are on the same page when it comes to leveraging the personalities that already exist within an organization.

Can you tell us a little bit more about the book, what it took to put the book together, how it is structured, who the audience is, … anything that will assist people in understanding what they can expect by reading the volumes? LS: This book is based upon decades of research that we have gathered from many experiences implementing hundreds of data models internationally.

Even before we started developing the book, we had developed many of these patterns over the years and had a great amount of experience successfully implementing them at many clients and giving presentations and seminars about the use of these patterns. It took thousands of hours to develop the book, and we spent a lot of time reviewing the patterns and going through many iterations of each pattern to make sure that the patterns represented very high quality, reusable constructs.

This book is designed for anyone that has a basic knowledge of data modeling and wants to be able to be more productive and increase the quality of their data models. This includes data modelers, data architects, data analysts, database administrators, database designers, data stewards, computer science teachers and students, corporate data integrators, as well as anyone involved in any aspect of data modeling.

The content of this book is suitable for use by professionals in the fields of data management, data quality, metadata management, master data management, data warehousing, data governance, and any other field where data models are used. The models in this book can help anyone involved in a data modeling effort to reuse constructs, see different alternatives, understand the pros and cons of each alternative, and make effective choices when modeling. The book is organized into 10 chapters which includes an introduction, 7 chapters that each offer a particular pattern e.

For each variation or level of the pattern, there is an example showing how to apply the pattern to a fictitious scenario. For example, in the classification chapter, there are numerous classification patterns and each of those patterns is applied to fictitious scenarios where there are needs to create specific, less specific and more generalized data models. Each data model that is rendered has data illustration tables that show examples of the instances of data that may be included in the model, in order to clarify and illustrate the models.

RSS: I will ask you the same question I have asked others through these interviews. LS: Interesting question. This book was very difficult for us to produce as the hours were very long, and it took a great deal of effort and a great number of iterations to put out what we thought was a very high quality product.

We thought it was so important to release this information as we believe that this can really change the way that data modeling is conducted and that this can help revolutionize the data modeling field by moving us to what only makes sense: reuse what we know works.

LS: I think that the publications that I have produced have been very practical and the idea of reusing data model constructs is such as an intrinsically productive prospect. Over the past several years, these works have not only evolved to incorporate new learnings on the universal models and patterns, but the more recent publications have focused on human dynamics, and are just as important if not more important as the publications on reusable data models. How much of the content of these books comes directly from your experiences at clients and at these events?

We are also able to get a great deal of feedback from many presentations and seminars at DAMA and other national and international conferences.


Len Silverston

Trusted Advisor podcast. We share our knowledge, we share our connections, we do business together. And twice a month I get to harass my fellow DEA members on this podcast. We serve small and mid-sized business owners in Denver and all over the world with website design and development, search engine optimization, pay per click advertising, marketing automation and more. He is also an internationally recognized human behavior and technology consultant.


Universal data models len silverston

Contact Len Silverston is a corporate and personal mindfulness facilitator, coach, and an internationally recognized human behavior and technology consultant. He has studied and practiced Vipassana meditation taught by S. Goenka starting in and has spent hundreds of days in silent, intensive, beginning and advanced Vipassana retreats, most of them with over 10 hours of meditation per day. He has studied and attended many workshops including intensive training from The Hoffman Institute, 6 month voice dialogue program from Martha Lou Cohen, many courses through from Dr.


Books by Len Silverston


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