Database Processing

Chapter Overview

  • Title: Database Processing

  • Focus: Understanding databases, data management, and the role of information within organizations.

The Big Picture

Evolution of Data Management

  • Batch Era (1950): Automate clerical work.

  • Transaction Era (1970): Introduced self-service for customers and suppliers to reduce costs and improve efficiency.

  • BI Era (1990): Focus on mining data for insights.

  • Cognition Era (2010): Computerized human thought simulation for automated enterprise actions.

  • Big Data Era (2020+): Real-time access to massive data reflecting actual events.

Information Technology Infrastructure

Key Components

  • Cloud Services: Public, enterprise, and hybrid clouds support various operational environments.

  • Data Centers: Central hubs where vast amounts of data are managed and processed, characterized by:

    • Very high electricity consumption.

    • High concentration of heat.

    • 24/7 data availability and management of huge amounts of data.

Understanding Databases

Definitions

  • Database: Structured collection of data.

  • Database Management System (DBMS): Software for managing databases, allowing for efficiency in data processing and administration.

  • Applications: Tools that interface with the DBMS to deliver functionality and user accessibility.

Components of a Database Application System

  • Forms: User input methods.

  • Reports: Output formats for data processing.

  • Queries: Requests to manipulate or retrieve data.

  • Users: Interact with applications for data access and analysis.

Types of Data and Information Flow

Importance of Data

  • Organizations must collect and analyze various levels and types of information to make informed decisions.

  • Successful data management contributes to organizational performance.

Data Flow Example in Retail

  • Indicates how various systems (CRM, POS, analytics) interact to create insights from disparate sources, highlighting the 4 Vs of Big Data:

    • Volume: Amount of data generated.

    • Variety: Different types of data.

    • Velocity: Speed of data generation and processing.

    • Veracity: Quality of data and its sources.

Database Concepts

Data vs. Information

  • Data: Raw facts.

  • Information: Data converted into a meaningful context.

  • Knowledge: Application of information to derive value or actions in decision-making.

Relationship Models

  • Entities and Relationships: Key components visualized in an Entity-Relationship model, where:

    • Primary Key: Uniquely identifies a record.

    • Foreign Key: Establishes relationships between tables.

Database Management System (DBMS)

Operations and Structure

  • DBMS operations include reading, inserting, modifying, and deleting data.

  • Uses Structured Query Language (SQL) for data manipulation.

Administration of DBMS

  • Setting up user permissions, security measures, backups, and performance enhancements.

  • Organizations often dedicate personnel to manage database administration tasks.

User Involvement

Role in Processing

  • Users determine data needs, table relationships, and interface designs.

  • Accurate modeling requires users to validate the structure to ensure business alignment.

Organizational Levels of Information

  • Different management levels require varying types of data:

    • Executive Management: Strategic insights.

    • General Management: Budgeting and reporting.

    • Front Line Employees: Transactional task management.

Data Integrity Challenges

Quality Measures

  • Successful decision-making hinges on high-quality information, characterized by:

    • Accuracy.

    • Completeness.

    • Consistency.

    • Uniqueness.

    • Timeliness.

Pitfalls

  • Poor data can arise from inaccuracies in entry, different format standards, and external data sources.

Future Trends in Data Management (202x)

Expected Changes

  • Advances in query and reporting technologies.

  • Continued growth in data storage and processing capabilities.

Privacy Concerns from Data Aggregators

  • Data aggregation practices raise issues about privacy, as vast information is processed and analyzed from multiple sources.