The Book Look: Achieving Buzzword Compliance

COL03x - feature image HobermanCome back in time with me to 1999. What a year for both movies and data! You probably can guess three good movies released in 1999 just based on these quotes:

  • “I see dead people.”
  • “Get in my belly.”
  • “You take the blue pill – the story ends…”

For data, us in IT were all in the last Y2K push to improve the structure and quality of the data structures within our applications. Even with so much work completed, there remained a level of uneasiness as to what would actually happen when the clock struck midnight to welcome the new millennium.

Speaking of data, 1999 was also exciting for me as I presented at my first major data management conference. I spoke about abstraction, and during my talk there was this guy in the audience who kept asking questions. He liked to challenge and was very articulate and precise with his words. Who was this guy? So passionate about data modeling – it was Dave Hay. This was the beginning of a friendship, where over the years Dave has participated in our monthly design challenges, spoken at our Data Modeling Zone conferences, and written three awesome books:

  • Enterprise Model Patterns
  • UML and Data Modeling
  • Achieving Buzzword Compliance

Achieving Buzzword Compliance just got released last month. It fills a very important niche in data architecture. The goal of the book in one phrase: To explain the language and vocabulary of Data Architecture. Dave uses his skill of making things precise to define many of the concepts within the data management field.

For example, right on page 1 Dave defines the conceptual data model. Want a precise definition of a logical data model? Turn to page 18. Page 19 presents you with a clear definition of the physical data model. Dave raises the ambiguity in many terms we use as part of doing our jobs. I like how Dave contrasts the views of other experts in the industry yet also provides his own opinions and experiences.

The legendary John Zachman, in his foreword to this book, has a fantastic summary on Achieving Buzzword Compliance and the data management industry in general:

“I like the premise of the book. Clearly, the Information domain is in a kind of pre-puberty stage of development. We do not have an agreed-upon ontological structure or even a lexicon, definitions of the essential concepts of our practices. We think we are communicating with each other but we are simply talking. We do not have a “Periodic Table” equivalent that would form the basis of an articulation of the natural laws that govern our practices. My personal opinion is that we, Information People, are much like the Alchemists of Chemistry before it became Chemistry, that is, before Mendeleev published the early versions of the Periodic Table.

In fact, I do not believe that there is a “discipline,” ANY discipline, until the body of serious practitioners have an agreed-upon ontological structure, an identification and formal expression of theoretical constructs, a basis for finding and validating the domain, “laws of nature.” Of course, the domain I personally have in mind is the “Enterprise” domain and David Hay takes a significant step in this direction in “Achieving Buzzword Compliance.”

By the way, it was Kurt Lewin that said, “there is nothing so practical as a good theory.” I would add that if you cannot articulate for me what you are arguing or proposing in the context of “physics” (i.e., laws of nature), I know I am banking on a miracle happening. Magic. Typically, a “silver bullet” … and there are no “silver bullets.”

This is another reason I like “Achieving Buzzword Compliance.” David is explaining to us the causes and effects of choices to be made based on his experience and research and the other practical work of acknowledged thought-leaders.”

My favorite part of Achieving Buzzword Compliance is the distinction between the Semantic Data Model and the Essential Data Model. The Semantic Data Model contains terms that the business is comfortable while the Essential Data Model is much more abstract. The Semantic Data Model for example would include terms such as Customer and Employee, while the Essential Data Model might have the Party Role concept.

A sample of the book can be found at this link.

Also, as a reader, get $10 off the print or PDF version of Achieving Buzzword Compliance using promo code TDAN10 at this link.

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About Steve Hoberman

Steve Hoberman has trained more than 10,000 people in data modeling since 1992. Steve is known for his entertaining and interactive teaching style (watch out for flying candy!), and organizations around the globe have brought Steve in to teach his Data Modeling Master Class, which is recognized as the most comprehensive data modeling course in the industry. Steve is the author of nine books on data modeling, including the bestseller Data Modeling Made Simple. Steve is also the author of the bestseller, Blockchainopoly. One of Steve’s frequent data modeling consulting assignments is to review data models using his Data Model Scorecard® technique. He is the founder of the Design Challenges group, Conference Chair of the Data Modeling Zone conferences, director of Technics Publications, and recipient of the Data Administration Management Association (DAMA) International Professional Achievement Award. He can be reached at

  • Richord1

    As a practicing Data Alchemist and someone who has been professing the need for Data Literacy for years, I disappointed with the title of the book. I read David Hay’s previous books and articles which I found to be insightful. The title of the book gave me the impression of someone writing a book that contains their rants about buzzwords.
    However, I was encouraged with the subtitle containing the words “Language and vocabulary” which resonated with my premise of the need for Data Literacy. But with data we suffer with a multitude of languages and vocabularies. The Tower of Babel.
    Data has various languages and a vocabularies. With the exception of the use of ontologies however we are attempting to architect these languages and vocabularies using traditional technical constructs. Technologists tend to chase the latest new, new thing and perhaps that’s perhaps where the use of the term buzzword came in. But that’s not the problem. We are treating data like a technology. It’s not. Its a language with all the frailties of language. We need new constructs that focus less on the data model and more of the human factors that affect the data language.
    Most data has no intrinsic semantics. The “metadata” is the technical construct we use for capturing the semantics of data. The data model is not the problem. The understanding of the data is the problem. Software is defined as a programming language. With syntax, semantics and rules. You can become programming language “literate” through understanding the constructs underlying the programming language.
    Before we can design data models and design a data architecture we need well defined and understood raw materials; data. In building architecture you can build models that use materials in anyway you want as long as their intrinsic characteristics are understood and complied with.
    I will purchase David’s book in spite of my reservations about the title following the idiom “don’t judge a book by its cover”.

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