Chapter 5

Chapter 5

Foundations of Business
Intelligence: Databases
and Information
Management

5.1 © 2007 by Prentice Hall

Essentials of Business Information Systems
Chapter 5 Foundations of Business Intelligence: Databases and
Information Management

The Database Approach to Data Management

• Database: a collection of related files containing records on
people, places, or things
• Entities: anything I’m interested in keeping data about. It
may be physical (car), conceptual (account), or event
(accident)
• Attributes: characteristics I’m interested in an entity OR the
important characteristics that define an entity
• Organizing data in a relational database: Tables include: fields,
records, and key fields (primary key, secondary key, or foreign key)

• Establishing relationships (associations among tables)
5.2 • Entity-relationship diagram, normalization, join table© 2007 by Prentice Hall



A Relational Database Table

A relational database organizes data in the form of two-dimensional tables.
Illustrated here is a table for the entity SUPPLIER showing how it
represents the entity and its attributes. Supplier_Number is the key field.

Relationships among Tables
of a Database
BookNo Title Author
1 IT Intro Ali
2 Adv IS Ahm
BwngNo Date BookNo StdNo 3 Program Sana
111 2/1/11 3 44 4 DBMS Samy
112 3/3/11 2 55
primary keys
113 5/3/11 3 55
secondary key
114 8/4/11 2 33
StdNo Name Address
33 Omar Somoha
primary key foreign keys 44 Hasan Roshdy
55 Mona Stanly
This is a Logical View of the Database 66 Maha AboQeer




A Simple Entity-Relationship Diagram

This diagram shows the relationship between the entities
SUPPLIER and PART (1 Supplier has many Parts).
It is called One to Many OR 1:M Relationship.

Keys of Table

• Primary Key: a column (or more) includes unique
values that can uniquely identify each row in a table. It
is a unique identifier for a database record.
• Secondary Key: a key that is not a primary key, but for
which an index is maintained and that may identify
more than one record such as in case we don’t
remember the student number as a primary key we use
student name as a secondary key.
• Foreign Key: a column (or more) in one table that refers
to another table as a relationship.


Database Management Systems

DBMS
• A specific type of software for creating, storing,
organizing, and accessing data from a database
• Separates the logical and physical views of the
data
• Logical view: how end users view data
• Physical view: how data are actually structured
and organized (sectors on a track on a HD)
• Examples of DBMS: Microsoft Access, DB2
(IBM), Oracle Database, Microsoft SQL Server,
MYSQL (OpenSource)


Books Table
Operations of a Relational DBMS
Borrowing Table a DBMS can do on a Database)
(What

Students Table

DBMS Operations:
• (1) Select: creates a subset of records based on stated criteria.
• Example: Select students who live in Stanly from the Students Table.


Books Table
(What

Students Table

DBMS Operations:
• (2) Join: combines relational tables to present
the user with more information than is available
from individual tables.
• Example: Join Borrowing Table with Students
Table to show all borrowing dates by Mona.



Books Table
(What

Students Table

DBMS Operations:
• (3) Project: creates a subset consisting of
columns in a table.
• Example: Project the addresses of students
from Students Table to know how many
students live in each district of Alexandria.




Capabilities of Database Management Systems
• Data definition (meaning of data items e.g. Nam: Name)
• Data dictionary (structure of DB ”tables, keys,
relationships,…”)
• Structured query language (SQL) is the language of dealing with
DBMSs as an end user or a program. It consists of:
• Data Retrieval Language (DRL) is the language of querying and
reporting, such as “Select * from Std;”
• Data manipulation language (DML) is the language of changing,
inserting and deleting data, such as “Update, Insert, Delete,…”
• Data definition language (DDL) is the language of building the
database components, such as “Create table, Delete Table,
Restructured Table,..…”




Capabilities of Database Management Systems
• Object-oriented databases (OODB):
• Data is stored as objects (special identity that has characteristic
and behavior “student”)
• It can be interpreted (explanation) only using the methods and
attributes specified by its class.


OODB

• Entity = anything I’m interested in keeping data about. It may be
physical (student), conceptual (account), or event (accident).
• Class (Set of attributes) = model or specifications of an entity
(Student: ID, Name, Phone,…).
• Object = an instance (case or example) of a Class (Student1:
225, Ali, 012223456,…).
• Method = behavior of object at a specific event.
• Attributes (Column) = characteristics I’m interested in an entity
OR the important characteristics that define an entity.
• State = the values of object attributes at specific time.


Using Databases to Improve Business Performance and Decision Making

Data Warehouses
• What is a data warehouse?
• A database that stores current and historical data
that may be of interest to decision makers. (layer of
data accumulated over time to support analysis
before decision.

• Data marts
• Subsets of data warehouses that are highly focused
and isolated for a specific population of users




Components of a Data Warehouse
The data warehouse
extracts current and
historical data from multiple
operational systems inside
the organization.
These data are combined
with data from external
sources and reorganized
into a central database
designed for management
reporting and analysis DSS.
The information directory
provides users with
information about the data
available in the warehouse.
Figure 5-13



Business Intelligence

A series of analytical
tools works with data
stored in databases to
find patterns and
insights for helping
managers and
employees make better
decisions to improve
organizational
performance.




Business Intelligence, Multidimensional Data
Analysis, and Data Mining
• Business Intelligence (BI): set of tools for consolidating
(merge), analyzing, and providing access to large amounts of
data to improve decision making. It includes Online Analytical
Processing (OLAP), data mining (removal), and Predictive analysis.

• Online Analytical Processing (OLAP): is an approach to quickly answer
multi-dimensional analytical queries. It gives answers to important queries
for decision making such as: What are the cities of highest growth rate of
milk products consumption in the last 10 years?




Business Intelligence, Multidimensional Data
Analysis, and Data Mining
• Data mining: is data processing using sophisticated data search
capabilities and statistical algorithms to discover patterns and correlations in
large preexisting historical databases or data-warehouses.
• Predictive analysis: includes study of:
• Associations (things happened together)
• Sequences (something happened after another)
• Classifications (things classified in categories)
• Clusters (things assembled together in something)
• Forecasts (things expected to happen after happening of others)




Databases and the Web
• Firms use the Web to make information from their internal
databases available to customers and partners
• Middleware (software running at the middle between the
Web pages and the database servers) and other software
make this possible
• Database servers
• CGI (Common Gate Interface)

• Web interfaces provide familiarity to users and savings
over redesigning and rebuilding legacy systems
(traditional old systems)


Managing Data Resources

Establishing an Information Policy
• An information policy states an organization’s
rules for managing and storing information
• Data administration is responsible for the specific
policies and procedures through which data can be
managed as a resource
• Large organizations use a database design and
management group to perform database
administration



Managing Data Resources

Ensuring Data Quality
• Poor data quality is a major obstacle to successful customer
relationship management (CRM)
• Data quality problems can be caused by redundant and
inconsistent data produced by multiple systems (in multiple not
linked systems some may be updated and some may not)
• Data input errors are the cause of many data quality problems
(they use check digits to improve the quality of data-entry)
• A data quality audit is a structured survey of the accuracy and
completeness of data
• Data cleansing detects and corrects incorrect, incomplete,
improperly formatted, and redundant data


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