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.