Database environment and development

• Chapter 1 discusses the database environment and development process

Key Definitions

• Database: An organized collection of logically related data
• Data: Stored representations of meaningful objects and events, which can be structured (numbers, text, dates) or unstructured (images, video, documents)
• Information: Data processed to increase knowledge
• Metadata: Data that describes the properties and context of user data.

Disadvantages of File Processing

• Program-data dependence: All programs maintain metadata for each file they use
• Data duplication: Different systems/programs have separate copies of data
• Limited data sharing: No centralized control
• Lengthy development times: Programmers design own file formats
• Excessive program maintenance: 80% of information systems budget

Problems with Data Dependency

• Each application programmer must maintain their own data
• Application programs need to include code for the metadata of each file
• Each application program must have its own processing routines
• Lack of coordination and central control results in non-standard file formats

Problems with Data Redundancy

• Waste of space due to duplicate data
• Increased maintenance headaches
• Data changes in one file can cause inconsistencies, which compromises data integrity

The Database Approach

• A centralized repository of shared data is managed by a controlling agent
• Data is stored in a standardized, convenient form
• Requires a Database Management System (DBMS)

Database Management System (DBMS)

• Software to create, maintain, and provide controlled database access
• It manages employee data, orders, inventory, pricing, and customer data
• It manages data resources like an operating system manages hardware

Advantages of the Database Approach

• Program-data independence and planned data redundancy
• Improved data consistency, sharing, and quality
• Increased application development productivity
• Enforcement of standards
• Improved data accessibility and responsiveness
• Reduced program maintenance and improved decision support

Costs and Risks of the Database Approach

• Requires new, specialized personnel
• Installation and management complexity and cost
• Conversion costs
• A need for explicit backup and recovery
• Organizational conflict

Elements of the Database Approach

• Data models: Graphical systems capturing nature and relationship of data
  • Enterprise Data Model: High-level entities and relationships for the organization
  • Project Data Model: Detailed view, matching data structure in database or data warehouse
• Entities: Noun form describing a person, place, object, event, or concept composed of attributes
• Relationships: Between entities, usually one-to-many (1:M) or many-to-many (M:N)
• Relational Databases: Use tables (relations) representing entities and primary/foreign keys

Components of the Database Environment

• CASE Tools: Computer-aided software engineering, and Repository, a central metadata storehouse
• Database Management System (DBMS): Software for database management
• Database: Storehouse of data, and Application Programs, the software using the data
• User Interface: Text and graphical displays for users
• Data/Database Administrators: Personnel maintaining the database
• System Developers: Personnel designing databases and software
• End Users: People who use the applications and databases

Enterprise Data Model

• The first step in database development
• Specifies scope and general content
• An overall picture of organizational data at high level of abstraction
• Includes an entity-relationship diagram, descriptions of entity types, relationships between entities, and business rules

Database and IS Development Approaches

• System Development Life Cycle (SDLC):
  • Detailed, well-planned, time-consuming, and involves a long development cycle
• Prototyping:
  • Rapid application development (RAD)
  • Involves a cursory attempt at conceptual data modeling, and defines the database during initial prototype development, repeating implementation and maintenance activities with new prototype versions

Systems Development Life Cycle Phases

• Planning: Preliminary understanding and a request for study is the deliverable
• Analysis: Thorough requirements analysis and structuring for functional system specifications
• Logical Design: Information requirements elicitation and structure for detailed design specifications
• Physical Design: Develop technology and organizational specifications, with program/data structures, technology purchases, and organization redesigns as deliverables
• Implementation: Programming, testing, training, and documentation, producing operational programs, documentation, and training materials
• Maintenance: Monitor, repair, and enhance, with periodic audits.

Database Schema Elements

• External Schema: User views, subsets of Conceptual Schema
  • Can be determined from business-function/data entity matrices, and the DBA determines schema for different users
• Conceptual Schema: E-R models-covered in Chapters 2 and 3
• Internal Schema: Logical structures and Physical structures

Managing Projects

• Project: planned undertaking of related activities to reach an objective that has a beginning and an end
  • Initiated and planned in the planning stage of SDLC
  • Executed during analysis, design, and implementation
  • Closed at the end of implementation
• Involved Personnel: Business analysts, systems analysts, database analysts and data modelers, users, programmers, database architects, data administrators, project managers, other technical experts

Evolution of Database Systems

• Driven by four main objectives:
  • The need for program-data independence, reduced maintenance, managing more complex data types and structures, ease of data access for less technical personnel, and the need for more powerful decision support platforms.

Range of Database Applications

• Personal databases, Two-tier and N-tier Client/Server databases, and Enterprise applications
• Enterprise applications use Enterprise resource planning (ERP) systems

ERP and Data Warehouse

• Enterprise Resource Planning (ERP): Integrates all enterprise functions, including manufacturing, finance, sales, marketing, inventory, accounting, and human resources
• Data Warehouse: Integrated decision support system derived from various operational databases