MIS 260 Exam #1 Review Sheet Notes
Exam #1 Review Sheet: Chapters 1, 2, 5, Technology Guide 1 & 2
CHAPTER 1: Introduction to Information Systems
Introduction (p. 1-2)
Information Technology (IT): Refers to any computer-based tool that people use to work with information and support the information and information-processing needs of an organization.
Why Should I Study Information Systems? (p. 3-4)
Information System (IS): A system that collects, processes, stores, analyzes, and disseminates information for a specific purpose.
Informed User: An individual who is knowledgeable about information systems and information technology.
Overview of Computer-based Information Systems (CBIS) (p. 11-14)
Noise (not in textbook): Unwanted or meaningless data or information.
Data (or data items): Raw, unorganized facts, figures, and symbols with no meaning yet.
Information: Data that has been organized, processed, and given meaning by placing it in context, making it useful to a human recipient.
Knowledge: Organized and processed information that helps in decision-making and problem-solving, often through accumulated experience and learning.
Computer-based Information System (CBIS): An information system that uses computer technology to perform some or all of its intended tasks. Often referred to as having the following components:
Hardware: Physical devices such as the processor, monitor, keyboard, and printer.
Software: A program or collection of programs that enable the hardware to process data.
Database: A collection of related files or tables containing data.
Network: A connecting system (wired or wireless) that permits different computers to share resources.
Procedures: The instructions for combining components to process information and generate the desired output.
People: Individuals who use the hardware and software, interface with the data, and use the output.
Application program (a.k.a. "app"): A computer program designed to perform a specific function for the user.
Types of Computer-based Information Systems (p. 14-16)
Enterprise Resource Planning (ERP): Information systems that correct a lack of communication among the functional area ISs by tightly integrating the functional area ISs with a common database.
Transaction Processing System (TPS): Supports the monitoring, collection, storage, and processing of data from the organization's basic business transactions, each of which generates data.
E-Commerce Systems: Information systems that conduct business electronically over computer networks.
Support for Organizational Employees (p. 17)
Knowledge Workers: Professional employees such as financial and marketing analysts, engineers, lawyers, and accountants who are experts in a particular subject area and skilled at creating, using, and distributing information.
Business Analytics (BA): The process of developing actionable recommendations and strategies based on data analysis. Uses predictive analysis, data mining, and machine learning to understand and improve business performance.
Expert System (ES): Attempts to mimic human experts by applying expertise in a specific domain. Provides advice or makes decisions in narrow areas.
Dashboards (or digital dashboards): Provide rapid access to timely information, access to reports, and direct access to structured information of all kinds, often presented in a visual or graphical format.
The Emergence of Cognitive Computing (p. 26)
Cognitive Computing: A type of computing that aims to simulate human thought processes in a computerized model. It uses self-learning systems that use data mining, pattern recognition, and natural language processing to mimic the way the human brain works.
CHAPTER 2: Organizational Strategy, Competitive Advantage, and Information Systems
Introduction (p. 33)
Competitive Advantage: An advantage over competitors in some measure, such as cost, quality, or speed; it enables a company to outperform its competitors.
Business Processes (p. 33-34)
Business Process: A collection of related activities that produce a product or a service of value to the organization, its business partners, or its customers. It comprises three fundamental elements:
Input: Materials, services, and information that flow through and are transformed as a result of process activities.
Resources: People and equipment that perform process activities.
Outputs: The product or a service created by the process.
Information Systems & Business Processes (p. 37)
Information systems play a crucial role in business processes by:
Executing the Process: Automating parts of the process (e.g., calculations, email generation, approving credits).
Capturing & Storing Process Data: Recording process flow and transaction data.
Monitoring Process Performance: Tracking the process status and identifying metrics for evaluation.
Capturing and Storing Process Data (p. 37)
Process Data: Data generated by executing a business process, which includes a wide range of information about each activity in the process.
Transaction Data: Data describing an event (a transaction) that takes place in a business, such as buying, selling, or paying. This is typically part of process data.
Monitoring Process Performance (p. 38)
Monitoring can occur at two levels:
Instance Level: Tracking the performance of a single instance of a process (e.g., a specific customer order).
Process Level: Tracking the performance of the overall process (e.g., total sales orders processed per day).
Robotic Process Automation (p. 38)
Robotic Process Automation (RPA): A technology that allows computer software to mimic human actions when interacting with digital systems, automating repetitive tasks without human intervention.
Business Pressures, Organizational Responses, & Information Technology Support (p. 43-50)
Organizations face various pressures that drive them to adjust their strategies and leverage information technology:
Market Pressures:
Globalization: The integration and interdependence of economic, social, cultural, and ecological facets of life, facilitated by information technology.
Changing nature of the workforce: Workforce diversity, growth in knowledge workers, and increased use of temporary employees.
Powerful customers: Increased customer sophistication and expectations due to readily available information and customized products/services.
Technology Pressures:
Technological innovation & obsolescence: Rapid development of new, disruptive technologies and the quick decline of existing ones.
Information overload: The sheer volume of digital information, often making it difficult for individuals to process and make decisions effectively.
Societal, Political, & Legal Pressures:
Organizational social responsibility or individual social responsibility: The obligations of organizations or individuals to society, extending beyond profit-making.
Compliance with government regulations: Adhering to laws and regulations, which often apply to information systems.
Sarbanes-Oxley Act of 2002 (SOX): A federal law that established sweeping auditing and financial regulations for public companies, designed to protect shareholders, employees, and the public from accounting errors and fraudulent financial practices.
USA Patriot Act: Significantly expanded the search and surveillance powers of federal law-enforcement and intelligence agencies following the attacks.
Gramm-Leach-Bliley Act (Financial Modernization Act of 1999): Requires financial institutions (offering loans, investment advice, insurance) to explain their information-sharing practices to customers and to safeguard sensitive data.
Health Insurance Portability and Accountability Act of 1996 (HIPAA): Includes the Privacy Rule that gives individuals rights over their health information and sets rules and limits on who can access it (electronic, written, or oral), and the Security Rule which requires security for electronic health information.
Protection against terrorist attacks: Increased need for security measures and information sharing in response to global threats.
Ethical issues (Ethics): Moral principles related to the collection, use, and dissemination of information, and the societal impact of IT.
Competitive Advantage & Strategic Information Systems (p. 53)
Competitive Strategy: A statement identifying a business's approach to competing, its goals, and the plans and policies required to achieve those goals.
Porter’s Competitive Forces Model (p. 54-56)
This model analyzes the competitive intensity and attractiveness of an industry by identifying five fundamental forces that shape competition:
Threat of Entry of New Competitors: The ease with which new companies can enter an industry and compete with existing firms.
Bargaining Power of Suppliers: The power of suppliers to drive up the prices of their goods and services.
Bargaining Power of Customers: The ability of customers to force down prices, demand higher quality, or play competitors against each other.
Threat of Substitute Products or Services: The likelihood of customers finding different ways to satisfy the same need.
Rivalry Among Existing Firms in the Industry: The intensity of competition between existing firms in the industry.
CHAPTER 5: Data and Knowledge Management
Difficulties of Managing Data (p. 120-121)
The amount of data is increasing exponentially over time.
Data are scattered throughout organizations, collected by many individuals using various methods & devices.
Data are generated from multiple sources.
Data Governance (p. 123)
Data Governance: An approach to managing information across an entire organization. It involves policies, procedures, and responsibilities for ensuring that data is accurate, consistent, and available.
Master Data: A set of core data (e.g., customer, product, employee, vendor, geographic location data) that spans across all of an organization’s information systems.
Transactional Data: Data generated by business transactions (e.g., sales, payments, shipments).
The Data Hierarchy (p. 124-125)
Data is organized in a hierarchy from smallest to largest:
Bit: A binary digit, either or .
Byte: A group of bits; represents a single character.
Character (not in textbook): A letter, number, or symbol.
Field: A column in a database table; represents an attribute of an entity (e.g., customer name).
Record: A row in a database table; a group of logically related fields (e.g., all information about one customer).
Table/File: A collection of related records (e.g., all customer records).
Database: A collection of related files or tables.
The Relational Database Model (p. 126-127)
Relational Database Model: A database model based on the concept of two-dimensional tables, enabling the representation of relationships among data.
Entity: A person, place, thing, or event on which information is stored (e.g., CUSTOMER, PRODUCT).
Instance: A specific, unique representation of an entity (e.g., a specific customer like 'John Doe').
Attribute: A characteristic or quality of a particular entity (e.g., customer name, product price).
Primary Key: A field (or group of fields) that uniquely identifies a record in a table.
Secondary Key: A field (or group of fields) that has some identifying information but typically does not uniquely identify a record.
Foreign Key: A field (or group of fields) in one table that uniquely identifies a row of another table. It is used to establish and enforce a link between the data in two tables.
Structured Data: Data that is organized in a highly formatted manner, typically in tables with rows and columns, easily stored and processed by computers.
Unstructured Data: Data that does not have a predefined format or is not organized in a predefined manner (e.g., text documents, emails, social media posts).
Big Data (p. 128)
Big Data: Diverse, high-volume, high-velocity information assets that require innovative forms of information processing for enhanced insight and decision-making.
Defining Big Data (p. 128)
Big Data is characterized by:
Exhibit Variety: Includes structured, semi-structured, and unstructured data.
Generated at High Velocity & Uncertain Patterns: Data streams in very fast and often unpredictably.
Does not fit neatly into structured databases: Its volume and variety make traditional database management challenging.
Can be analyzed only by sophisticated information systems: Requires advanced analytical tools and techniques.
Characteristics of Big Data (p. 129)
Often referred to as the "Three Vs":
Volume: Enormous amount of data.
Velocity: The rate at which data is generated, collected, and processed.
Variety: The diverse types of data, from structured numerical data to unstructured text, audio, and video.
Issues with Big Data (p. 130)
Unstructured Sources: A significant portion of big data comes from unstructured sources, making analysis complex.
"Dirty": Data often contains errors, inconsistencies, and duplicates, requiring cleansing.
Changes, especially in data streams: Data from streaming sources is constantly changing, making real-time analysis challenging.
Data Warehouses and Data Marts (p. 135-136)
Describing Data Warehouses and Data Marts:
Data Warehouse: A repository of historical data that is organized by subject to support decision-makers in the organization. Aggregates data from multiple sources.
Data Mart: A low-cost, scaled-down version of a data warehouse that is designed for the needs of a single department or functional area.
Online Transaction Processing (OLTP): Processes business transactions as soon as they occur. Deals with operational data. A transactional system.
Online Analytical Processing (OLAP): Involves the analysis of accumulated data from databases or data warehouses. Deals with analytical data. A reporting system.
Multi-dimensional Structure: Data in data warehouses and data marts is often stored in a multi-dimensional structure (represented as cubes), allowing for complex analysis from various perspectives.
Concepts & Definitions (p. 144-145)
Knowledge Management (KM): A process that helps organizations manipulate important knowledge that comprises part of the organization's memory, usually in an unstructured format.
Knowledge: Information that is contextual, relevant, and actionable, derived from data and information and often embedded in human expertise.
Intellectual Capital: The collective brainpower, expertise, and know-how of an organization's employees; it includes both explicit and tacit knowledge.
Explicit Knowledge: Knowledge that is objective, rational, and easily documented, transferred, and stored (e.g., procedures, policies, manuals).
Tacit Knowledge: Knowledge that is subjective, experiential, and difficult to document or share. It resides in the minds of employees.
Technology Guide 1: Hardware
Introduction to Hardware (p. 470)
Hardware consists of:
Central Processing Unit (CPU): The hardware component that performs most of the calculations and processing.
Primary Storage (e.g., RAM): Volatile memory used to store data, instructions, and results temporarily.
Secondary Storage (e.g., solid state drive): Non-volatile memory for long-term storage of data and programs.
Input Technologies (e.g., keyboard): Devices that gather data and convert them into electronic form.
Output Technologies (e.g., monitor): Devices that display, project, or physically produce results and information.
Communication Technologies (e.g., network access): Devices and systems that allow computers to exchange data.
Computer Hierarchy (p. 471-473)
From most powerful to least:
Supercomputers: The fastest, most powerful computers, used for highly intensive computational tasks (e.g., scientific research, weather forecasting).
Mainframe Computers: Large, expensive, and powerful computers that can handle high volumes of transactions and support thousands of users simultaneously (e.g., for large businesses, government agencies).
Midrange Computers or Servers (not in textbook): Generally smaller and less powerful than mainframes but more powerful than microcomputers. Used for network servers and other specialized tasks.
Microcomputers: Smallest and least expensive category of general-purpose computers. Used by individuals (e.g., desktop computers).
Laptop and Notebook Computers: Portable, compact microcomputers.
Tablet Computers: Portable, slate-shaped computers with a touch screen, smaller than a typical laptop.
Wearable Computers (e.g., augmented reality): Computers integrated into clothing or accessories, often used for specific applications like augmented reality.
Input Technologies (p. 473)
Human Data-Entry: Devices requiring human input (e.g., keyboard, mouse, joystick, scanner, microphone, webcam, stylus).
Gesture Recognition: Technology that interprets human movements as commands or data input.
Output Technologies (p. 474)
Monitors: Display visual output.
Printers: Produce hard copies of text and graphics.
Heads-up Displays (HUDs): Project information into the user's field of view.
Augmented Reality, Virtual Reality, & Mixed Reality (p. 474)
Augmented Reality (AR): A live, direct or indirect, view of a physical, real-world environment whose elements are augmented (or supplemented) by computer-generated sensory input.
Virtual Reality (VR): Provides a simulated environment that can be similar to or completely different from the real world. Users can interact with the virtual world.
Mixed Reality (MR): Merges real and virtual worlds to produce new environments and visualizations where physical and digital objects coexist and interact in real time.
Central Processing Unit (p. 480)
Central Processing Unit (CPU): The hardware component that performs most of the calculations and processing within a computer. It is the