This course introduces the students to the process for taking knowledge of the business and its rules and converting these into a stable data model. The data model is a representation of the objects that the business uses, the characteristics of those objects and the rules that govern their relationship. It is important for analysts to understand the rules for building a data model and the processes for doing so. Students will understand the benefits of data modeling.
This is a pragmatic workshop in that there are many realistic examples used throughout. The examples show mainstream data modeling concepts. You learn a rigorous method for defining data. You learn how data modelers gather the data, define and analyze business rules, perform normalization, and use the results to create a stable model of the data within a business area. You learn how data modelers use state-of-the-art refinement techniques like subtyping and recursive relationships. Above all, you learn that data modeling can be done rapidly.
A good data model is non-redundant. The basic goals and some examples of normalization are presented.
You will learn the importance of business rules. A business rule is a constraint or policy that the business must follow.You will also learn that it is possible to validate a data model. Three methods will be reviewed, namely, a CRUD matrix, a data view and a data usage map.
It is important to know when a data model is done. The workshop covers various criteria for determining when you have finished a data model. Specific steps for the conversion of a logical data model to a physical data model will be discussed and exercised.
The workshop covers the definition and purpose of data modeling. It reviews the steps in creating a rigorous data model. Where data modeling fits into the overall life cycle is stressed. To accomplish this, a framework is established into which data modeling fits. CASE technology is discussed and how it supports data modeling.Practical examples are used throughout the workshop.
Business and Systems Managers, Business and Systems Users, Business Systems Analysts, Systems Analysts, Project Managers, Project Team Members, Data/Database Administrators.
Upon successful completion of this course, the student will be able to understand data models that are:
- Independent of implementation and organizational structure
- Accurate representation of the business
- Simple (because they use refinement)
- Appropriately scoped
- Based on sound theoretical principles
- Easy to understand.
There are no prerequisites. Everything you need to know to understand the central goals of data modeling is taught during the course itself.