Cardinality is represented by the symbols that appear on both ends of a relationship line. For cardinality, the choices are simple: zero, one, or many (“many” referring to any number higher than one). Each side of a relationship can have any combination of zero, one, or many.

Figure 17 shows different cardinality relationships. An Organization employs one or more Employees. An Employee can support zero, one, or many Dependents. But an Employee has one and only one Job during a time period. Cardinality relationships are a way of capturing rules and expectations related to data. If data shows that an Employee holds more than one Job during a set time period, then there is an error in the data, or the Organization is breaking a rule.

Attribute

An attribute is a property which identifies, describes, or measures an entity. The physical correspondent of an attribute in an entity is a column, field, tag, or node in a table, view, document, graph, or file. In the example in Figure 18, the entity Organization has the attributes of Organization Tax ID, Organization Phone Number, and Organization Name. Employee has the attributes of Employee Number, Employee First Name, Employee Last Name, and Employee Birth Date. Dependent and Job details have attributes that describe their characteristics.

Domain

In data modeling, a domain is the complete set of possible values that an attribute can be assigned. A domain provides a means of standardizing the characteristics of the attributes and constrains the data that can be populated in the field. For example, the domain Date, which contains all possible valid dates, can be assigned to any date attribute in a logical data model or date columns/fields in a physical data model such as:

• EmployeeHireDate
• OrderEntryDate
• ClaimSubmitDate
• CourseStartDate

Domains are critical to understanding the quality of data. All values inside the domain are valid values. Those outside the domain are referred to as invalid values. An attribute should not contain values outside of its assigned domain. The domain for EmployeeHireDate may be defined simply as valid dates. Under this rule, the domain for EmployeeHireDate does not include February 30 of any year.

Data modeling and data management

Data Modeling is a process of discovering and documenting information that is critical to an organization’s understanding of itself through its data. Models capture and enable use of knowledge within an organization. (That is, they are a critical form of and source of Metadata.) They can even be used to improve the quality of that information, through enforcement of naming conventions and other standards that make information more consistent and reliable.

Data analysts and designers act as intermediaries between information consumers (the people with business requirements for data) and the data producers who capture the data in usable form. Data professionals must balance the data requirements of the information consumers and the application requirements of data producers.

Data designers must also balance the short-term versus long-term business interests. Information consumers need data in a timely fashion to meet short-term business obligations and to take advantage of current business opportunities. System-development project teams must meet time and budget constraints. However, they must also meet the long-term interests of all stakeholders by ensuring that an organization’s data resides in data structures that are secure, recoverable, sharable, and reusable, and that this data is as correct, timely, relevant, and usable as possible. Therefore, data models and database designs should provide a reasonable balance between the short-term and long-term needs of the enterprise.

What you need to know

• Architecture is critical to an organization’s ability to understand itself – its systems, its data, and the relationship between business and technical processes.
• A strategic approach to overall architecture enables an organization to make better decisions.
• Data architecture focuses on enabling an organization to understand and capture explicit knowledge about its own data.
• The Metadata created and managed through data architectural processes is critical to using and managing data over time.
• Data modeling is critical to data management because data models define entities that are important to the organization, concisely capture data requirements, and clarify rules and relationships that are necessary to manage data and the quality of data.