6 - Foundations of BI: Database & Info Mgmt

Chapter 6

Foundations of
Business Intelligence:
Databases and
Information
Management
6.1

Management Information Systems
Chapter 6 Foundations of Business Intelligence: Databases
and Information Management
LEARNING OBJECTIVES

• Describe how the problems of managing data resources
in a traditional file environment are solved by a database
management system
• Describe the capabilities and value of a database
management system
• Apply important database design principles
• Evaluate tools and technologies for accessing
information from databases to improve business
performance and decision making
• Assess the role of information policy, data administration,
and data quality assurance in the management of firm’s
data resources
6.2

Can HP Mine Success from an Enterprise Data Warehouse?

• Problem: HP’s numerous systems unable to deliver the
information needed for a complete picture of business
operations, lack of data consistency
• Solutions: Build a data warehouse with a single global
enterprise-wide database; replacing 17 database
technologies and 14,000 databases in use
• Created consistent data models for all enterprise data
and proprietary platform
• Demonstrates importance of database management in
creating timely, accurate data and reports
• Illustrates need to standardize how data from disparate
sources are stored, organized, and managed

6.3

Organizing Data in a Traditional File Environment

• File organization concepts
• Computer system organizes data in a hierarchy
• Field: Group of characters as word(s) or number
• Record: Group of related fields
• File: Group of records of same type
• Database: Group of related files
• Record: Describes an entity
• Entity: Person, place, thing on which we store
information
• Attribute: Each characteristic, or quality, describing entity
• E.g., Attributes Date or Grade belong to entity COURSE

6.4


The Data Hierarchy

A computer system
organizes data in a
hierarchy that starts with the
bit, which represents either
a 0 or a 1. Bits can be
grouped to form a byte to
represent one character,
number, or symbol. Bytes
can be grouped to form a
field, and related fields can
be grouped to form a record.
Related records can be
collected to form a file, and
related files can be
organized into a database.

Figure 6-1
6.5


• Problems with the traditional file environment (files
maintained separately by different departments)
• Data redundancy and inconsistency
• Data redundancy: Presence of duplicate data in multiple files
• Data inconsistency: Same attribute has different values
• Program-data dependence:
• When changes in program requires changes to data accessed by
program
• Lack of flexibility
• Poor security
• Lack of data sharing and availability

6.6


Traditional File Processing

The use of a traditional approach to file processing encourages each functional area in a corporation to
develop specialized applications and files. Each application requires a unique data file that is likely to be a
subset of the master file. These subsets of the master file lead to data redundancy and inconsistency,
processing inflexibility, and wasted storage resources.

Figure 6-2
6.7

The Database Approach to Data Management

• Database
• Collection of data organized to serve many applications by
centralizing data and controlling redundant data
• Database management system
• Interfaces between application programs and physical data files
• Separates logical and physical views of data
• Solves problems of traditional file environment
• Controls redundancy
• Eliminates inconsistency
• Uncouples programs and data
• Enables organization to central manage data and data security

6.8

Advantages

DBMS

6.9

Better Data Accessibility

• DBMS allows on-line access by users by
providing query languages that permit
users to ask questions and obtain
information rather then waiting for
programmers.

6.10

Economy of scale
• When all the organization’s data requiremnets are
satisfied by oen data base instead of many separate files,
the size of the combined operation provides several
advantages, like , the portion of the budget that would
ordinarily be allocated to various departments for their
data design,storage and maintenance costs can be pooled,
possibly resources may be used to purchase a more
sophisticated and powerful system than any department
could afford individually. Programming time that would
ordinarily be devoted to designing fiels can be spent on
improving that database.

6.11

More Control Over Concurrency

• In a file system if two users are permitted
to access data simultaneously or both
attempt to perform updates, they wil
interfere with each other and one might
overwrite the value recorded by the other.
A DBMS have subsystems to control
concurrency so that transactions are not
lost or performed incorrectly.

6.12

Better Backup & Recovery
Procedure
• A backup tape is maintained in a databse
environment which is supplemented by a
lot of changes. Whenever the data base is
modified, a log entry is made. If the system
fails, the tape and log are used to bring he
data in original state it was in before
failure.

6.13

Disadvantages of Database

6.14

High Cost of DBMS

• Because a complete DBMS is a very large
and sophisticated piece of software, it is
expensive to purchase or lease/rent.

6.15

High Hardware Cost

• Additional memory and processing power
may be required to run the DBMS,
resulting in the need to upgrade the
hardware.

6.16

High Programming Cost

• Because a DBMS is a complex tool with
many features. It requires experienced
programmers with through knowledge
resulting in extra payment for their hire
and expertise.

6.17

High Conversion Costs

• When an organization converts to a data
base system, data has to be removed from
files and loaded into the database which
may be difficult and time consuming
process.

6.18

Slower processing in some
applications
• Although integrated database in desinged
to provide better information, certain
applications may be slower due to the
integration of data.

6.19

Increased Vulnerabilities
( Central Dependency)
• Since all applications depend on database
system, the failure or any component can
bring operations to a standstill.

6.20

More Difficult Recovery

• The recovery process after a system failure
is more complicated because the system
must determine which transactions were
completed and which were in progress at
the time of failure. The fact that a database
allows users to make updated concurrently
further complicated the recovery the
recovery process.

6.21

When not to use a DBMS

• Main Costs of using a DBMS
• High initial investment in hardware,
software, training
• Overhead for providing generality, secutiry
recovery, integrity and concurrency
control.
• Generality that a DBMS provides for
defining and processing data….

6.22

….
• When a DBMS may be unnecessary
• If the database and applications are simple,
well defined, and not expected to change.
• If there are stringent real-time
requirements that may not be met because
of DBMS overhead.
• If access to data by multiple users is not
required.

6.23


Human Resources Database with Multiple Views

A single human resources database provides many different views of data, depending on the information
requirements of the user. Illustrated here are two possible views, one of interest to a benefits specialist and
one of interest to a member of the company’s payroll department.

Figure 6-3
6.24


• Relational DBMS
• Represent data as two-dimensional tables called relations or files
• Each table contains data on entity and attributes
• Table: grid of columns and rows
• Rows (tuples): Records for different entities
• Fields (columns): Represents attribute for entity
• Key field: Field used to uniquely identify each record
• Primary key: Field in table used for key fields
• Foreign key: Primary key used in second table as look-up field to
identify records from original table

6.25

The goal of a relation database
design
The goal is to generate a set of relation
schemes that allow us to store information
without redundant data and allow us to
retrieve information easily and efficiently.
The approach followed is to design
schemes that are in an appropriate form
one of the so called Normal Form.

6.26 2-26


Relational Database Tables

A relational database organizes data in the form of two-dimensional tables. Illustrated here are tables for
the entities SUPPLIER and PART showing how they represent each entity and its attributes.
Supplier_Number is a primary key for the SUPPLIER table and a foreign key for the PART table.

Figure 6-4A
6.27


Relational Database Tables (cont.)

Figure 6-4B
6.28


• Operations of a Relational DBMS
• Three basic operations used to develop useful sets of data
• SELECT: Creates subset of data of all records that
meet stated criteria
• JOIN: Combines relational tables to provide user with
more information than available in individual tables
• PROJECT: Creates subset of columns in table,
creating tables with only the information specified

6.29


The Three Basic Operations of a Relational DBMS

The select, project, and join operations enable data from two different tables to be combined and only
selected attributes to be displayed.

Figure 6-5
6.30


• Object-Oriented DBMS (OODBMS)
• Stores data and procedures as objects
• Capable of managing graphics, multimedia, Java
applets
• Relatively slow compared with relational DBMS for
processing large numbers of transactions
• Hybrid object-relational DBMS: Provide capabilities
of both OODBMS and relational DBMS

6.31


• Capabilities of Database Management Systems
• Data definition capability: Specifies structure of database
content, used to create tables and define characteristics of fields
• Data dictionary: Automated or manual file storing definitions of
data elements and their characteristics
• Data manipulation language: Used to add, change, delete,
retrieve data from database
• Structured Query Language (SQL)
• Microsoft Access user tools for generation SQL
• Many DBMS have report generation capabilities for creating
polished reports (Crystal Reports)

6.32


Microsoft Access Data Dictionary Features

Figure 6-6
Microsoft Access has a
rudimentary data dictionary
capability that displays
information about the size,
format, and other
characteristics of each field
in a database. Displayed
here is the information
maintained in the SUPPLIER
table. The small key icon to
the left of Supplier_Number
indicates that it is a key field.

6.33


Example of an SQL Query

Illustrated here are the SQL statements for a query to select suppliers for parts 137 or 150. They produce a
list with the same results as Figure 6-5.

Figure 6-7
6.34


An Access Query

Illustrated here is how the query in Figure 6-7 would be constructed using query-building tools in the
Access Query Design View. It shows the tables, fields, and selection criteria used for the query.

Figure 6-8
6.35


• Designing Databases
• Conceptual (logical) design: abstract model from business
perspective
• Physical design: How database is arranged on direct-access
storage devices
• Design process identifies
• Relationships among data elements, redundant database
elements
• Most efficient way to group data elements to meet business
requirements, needs of application programs
• Normalization
• Streamlining complex groupings of data to minimize redundant
data elements and awkward many-to-many relationships

6.36

Normalization

• The process of refining the data model
built by the Entity-Relationship diagram.
The Normalization technique, logically
groups the data over a number of tables,
which are independent and contain no
duplicate data. The entities or tables
resulting from normalization contain
simple data items, with relationships being
represented by replication of key data
item(s).

6.37 2-37

Steps towards Normalization

• 1. To convert The E-R Model into
Tables/Relations.
• 2. Examine the tables for redundancy and if
necessary, change them to non redundant
forms.
• 3. This non-redundant model is then
converted to a database definition, which
achieve the objective of the Database
Design Phase.

6.38 2-38

So Normalization is

• Refinement of the E-R Model

• Segregation of data over many entities or
tables

• Normalized model converted to physical
database tables.

6.39 2-39

Need For Normalization

• Improves database design
• Ensures minimum redundancy of data
• Reduces need to reorganize data when
design is modified/enhanced
• Removes anomalies for database activities

6.40 2-40

…

• The tables derived from the E-R Models
may be complex attributes, which have
to be removed. The process of
normalization also removes any
undesirable consequences, resulting
from database activities like inserting,
updating and deleting values in a
table.Storage space may also be
conserved, thus resulting in a more
efficient database.
6.41 2-41

Unnormalized Data Structure

• Unnormalized data structure contains
redundant and disorganized data, which
needs to be organized, by dividing the data
over several, tables to avoid redundancy.
This is achieved by gong through the
process of normalization.
• Example……

6.42 2-42

Suppli Supplier Name Suppli City Part Part W Qty City Design
er er Supplier Cod Name eig
Code Status e ht

SO1 RAM 15 CALCUTTA 101 SCRE 8 500 MUMBAI
W

SO2 SHYAM 20 CHENNAI 102 NUT 16 200 HYDERABA
D

SO3 AMIT 25 HYDERABA 103 BOLT 20 250 CHENNAI
D

SO4 RAJESH 15 MEMBAI 104 NUT 16 300 MUMBAI

SO5 SUNDER 25 HYDERABA 105 BOLT 20 50 CALCUTTA
D

SO6 MURLIDHARAN 15 JAIPUR 106 NUT 16 200 CALCUTTA

6.43 2-43

The Above table presents several
difficulties in operations like..
• Insertion of Fields: If a new field is
introduced into the system, it cannot be
added to the database, until it becomes
related to another existing field, eg if a
supplier name is introduced, its details
cannot be entered in the table unless a part
name is present for that supplier.

6.44 2-44

• Updation of Fields : If the supplier code of
a supplier is to be modified , it has to be
changed throughout the table, in all
occurrences of the supplier record. Missing
out even a single correction, would result
in inaccurate data.

6.45 2-45

• Deletion of Fields : if information related to
a specific column is to be deleted, the
entire row has to be deleted, which results
in loss of required information. For
example, if the row of the supplier, ‘Amit’ is
deleted, the information about the ‘Part
Name’ is lost. Deletion of supplier details
also deletes other details.

6.46 2-46

Relational Data Integrity

• Candidate Key: Most of the relations have
an attribute, which can uniquely identify
each tuple in a the relation. In some .cases
attribute is called the CANDIDATE KEY.
Candidate Key: Is an attribute that can
uniquely identify each tuple in the relation.

6.47 2-47

Steps in Normalization

• First Normal Form (1NF) : Underline
domain should have atomic values ie
there should be no nested relations.
• Identify repeating groups of fields
• Remove repeating to a separate table
• Identify the keys for the tables
• Key of parent table is brought as part of
the concatenated key of the second
table.
6.48 2-48

Second Normal Form (2NF)
• No subset of primary key should identify a
record uniquely.
• Check if all fields are dependent on the
whole key.
• Remove fields that depend on part of the
key
• Group partially dependent fields as a
separate table.
• Name the tables
• Identify key(s) to the tables(s)

6.49 2-49

Third Normal Form (3NF)
• All the non-key fields of the table are
independent of all other non-key fields of the
same table. There should be no transitive-
dependency of non-key fields on other non-key
fields. OR All the non-key attributes of
the table are independent of all other
non-key attributes of the same table.
• Remove fields that
– Depend on other non-key fields.
– Can be calculated or derived from logic
• Group interdependent fields as separate tables,
identify the key and names the table
6.50 2-50


An Unnormalized Relation for Order

An unnormalized relation contains repeating groups. For example, there can be many parts and suppliers
for each order. There is only a one-to-one correspondence between Order_Number and Order_Date.

Figure 6-9
6.51


Normalized Tables Created from Order

After normalization, the original relation ORDER has been broken down into four smaller relations. The
relation ORDER is left with only two attributes and the relation LINE_ITEM has a combined, or
concatenated, key consisting of Order_Number and Part_Number.

Figure 6-10
6.52


• Entity-relationship diagram
• Used by database designers to document the data model
• Illustrates relationships between entities
• Distributing databases: Storing database in more than
one place
• Partitioned: Separate locations store different parts of database
• Replicated: Central database duplicated in entirety at different
locations

6.53


An Entity-Relationship Diagram

This diagram shows the relationships between the entities ORDER, LINE_ITEM, PART, and SUPPLIER that
might be used to model the database in Figure 6-10.

Figure 6-11
6.54


• Distributing databases
• Two main methods of distributing a database
• Partitioned: Separate locations store different parts of
database
• Replicated: Central database duplicated in entirety at
different locations
• Advantages
• Reduced vulnerability
• Increased responsiveness
• Drawbacks
• Departures from using standard definitions
• Security problems
6.55


Distributed Databases

There are alternative ways of distributing a database. The central database can be partitioned (a) so that each remote
processor has the necessary data to serve its own local needs. The central database also can be replicated (b) at all remote
locations.

Figure 6-12
6.56

Using Databases to Improve Business Performance and Decision Making

• Very large databases and systems require special
capabilities, tools
• To analyze large quantities of data
• To access data from multiple systems
• Three key techniques
• Data warehousing
• Data mining
• Tools for accessing internal databases through the Web

6.57


• Data warehouse:
• Stores current and historical data from many core operational
transaction systems
• Consolidates and standardizes information for use across
enterprise, but data cannot be altered
• Data warehouse system will provide query, analysis, and reporting
tools
• Data marts:
• Subset of data warehouse
• Summarized or highly focused portion of firm’s data for use by
specific population of users
• Typically focuses on single subject or line of business

6.58


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. The information directory provides users
with information about the data available in the warehouse.

Figure 6-13
6.59


The IRS Uncovers Tax Fraud with a Data Warehouse

• Read the Interactive Session: Organizations, and then
discuss the following questions:
• Why was it so difficult for the IRS to analyze the taxpayer data
it had collected?
• What kind of challenges did the IRS encounter when
implementing its CDW? What management, organization, and
technology issues had to be addressed?
• How did the CDW improve decision making and operations at
the IRS? Are there benefits to taxpayers?
• Do you think data warehouses could be useful in other areas
of the federal sector? Which ones? Why or why not?

6.60


• Business Intelligence:
• Tools for consolidating, analyzing, and providing access
to vast amounts of data to help users make better
business decisions
• E.g., Harrah’s Entertainment analyzes customers to
develop gambling profiles and identify most profitable
customers
• Principle tools include:
• Software for database query and reporting
• Online analytical processing (OLAP)
• Data mining

6.61


Business Intelligence

Figure 6-14
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.

6.62


• Online analytical processing (OLAP)
• Supports multidimensional data analysis
• Viewing data using multiple dimensions
• Each aspect of information (product, pricing, cost,
region, time period) is different dimension
• E.g., how many washers sold in East in June
compared with other regions?
• OLAP enables rapid, online answers to ad hoc queries

6.63


Multidimensional Data Model

Figure 6-15
The view that is showing is
product versus region. If
you rotate the cube 90
degrees, the face that will
show is product versus
actual and projected sales. If
you rotate the cube 90
degrees again, you will see
region versus actual and
projected sales. Other views
are possible.

6.64


• Data mining:
• More discovery driven than OLAP
• Finds hidden patterns, relationships in large databases and
infers rules to predict future behavior
• E.g., Finding patterns in customer data for one-to-one
marketing campaigns or to identify profitable customers.
• Types of information obtainable from data mining
• Associations
• Sequences
• Classification
• Clustering
• Forecasting

6.65


• Predictive analysis
• Uses data mining techniques, historical data, and
assumptions about future conditions to predict
outcomes of events
• E.g., Probability a customer will respond to an offer or
purchase a specific product
• Text mining
• Extracts key elements from large unstructured data sets
(e.g., stored e-mails)

6.66


• Web mining
• Discovery and analysis of useful patterns and information
from WWW
• E.g., to understand customer behavior, evaluate
effectiveness of Web site, etc.
• Techniques
• Web content mining
• Knowledge extracted from content of Web pages
• Web structure mining
• E.g., links to and from Web page
• Web usage mining
• User interaction data recorded by Web server

6.67


• Databases and the Web
• Many companies use Web to make some internal
databases available to customers or partners
• Typical configuration includes:
• Web server
• Application server/middleware/CGI scripts
• Database server (hosting DBM)
• Advantages of using Web for database access:
• Ease of use of browser software
• Web interface requires few or no changes to database
• Inexpensive to add Web interface to system

6.68


Linking Internal Databases to the Web

Users access an organization’s internal database through the
Web using their desktop PCs and Web browser software.

Figure 6-16
6.69

Managing Data Resources

The Databases Behind MySpace

• Read the Interactive Session: Technology, and then
discuss the following questions:
• What kind of databases and database servers does MySpace
use?
• Why is database technology so important for a business such
as MySpace?
• How effectively does MySpace organize and store the data on
its site?
• What data management problems have arisen? How has
MySpace solved or attempted to solve these problems?

6.70


• Establishing an information policy
• Firm’s rules, procedures, roles for sharing, managing, standardizing
data
• E.g., What employees are responsible for updating sensitive
employee information
• Data administration: Firm function responsible for specific policies
and procedures to manage data
• Data governance: Policies and processes for managing availability,
usability, integrity, and security of enterprise data, especially as it
relates to government regulations
• Database administration : Defining, organizing, implementing,
maintaining database; performed by database design and
management group

6.71


• Ensuring data quality
• More than 25% of critical data in Fortune 1000
company databases are inaccurate or incomplete
• Most data quality problems stem from faulty input
• Before new database in place, need to:
• Identify and correct faulty data
• Establish better routines for editing data once
database in operation

6.72


• Data quality audit:
• Structured survey of the accuracy and level of
completeness of the data in an information system
• Survey samples from data files, or
• Survey end users for perceptions of quality
• Data cleansing
• Software to detect and correct data that are incorrect,
incomplete, improperly formatted, or redundant
• Enforces consistency among different sets of data from
separate information systems

6.73

6 - Foundations of BI: Database & Info Mgmt

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6 - Foundations of BI: Database & Info Mgmt