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McGraw-Hill/Irwin Copyright © 2007 by The McGraw-Hill Companies, Inc. All rights reserved.
Data Modeling and
Analysis

Objectives
• Define data modeling and explain its benefits.
• Recognize and understand the basic concepts and constructs of
a data model.
• Read and interpret an entity relationship data model.
• Explain when data models are constructed during a project and
where the models are stored.
• Discover entities and relationships.
• Construct an entity-relationship context diagram.
• Discover or invent keys for entities and construct a key-based
diagram.
• Construct a fully attributed entity relationship diagram and
describe data structures and attributes to the repository.
• Normalize a logical data model to remove impurities that can
make a database unstable, inflexible, and nonscalable.
• Describe a useful tool for mapping data requirements to business
operating locations.

8-3
Data Modeling
Data modeling – a technique for organizing
and documenting a system’s data.
Sometimes called database modeling.
Entity relationship diagram (ERD) – a
data model utilizing several notations to
depict data in terms of the entities and
relationships described by that data.

8-4
Sample Entity Relationship Diagram
(ERD)

8-5
 Persons: agency, contractor, customer,
department, division, employee,
instructor, student, supplier.
 Places: sales region, building, room,
branch office, campus.
 Objects: book, machine, part, product, raw material, software
license, software package, tool, vehicle model, vehicle.
 Events: application, award, cancellation, class, flight, invoice,
order, registration, renewal, requisition, reservation, sale, trip.
 Concepts: account, block of time, bond, course, fund,
qualification, stock.
Data Modeling Concepts: Entity
Entity – a class of persons, places, objects,
events, or concepts about which we need to
capture and store data.
• Named by a singular noun

8-6
Data Modeling Concepts: Entity
Entity instance – a single occurrence of an entity.
Student ID Last Name First Name
2144 Arnold Betty
3122 Taylor John
3843 Simmons Lisa
9844 Macy Bill
2837 Leath Heather
2293 Wrench Tim
instances
entity

8-7
Data Modeling Concepts:
Attributes
Attribute – a descriptive property or
characteristic of an entity. Synonyms
include element, property, and field.
• Just as a physical student can have
attributes, such as hair color, height,
etc., data entity has data attributes
Compound attribute – an attribute
that consists of other attributes.
Synonyms in different data modeling
languages are numerous:
concatenated attribute, composite
attribute, and data structure.

8-8
Data Modeling Concepts: Data
Type
Data type – a property of an attribute that identifies what
type of data can be stored in that attribute.
Representative Logical Data Types for Attributes
Data Type Logical Business Meaning
NUMBER Any number, real or integer.
TEXT A string of characters, inclusive of numbers. When numbers are included in a TEXT
attribute, it means that we do not expect to perform arithmetic or comparisons with
those numbers.
MEMO Same as TEXT but of an indeterminate size. Some business systems require the
ability to attach potentially lengthy notes to a give database record.
DATE Any date in any format.
TIME Any time in any format.
YES/NO An attribute that can assume only one of these two values.
VALUE SET A finite set of values. In most cases, a coding scheme would be established (e.g.,
FR=Freshman, SO=Sophomore, JR=Junior, SR=Senior).
IMAGE Any picture or image.

8-9
Domains
Domain – a property of an attribute that defines what
values an attribute can legitimately take on.
Representative Logical Domains for Logical Data Types
Data Type Domain Examples
NUMBER For integers, specify the range.
For real numbers, specify the range and precision.
{10-99}
{1.000-799.999}
TEXT Maximum size of attribute. Actual values usually infinite;
however, users may specify certain narrative restrictions.
Text(30)
DATE Variation on the MMDDYYYY format. MMDDYYYY
MMYYYY
TIME For AM/PM times: HHMMT
For military (24-hour times): HHMM
HHMMT
HHMM
YES/NO {YES, NO} {YES, NO} {ON, OFF}
VALUE SET {value#1, value#2,…value#n}
{table of codes and meanings}
{M=Male
F=Female}

8-10
Default Value
Default value – the value that will be recorded if a
value is not specified by the user.
Permissible Default Values for Attributes
Default Value Interpretation Examples
A legal value from
the domain
For an instance of the attribute, if the user does not specify
a value, then use this value.
0
1.00
NONE or NULL For an instance of the attribute, if the user does not specify
a value, then leave it blank.
NONE
NULL
Required or NOT
NULL
For an instance of the attribute, require that the user enter
a legal value from the domain. (This is used when no value
in the domain is common enough to be a default but some
value must be entered.)
REQUIRED
NOT NULL

8-11
Identification
Key – an attribute, or a group of
attributes, that assumes a unique value
for each entity instance. It is sometimes
called an identifier.
• Concatenated key - group of attributes
that uniquely identifies an instance.
Synonyms: composite key, compound
key.
• Candidate key – one of a number of
keys that may serve as the primary key.
Synonym: candidate identifier.
• Primary key – a candidate key used to
uniquely identify a single entity instance.
• Alternate key – a candidate key not
selected to become the primary key.
Synonym: secondary key.

8-12
Relationships
Relationship – a natural business
association that exists between one or
more entities.
The relationship may represent an event that
links the entities or merely a logical affinity
that exists between the entities.

8-13
Cardinality
Cardinality – the minimum and maximum
number of occurrences of one entity that may be
related to a single occurrence of the other entity.
Because all relationships are bidirectional,
cardinality must be defined in both directions for
every relationship.
bidirectional

8-14
Degree
Degree – the number of entities that
participate in the relationship.
A relationship between two entities is called
a binary relationship.
A relationship between three entities is
called a 3-ary or ternary relationship.
A relationship between different instances of
the same entity is called a recursive
relationship.

8-15
Degree
Relationships may
exist between more
than two entities
and are called
N-ary relationships.
The example ERD
depicts a ternary
relationship.

8-16
Degree
Associative entity
– an entity that
inherits its primary
key from more than
one other entity
(called parents).
Each part of that
concatenated key
points to one and
only one instance of
each of the
connecting entities.
Associative
Entity

8-17
Recursive Relationship
Recursive relationship - a relationship that
exists between instances of the same entity

8-18
Nonidentifying Relationships
Nonidentifying relationship – relationship where each
participating entity has its own independent primary key
• Primary key attributes are not shared.
• The entities are called strong entities

8-19
Identifying Relationships
Identifying relationship – relationship in which the parent
entity’ key is also part of the primary key of the child entity.
• The child entity is called a weak entity.

8-20
Nonspecific Relationships
Nonspecific
relationship –
relationship where
many instances of
an entity are
associated with
many instances of
another entity.
Also called many-
to-many
relationship.
Nonspecific
relationships must
be resolved,
generally by
introducing an
associative entity.

8-21
Resolving Nonspecific
Relationships
The verb or verb phrase of a many-
to-many relationship sometimes
suggests other entities.

8-22
Generalization
Generalization – a concept wherein the attributes
that are common to several types of an entity are
grouped into their own entity.
Supertype – an entity whose instances store
attributes that are common to one or more entity
subtypes.
Subtype – an entity whose instances may inherit
common attributes from its entity supertype
And then add other attributes unique to the subtype.

8-23
Process of Logical Data
Modeling
• Strategic Data Modeling
• Many organizations select IS development
projects based on strategic plans.
• Includes vision and architecture for information
systems
• Identifies and prioritizes develop projects
• Includes enterprise data model as starting point
for projects
• Data Modeling during Systems Analysis
• Data model for a single information system is
called an application data model.

8-24
Logical Model Development
Stages
1. Context Data model
• Includes only entities and relationships
• To establish project scope
2. Key-based data model
• Eliminate nonspecific relationships
• Add associative entities
• Include primary and alternate keys
• Precise cardinalities
3. Fully attributed data model
• All remaining attributes
• Subsetting criteria
4. Normalized data model
Metadata - data about data.

8-25
What is a Good Data Model?
• A good data model is simple.
• Data attributes that describe any given entity
should describe only that entity.
• Each attribute of an entity instance can have only
one value.
• A good data model is essentially
nonredundant.
• Each data attribute, other than foreign keys,
describes at most one entity.
• Look for the same attribute recorded more than
once under different names.
• A good data model should be flexible and
adaptable to future needs.

Data Analysis & Normalization
Data analysis – a technique used to
improve a data model for implementation
as a database.
Goal is a simple, nonredundant, flexible, and
adaptable database.
Normalization – a data analysis technique
that organizes data into groups to form
nonredundant, stable, flexible, and
adaptive entities.

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