Chapter3

Learning Outcomes

• Define a database and a database management system.

• Explain logical database design and the relational database model.

• Define the components of a database management system.

• Summarize recent trends in database design and use.

• Explain the components and functions of a data warehouse.

Databases

Definition

• Database: A collection of related data stored in a central or multiple locations.

• Data Hierarchy: Structure and organization involving fields, records, and files.

• Database Management System (DBMS): Software that enhances efficiency in creating, storing, maintaining, and accessing database files.

Interaction with User, DBMS, and Database

• User interacts with the DBMS to perform data operations.

Types of Data in a Database

Internal Data

• Collected within an organization and stored in internal databases.

External Data

• Gathered from various external sources and stored in a data warehouse.

Methods for Accessing Files

Sequential Access

• Records are organized and processed in numerical order based on a primary key.

• Typically used for backup and archive files.

Random Access

• Records can be accessed in any order.

• Suitable for processing a small number of records at a time.

Indexed Sequential Access Method (ISAM)

• Provides options for accessing records either sequentially or randomly based on the access requirements.

Logical Database Design

Views of Information in a Database

• Physical View: Concerned with how data is stored and retrieved from storage media.

• Logical View: How information is organized and presented to users; could vary for different users.

Data Model

• Defines how data is created, represented, organized, and maintained.

• Includes structure, operations, and integrity rules.

Common Data Models

Hierarchical Model

• Represents relationships in a tree-like structure (nodes and branches).

Network Model

• Allows multiple parent-child relationships.

Relational Model

• Comprises tables (two-dimensional) with rows (records) and columns (fields), alongside definitions in a data dictionary.

Relational Model Keys

Primary Key

• Uniquely identifies each record in the database.

Foreign Key

• Links to another table's primary key for cross-referencing.

Normalization

• Process to eliminate redundant data and ensure relevant data storage.

• Staged improvement from first normal form (1NF) to fifth normal form (5NF).

DBMS Components

• Database Engine: Handles storage, manipulation, and retrieval operations.

• Data Definition: Defines structure and schema of database files.

• Data Manipulation: Allows addition, deletion, modification, and retrieval through query languages.

• Application Generation: Designs application elements using database data.

• Data Administration: Manages backup, recovery, security, and permissions.

Recent Trends in Database Design and Use

• Data-driven websites, natural language processing, distributed databases, and object-oriented databases.

• Advances in artificial intelligence impacting functionalities.

Data Warehouses

Definition

• Collections of data from various sources designed to support decision-making.

• Also referred to as hypercubes, as they store multidimensional data.

Characteristics

• Subject-oriented, integrated, time-variant, and capable of capturing aggregated data for analytics.

Storage in Data Warehouses

• Raw Data: Original state of collected information.

• Summary Data: Contains subtotals across different categories.

• Metadata: Data about data’s origin, quality, and other characteristics.

Output from Data Warehouses

• Utilizes Online Analytical Processing (OLAP) for multidimensional analysis generating business intelligence.

• Data-mining analysis identifies patterns and relationships within data.

Benefits of Data Warehouses

• Cross-reference operations for comparative analysis.

• Rapid report generation using aggregated data from multiple sources.

• Support for extensive historical data analysis aiding management decisions across varying demands.

Data Marts

Definition

• Smaller, focused versions of data warehouses serving specific departments.

Advantages

• Faster access, improved response time, ease of creation, and cost-effectiveness.

Disadvantages

• Limited scope and challenges in consolidating data across departments.

Business Analytics (BA)

Purpose

• Employs data and statistical methods to extract insights for decision-makers.

Methods

• Descriptive Analytics: Reviews past events to inform future actions.

• Predictive Analytics: Prepares users for future occurrences.

• Prescriptive Analytics: Suggest courses of action using data-driven insights.

Big Data

Definition

• Large volume of data that exceeds the ability of traditional processing methods.

The 3 Vs

• Volume: Transaction quantity.

• Variety: Mix of structured and unstructured data.

• Velocity: Speed of data gathering and processing.

Applications and Risks

• Provides competitive advantage in sectors like retail, finance, and healthcare, but poses privacy risks including discrimination and ethical concerns.

Commonly Used Platforms for Big Data Analytics

• Apache Hadoop, NoSQL, Cassandra, and commercial platforms like SAP Big Data Analytics and Tableau.

Database Marketing

Definition

• Utilizes customer data for effective promotion and strategies.

Techniques

• Involves data segmentation, multivariate analysis, and automated tools.

Goals

• Focused on increasing profits by transforming marketing to a proactive approach and engaging customers through targeted strategies.