IT CH4 PDF

IT CH4 1. Uses of Database Application Software Definition of a Database A structured collection of information stored for easy access and retrieval. Business-Level Advantages of Databases 1. Multi-user Access: Allows multiple users to a...

IT CH4 1. Uses of Database Application Software Definition of a Database A structured collection of information stored for easy access and retrieval. Business-Level Advantages of Databases 1. Multi-user Access: Allows multiple users to access data simultaneously. 2. Distributed Access: Ensures users in different departments can access relevant data. 3. Speed: Rapid access to large datasets for analysis or reporting. 4. Data Quality: Sophisticated checks ensure accurate, integrated and consistent data. 5. Security: Restricts access to sensitive data based on roles. 6. Space Efficiency: Optimizes storage by splitting up DB into multiple tables Business-Level Advantages of Databases Space Multi-user Efficiency Access Optimizes storage Enables simultaneous with organized data access for structures multiple users Distributed Security Access Protects sensitive Allows data access data with role-based across different access departments Data Quality Speed Ensures data accuracy Provides rapid access to and consistency large datasets 2. Types of Databases 1. File Processing Databases: Early systems using numerous files. Issues: Limited usability and inconsistent data formats. Subtypes: Flat-file: Single table format. only contains 1 type of record and cont access data held in other DB Free-form: Stores unstructured data like text or notes. it is organized and retrieved by using categories or keywords Hypertext: Multimedia data with linked objects. 2. Database Management Systems (DBMS): Programs that manage database operations like input, retrieval, and manipulation. Features: Tools for reporting”genera purpose tools”, user-friendly interfaces for non-technical users, and standardized development practices.=‫ةدحوملا ةيمنتلا تاسرامم‬ File Processing Databases Database Management Systems (DBMS) Flat-file Data Free-form Reporting Tools Management Hypertext Systems User Interfaces Development Practices 3. Data Warehouses Definition: Large systems storing historical and current data for analysis. Data Marts: Smaller departmental versions for focused operations and easier control. Data Warehousing: Process of creating and maintaining data warehouses. ETL (Extract, Transform, Load): 1. Extract data from various sources. 2. Transform data into usable formats. 3. Load data into the warehouse. Data Warehousing Process internal & Load Data Deliver external into Insights to data Warehouse Client Extract and Analyze Data Transform Data notice that in the 1st step : ?data come from sources such as: legacy DB / ERP=enterprise resource planning / EPOS=electronic-point-of-sale data / EDI=electronic data interchange 4. Concept of Analytics and Big Data 1. Analytics: Data-driven “the data are either qualitative or quantitative”methods for better decision-making. it can also be seen as the integration of business intelligence and information systems Types: Descriptive: Analyzes past and present data. what happened? by utilizing (business inteligence/web analytics/statistical techniques) Predictive: Forecasts future trends. what will happen? by utilizing (regression analysis/data mining/forecasting techniques) Prescriptive: Suggests optimal actions. what should happen ? by utilizing (linear programming/decision trees/simulation) Which type of analytics should be used for the specific purpose? Predictive Descriptive Analytics Prescriptive Analytics Ideal for forecasting Analytics future trends and Useful for Best for outcomes based on understanding past recommending historical data. and present data to optimal actions to inform current achieve desired decisions. outcomes. Data Types: Structured: Traditional, organized formats (e.g., financial data). Unstructured: Multimedia and social media content (e.g.,web sites/e-mails). represent the majority Semi-structured: Combination of structured and unstructured data (e.g., emails). Which data type is being analyzed? Structured Data Unstructured Data Organized and traditional Involves multimedia and formats like financial data social media content Semi-structured Data Combines elements of both structured and unstructured data, such as emails 2. Big Data: Large datasets managed using IT systems. Innovations like faster networks and cloud storage enhance analysis. Emphasis on unstructured data analysis. 3. Data Mining: Process of extracting patterns and trends from large datasets using mathematical & AI techniques. Applications: Enhancing profitability and identifying actionable insights.