Click to add Text

McGraw-Hill/Irwin ©2005 The McGraw-Hill Companies, All rights reserved

©2005 The McGraw-Hill Companies,

All rights reservedMcGraw-Hill/Irwin

Click to add Text

McGraw-Hill/Irwin ©2005 The McGraw-Hill Companies, All rights reserved

CHAPTER 3

DATABASES AND DATA WAREHOUSES

3-3

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

3-4

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

3-5

STUDENT LEARNING OUTCOMES

1. Describe business intelligence and its role2. Compare databases and data warehouses

by OLTP and OLAP3. List/describe key characteristics of a

relational database4. Define 5 software components of a DBMS

3-6

STUDENT LEARNING OUTCOMES

5. List/describe key characteristics of a data warehouse

6. Define 4 major types of data-mining tools7. List key considerations in managing

information as a resource

3-7

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

3-8

INTRODUCTION

• IT tools help process information to create business intelligence according to:– OLTP– OLAP

3-9

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

3-10

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

3-11

INTRODUCTION

3-12

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

3-13

Databases Are…

• Collections of information• Created with logical structures• With logical ties within the information• With built-in integrity constraints

3-14

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

3-15

Databases – Collections of Information

3-16

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

3-17

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?

3-18

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

3-19

Databases – With Logical Ties Within the Information

3-20

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

3-21

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

3-22

DATABASE MANAGEMENT SYSTEM TOOLS

• 5 software components:1. DBMS engine2. Data definition subsystem3. Data manipulation subsystem4. Application generation subsystem5. Data administration subsystem

3-23

DATABASE MANAGEMENT SYSTEM TOOLS

3-24

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

3-25

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

3-26

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

3-27

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

3-28

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

3-29

Views

3-30

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

3-31

Report Generators

3-32

Report Generators

3-33

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?”

3-34

QBE Tools

3-35

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 point-

and-click interface• SQL is used mostly by IT people

3-36

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

3-37

Data Administration Subsystem

• Data administration subsystem – helps you manage the overall database environment– Backup and recovery– Security management– Query optimization– Concurrency control– Change management

3-38

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

3-39

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?

3-40

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?

3-41

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

3-42

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

3-43

What Is a Data Warehouse?

3-44

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?

3-45

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

3-46

What Are Data-Mining Tools?

3-47

Query-And-Reporting Tools

• Query-and-reporting tools – similar to QBE tools, SQL, and report generators in the typical database environment

3-48

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

3-49

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

3-50

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

3-51

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

3-52

Data Marts

3-53

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)

3-54

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?

3-55

MANAGING THE INFORMATION RESOURCE

• Information is an organizational resource• Just like people, capital, and equipment• It must be managed effectively

3-56

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

3-57

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

3-58

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

3-59

CAN YOU…

1. Describe business intelligence and its role2. Compare databases and data warehouses

by OLTP and OLAP3. List/describe key characteristics of a

relational database4. Define 5 software components of a DBMS

3-60

CAN YOU…

5. List/describe key characteristics of a data warehouse

6. Define 4 major types of data-mining tools7. List key considerations in managing

information as a resource

Click to add Text

McGraw-Hill/Irwin ©2005 The McGraw-Hill Companies, All rights reserved

CHAPTER 3

End of Chapter 3