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Managing Information Systems
Seventh Canadian Edition
Laudon, Laudon and Brabston
CHAPTER 6
Databases and Information
Management
Copyright © 2015 Pearson Canada Inc.
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Organizing Data in a Traditional File
Environment
File organization terms and concepts
• Bit: Smallest unit of data; binary digit (0,1)
• Byte: Group of bits that represents a single character
• Field: Group of words or a complete number
• Record: Group of related fields
• File: Group of records of same type
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Organizing Data in a Traditional File
Environment
File Organization Term and Concepts
• Database: Group of related files
• 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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Problems with the Traditional File
Environment
• Data redundancy and inconsistency
• Program-data dependence
• Lack of flexibility
• Poor security
• Lack of data sharing and availability
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Problems with the Traditional File Environment:
Data Redundancy & Inconsistency
• Data redundancy: The presence of duplicate data in
multiple data files so that the same data are stored in
more than one place or location
• Data inconsistency: The same attribute may have
different values.
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Problems with the Traditional File Environment:
Program-Data Dependence
• The coupling of data stored in files and the
specific programs required to update and
maintain those files such that changes in
programs require changes to the data
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Problems with the Traditional File Environment:
Lack of Flexibility
• A traditional file system can deliver routine
scheduled reports after extensive programming
efforts, but it cannot deliver ad-hoc reports or
respond to unanticipated information requirements
in a timely fashion
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Problems with the Traditional File Environment:
Security & Data Sharing
• Poor security
• Management may have no knowledge of who is
accessing or making changes to the organization’s
data
• Lack of data sharing and availability
• Information cannot flow freely across different
functional areas or different parts of the
organization.
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The Database Approach to Data
Management
Database management systems
• How a DBMS solves the problems of the traditional
file environment
• Relational DBMS
• Operations of a relational DBMS
• Hierarchical and network DBMS
• Object-oriented DBMS
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Relational DBMS
• Represents data as two-dimensional tables called
relations (a group of records makes a table (file) and
a group of values for the set of fields makes a record
(tuple, row); a field name labels each column of the
table))
• Relates data across tables based on common data
element (a group of tables (files) makes a database)
• Examples: Access, DB2, Oracle, MS SQL Server
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Operations of a Relational DBMS
• Select: Creates subset of rows that meet specific
criteria
• Join: Combines relational tables to provide users with
information (requires a field in common between the
tables being joined)
• Project: Create a subset consisting of certain columns
of the table; enables users to have new tables
containing only relevant information
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Capabilities of Database
Management Systems
• Data Definition Language:
• Specifies structure of the database
• Data Dictionary
• Stores definition of data elements and their
characteristics
• Querying and Reporting
• Data manipulation language
• Structured query language (SQL)
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Designing Databases
• Conceptual design: Abstract model of database from
a business perspective
• Physical design: Detailed description of business
information needs
• 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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Business Intelligence Infrastructure
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
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Business Intelligence Infrastructure
Hadoop
• Designed to handle big data
• enables distributed parallel processing of huge
amounts of data across inexpensive computers
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Tools for Business Intelligence
• Tools for consolidating, analyzing, and providing access
to vast amounts of data to help users make better
business decisions
• Tools include:
• Software for database query and reporting
• Online analytical processing (OLAP)
• Data mining
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Data Mining
• Tools for analyzing large pools of data
• Find hidden patterns and infer rules to predict trends
– Associations
– Sequences
– Classifications
– Clusters
– Forecasts
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Text Mining and Web Mining
Text Mining
• Extracts key elements from large unstructured
data sets (e.g., stored e-mails)
Sentiment analysis
• Mines text comments in an e-mail message, blog,
social media conversation, or survey to detect
favourable and unfavourable opinions about
specific subjects
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Text Mining and Web Mining
Web Mining
• The discovery and analysis of useful patterns and
information from the World Wide Web
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Managing Data Resources
Establishing an information policy
• Specifies the organization’s rules for sharing,
disseminating, acquiring, standardizing, classifying,
and inventorying information
• Data administration is responsible for specific
policies and procedures through which data is
managed
• Data governance
• Database administration
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Managing Data Resources
Ensuring Data Quality
• Data Quality Audit
– Structured survey of the accuracy and
completeness of data in an information system
• Data cleansing
– consists of activities for detecting and correcting
data in an information system
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Managing Information Systems
Seventh Canadian Edition
Laudon, Laudon and Brabston
CHAPTER 6
Databases and Information
Management
Copyright © 2015 Pearson Canada Inc.
6-28