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The Data Modeling Addict - January 2012
Step Four of the Ten Steps to Completing the High Level Data Model – Determine Type of Model
Published: January 1, 2012 This month's column describes the 4 variations of model and helps determine how to select the appropriate one.
Excerpt from Data Modeling for the Business: A Handbook for Aligning the Business with IT using High-Level Data Models
This is the fifth 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 series so far, we covered an overview of the HDM and three of the ten steps to building one: Identify Model Purpose, Identify Model Stakeholders, and Inventory Available Resources. In this article we will discuss the fourth step, Determine Type of Model. Here are all ten steps as a reference (the step in bold is the focus of this column):
The purpose of the model from Step 1 aids in determining the type of model to build in Step 4. The HDM needs to be one of four different variations, as shown in the table below.
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. Relational Data ModelA relational data model describes the operational databases that support business processes. The scope of a high-level relational data model can range from an individual business process, such as order processing, student registration, or account billing, to an enterprise perspective of all of the concepts encompassed by the business. A sample business rule from a relational model would state, “A Customer must place one or more Orders. An Order must be placed by one and only one Customer.”Dimensional Data ModelA dimensional model is used exclusively for reporting. A number such as Gross Sales Amount might need to be viewed at a month or year level, or at a region or country level, for example. The relationships on a high-level dimensional model represent navigation paths between concepts instead of business rules, as in the relational model. For example, a business rule on a dimensional data model would state, “I need to see Sales Amount by Customer, Month, and Product. Then I plan on going up to a Year and Brand level.”The scope of a dimensional model is a collection of related measures that together address some business concern. For example, the metrics Number of Product Complaints and Number of Product Inquiries can be used to gauge product satisfaction. Business PerspectiveA business perspective is a high-level data model of a defined portion of the business. The scope can be limited to a department or function such as manufacturing or sales or it can be as broad as the entire enterprise or industry. The business perspective is chosen more frequently over the application perspective. Many times when we say we are creating a high-level data model, we mean the business perspective. Before embarking on any large development effort, we first need to understand the business. If an organization needs a new claims-processing system, it needs to have a common understanding of claims and related concepts. The business perspective can be created simply to understand a business area, or as a beginning to a large development effort, such as introducing third-party software into your organization.Choose the business perspective for any of the following situations:
Application PerspectiveAn application perspective is a high-level data model of a defined portion of a particular application. This perspective can be for either a proposed or existing application. In many cases, the application perspective is built after first understanding the business perspective and is usually a subset of its business perspective. For example, if a business perspective models order processing, the application perspective may focus on an order entry system.Choose the application perspective for any of the following situations:
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Steve Hoberman -
Steve Hoberman is a world-recognized innovator and thought-leader in the field of data modeling. He has worked as a business intelligence and data management practitioner and trainer since 1990. Steve is known for his entertaining, interactive teaching and lecture style (watch out for flying candy!) and is a popular, frequent presenter at industry conferences, both nationally and internationally. Steve is a columnist and frequent contributor to industry publications, as well as the author of Data Modeler’s Workbench and Data Modeling Made Simple. He is the founder of the Design Challenges group and inventor of the Data Model Scorecard™. Please visit his website www.stevehoberman.com to learn more about his training and consulting services, and to sign up for his Design Challenges! He can be reached at me@stevehoberman.com. |