The Data Modeling Addict October 2012

Excerpt from Data Modeling for the Business: A Handbook for Aligning the Business with IT using High-Level Data Models
By Steve Hoberman, Donna Burbank, and Chris Bradley
ISBN: 9780977140077

This is the eighth in a series of articles covering the ten steps for completing the High-Level Data Model (HDM), which is also known as a subject area model or conceptual data model. In our article series so far, we covered an overview of the HDM and six of the ten steps to building one: Identify Model Purpose, Identify Model Stakeholders, Inventory Available Resources, Determine Type of Model, Select Approach, and Complete an audience-view HDM. In this article we will discuss the seventh step, Incorporate Enterprise Terminology. Here are all ten steps as a reference (the step in bold is the focus on this article):

  1. Identify model purpose.
  2. Identify model stakeholders.
  3. Inventory available resources.
  4. Determine type of model.
  5. Select approach.
  6. Complete an audience-view HDM.
  7. Incorporate enterprise terminology.
  8. Signoff.
  9. Market.
  10. Maintain.

Once you’ve captured your stakeholders’ view in the boxes and lines of the audience high-level model, you can move on to the enterprise perspective. To build the enterprise perspective, modify the audience model to be consistent with enterprise terminology and rules. Ideally, this enterprise perspective is captured within an enterprise data model.

An enterprise data model is a subject-oriented and integrated data model containing all of the data concepts produced and consumed across an entire organization. Subject-oriented means that the concepts on a data model fit together as the CEO sees the company, as opposed to how individual functional or department heads see their view of the company. There is one Customer concept, one Order concept, etc. Integration goes hand in hand with subject-orientation. Integration means that all of the data and rules in an organization are depicted once and fit together seamlessly. Every data concept has a single definition and name. Integration implies that with this single version of the truth comes a mapping back to the chaotic real world.

An enterprise data model could exist at the HDM level, or it could exist at the logical or even possibly the physical level of detail. If the enterprise data model exists at the HDM level, it should still fit on one page, although the paper size might have to be a bit larger . We have built an enterprise HDM that contained over 100 concepts and still fit in a readable font on an 8.5 by 14 inch piece of paper. The comparison between your HDM and the enterprise data model becomes a more tedious task if the enterprise data model exists in a more detailed format, such as logical or physical. For example, there could be over 100 entities that have the word ‘Order’ in their name on a logical enterprise data model. All of these order entities could map to your HDM concept of ‘Purchase Order’.

Most organizations do not have a useable enterprise data model. If you work for one of these organizations, don’t worry—there are other ways to ensure an enterprise fit. Other options include matching a system of record, ensuring consistency with a global data warehouse, or comparing your model against an industry standard data model.

You might find yourself renaming concepts or modifying scope to stay in line with the enterprise. More than likely, you will need to go back to the business people for your HDM and confirm both your understanding and theirs of the important concepts on the model. You might have to schedule meetings between the people whose view is captured in the audience-specific model and those who share the enterprise perspective to resolve any terminology or definition issues.

Reinforcing the Top-down Approach – Recipe Management Enterprise-View HDM

– Steve’s real-world experienceReturning to the earlier Recipe Management example, if we were able to achieve consensus across all countries, such as the US and Spain, on a single recipe management view, we now need to compare this single audience-view HDM with the enterprise. If we did not achieve consensus across countries, have no fear. We can use the enterprise data model as a medium to bridge concept terminology and definition gaps across the separate HDMs that have been created for each country.

Although this organization did not have a fully-embraced enterprise data model, there were organization-recognized experts. Incorporating these experts’ views would guarantee an enterprise view. There were quite a number of passionate conversations and debates at this point to gain alignment between the audience- and enterprise- views.

At the conclusion of many meetings spanning over two months, we finished our model. A subset is shown in Figure 8.12.


Figure 8.12 – Subset of Ingredient Model

Concept definitions:

Country – A politically organized body of people under a single government.

Finished Product – The final product recognized by the Consumer and in a state ready for sale to our Customers.

Ingredient – Something tangible used in the production process which could be a raw material, the packaging, semi-finished product, or the finished product ready for sale to the consumer.

Plant – The building or buildings, or parts thereof, used for or in connection with the manufacturing, processing, packaging, labeling, or storage of our ingredients.

Raw Material – An unprocessed natural resource or product used in manufacturing.

Recipe – A set of instructions for how to make a finished item ready for sale to the consumer.

Region – A geographic term used to describe one or more countries.

Semi-Finished Material – An ingredient that contains one or more raw materials that have been modified according to a recipe with a goal of being transformed into a finished product.

Business Rules (listed in the order we would typically walk someone through the model):

  • An Ingredient must contain many Ingredients.
  • An Ingredient must be part of many Ingredients.
  • An Ingredient can be a Semi-Finished Material, Raw Material, or Finished Product.
  • An Ingredient must be used by many Recipes.
  • A Recipe must include many Ingredients.
  • Each Recipe must vary across many Regions.
  • Each Region must use many Recipes.
  • Each Region may contain one or many Countries.
  • Each Country must belong to one Region.
  • Each Country may contain one or many Plants.
  • Each Plant must belong to one Country.
  • Each Country must offer many Finished Products.
  • Each Finished Product must be offered in many Countries.
  • Each Plant must contain many Raw Materials.
  • Each Raw Material must be available in many Plants.

In the next column I will go into detail on Step 8, Signoff.


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Steve Hoberman

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

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