McGraw-Hill/Irwin
McGraw-Hill/Irwin
©2005 The McGraw-Hill
Companies,
©2005 The McGraw-Hill Companies, All rights reserved
All rights reserved
CHAPTER 3
DATABASES AND DATA
WAREHOUSES
McGraw-Hill/Irwin
©2005 The McGraw-Hill Companies, All rights reserved
OPENING CASE STUDY
• Chrysler Spins a Competitive Advantage with
Supply Chain Management Software
• Chapter 2 – supply chain management is a
key business initiative
• Chrysler’s SCM is called SPIN, a Web-based
system
3-3
OPENING CASE STUDY
• Behind SPIN are powerful databases
• Databases store a wealth of information
–
–
–
–
–
Inventory
Work-in-progress
Supplier information
Recall notices
Customer purchases
• This chapter – databases and data
warehouses
3-4
STUDENT LEARNING OUTCOMES
1. Describe business intelligence and its role
2. Compare databases and data warehouses
by OLTP and OLAP
3. List/describe key characteristics of a
relational database
4. Define 5 software components of a DBMS
3-5
STUDENT LEARNING OUTCOMES
5. List/describe key characteristics of a data
warehouse
6. Define 4 major types of data-mining tools
7. List key considerations in managing
information as a resource
3-6
INTRODUCTION
• Organizations need business intelligence
• Business intelligence (BI) – knowledge
about your customers, competitors, business
partners, competitive environment, and
internal operations to make effective,
important, and strategic business decisions
3-7
INTRODUCTION
• IT tools help process information to create
business intelligence according to:
– OLTP
– OLAP
3-8
INTRODUCTION
• Online transaction processing (OLTP) –
the gathering of input information, processing
that information, and updating existing
information to reflect the gathered and
processed information
– Databases support OLTP
– Operational database – databases that support
OLTP
3-9
INTRODUCTION
• Online analytical processing (OLAP) – the
manipulation of information to support
decision making
– Databases can support some OLAP
– Data warehouses only support OLAP, not OLTP
– Data warehouses are special forms of databases
that support decision making
3-10
INTRODUCTION
3-11
THE RELATIONAL DATABASE
MODEL
• There are many types of databases
• The relational database model is the most
popular
• Relational database – uses a series of
logically related two-dimensional tables or
files to store information in the form of a
database
3-12
Databases Are…
•
•
•
•
Collections of information
Created with logical structures
With logical ties within the information
With built-in integrity constraints
3-13
Databases – Collections of Information
• Databases have many tables
• Consider Solomon Enterprises that provides
concrete to home and commercial builders.
Tables or files include:
–
–
–
–
–
Order
Customer
Concrete Type
Employee
Truck
3-14
Databases – Collections of Information
3-15
Databases – Created with Logical
Structures
• In databases, the row number is irrelevant
• Not true in spreadsheet software
• In databases, column names are very
important. Column names are created in the
data dictionary
• Data dictionary – contains the logical
structure of the information in a database
3-16
Databases – With Logical Ties Within
the Information
• Logical ties must exist between the tables or
files in a database
• Logical ties are created with primary and
foreign keys
• Primary key – field (or group of fields in
some cases) that uniquely describes each
record
• Can you find primary keys in Figure 3.1 on
page 129?
3-17
Databases – With Logical Ties Within
the Information
• Foreign key – primary key of one file that
appears in another file
• Foreign keys help you create logical ties
within the information in a database
3-18
Databases – With Logical Ties Within
the Information
3-19
Databases – With Built-In Integrity
Constraints
• Integrity constraints – rules that help
ensure the quality of the information
• Examples
–
–
–
–
Primary keys must be unique
Foreign keys must be present
Sales price cannot be negative
Phone number must have area code
3-20
DATABASE MANAGEMENT SYSTEM
TOOLS
• Database management system (DBMS) –
helps you specify the logical organization for
a databases and access and use the
information within a database
– Word processing software = document
– Spreadsheet software = workbook
– DBMS software = database
3-21
DATABASE MANAGEMENT SYSTEM
TOOLS
•
5 software components:
1.
2.
3.
4.
5.
DBMS engine
Data definition subsystem
Data manipulation subsystem
Application generation subsystem
Data administration subsystem
3-22
DATABASE MANAGEMENT SYSTEM
TOOLS
3-23
DBMS Engine
• DBMS engine – accepts logical requests
from the various other DBMS subsystems,
converts them into their physical equivalent,
and actually accesses the database and data
dictionary as they exist on a storage device
• DBMS engine separates the logical from the
physical
3-24
DBMS Engine
• Physical view – how information is
physically arranged, stored, and accessed on
some type of storage device
• Logical view – how you as a knowledge
worker need to arrange and access
information
• With a database, you only concern yourself
with your logical view
3-25
Data Definition Subsystem
• Data definition subsystem – helps you
create and maintain the data dictionary and
define the structure of the files in a database
• You must create a data dictionary before
entering information into a database
• Module J covers this for Microsoft Access
3-26
Data Manipulation Subsystem
• Data manipulation subsystem – helps you
add, change, and delete information
• This is your primary DBMS interface as you
work with a database
–
–
–
–
Views
Report generators
QBE tools
SQL
3-27
Views
• View – allows you to see the contents of a
database file
–
–
–
–
Make whatever changes you want
Perform simple sorting
Query to find the location of information
Looks similar to a workbook with no row numbers
3-28
Views
3-29
Report Generators
• Report generator – helps you quickly define
formats of reports and what information you
want to see in a report
• You can save report formats and generate
reports at any time with up-to-date
information
3-30
Report Generators
3-31
Report Generators
3-32
QBE Tools
• Query-by-example (QBE) tool – helps you
graphically design the answer to a question
• “What driver most often delivers concrete to
Triple A Homes?”
3-33
QBE Tools
3-34
SQL
• Structured query language (SQL) –
standardized fourth-generation language
found in most DBMSs
• Performs the same task as a QBE tool
– But uses a sentence structure instead of pointand-click interface
• SQL is used mostly by IT people
3-35
Application Generation Subsystem
• Application generation subsystem –
contains facilities to help you develop
transaction-intensive applications
– Data entry screen (called forms)
– Programming languages
• Used mostly by IT specialists
3-36
Data Administration Subsystem
• Data administration subsystem – helps you
manage the overall database environment
–
–
–
–
–
Backup and recovery
Security management
Query optimization
Concurrency control
Change management
3-37
Data Administration Subsystem
• Backup and recovery
– Periodically back up information
– Recover a database if a failure occurs
• Security management
– Who has access to what information
– Who can perform certain tasks (e.g., add,
change, or delete) on information
3-38
Data Administration Subsystem
• Query optimization
– Restructure physical view of information to
optimize response times to queries
• Concurrency control
– What happens if two people makes changes to
the same information at the same time?
3-39
Data Administration Subsystem
• Change management
– What is the effect of structural changes to a
database?
– What if you add a new column?
– What happens if you delete a column?
– What happens if you change a column’s
attributes?
3-40
DATA WAREHOUSES AND DATA
MINING
• Data warehouses support OLAP and decision
making
• Data warehouses do not support OLTP
• Data-mining tools are the tools you use to
work with a data warehouse
– DBMS software = database
– Data-mining tools = data warehouse
3-41
What Is a Data Warehouse?
• Data warehouse – logical collection of
information – gathered from operational
databases – used to create business
intelligence that supports business analysis
activities and decision-making tasks
3-42
What Is a Data Warehouse?
3-43
What Is a Data Warehouse?
•
•
•
•
•
Multidimensional
Rows and columns
Also layers
Many times called hypercubes
What are the dimensions in Figure 3.8 on
page 142?
3-44
What Are Data-Mining Tools?
• Data-mining tools – software tools that you
use to query information in a data warehouse
–
–
–
–
Query-and-reporting tools
Intelligence agents
Multidimensional analysis tools
Statistical tools
3-45
What Are Data-Mining Tools?
3-46
Query-And-Reporting Tools
• Query-and-reporting tools – similar to QBE
tools, SQL, and report generators in the
typical database environment
3-47
Intelligent Agents
• Use various artificial intelligence tools such
as neural networks and fuzzy logic to form
the basis for “information discovery” and
building business intelligence
• Help you find hidden patterns in information
• Chapter 4 focuses more on these
3-48
Multidimensional Analysis Tools
• Multidimensional analysis (MDA) tools –
slice-and-dice techniques that allow you to
view multidimensional information from
different perspectives
– Bring new layers to the front
– Reorganize rows and columns
3-49
Statistical Tools
• Help you apply various mathematical models
to the information stored in a data warehouse
to discover new information
– Regression
– Analysis of variance
– And so on
3-50
Data Marts
• Data warehouses can support all of an
organization’s information
• Data marts have subsets of an
organizationwide data warehouse
• Data mart – subset of a data warehouse in
which only a focused portion of the data
warehouse information is kept
3-51
Data Marts
3-52
Data Mining as a Career Opportunity
• Knowledge of data mining can be a
substantial career opportunity for you
– Query and Analysis and Enterprise Analytic Tools
(Business Objects)
– Business Intelligence and Information Access
tools (SAS)
– Many in Cognos (the data warehouse leader)
– PowerAnalyzer (Informatica)
3-53
Considerations in Using a Data
Warehouse
• Do you need a data warehouse?
– Perhaps database OLAP is sufficient
• Do all employees need the entire data
warehouse?
– If no, build smaller data marts
• How up-to-date must the information be?
• What data-mining tools do you need?
3-54
MANAGING THE INFORMATION
RESOURCE
• Information is an organizational resource
• Just like people, capital, and equipment
• It must be managed effectively
3-55
MANAGING THE INFORMATION
RESOURCE
• Who should oversee your organization’s
information resource?
– Chief information officer (CIO) – oversees an
organization’s information resource
– Data administration – plans for, oversees the
development of, and monitors the information
resource
– Database administration – technical and
operational aspects of managing information
3-56
MANAGING THE INFORMATION
RESOURCE
• Is information ownership a consideration?
– If you create information, you “own” it
– You will also share it with others
– Because you “own” it, you are responsible for its
quality
3-57
MANAGING THE INFORMATION
RESOURCE
• How “clean” must your information be?
– Duplicate information (records) must be
eliminated
– Inaccurate information must be corrected
– Information forms the basis of business
intelligence
– If your business intelligence is bad, you will make
poor decisions
3-58
CAN YOU…
1. Describe business intelligence and its role
2. Compare databases and data warehouses
by OLTP and OLAP
3. List/describe key characteristics of a
relational database
4. Define 5 software components of a DBMS
3-59
CAN YOU…
5. List/describe key characteristics of a data
warehouse
6. Define 4 major types of data-mining tools
7. List key considerations in managing
information as a resource
3-60
CHAPTER 3
End of Chapter 3
McGraw-Hill/Irwin
©2005 The McGraw-Hill Companies, All rights reserved
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