Chapter 3 and Module CChapter 3 and Module C

DATABASES AND DATA DATABASES AND DATA WAREHOUSESWAREHOUSES

Supporting the Analytics-Supporting the Analytics-Driven OrganizationDriven Organization

Opening Case: Opening Case: Did You Know CDs Come from Did You Know CDs Come from

Dead Dinosaurs?Dead Dinosaurs?

In 2010, more than half of all music was in digital form; physical music will never again be the norm

INTRODUCTIONINTRODUCTION Business intelligence (BI)Business intelligence (BI)

Knowledge about your customers, Knowledge about your customers, competitors, business partners, competitors, business partners, competitive environment, and internal competitive environment, and internal operations to make effective, important, operations to make effective, important, and strategic business decisionsand strategic business decisions

AnalyticsAnalytics Fact-based decision-makingFact-based decision-making Integrated use of IT and statistical Integrated use of IT and statistical

techniques to create BItechniques to create BI

Data ProcessingData Processing

IT tools help process information to IT tools help process information to create business intelligence create business intelligence according to…according to… OLTPOLTP OLAPOLAP

Data ProcessingData Processing Online transaction processing (OLTP)Online transaction processing (OLTP)

The gathering and processing transaction The gathering and processing transaction information, and updating existing information to information, and updating existing information to reflect the transactionreflect the transaction

Databases support OLTPDatabases support OLTP Operational databaseOperational database – databases that support OLTP – databases that support OLTP

Online analytical processing (OLAP)Online analytical processing (OLAP) TThe manipulation of information to support decision he manipulation of information to support decision

makingmaking Databases can support some OLAPDatabases can support some OLAP Data warehouses only support OLAP, not OLTPData warehouses only support OLAP, not OLTP Data warehouses are special forms of databases that Data warehouses are special forms of databases that

support decision making and help build BIsupport decision making and help build BI

OLTP, OLAP, and Business OLTP, OLAP, and Business IntelligenceIntelligence

THE RELATIONAL DATABASE THE RELATIONAL DATABASE MODELMODEL

There are many types of databasesThere are many types of databases The relational database model is the The relational database model is the

most popularmost popular

Relational databaseRelational database

Database CharacteristicsDatabase Characteristics

1.1. Collections of informationCollections of information

2.2. Created with logical structuresCreated with logical structures

3.3. Include logical ties within the Include logical ties within the informationinformation

4.4. Include built-in integrity constraintsInclude built-in integrity constraints

1. Database – Collection of 1. Database – Collection of InformationInformation

2. Database – Logical 2. Database – Logical StructureStructure

CharacterCharacter FieldField RecordRecord File File

(Table)(Table) DatabaseDatabase Data Data

WarehouWarehousese

Advisor

Advisor IDALastNam

eAFirstNam

e

101 Leonard Lori

102 Aurigemma Sal

103 Bajaj Akhilesh

104 Platner Steve

105 McCrary Mike

ClassClass

SynonymClass Prefix

Class NoClass

Section

10342 MIS 3003 3

10344 MIS 1123 2

10359 MIS 4133 2

10450 MIS 1123 1

10578 MIS 2013 3

10643 MIS 4053 1

Student-ClassStudent ID Class Synonym

1011 10342

1011 10643

1013 10578

1014 10342

1014 10359

1014 10450

1015 10578

1016 10342

1017 10344

1017 10450

Student

Student IDSLastNam

eSFirstNam

eAdvisor ID

1011 Berry Jeff 101

1012 Smith Tom 103

1013 Sanders Tally 101

1014 Anderson Cindy 103

1015 Whitman Amy 102

1016 Jones Kelsi 105

1017 Phillips Susan 104

Logical Structure: CharacterLogical Structure: Character

CharacterCharacter FieldField RecordRecord File File

(Table)(Table) DatabaseDatabase Data Data

WarehouWarehousese

Advisor

Advisor IDALastNam

eAFirstNam

e

101 Leonard Lori

102 Aurigemma Sal

103 Bajaj Akhilesh

104 Platner Steve

105 McCrary Mike

ClassClass

SynonymClass Prefix

Class NoClass

Section

10342 MIS 3003 3

10344 MIS 1123 2

10359 MIS 4133 2

10450 MIS 1123 1

10578 MIS 2013 3

10643 MIS 4053 1

Student-ClassStudent ID Class Synonym

1011 10342

1011 10643

1013 10578

1014 10342

1014 10359

1014 10450

1015 10578

1016 10342

1017 10344

1017 10450

Student

Student IDSLastNam

eSFirstNam

eAdvisor ID

1011 Berry Jeff 101

1012 Smith Tom 103

1013 Sanders Tally 101

1014 Anderson Cindy 103

1015 Whitman Amy 102

1016 Jones Kelsi 105

1017 Phillips Susan 104

Logical Structure: FieldLogical Structure: Field

CharacterCharacter FieldField RecordRecord File File

(Table)(Table) DatabaseDatabase Data Data

WarehouWarehousese

Advisor

Advisor IDALastNam

eAFirstNam

e

101 Leonard Lori

102 Aurigemma Sal

103 Bajaj Akhilesh

104 Platner Steve

105 McCrary Mike

ClassClass

SynonymClass Prefix

Class NoClass

Section

10342 MIS 3003 3

10344 MIS 1123 2

10359 MIS 4133 2

10450 MIS 1123 1

10578 MIS 2013 3

10643 MIS 4053 1

Student-ClassStudent ID Class Synonym

1011 10342

1011 10643

1013 10578

1014 10342

1014 10359

1014 10450

1015 10578

1016 10342

1017 10344

1017 10450

Student

Student IDSLastNam

eSFirstNam

eAdvisor ID

1011 Berry Jeff 101

1012 Smith Tom 103

1013 Sanders Tally 101

1014 Anderson Cindy 103

1015 Whitman Amy 102

1016 Jones Kelsi 105

1017 Phillips Susan 104

Logical Structure: RecordLogical Structure: Record

CharacterCharacter FieldField RecordRecord File File

(Table)(Table) DatabaseDatabase Data Data

WarehouWarehousese

Advisor

Advisor IDALastNam

eAFirstNam

e

101 Leonard Lori

102 Aurigemma Sal

103 Bajaj Akhilesh

104 Platner Steve

105 McCrary Mike

ClassClass

SynonymClass Prefix

Class NoClass

Section

10342 MIS 3003 3

10344 MIS 1123 2

10359 MIS 4133 2

10450 MIS 1123 1

10578 MIS 2013 3

10643 MIS 4053 1

Student-ClassStudent ID Class Synonym

1011 10342

1011 10643

1013 10578

1014 10342

1014 10359

1014 10450

1015 10578

1016 10342

1017 10344

1017 10450

Student

Student IDSLastNam

eSFirstNam

eAdvisor ID

1011 Berry Jeff 101

1012 Smith Tom 103

1013 Sanders Tally 101

1014 Anderson Cindy 103

1015 Whitman Amy 102

1016 Jones Kelsi 105

1017 Phillips Susan 104

Logical Structure: FileLogical Structure: File

CharacterCharacter FieldField RecordRecord File File

(Table)(Table) DatabaseDatabase Data Data

WarehouWarehousese

Advisor

Advisor IDALastNam

eAFirstNam

e

101 Leonard Lori

102 Aurigemma Sal

103 Bajaj Akhilesh

104 Platner Steve

105 McCrary Mike

ClassClass

SynonymClass Prefix

Class NoClass

Section

10342 MIS 3003 3

10344 MIS 1123 2

10359 MIS 4133 2

10450 MIS 1123 1

10578 MIS 2013 3

10643 MIS 4053 1

Student-ClassStudent ID Class Synonym

1011 10342

1011 10643

1013 10578

1014 10342

1014 10359

1014 10450

1015 10578

1016 10342

1017 10344

1017 10450

Student

Student IDSLastNam

eSFirstNam

eAdvisor ID

1011 Berry Jeff 101

1012 Smith Tom 103

1013 Sanders Tally 101

1014 Anderson Cindy 103

1015 Whitman Amy 102

1016 Jones Kelsi 105

1017 Phillips Susan 104

Logical Structure: DatabaseLogical Structure: Database

CharacterCharacter FieldField RecordRecord File File

(Table)(Table) DatabaseDatabase Data Data

WarehouWarehousese

Advisor

Advisor IDALastNam

eAFirstNam

e

101 Leonard Lori

102 Aurigemma Sal

103 Bajaj Akhilesh

104 Platner Steve

105 McCrary Mike

ClassClass

SynonymClass Prefix

Class NoClass

Section

10342 MIS 3003 3

10344 MIS 1123 2

10359 MIS 4133 2

10450 MIS 1123 1

10578 MIS 2013 3

10643 MIS 4053 1

Student-ClassStudent ID Class Synonym

1011 10342

1011 10643

1013 10578

1014 10342

1014 10359

1014 10450

1015 10578

1016 10342

1017 10344

1017 10450

Student

Student IDSLastNam

eSFirstNam

eAdvisor ID

1011 Berry Jeff 101

1012 Smith Tom 103

1013 Sanders Tally 101

1014 Anderson Cindy 103

1015 Whitman Amy 102

1016 Jones Kelsi 105

1017 Phillips Susan 104

Database – Physical Database – Physical StructureStructure

BitsBits BytesBytes WordsWords

Databases – Created with Databases – Created with Logical StructuresLogical Structures

Databases have many tablesDatabases have many tables In databases, the row number is In databases, the row number is

irrelevant; not true in spreadsheet irrelevant; not true in spreadsheet softwaresoftware

In databases, column names are very In databases, column names are very important. Column names are important. Column names are created in the data dictionarycreated in the data dictionary

Database – Created with Logical Database – Created with Logical StructuresStructures

Data dictionary Data dictionary – contains the logical – contains the logical structure for the information in a databasestructure for the information in a database

Before you can enter information into a database, you must define the data dictionary for all the tables and their fields. For example, when you create the Truck table, you must specify that it will have three pieces of information and that Date of Purchase is a field in Date format.

3. Databases – With Logical 3. Databases – With Logical Ties Within the InformationTies Within the Information

Logical ties must exist between the Logical ties must exist between the tables or files in a databasetables or files in a database

Logical ties are created with primary Logical ties are created with primary and foreign keysand foreign keys

Primary key (PK)Primary key (PK) Composite primary key (CPK)Composite primary key (CPK) Foreign key (FK)Foreign key (FK)

Database – Logical Ties within Database – Logical Ties within the Informationthe Information

Customer Number is the primary key for Customer and appears in Order as a foreign key

Logical Ties – KeysLogical Ties – Keys A PK and a FK do not have to have the A PK and a FK do not have to have the

same name.same name. If a record can be uniquely identified If a record can be uniquely identified

with only one PK, then the file should with only one PK, then the file should only have one.only have one.

A PK is required (or CPKs) for each file.A PK is required (or CPKs) for each file. A FK may or may not exist for each file.A FK may or may not exist for each file. All CPKs do not have to be FKs.All CPKs do not have to be FKs.

4. Databases – Built-In 4. Databases – Built-In Integrity ConstraintsIntegrity Constraints

Integrity constraintsIntegrity constraints – rules that help – rules that help ensure the quality of the informationensure the quality of the information

ExamplesExamples Primary keys must be uniquePrimary keys must be unique Foreign keys must be presentForeign keys must be present Sales price cannot be negativeSales price cannot be negative Phone number must have area codePhone number must have area code

Steps in Developing a Steps in Developing a DatabaseDatabase

Step 1: Define Entity Classes (tables) Step 1: Define Entity Classes (tables) and Primary Keysand Primary Keys

Step 2: Defining Relationships Among Step 2: Defining Relationships Among Entity ClassesEntity Classes ERD (entity relationship diagram)ERD (entity relationship diagram) NormalizationNormalization: (1) eliminate M:M; (2) : (1) eliminate M:M; (2)

fields must depend on PK; (3) no derived fields must depend on PK; (3) no derived fieldsfields

Step 3: Defining Information For Each Step 3: Defining Information For Each RelationRelation

Step 4: Use A Data Definition Language Step 4: Use A Data Definition Language To Create Your DatabaseTo Create Your Database

DATABASE MANAGEMENT DATABASE MANAGEMENT SYSTEM TOOLSSYSTEM TOOLS

5 Components of a DBMS5 Components of a DBMS1.1. DBMS engineDBMS engine

2.2. Data definition subsystemData definition subsystem

3.3. Data manipulation subsystemData manipulation subsystem ViewsViews Report generatorsReport generators QBE toolsQBE tools SQLSQL

4.4. Application generation subsystemApplication generation subsystem

5.5. Data administration subsystemData administration subsystem

ViewView

ViewView – allows you to see the contents of a database – allows you to see the contents of a database file, make changes, and query it to find informationfile, make changes, and query it to find information

Report GeneratorReport Generator

Report generator Report generator – helps – helps you quickly define formats you quickly define formats of reports and what of reports and what information you want to information you want to see in a reportsee in a report

Query-by-Example ToolQuery-by-Example Tool QBE tool QBE tool – helps you graphically – helps you graphically

design the answer to a questiondesign the answer to a question

Structured Query LanguageStructured Query Language

SQLSQL – standardized fourth-generation – standardized fourth-generation query language found in most DBMSsquery language found in most DBMSs

Sentence-structure equivalent to QBESentence-structure equivalent to QBEMostly used by IT professionalsMostly used by IT professionalsNon-procedural language, which Non-procedural language, which makes it different from other makes it different from other programming languagesprogramming languages

DATA WAREHOUSES AND DATA WAREHOUSES AND DATA MININGDATA MINING

Data warehouses support OLAP and Data warehouses support OLAP and decision makingdecision making

Data warehouses do not support OLTPData warehouses do not support OLTP

Data warehouseData warehouse Data martData mart Data-miningData-mining

Data Warehouse ExampleData Warehouse Example

Data Mart ExampleData Mart Example

Data-Mining ToolsData-Mining Tools

Data Warehouse Data Warehouse ConsiderationsConsiderations

Do you really need one, or does your Do you really need one, or does your database environment support all your database environment support all your functions?functions?

Do all employees need a big data Do all employees need a big data warehouse or a smaller data mart?warehouse or a smaller data mart?

How up-to-date must the information How up-to-date must the information be?be?

What data-mining tools do you need?What data-mining tools do you need?

INFORMATION OWNERSHIPINFORMATION OWNERSHIP

Information is a resource you must Information is a resource you must manage and organize to help the manage and organize to help the organization meet its goals and organization meet its goals and objectivesobjectives

You need to considerYou need to consider Strategic management supportStrategic management support Sharing information with responsibilitySharing information with responsibility Information cleanlinessInformation cleanliness

Strategic Management Strategic Management SupportSupport

• Data administration Data administration – function – function that plans for, oversees the that plans for, oversees the development of, and monitors the development of, and monitors the information resourceinformation resource

• Database administration Database administration – – function responsible for the more function responsible for the more technical and operational aspects of technical and operational aspects of managing organizational informationmanaging organizational information

Sharing InformationSharing Information

Everyone can share – while not Everyone can share – while not consuming – informationconsuming – information

But someone must “own” it by But someone must “own” it by accepting responsibility for its quality accepting responsibility for its quality and accuracyand accuracy

Information CleanlinessInformation Cleanliness

Related to ownership and Related to ownership and responsibility for quality and accuracyresponsibility for quality and accuracy

No duplicate informationNo duplicate informationNo redundant records with slightly No redundant records with slightly different data, such as the spelling of different data, such as the spelling of a customer namea customer name

GIGO – if you have garbage GIGO – if you have garbage information you get garbage information you get garbage information for decision making information for decision making