Foundations of Business Intelligence: Databases and Information Management

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

• Describe basic file organization concepts and the
  problems of managing data resources in a
  traditional file environment.
• Describe the principles of a database management
  system and the features of a relational database.
• Apply important database design principles.

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Management Information Systems Foundations of
Business Intelligence Databases and Information
Management
LEARNING OBJECTIVES (contd)

• Evaluate tools and technologies for providing
  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 organizational data resources.

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Management Information Systems Foundations of
Business Intelligence Databases and Information
Management
Nascar Races to Manage Its Data

• Problem Gaining knowledge of customers and
  making effective use of fragmented customer data.
• Solutions Use relational database technology to
  increase revenue and productivity.
• Data access rules and a comprehensive customer
  database consolidate customer data.
• Demonstrates ITs role in creating customer
  intimacy and stabilizing infrastructure.
• Illustrates digital technologys role in
  standardizing how data from disparate sources are
  stored, organized, and managed.

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ORGANIZING DATA IN A TRADITIONAL FILE ENVIRONMENT
File Organization Terms and Concepts

• Entity Person, place, thing, event about which
  information is maintained
• Attribute Description of a particular entity
• Key field Identifier field used to retrieve,
  update, sort a record

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ORGANIZING DATA IN A TRADITIONAL FILE ENVIRONMENT
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• Select Works on single table and takes rows that
  meet a specified condition, copy them into a new
  table
• (Table name)
• Condition(s)
• SQL (Structured Query language)
• SELECT
• FROM (table name)
• WHERE condition 1
• AND condition 2
• AND condition 3

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ORGANIZING DATA IN A TRADITIONAL FILE ENVIRONMENT
Problems with the Traditional File Environment

• Data redundancy
• Program-Data dependence
• Lack of flexibility
• Poor security
• Lack of data-sharing and availability

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THE DATABASE APPROACH TO DATA MANAGEMENT
Database Management System (DBMS)

• Creates and maintains databases
• Eliminates requirement for data definition
  statements
• Acts as interface between application programs
  and physical data files
• Separates logical and physical views of data

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THE DATABASE APPROACH TO DATA MANAGEMENT
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THE DATABASE APPROACH TO DATA MANAGEMENT
Components of DBMS

• Data definition language Specifies content and
  structure of database and defines each data
  element
• Data manipulation language
• Manipulates data in a database
• Data dictionary Stores definitions of data
  elements, and data characteristics

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THE DATABASE APPROACH TO DATA MANAGEMENT
Types of Databases

• Relational DBMS
• Hierarchical and Network DBMS
• Object-Oriented Databases

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THE DATABASE APPROACH TO DATA MANAGEMENT
Relational DBMS

• Represents data as two-dimensional tables called
  relations
• Relates data across tables based on common data
  element
• Examples DB2, Oracle, MS SQL Server

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THE DATABASE APPROACH TO DATA MANAGEMENT
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THE DATABASE APPROACH TO DATA MANAGEMENT
Hierarchical and Network DBMS

• Hierarchical DBMS
• Organizes data in a tree-like structure
• Supports one-to-many parent-child relationships
• Prevalent in large legacy systems

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THE DATABASE APPROACH TO DATA MANAGEMENT
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THE DATABASE APPROACH TO DATA MANAGEMENT
Hierarchical and Network DBMS

• Network DBMS
• Depicts data logically as many-to-many
  relationships

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THE DATABASE APPROACH TO DATA MANAGEMENT
Network DBMS
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THE DATABASE APPROACH TO DATA MANAGEMENT
Hierarchical and Network DBMS

• Disadvantages
• Outdated
• Less flexible compared to RDBMS
• Lack support for ad-hoc and English language-like
  queries

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THE DATABASE APPROACH TO DATA MANAGEMENT
Object-Oriented Databases

• Object-oriented DBMS Stores data and procedures
  as objects that can be retrieved and shared
  automatically
• Object-relational DBMS Provides capabilities of
  both object-oriented and relational DBMS

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THE DATABASE APPROACH TO DATA MANAGEMENT
Three Basic Operations in a Relational Database

• Select Creates subset of rows that meet specific
  criteria
• Join Combines relational tables to provide users
  with information
• Project Enables users to create new tables
  containing only relevant information

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THE DATABASE APPROACH TO DATA MANAGEMENT
Querying Databases Elements of SQL

• Basic SQL Commands
• SELECT Specifies columns
• FROM Identifies tables or views
• WHERE Specifies conditions
• Example Access database

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THE DATABASE APPROACH TO DATA MANAGEMENT
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THE DATABASE APPROACH TO DATA MANAGEMENT
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CREATING A DATABASE ENVIRONMENT
Designing Databases

• Conceptual design Abstract model of database
  from a business perspective
• Physical design Detailed description of business
  information needs

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CREATING A DATABASE ENVIRONMENT
Designing Databases

• Entity-relationship diagram Methodology for
  documenting databases illustrating relationships
  between database entities
• Normalization Process of creating small stable
  data structures from complex groups of data

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CREATING A DATABASE ENVIRONMENT
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CREATING A DATABASE ENVIRONMENT
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CREATING A DATABASE ENVIRONMENT
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CREATING A DATABASE ENVIRONMENT
Distributing Databases

• Centralized database
• Used by single central processor or multiple
  processors in client/server network

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CREATING A DATABASE ENVIRONMENT
Distributing Databases

• Distributed database
• Stored in more than one physical location
• Partitioned database
• Duplicated database

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CREATING A DATABASE ENVIRONMENT
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CREATING A DATABASE ENVIRONMENT
Management Requirements for Database Systems

• Key elements in a database environment
• Data Administration
• Data Planning and Modeling Methodology
• Database Technology and Management
• Users

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CREATING A DATABASE ENVIRONMENT
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DATABASE TRENDS
Multidimensional Data Analysis

• On-line analytical processing (OLAP)
• Multidimensional data analysis
• Supports manipulation and analysis of large
  volumes of data from multiple dimensions/perspecti
  ves

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DATABASE TRENDS
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DATABASE TRENDS
Data Warehousing and Datamining

• Data warehouse
• Supports reporting and query tools
• Stores current and historical data
• Consolidates data for management analysis and
  decision making

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DATABASE TRENDS
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DATABASE TRENDS
Data Warehousing and Datamining

• Data mart
• Subset of data warehouse
• Contains summarized or highly focused portion of
  data for a specified function or group of users

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DATABASE TRENDS
Data Warehousing and Datamining

• Datamining association, sequence,
  classification, prediction, clustering.
• Tools for analyzing large pools of data
• Find hidden patterns and infer rules to predict
  trends

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DATABASE TRENDS
Benefits of Data Warehouses

• Improved and easy accessibility to information
• Ability to model and remodel the data

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DATABASE TRENDS
Databases and the Web

• Hypermedia database
• Organizes data as network of nodes
• Links nodes in pattern specified by user
• Supports text, graphic, sound, video and
  executable programs

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Management Information Systems Chapter 6
Foundations of Business Intelligence Databases
and Information Management
Using Databases to Improve Business Performance
and Decision Making
DNA Databases Crime-Fighting Weapon or Threat to
Privacy?

• Read the Interactive Session Management, and
  then discuss the following questions
• What are the benefits of DNA databases?
• What problems do DNA databases pose?
• Who should be included in a national DNA
  database? Should it be limited to convicted
  felons? Explain your answer.
• Who should be able to use DNA databases?

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Management Information Systems Chapter 6
Foundations of Business Intelligence Databases
and Information Management
Managing Data Resources
What Can Be Done About Data Quality?

• Read the Interactive Session Management, and
  then discuss the following questions
• What was the impact of data quality problems on
  the companies described in this case study? What
  management, organization, and technology factors
  caused these problems?
• How did the companies described in this case
  solve their data quality problems? What
  management, organization, and technology issues
  had to be addressed?
• It has been said that the biggest obstacle to
  improving data quality is that business managers
  view data quality as a technical problem. Discuss
  how this statement applies to the companies
  described in this case study.