Database Concepts and Data Hierarchy

• A database is a structured collection of data, while a Database Management System (DBMS) is software that manages and provides access to the database.
• Data is organized hierarchically: characters form fields, fields form records, records form files, and files form databases.

Data Hierarchy

• Character: A basic unit of data like letters, digits, or symbols.
• Field: A combination of characters describing an aspect of an object or activity.
• Record: A collection of related fields about one object, activity, or individual.
• File: A collection of related records.

Key Database Concepts

• Entity: A person, place, or thing for which data is collected.
• Attribute: A characteristic of an entity.
• Key Field: An attribute uniquely identifying records.
• Data Item: A specific value of an attribute.
• Primary Key: A field uniquely identifying a record.

Data Management Approaches

• Traditional Approach: Each system uses its own dedicated data files.
• Database Approach: Multiple systems share a common pool of data.
• Data Modeling: Data entities and their relationships.
• Enterprise Data Modeling: Data modeling at the enterprise level.
• ER Diagrams: Data models showing organization and relationships graphically.
• Relational Database Model: Data organized in two-dimensional tables or relations.
• Relational Model: Organizing data into tables (relations).
• Domain: Allowable values for a data attribute.

Data Manipulation

• Selecting: Eliminating rows based on criteria.
• Projecting: Eliminating columns.
• Joining: Combining two or more tables.
• Linking: Combining tables through common attributes.
• Data cleansing: Detecting and correcting inaccurate records.
• Bad Data: Data due to user errors or corruption.
• DBMS acts as an interface between databases, users, and applications.

Types of DBMS

• Single-User DBMS: For one user at a time.
• Multiple-User DBMS: Allows simultaneous access by many users.
• Flat Files: Simple databases with no relationships between records.
• SQL: A programming language for accessing relational data.
• NoSQL Databases: Faster performance by storing and retrieving data without strict relational conditions.
• Visual, Audio, and Other Database Systems: Organized storage of visual and audio data.

Database Activities

• Schema: Description of the entire database.
• DDL: Instructions and commands to define data and relationships.
• Data Dictionary: Detailed description of all data in the database.
• Concurrency Control: Managing access when multiple users need the same record.
• DML: Language for accessing, modifying data, and generating reports.
• Database Administrators (DBAs): Professionals who manage databases.
• Data Administrator: Defines and implements principles for data issues.
• Database as a Service (DaaS): Remote database management.
• Front-end application: Direct user interaction.
• Back-end application: Interacts with other programs.

Big Data

• Big Data: Large, complex data collections that cannot be handled by traditional tools.
• In-Memory Database (IMDB): Stored in RAM for fast access.
• Traditional Online Transaction Processing (OLTP): Systems for quickly putting data into databases.
• Data Warehouse: Stores business information from many sources.
• ETL (Extract, Transform, Load): Process for loading data into a warehouse.
• Data Mart: Subset of a data warehouse for smaller units.

Business Intelligence

• Business Intelligence: Transforms structured data into useful information.
• Data Mining: Discovers patterns and relationships in data.
• Online Analytical Processing (OLAP): Explores data from multiple perspectives.
• Data-Mining Software: Analyzes transactions and detects patterns.
• Predictive Analysis: Uses past data to predict future events.
• Data Visualization: Uses graphs and dashboards to reveal data relationships.
• Social Graph Analysis: Visualizes data as user networks.
• Key Performance Indicators (KPIs): Measures progress toward goals.
• Dashboard: Visual tool displaying KPI status.
• Competitive Intelligence: Information about competitors’ strategies.
• Counter Intelligence: Protecting organizational information.

Networks and Cloud Computing

• Computer Network: Connects computers for data sharing.
• Network Topology: Arrangement of communication links.
• Star Network: Devices connect through a central hub.
• Bus Network: Devices share a single communication line.
• Mesh Network: Multiple access points for reliable communication.
• Personal Area Network (PAN): Wireless network around a user.
• Local Area Network (LAN): Connects devices in a small area.
• Metropolitan Area Network (MAN): Connects users across a city.
• Wide Area Network (WAN): Connects large geographic regions.
• Centralized Processing: All tasks in one location.
• Decentralized Processing: Separate systems at different locations.
• Distributed Processing: Data processing across networked devices.
• Client / Server Architecture: Servers manage tasks and share with clients.
• Cloud Computing: On-demand access to shared computing resources.

Key Characteristics of Cloud Computing

• On-Demand Self-Service: Access resources without human interaction.
• Broad Network Access: Accessible over standard devices.
• Resource Pooling: Sharing resources among multiple customers.
• Rapid Elasticity: Quick scaling of resources based on demand.
• Measured Service: Monitoring and billing of resource usage.

Cloud Service Models

• Infrastructure as a Service (IaaS): Virtualized computing resources.
• Platform as a Service (PaaS): Cloud platform for application development.
• Software as a Service (SaaS): Software applications online via subscription.

Deployment Models

• Public Cloud: Shared resources over the internet.
• Private Cloud: Dedicated environment for a single organization.
• Hybrid Cloud: Combination of public and private clouds.
• Community Cloud: Shared environment for organizations with common goals.

Electronic and Mobile Commerce and Enterprise Systems

• Electronic Commerce (E-commerce): Conducting business activities electronically.
• Mobile Commerce (M-commerce): Conducting business using mobile devices.

Types of E-commerce

• Business-to-Business (B2B) E-commerce: Transactions between organizations and connects business partners in a virtual supply chain and is currently the largest type of e-commerce.
• Business-to-Consumer (B2C) E-commerce: Direct transactions between businesses and customers with reasons for steady growth including cheaper goods and services online, personalized product options, and the use of social media to reach customers.
• Consumer-to-Consumer (C2C) E-commerce: Transactions between consumers facilitated by a third party.
• E-Government: Use of ICT to improve government services and relationships with citizens.
  • Government-to-business (G2B): interactions and transactions between governments and businesses
  • Government-to-citizen (G2C): provide one-stop, online access to information and services to individuals.
  • Government-to-government (G2G): interactions and collaborations between different government agencies to improve efficiency and coordination in delivering services. Areas in which application are used: Retail and Wholesale, Manufacturing, E-Boutiques, Marketing and advertising, Bartering

Applications of E-commerce

• Retail and Wholesale: Includes e-tailing (direct sales to consumers) and cybermalls (single websites offering multiple products and services).
• Manufacturing: Utilizes electronic exchanges where companies buy and sell goods, trade market information, and run back-office operations. Three types of exchanges are Private, Consortium operated and Independent exchanges.
• Marketing: Involves market segmentation to target advertising messages.
• Advertising: Employs CPM, CPC, and CPA models; success measured by users reached, CTR, and user actions.
• Bartering: Utilizes Web sites that have been created to support bartering for goods and services.

Tools for E-commerce

• Retargeting: Uses targeted ads to recapture shoppers who left a retailer’s site.
• Price Comparison: Mobile apps enable shoppers to compare prices and products on the Web.
• Couponing: Direct mobile coupons sent to consumers’ smartphones.

Other E-commerce Aspects:

• Investment and Finance: Revolutionized with online brokerage services.
• Banking: Allows online balance checks, money transfers, and bill payments.
• E-Boutiques: Online stores selling fashionable clothing and accessories.

Electronic Payment Systems

• Digital certificate: Verifies the identity of a sender or Web site.
• Certificate authority (CA): Issues digital certificates.
• Secure Sockets Layer (SSL): Secures sensitive data during e-commerce, and a communications protocol.
• Electronic Cash: Computerized money for e-commerce transactions.
• Credit, Charge, Debit, and Smart Cards: Common payment methods for online purchases.
• P-card: Used to streamline purchase order and invoice payment processes.

Transaction Processing Systems (TPSs)

• Capture and process detailed data for fundamental business operations
• Organizations expect their TPSs to maintain a high degree of accuracy, avoid processing fraudulent transactions, produce timely reports, improve labor efficiency, and improve customer service.
• Types of transaction processing system are Order processing Systems, Accounting system and Purchasing system.

Traditional Transaction Processing Methods and Objectives

• Batch Processing System: Transactions accumulated and processed as a single batch.
• Online Transaction Processing (OLTP): Each transaction is processed immediately.

Transaction Processing Systems for Entrepreneurs and Small and Medium-Sized Enterprises

• SME's typically use Integrated TPS solutions that are easy to install and operate and have a low total cost of ownership.

Transaction processing cycle:

• Data Collection: Capturing necessary transaction data.
• Data Editing: Checking data validity and completeness.
• Data Correction: Reentering incorrectly entered data.
• Data Processing: Performing calculations and transformations.
• Data Storage: Updating databases with new transactions.
• Document Production: Generating output records, documents, and reports.

Enterprise System

• Ensures information is shared across all business functions and management levels.
• Enterprise Resource Planning (ERP): Manages vital business operations for an entire organization.

Advantages of ERP:

• Improved Access to Data for Operational Decision Making
• Elimination of Costly, Inflexible Legacy Systems
• Improvement of Work Processes
• Upgrade of Technology Infrastructure
• Supply Chain Management (SCM): Manages activities from raw materials to delivery.
• Customer relationship management (CRM) System: Manages all aspects of customer encounters, including marketing, sales, distribution, accounting, and customer service.

Key features of a CRM system:

Contact management Sales management Customer support Marketing automation Analysis Social networking Access by smartphone Import contact data

• Product Lifecycle Management (PLM): Manages product information and processes collaboratively.
• Product Lifecycle Management (PLM) Software: Manages data and processes associated with product life cycle phases.

Business Intelligence (BI)

• A set of processes and techniques to analyze raw data and extract information that helps drive business solutions.

General Categories of BI/Analytic Techniques

• Descriptive Analysis: Summarizes historical data to identify trends, patterns, and insights.
• Predictive Analysis: Analyzes current data to identify future probabilities and trends, and make predictions about the future.
• Time Series Analysis: Analyzing a sequence of well-defined data points measured at uniform time intervals over a long period.
• Data Mining: Explores large datasets for hidden patterns to predict future trends and behaviors.
• Optimization: Techniques used daily within organizations to allocate scarce resources in ways that minimize costs or maximize profits.
• Text and Video Analysis: Involves various techniques to extract insights from text and video content. Text Analysis: a process of extracting value from large quantities of unstructured text data. Video Analysis: Uses computer algorithms and AI to automatically analyze video content for insights and decision-making.

Popular Business Intelligence Tools

• Power BI: A BI and data visualization tool developed by Microsoft that helps organizations transform raw data into actionable insights through interactive reports, dashboards, and AI-powered analytics.
• Tableau: A data visualization tool that allows users to create interactive dashboards and reports. Known for its ease of use and its ability to connect to a wide variety of data sources.
• Qlik Sense: A data visualization and discovery tool allowing users to explore data and create interactive visualizations. Known for its associative data model that supports non-linear data exploration.
• SAS Analytics: Part of the SAS suite, offering powerful analytics and reporting features.
• Google Analytics: A free web analytics service providing insights into website traffic and user behavior.