The Data Modeling Addict – October 2010

In this article I will provide an overview to the 10 steps for completing the high-level data model (HDM), which is also known as a subject area model or conceptual data model. In subsequent columns, I will go into detail into each one of these 10 steps. Although you can start some of the steps out of sequence, they need to be completed in the order they appear. For example, you might find yourself jotting down stakeholders (Step 2) before identifying the purpose of the model (Step 1). However, you will need to revisit your model stakeholder list after finalizing the purpose of the model.

The ten steps for completing the HDM are:

  1. Identify Model Purpose. Determine and agree on the primary reason for having a HDM. Always begin with the end in mind. Before starting any data modeling effort, first identify why the model needs to be built. It is important to remember to focus the purpose of the high-level data model around a business need or process improvement. 
  2. Identify Model Stakeholders. Document the names and departments of those who will be involved in building the HDM, as well as those who will use it after its completion. A HDM stakeholder is someone who will be affected directly or indirectly by the model that is produced during the modeling sessions. There are a number of roles that would be useful in building and using the HDMs.
  3. Inventory available resources. Leverage the results of Step 2 to determine what people will be involved in building the HDM and also identify any documentation that could provide useful content to the HDM.
  4. Determine Type of Model. The purpose of the model from Step 1 aids in determining the type of model to build in Step 4. The modeler will need to select one of the cells in this chart, depending on the characteristics of their model. Application and business represent focus, while relational and dimensional represent functions.
  5. Select approach. There are 3 approaches for building a high-level data model: top-down, bottom-up and hybrid. Even though these approaches sound completely different from each other, there is really quite a lot in common across them. In fact, the major difference between the approaches lies in the initial information gathering step.
  6. Complete an Audience-View HDM. Once we are confident about which approach we should take, we need to build the audience-view HDM. This is the first high-level model we build. Our purpose is to capture the viewpoint of the audience without complicating information capture with including how their viewpoint fits with other departments or with the organization as a whole. Our next step will reconcile the deliverable from this step with enterprise terminology. Here our purpose is just to capture their view of the world.
  7. Incorporate Enterprise Terminology. 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.
  8. Signoff. After the initial information gathering, make sure the model is reviewed for data modeling best practices as well as the fact that it meets the requirements. The sign-off process on a HDM does not require the same formality as signoff on a physical design, but it should still be taken seriously. Usually email verification that the model looks accurate will suffice.
  9. Market. Think of yourself as a product vendor of sort – the best product on the market won’t necessarily sell unless it is marketed effectively. And the best-advertised product won’t sell unless it solves a need of the consumer. Marketing is about understanding the customers’ motivations (i.e., what’s in it for me?), creating a plan to accomplish a win-win scenario, and then making sure to get the word out to the appropriate audience.
  10. Maintain. Remember that even after the model is complete, there is still a maintenance task that we must stay on top of. The HDM will not change often, but it will change. We need to have formal processes for keeping the model up to date and aligned with the other model levels. We also want to make sure that the HDM is actively used by other groups and processes in the organization and doesn’t become a passive artifact.

In the next column, I will go into detail on Step 1 – Identify Model Purpose.

This column is an 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 – Link to Amazon

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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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