Fundamentals of Information Systems, Second Edition 1 Organizing Data and Information
Fundamentals of Information Systems, Second Edition
1
Organizing Data and Information
Fundamentals of Information Systems, Second Edition
2
Principles and Learning Objectives
• The database approach to data management provides significant advantages over the traditional file-based approach.
Fundamentals of Information Systems, Second Edition
3
Principles and Learning Objectives
• A well-designed and well-managed database is an extremely valuable tool in supporting decision making.
Fundamentals of Information Systems, Second Edition
4
Principles and Learning Objectives
• Further improvements in the use of database technology will continue to evolve and yield real business benefits.
Fundamentals of Information Systems, Second Edition
5
The Traditional Approach To Data Management
Fundamentals of Information Systems, Second Edition
6
The Database Approach to Data Management
Fundamentals of Information Systems, Second Edition
7
Advantages of the Database Approach
Fundamentals of Information Systems, Second Edition
8
Disadvantages of the Database Approach
Fundamentals of Information Systems, Second Edition
9
Data Modeling
• Entity-relationship diagrams
Fundamentals of Information Systems, Second Edition
10
Entity-Relationship Diagram for a Customer Ordering Database
Fundamentals of Information Systems, Second Edition
11
Relational Database Model
Fundamentals of Information Systems, Second Edition
12
Relational Models
Describe data using a standard tabular format with all data elements placed in two-dimensional tables, called relations.
– Domain– Selecting– Projecting– Joining
Fundamentals of Information Systems, Second Edition
13
Linking Database Tables to Answer an Inquiry
Fundamentals of Information Systems, Second Edition
14
Database Management Systems
Fundamentals of Information Systems, Second Edition
15
Providing a User View
• Schema - a description of the entire database• Subschema - a file that contains a description
of a subset of the database and identifies which users can modify the data items in that subset
Fundamentals of Information Systems, Second Edition
16
The Use of Schemas and Subschemas
Fundamentals of Information Systems, Second Edition
17
Creating and Modifying the Database
• Data definition language (DDL) - a collection of instructions and commands used to define and describe data and data relationships in a specific database
• Data dictionary – detailed description of data in a database
Fundamentals of Information Systems, Second Edition
18
Typical Uses of a Data Dictionary
• Provide a standard definition of terms and data elements• Assist programmers in designing and writing programs• Simplify database modification• Reduce data redundancy• Increase data reliability• Speed program development• Ease modification of data and information
Fundamentals of Information Systems, Second Edition
19
Storing and Retrieving Data
Fundamentals of Information Systems, Second Edition
20
Structured Query Language
Fundamentals of Information Systems, Second Edition
21
Database Output
Fundamentals of Information Systems, Second Edition
22
Database Applications
Fundamentals of Information Systems, Second Edition
23
Data Warehouses, Data Marts, and Data Mining
• Data Warehouse - a database that collects business information from many sources in the enterprise, covering all aspects of the company’s processes, products, and customers.
• Data Mart – a subset of a data warehouse.
• Data Mining - an information analysis tool that involves the automated discovery of patterns and relationships in a data warehouse.
Fundamentals of Information Systems, Second Edition
24
Elements of a Data Warehouse
Fundamentals of Information Systems, Second Edition
25
Common Data Mining Applications
Fundamentals of Information Systems, Second Edition
26
Business Intelligence
Gathering enough of the right information in a timely manner and usable form.
– Competitive intelligence– Counterintelligence– Knowledge management
Fundamentals of Information Systems, Second Edition
27
Comparison of OLAP and Data Mining