Data Modeling As Part of Database Design

The data model is one part of the conceptual design process. The other is the function model. The data model focuses on what data should be stored in the database while the function model deals with how the data is processed. To put this in the context of the relational database, the data model is used to design the relational tables. The functional model is used to design the queries that will access and perform operations on those tables.

Data modeling is preceeded by planning and analysis. The effort devoted to this stage is proportional to the scope of the database. The planning and analysis of a database intended to serve the needs of an enterprise will require more effort than one intended to serve a small workgroup.

The information needed to build a data model is gathered during the requirments analysis. Although not formally considered part of the data modeling stage by some methodologies, in reality the requirements analysis and the ER diagramming part of the data model are done at the same time.

Requirements Analysis

The goals of the requirements analysis are:

• to determine the data requirements of the database in terms of primitive objects
• to classify and describe the information about these objects
• to identify and classify the relationships among the objects
• to determine the types of transactions that will be executed on the database and the interactions between the data and the transactions
• to identify rules governing the integrity of the data

The modeler, or modelers, works with the end users of an organization to determine the data requirements of the database. Information needed for the requirements analysis can be gathered in several ways:

• review of existing documents - such documents include existing forms and reports, written guidelines, job descriptions, personal narratives, and memoranda. Paper documentation is a good way to become familiar with the organization or activity you need to model.
• interviews with end users - these can be a combination of individual or group meetings. Try to keep group sessions to under five or six people. If possible, try to have everyone with the same function in one meeting. Use a blackboard, flip charts, or overhead transparencies to record information gathered from the interviews.
• review of existing automated systems - if the organization already has an automated system, review the system design specifications and documentation

The requirements analysis is usually done at the same time as the data modeling. As information is collected, data objects are identified and classified as either entities, attributes, or relationship; assigned names; and, defined using terms familiar to the end-users. The objects are then modeled and analysed using an ER diagram. The diagram can be reviewed by the modeler and the end-users to determine its completeness and accuracy. If the model is not correct, it is modified, which sometimes requires additional information to be collected. The review and edit cycle continues until the model is certified as correct.

Three points to keep in mind during the requirements analysis are:

1. Talk to the end users about their data in "real-world" terms. Users do not think in terms of entities, attributes, and relationships but about the actual people, things, and activities they deal with daily.
2. Take the time to learn the basics about the organization and its activities that you want to model. Having an understanding about the processes will make it easier to build the model.
3. End-users typically think about and view data in different ways according to their function within an organization. Therefore, it is important to interview the largest number of people that time permits.

Steps In Building the Data Model

While ER model lists and defines the constructs required to build a data model, there is no standard process for doing so. Some methodologies, such as IDEFIX, specify a bottom-up development process were the model is built in stages. Typically, the entities and relationships are modeled first, followed by key attributes, and then the model is finished by adding non-key attributes. Other experts argue that in practice, using a phased approach is impractical because it requires too many meetings with the end-users. The sequence used for this document are:

1. Identification of data objects and relationships
2. Drafting the initial ER diagram with entities and relationships
3. Refining the ER diagram
4. Add key attributes to the diagram
5. Adding non-key attributes
6. Diagramming Generalization Hierarchies
7. Validating the model through normalization
8. Adding business and integrity rules to the Model

In practice, model building is not a strict linear process. As noted above, the requirements analysis and the draft of the initial ER diagram often occur simultaneously. Refining and validating the diagram may uncover problems or missing information which require more information gathering and analysis

Summary

Data modeling must be preceded by planning and analysis. Planning defines the goals of the database , explains why the goals are important, and sets out the path by which the goals will be reached. Analysis involves determining the requirements of the database. This is typically done by examining existing documentation and interviewing users.

An effective data model completely and accurately represents the data requirements of the end users. It is simple enough to be understood by the end user yet detailed enough to be used by a database designer to build the database. The model eliminates redundant data, it is independent of any hardware and software constraints, and can be adapted to changing requirements with a minimum of effort.

Data modeling is a bottom up process. A basic model, representing entities and relationships, is developed first. Then detail is added to the model by including information about attributes and business rules.

The next section discusses Identifying Data Objects and Relationships.