1. Mgt 20600: IT Management & Applications Databases
Tuesday
October 25, 2005

2. Reminders Reading For today
Strategies for Effective Data Storage and Management
For next week
Fundamentals text, Chapter 6, Information and Decision Support Systems
Homework
Database homework due this Friday by 5pm
Exams
Exam 1
Nice work for many of you!
If you didn’t do as well as you like, still many hundreds of points to go in the course
Suggestions
Always do reading ahead of time, not just before exam
Always come to class
Take the homeworks seriously and review them before the exams
Come and see me and Christine for help with exam review
Exam 2
Tuesday, November 8th
Covers networks and databases
Homeworks 3 and 4
Fundamentals text chapters 3 and 4
Strategies for Effective Data Storage and Management online reading
Lectures on networks and databases
75 points
Same kind of mix of questions as on first exam
In class next week: Decision Support Systems

3. Databases
A well-designed and well-managed database is an extremely valuable tool in supporting decision making
Databases are key corporate assets
Databases are the foundation for sophisticated analyses that provide business intelligence
What new products to design
How to market to particular customer groups
Which customer groups are the most profitable

4. Traditional Approach to Data Management
Traditional approach: separate data files are created for each application
Results in data redundancy (duplication)
Data redundancy conflicts with data integrity

5. Database Approach to Data Management
Database approach: pool of related data is shared by multiple applications
Significant advantages over traditional approach

6. Using Databases with Other Software
Database management systems are often used with other software packages or the Internet
A database management system can act as a front-end application or a back-end application
Front-end application: interacts with users
Back-end application: interacts with applications

7. Advantages of Database Approach
Improved strategic use of data
Reduced data redundancy
Improved data integrity
Easier modification and updating
Data and program independence
Better access to data and information
Standardization of data access
A framework for program development
Better overall protection of the data
Shared data and information resources

8. Disadvantages of the Database Approach
More complexity
More difficult to recover from a failure
More expensive

9. Distributed Databases
Data may be spread across several smaller databases connected via telecommunications devices
Corporations get more flexibility in how databases are organized and used
Replicated database
Holds a duplicate set of frequently used data

10. Databases Databases must contain Accurate information
Right kinds of information
Current information
Information from all organizational functions

11. Database Data Data regarding Important entities
Customers
Suppliers
Transactions
Each entity will have a number of attributes about which you want to collect and store information
Customer address
Customer phone number
Customer account number

12. Entities, Attributes, Keys
Entity: a generalized class of people, places, or things (objects) for which data is collected, stored, and maintained (Table and records)
Attribute: a characteristic of an entity (fields)
Data item: a value of an attribute (fields)
Key: field(s) that identify a record
Primary key: field(s) that uniquely identify a record

13. Hierarchy of Data
Field: name, number, or characters that describe an aspect of a business
object or activity
Record: a collection of related data fields
File: a collection of related records
Database: a collection of integrated and related files

14. Data Modeling and the Relational Database Model
When building a database, consider:
Content: What data should be collected, at what cost?
Access: What data should be provided to which users, and when?
Logical structure: How should data be arranged to make sense to a given user?
Physical organization: Where should data be physically located?

15. Data Modeling Building a database requires two types of design
Logical design
Shows an abstract model of how data should be structured and arranged to meet an organization’s information needs
Physical design
Fine-tunes the logical database design for performance and cost considerations

16. Data Modeling
Data model: a diagram of data entities and their relationships
Entity-relationship (ER) diagrams: data models that use basic graphical symbols to show the organization of and relationships between data

17. Data Modeling
An Entity-Relationship (ER) Diagram for a Customer Order Database

18. The Relational Database Model
Relational model: all data elements are placed in two-dimensional tables (relations), which are the logical equivalent of files
In the relational model:
Each table represents a data entity
Each row of a table represents a specific instance of a data entity
Columns of the table represent attributes

19. The Relational Database Model
A Relational Database Model

20. Manipulating Data Selecting: eliminates rows according to criteria
Projecting: eliminates columns in a table
Joining: combines two or more tables
Linking: relates or links two or more tables using common data attributes

21. Manipulating Data
Linking Data Tables to Answer an Inquiry

22. Database Management Systems (DBMS)
Interface between
Database and application programs
Database and the user
Database types
Flat file
Single user
Multiple users

23. Providing a User View Schema: description of the entire database
User view: user-accessible portion of the database
Subschema
Contains a description of a subset of the database
Identifies which users can view and modify the data items in the subset
Is used to create different user views

24. Providing a User View
The Use of Schemas and Subschemas

25. Creating and Modifying the Database
Data definition language (DDL)
Collection of instructions/commands that define and describe data and data relationships in a database
Allows database creator to describe the data and the data relationships that are to be contained in the schema and the subschemas
Data dictionary: a detailed description of all the data used in the database

26. Creating and Modifying the Database
A Typical Data Dictionary Entry

27. Storing and Retrieving Data
Logical and Physical Access Paths

28. Manipulating Data and Generating Reports
Query-By-Example (QBE): a visual approach to developing database queries or requests
Data manipulation language (DML): commands that manipulate the data in a database
Structured Query Language (SQL): ANSI standard query language for relational databases
Database programs can produce reports, documents, and other outputs

29. Database Administration
Database administrator (DBA): directs or performs all activities to maintain a database environment
Designing, implementing, and maintaining the database system and the DBMS
Establishing policies and procedures
Training employees

30. Selecting a Database Management System
Important characteristics of databases to consider:
Size of the database
Number of concurrent users
Performance
Ability to be integrated with other systems
Features of the DBMS
Vendor considerations
Cost of the system

31. Object-Oriented Database Management Systems
Stores both data and its processing instructions
Method: a procedure or action
Message: a request to execute or run a method

32. Object-Relational Database Management Systems
Object-relational database management system (ORDBMS)
A DBMS capable of manipulating audio, video, and graphical data

33. Data Warehouses, Data Marts, and Data Mining
Data warehouse: collects business information from many sources in the enterprise
Data mart: a subset of a data warehouse
Data mining: an information-analysis tool for discovering patterns and relationships in a data warehouse or a data mart

34. Data Warehouses, Data Marts, and Data Mining
Elements of a Data Warehouse

35. Data Warehouses, Data Marts, and Data Mining
Common Data-Mining Applications

36. Online Analytical Processing (OLAP)
Software that allows users to explore data from a number of different perspectives
Comparison of OLAP and Data Mining

37. Business Intelligence
Business intelligence (BI): gathering the right information in a timely manner and usable form and analyzing it to have a positive impact on business
Knowledge management: capturing a company’s collective expertise and distributing it wherever it can help produce the biggest payoff