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Chapter 3 and Module C
DATABASES AND DATA
WAREHOUSES
Building Business Intelligence
MORE CHERRIES PLEASE
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Ben & Jerry’s
 190,000 pints of ice cream and frozen
yogurt
 50,000 grocery stores
 In the U.S. and 12 other countries
 Meticulously tracks every piece of
information on every pint
MORE CHERRIES PLEASE
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Noticed a problem with Cherry Garcia Ice
Cream
Complaints of not enough cherries
Ben & Jerry’s could find no production
problems
Eventually found that the wrong photo was
on the ice cream container
Ben & Jerry’s analyzed all the information to
create business intelligence
INTRODUCTION
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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
INTRODUCTION
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IT tools help process information to create
business intelligence according to…
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OLTP
OLAP
INTRODUCTION
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Online transaction processing (OLTP) –
the gathering and processing transaction
information, and updating existing
information to reflect the transaction
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Databases support OLTP
Operational database – databases that
support OLTP
Batch Processing – processing all of the
transactions at once; can be used to update
a database
INTRODUCTION
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Online analytical processing (OLAP) –
the manipulation of information to support
decision making
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Databases can support some OLAP
Data warehouses only support OLAP, not
OLTP
Data warehouses are special forms of
databases that support decision making
OLTP, OLAP, and Business Intelligence
THE RELATIONAL DATABASE
MODEL
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There are many types of databases
The relational database model is the most
popular
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Relational database
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Database Characteristics
 Collections
of information
 Created with logical structures
 Include logical ties within the information
 Include built-in integrity constraints
Database – Collection of Information
Database – Logical Structure
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Character
Field
Record
File (Table)
Database
Data Warehouse
Database – Physical Structure
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Bits
Bytes
Words
Databases – Created with Logical
Structures
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Databases have many tables
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
Database – Created with Logical
Structures

Data dictionary – contains the logical
structure 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.
Databases – With Logical Ties
Within the Information
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Logical ties must exist between the tables
or files in a database
Logical ties are created with primary and
foreign keys
Primary key
Composite primary key
Foreign key
Database – Logical Ties within the
Information
Customer Number is the
primary key for Customer and
appears in Order as a foreign
key
Databases – With Built-In
Integrity Constraints
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Integrity constraints – rules that help
ensure the quality of the information
Examples
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Primary keys must be unique
Foreign keys must be present
Sales price cannot be negative
Phone number must have area code
Steps in Developing a Database
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Step 1: Define Entity Classes (tables) and
Primary Keys
Step 2: Defining Relationships Among
Entity Classes
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ERD (entity relationship diagram)
Normalization
Step 3: Defining Information For Each
Relation
Step 4: Use A Data Definition Language
To Create Your Database
DATABASE MANAGEMENT
SYSTEM TOOLS
5 Components of a DBMS
1.
2.
3.
DBMS engine
Data definition subsystem
Data manipulation subsystem
Views
 Report generators
 QBE tools
 SQL
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4.
5.
Application generation subsystem
Data administration subsystem
View
Binoculars
Report Generator
Query-by-Example Tool
Structured Query Language
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SQL – standardized fourth-generation
query language found in most DBMSs
 Sentence-structure equivalent to QBE
 Mostly used by IT professionals
 Non-procedural language, which makes it
different from other programming
languages
DATA WAREHOUSES AND DATA
MINING
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Data warehouses support OLAP and
decision making
Data warehouses do not support OLTP
Data warehouse
Data mart
Data-mining
DATA WAREHOUSES AND DATA
MINING
Data Marts
Data-Mining Tools
Data Warehouse Considerations
 Do
you really need one, or does your
database environment support all your
functions?
 Do all employees need a big data
warehouse or a smaller data mart?
 How up-to-date must the information be?
 What data-mining tools do you need?
INFORMATION OWNERSHIP
 Information
is a resource you must
manage and organize to help the
organization meet its goals and objectives
 You need to consider
 Strategic
management support
 Sharing information with responsibility
 Information cleanliness
Strategic Management Support
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Data administration – function that
plans for, oversees the development of,
and monitors the information resource
Database administration – function
responsible for the more technical and
operational aspects of managing
organizational information
Sharing Information
 Everyone
can share – while not consuming
– information
 But someone must “own” it by accepting
responsibility for its quality and accuracy
Information Cleanliness
 Related
to ownership and responsibility for
quality and accuracy
 No duplicate information
 No redundant records with slightly
different data, such as the spelling of a
customer name
 GIGO – if you have garbage information
you get garbage information for decision
making