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Transcript
Chapter 5
Data Resource
Management
McGraw-Hill/Irwin
Copyright © 2013 by The McGraw-Hill Companies, Inc. All rights reserved.
Learning Objectives
 Explain the value of implementing data resource
management processes and technologies in an
organization.
 Outline the advantages of a database management
approach to managing the data resources of a
business, compared with a file processing approach.
 Explain how database management software helps
business professionals and supports the operations
and management of a business.
5-2
Learning Objectives
Provide examples to illustrate each of the
following concepts:
Major types of databases
Data warehouses and data mining
Logical data elements
Fundamental database structures
Database development
5-3
Section 1
Technical Foundations of Database
Management
5-4
I. Database Management System
Data resources must be structured and
organized in some logical manner so
they can be accessed, processed,
retrieved, and managed easily
5-5
II. Fundamental Data Concepts
Character – the most basic logical data
element that can be observed, a single
alpha or numeric or other symbol,
represented by one byte
Field – a grouping of related characters,
as a last name or a salary, represents an
attribute of some entity General Purpose
Application Programs – perform
common information processing jobs for
end users
5-6
II. Fundamental Data Concepts
Record – a grouping of attributes that
describe an entity
File – a group of related data records
Database – a collection of logically
related data elements
5-7
II. Fundamental Data Concepts
5-8
III. Database Structures (Models)
 Hierarchical Structure – treelike structure of oneto-many parent-child relationships (each child can
have only one parent)
 Network Structure – similar to hierarchical but
allows many-to-many relationships (a child record
can have more than one parent)
 Relational Structure – the most widely used
database model today; data is represented as a
series of two-dimensional tables called Relations;
each column is a named attribute of the entity,
each row is an unnamed instance of that entity
5-9
III. Database Structures (Models)
Relational Operations
Select – create a subset that meets a criterion
Join – temporarily combine two or more tables for
comparison
Project – create a subset of the columns in the
temporary tables
Multidimensional Structure – variation of the
Relational model using multidimensional
structures to organize and express
relationships
5-10
III. Database Structures (Models)
5-11
III. Database Structures (Models)
5-12
III. Database Structures (Models)
5-13
III. Database Structures (Models)
Object-Oriented Structure – combining the
data of interest and the processes that act on
that data into a structure called an object
Encapsulation – allows complex data types
Inheritance – allows replication of some/all of the
characteristics of a parent object in the creation of
a child object
Evaluation of Database Structures – databases
have grown more complex as needs have
changed and grown in complexity
5-14
III. Database Structures (Models)
5-15
Database Pioneer Rethinks the Best Way
to Organize Data
How do databases currently focus on
data to be stored?
What is suggested as a better away to
focus on data warehouses?
What gains do these changes promise?
5-16
IV. Database Development
Database Administrator (DBA) – controls
development and administration of the
database
Data Definition Language (DDL) – used to
specify the contents, relationships, and
structure of the database
Data Dictionary – directory containing the
metadata
5-17
IV. Database Development
Metadata – data about the data
Data Planning and Database Design
Data Modeling (Entity-Relationship Diagrams) –
logical models of the data itself; this must be done
before choosing the database model
Schema – the physical/internal view of a system
Subschema – the logical/external view of a system
5-18
IV. Database Development
5-19
IV. Database Development
Entity Relationship Diagram
5-20
AAA Missouri: Data Quality Is an
Important First Step
What problem did AAA Missouri have?
How does Melissa solve this problem?
What other benefits does this software
provide?
What is the ultimate goal of using this
software? What business benefit will
that provide?
5-21
Hadoop: Ready for the Large-scale
Databases of the Future
What is the strength of Hadoop?
What does Hadoop do differently from
pervious databases?
Why do we need something different
today for handling data in databases?
What does this tell you about the future
od handling data?
5-22
Section 2
Managing Data Resources
5-23
I. Data Resource Management
Data are an organizational resource that
must be managed as any other resource
5-24
I. Data Resource Management
Types of Databases Used by
Organizations and End-Users
5-25
II. Types of Databases
Operational Databases – store detailed data to
support business processes and operations
Distributed Databases – many organizations
distribute their databases over multiple
locations
Replication – complex process of updating
distributed data
Duplication – simplified method of updating
distributed data
5-26
II. Types of Databases
External Databases – outside the firm,
free or fee-based
Hypermedia Databases – hyperlinked
pages of multimedia
5-27
Coty: Using Real-Time Analytics to Track
Demand
What percentage of retails products are
usually out of stock in the U.S.?
What percentage of promotional items
are usually out of stock in the U.S.?
What effect does this have on business?
How does Coty deal with these issues?
5-28
III. Data Warehouses and Data Mining
Data Warehouse – stores data extracted
from other databases
Data Mart – subset of a data warehouse
focusing on a single topic, customer,
product, etc.
Data Mining – analyzing a data
warehouse to reveal hidden patterns
and trends
5-29
III. Data Warehouses and Data Mining
Components of a Data Warehouse System
5-30
III. Data Warehouses and Data Mining
A Data Warehouse and its Data Mart Subsets
5-31
III. Data Warehouses and Data Mining
Data Mining Extracts Business Knowledge
from a Data Warehouse
5-32
Better Analytics Means Better Care
According to this case, what is the state
of healthcare and BI?
In what ways did the system improve
patients’ health?
How does SETMA view the cost and
benefits of the system?
5-33
IV. Traditional File Processing
Data was stored in independent files without
regard to other needs for that data
Problems of File Processing – databases seek
to solve these problems
1. Data Redundancy – the same data is kept in
more than one location; databases seek to
Control (NOT reduce!) Redundancy; this led to
Data Inconsistency – same data in multiple
locations but the Values were Different
5-34
IV. Traditional File Processing
Problems of File Processing – databases seek
to solve these problems
2. Lack of data Integration – data not easily
available for ad hoc requests
3. Data Dependence – data and programs were
“tightly coupled”, changing one meant having to
change the other
4. Lack of Data Integrity (Standardization) – data
was defined differently by different end users or
applications
5-35
Online Dating: The Technology Behind
Finding Love
Are all dating sites the same? For users,
what makes the difference between
different dating sites?
What is the biggest challenge for
eHarmony?
When is the demand for eHarmony’s
services greatest? Why might this be?
What does this mean from a business
perspective?
5-36
V. Database Management Approach
Consolidate the data from separate files into
databases accessible by multiple application
programs
Database Management System (DBMS) – a
collection of programs to create, maintain, and use
(retrieve) data in a database
Database Maintenance – organizational databases
need to be updated continually
Application Development – facilitated by the Data
Manipulation Language (DML) provided by the
DBMS
5-37
V. Database Management Approach
Database Interrogation – query (“ask”) the
database for information
 Query Language – allows ad hoc requests of the
database
 SQL Queries (Structured Query Language) –
standard query language found in many
databases
 Boolean Logic – 3 logical operators: AND, OR,
and NOT
 Graphical and Natural Queries – easier methods
of structuring SQL statements
5-38