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

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

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

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

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

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

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

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

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

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

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

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

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

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?

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

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

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

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

The Relational Database Model A Relational Database Model

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

Manipulating Data Linking Data Tables to Answer an Inquiry

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

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

Providing a User View The Use of Schemas and Subschemas

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

Creating and Modifying the Database A Typical Data Dictionary Entry

Storing and Retrieving Data Logical and Physical Access Paths

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

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

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

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

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

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

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

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

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

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