6 - Foundations of BI: Database & Info Mgmt

6.1

6 Chapter

Foundations of

Business Intelligence:

Databases and

Information

Management

6.2

LEARNING OBJECTIVES

Management Information Systems Chapter 6 Foundations of Business Intelligence: Databases

and Information Management

• 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

6.3

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



6.4

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



6.5

The Data Hierarchy

Figure 6-1

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.




6.6

• 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




6.7

Traditional File Processing

Figure 6-2

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.




6.8

• 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

The Database Approach to Data Management



6.9

Advantages

DBMS

6.10

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.

6.11

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.

6.12

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.

6.13

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.

6.14

Disadvantages of Database

6.15

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.

6.16

High Hardware Cost

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

6.17

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.

6.18

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.

6.19

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.

6.20

Increased Vulnerabilities ( Central Dependency)

• Since all applications depend on database system, the failure or any component can bring operations to a standstill.

6.21

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.

6.22

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….

6.23

…. • 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.

6.24



Figure 6-3

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.

Human Resources Database with Multiple Views


6.25

• 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




6.26 2-26

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.

6.27



Figure 6-4A

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.

Relational Database Tables


6.28



Figure 6-4B

Relational Database Tables (cont.)


6.29

• 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




6.30



Figure 6-5

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

selected attributes to be displayed.

The Three Basic Operations of a Relational DBMS


6.31

• 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




6.32

• 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)




6.33



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.

Microsoft Access Data Dictionary Features


6.34



Figure 6-7

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.

Example of an SQL Query


6.35



Figure 6-8

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.

An Access Query


6.36

• 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




6.37 2-37

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).

6.38 2-38

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.

6.39 2-39

So Normalization is

• Refinement of the E-R Model

• Segregation of data over many entities or tables

• Normalized model converted to physical database tables.

6.40 2-40

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

6.41 2-41

…

• 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.

6.42 2-42

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……

6.43 2-43

Suppli

er

Code

Supplier Name

Suppli

er

Status

City

Supplier

Part

Cod

e

Part

Name

W

eig

ht

Qty

City Design

SO1

RAM

15

CALCUTTA

101

SCRE

W

8

500

MUMBAI

SO2

SHYAM

20

CHENNAI

102

NUT

16

200

HYDERABA

D

SO3

AMIT

25

HYDERABA

D

103

BOLT

20

250

CHENNAI

SO4

RAJESH

15

MEMBAI

104

NUT

16

300

MUMBAI

SO5

SUNDER

25

HYDERABA

D

105

BOLT

20

50

CALCUTTA

SO6

MURLIDHARAN

15

JAIPUR

106

NUT

16

200

CALCUTTA

6.44 2-44

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.

6.45 2-45

• 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.

6.46 2-46

• 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.

6.47 2-47

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.

6.48 2-48

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.

6.49 2-49

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)

6.50 2-50

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

6.51



Figure 6-9

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.

An Unnormalized Relation for Order


6.52



Figure 6-10

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.

Normalized Tables Created from Order


6.53

• 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




6.54



Figure 6-11

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.

An Entity-Relationship Diagram


6.55

• 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




6.56

Distributed Databases

Figure 6-12

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.




6.57

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

6.58



• 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


6.59

Components of a Data Warehouse

Figure 6-13

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.




6.60

• 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?

The IRS Uncovers Tax Fraud with a Data Warehouse




6.61



• 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


6.62

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.




6.63



• 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


6.64

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.




6.65



• 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


6.66



• 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)


6.67



• 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


6.68



• 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


6.69

Linking Internal Databases to the Web

Figure 6-16

Users access an organization’s internal database through the

Web using their desktop PCs and Web browser software.




6.70

• 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?

The Databases Behind MySpace



Managing Data Resources

6.71


• 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



6.72

• 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




6.73

• 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


