Business Analysis. Introduction to data modeling

Topics: Entity-relationship model, Data modeling, Database model Pages: 30 (7007 words) Published: January 19, 2013
Lesson 3: An introduction to data modeling
3.1 Introduction: The importance of conceptual models
same: understand the problem before you start constructing a solution. There are two important things to keep in mind when learning about and doing data modeling: 1. Data modeling is first and foremost a tool for communication.Their is no single “right” model. Instead, a valuable model highlights tricky issues, allows users, designers, and implementors to discuss the issues using the same vocabulary, and leads to better design decisions. 2. The modeling process is inherently iterative: you create a model, check its assumptions with users, make the necessary changes, and repeat the cycle until you are sure you understand the critical issues. In this background lesson, you are going to use a data modeling technique—specifically, EntityRelationship Diagrams (ERDs)—to model the business scenario from Lesson 2. The data model you create in this lesson will form the foundation of the database that you use throughout the remaining lessons.

Before you sit down in front of the keyboard and start creating a database application, it is critical that you take a step back and consider your business problem—in this case, the kitchen supply scenario presented in Lesson 2— from a conceptual point of view. To facilitate this process, a number of conceptual modeling techniques have been developed by computer scientists, psychologists, and consultants.


For our purposes, we can think of a conceptual model as a picture of the information system we are going to build. To use an analogy, conceptual models are to information systems what blueprints are to buildings.

There are many different conceptual modeling techniques used in practice. Each technique uses a different set of symbols and may focus on a different part of the problem (e.g., data, processes, information flows, objects, and so on). Despite differences in notation and focus, however, the underlying rationale for conceptual modeling techniques is always the © Michael Brydon ( Last update: 02-May-01

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An introduction to data modeling

Introduction: The importance of conceptual Entities and attributes


What is data modeling?

A data model is a simply a diagram that describes the most important “things” in your business environment from a data-centric point of view. To illustrate, consider the simple ERD shown in Figure 3.1. The purpose of the diagram is to describe the relationship between the data stored about products and the data stored about the organizations that supply the products. FIGURE 3.1: An ERD showing a relationship between products and suppliers.

The rectangles in Figure 3.1 are called entity types (typically shortened to “entities”) and the ovals are called attributes. The entities are the “things” in the business environment about which we want to store data. The attributes provide us with a means of organizing and structuring the data. For example, we need to store certain information about the products that we sell, such as the typical selling price of the product (“Unit price”) and the quantity of the product currently in inventory (“Qty on hand”). These pieces of data are attributes of the Product entity. It is important to note that the precise manner in which data are used and processed within a particular business application is a separate issue from data modeling. For example, the data model says nothing about how the value of “Qty on hand” is changed over time. The focus in data modeling is on capturing data about the environment. You will learn how to change this data (e.g., process orders so that the inventory values are updated) once you have mastered the art of database design.

ProductID Unit price Qty on hand



supplied by


Attributes Supplier Relationship
Name Address


A data modeler assumes that if the right data is available, the other...
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