Tiêu đề CSC 122 Lecture Note.pdf ✅

- Executive Information Systems (EIS) : Offer top executives access to key performance indicators and critical data to assist with strategic decision-making. - Knowledge Management Systems (KMS) : Focus on managing and facilitating the use of knowledge and expertise within an organization. Summary: Information systems play a crucial role in modern organizations by enhancing efficiency, improving decision-making, and providing competitive advantages. Types of Information Systems: 1. Transaction Processing Systems (TPS): - Function : Automate and manage routine transactions and operations. Examples include order processing systems, payroll systems, and inventory management systems. - Characteristics : Handle large volumes of data, ensure accuracy, and provide real-time processing. - Examples : Point-of-Sale (POS) systems, online booking systems. 2. Management Information Systems (MIS): - Function : Provide managers with regular, routine information to assist in decision-making and performance monitoring. - Characteristics : Generate periodic reports, summarize data from TPS, and support structured decision-making. - Examples : Sales performance dashboards, inventory reports. 3. Decision Support Systems (DSS): - Function : Help with complex decision-making by analyzing large volumes of data and providing simulation models. - Characteristics : Offer interactive tools for querying data, forecasting, and scenario analysis. - Examples : Financial planning systems, marketing analysis tools. 4. Expert Systems: - Function : Emulate the decision-making abilities of human experts to solve complex problems in specific domains. - Characteristics : Use knowledge bases and inference engines to provide solutions or recommendations. - Examples : Medical diagnosis systems, legal advisory systems. 5. Executive Information Systems (EIS): - Function : Provide top executives with easy access to critical information and high-level summaries. - Characteristics : Offer real-time data, visual dashboards, and summary reports for strategic decision-making.
- Examples : Balanced scorecard systems, executive dashboards. 6. Knowledge Management Systems (KMS): - Function : Facilitate the creation, sharing, and utilization of organizational knowledge and expertise. - Characteristics : Include tools for collaboration, document management, and knowledge repositories. - Examples : Intranet wikis, document sharing platforms. Key Concepts in Information Systems: 1. Data Management: - Databases : Central repositories for storing and managing data. Examples include relational databases (SQL), NoSQL databases, and cloud-based data storage. - Data Warehousing : Consolidates data from multiple sources to support analysis and reporting. 2. Information Security: - Confidentiality, Integrity, and Availability (CIA) : Fundamental principles of information security ensuring that data is protected from unauthorized access, is accurate, and is accessible when needed. - Encryption : Protects data by converting it into a secure format that can only be read with the correct decryption key. - Access Control : Manages who can access specific data and system resources. 3. Systems Development Life Cycle (SDLC): - Phases : Includes planning, analysis, design, implementation, testing, and maintenance. - Methodologies : Agile, Waterfall, Scrum, and DevOps are various approaches to managing the development process. 4. Business Intelligence (BI): - Function : Involves analyzing data to provide actionable insights and support business decisions. - Tools : Includes data mining, reporting tools, and visualization software like Tableau and Power BI. 5. Enterprise Systems: - Enterprise Resource Planning (ERP) : Integrates core business processes such as finance, HR, manufacturing, and supply chain into a unified system. - Customer Relationship Management (CRM) : Manages interactions with customers, tracks sales, and enhances customer service. - Supply Chain Management (SCM) : Oversees the flow of goods and services from suppliers to customers.
6. Emerging Technologies: - Artificial Intelligence (AI) and Machine Learning (ML) : Enhance decision-making and automate tasks through algorithms that learn from data. - Big Data : Handles vast amounts of data that traditional systems cannot process efficiently, providing insights from large data sets. - Blockchain : Provides secure, decentralized record-keeping for transactions and data. Challenges and Trends: 1. Cybersecurity Threats : Organizations face increasing risks from cyberattacks, requiring robust security measures and incident response strategies. 2. Data Privacy Regulations : Compliance with regulations such as GDPR and CCPA is essential for protecting personal information. 3. Cloud Computing : Offers scalable resources and services over the internet, impacting how businesses manage and deploy information systems. 4. Integration and Interoperability : Ensuring different systems and technologies work seamlessly together to improve efficiency and data accuracy. 5. User Experience (UX) : Designing systems with intuitive interfaces and user-friendly features to enhance productivity and satisfaction. 6. Sustainability : Addressing the environmental impact of IT operations, such as energy consumption and electronic waste. Advanced Concepts and Applications: 1. Artificial Intelligence (AI) and Machine Learning (ML): - AI : Encompasses technologies that simulate human intelligence, such as natural language processing, robotics, and expert systems. AI applications can automate tasks, enhance decision- making, and provide personalized experiences. - ML : A subset of AI that involves training algorithms to recognize patterns and make predictions based on data. Examples include recommendation systems, fraud detection, and predictive analytics. 2. Big Data and Analytics: - Big Data : Refers to extremely large data sets that are complex and voluminous, making them difficult to process with traditional data processing tools. Big Data technologies handle massive amounts of unstructured and structured data. - Analytics : Involves examining data to draw insights and support decision-making. Techniques include descriptive analytics (what happened), predictive analytics (what might happen), and prescriptive analytics (what should we do). 3. Cloud Computing: