Steve Hoberman - Data Model Scorecard: Applying the Industry Standard on Data Model Quality

Data modeling is the process of discovering, analyzing, and scoping data requirements and then representing these data requirements in a visual format called the data model. A data model is a set of symbols and text used for communicating a precise representation of an information landscape. As with a model of any landscape, such as a map that models a geographic landscape, certain content is included and certain content excluded to facilitate understanding.

Discovering involves determining what information the business needs in its business processes and/or applications such as learning that Customer and Account are important concepts. Analyzing involves clarifying requirements such as coming up with clear definitions for Customer and Account and understanding the relationship between customers and their accounts. Scoping involves working with the business to determine what is most important for a particular project phase such as whether we need both Savings and Checking Accounts or just Checking Accounts for Phase 1. Representing means displaying what the information landscape looks like using an unambiguous precise language such as in the following data model:

• Each Customer may own one or many Accounts .
• Each Account must be owned by one or many Customers .

Once we document these requirements on the data model, we can then communicate them to business and information technology (IT) players involved in application development such as business users, business analysts, data modelers, data architects, database administrators, developers, testers, and managers.

Data models are the main medium used to communicate data requirements from business to IT, and within IT from analysts, modelers, and architects to database designers and developers. Regardless of whether the underlying database technology is a Relational Database Management System (RDBMS) such as Oracle or Teradata, or a NoSQL database such as MongoDB or Hadoop, we still need a way to communicate data requirements. Therefore, we need data models!

Our data models need to be of high quality to support current requirements yet also gracefully accommodate future requirements. The Data Model Scorecard is a tool you can use to improve the quality of your organizations data models.

Many of my consulting assignments are dedicated to applying the Data Model Scorecard to my clients data models and making recommendations to improve the design. This book will show you how to apply the Data Model Scorecard. This book is written for people who build, use, or review data models. There are three sections.

In .

In , we will explore each of the ten categories of the Data Model Scorecard. There are ten chapters in this section, each chapter dedicated to a specific Scorecard category: