Business Intelligence PDF

BUSINSS INTELLEGENCE DEPARTMENT = BCA NAME = SOUVIK MONDAL TOPIC= QUESTIONS AND ANSWERS Unit 1: Business Intelligence and Decision Support Systems 1. What is a Decision Support System (DSS)? Answer: A DSS is a computer-based system that helps people make decisions by analysing data and presenting...

BUSINSS INTELLEGENCE DEPARTMENT = BCA NAME = SOUVIK MONDAL TOPIC= QUESTIONS AND ANSWERS Unit 1: Business Intelligence and Decision Support Systems 1. What is a Decision Support System (DSS)? Answer: A DSS is a computer-based system that helps people make decisions by analysing data and presenting it in an easy-to-understand format. It includes databases, models, and user-friendly interfaces. 2. What are the components of a Decision Support System? Answer: The component of Decision Support System (DSS) is discussed as follows: A) Database Management System (DBMS): Stores relevant data. B) Model Base: Contains mathematical models and analytical tools. C) User Interface: Helps users interact with the system. D) Knowledge Base: Stores rules and domain knowledge. E) Software Tools: Tools for data analysis and simulations. F) Communication Network: Helps people collaborate.  G) Hardware: Supports the system's operations. H) Query and Reporting Tools: Generate reports for decision-making. I) Security: Protects data and processes. 3. What is Business Intelligence (BI)? Answer: BI is a set of tools and processes that analyse data to help businesses make better decisions. It includes tools for data collection, analysis, and reporting. 4. What are the key components of Business Intelligence? Answer: The key components of business intelligence are discussed as follows: A) Data Warehousing: Stores data for analysis. B) Data Mining: Finds patterns in data. C) OLAP (Online Analytical Processing): Helps analyse data from different angles. D) Reporting Tools: Present data in reports and dashboards. 5. What are Business Analytics? Explain its key types. Answer: Business Analytics refers to the process of using data to analyse past performance and make future decisions. It involves collecting, processing, and analysing data to gain insights. Types of Business Analytics: A) Descriptive Analytics: Summarizes past data to understand what has happened. Example: Sales reports, dashboards. B) Diagnostic Analytics: Explains why something happened by analysing past data. Example: Root cause analysis. C) Predictive Analytics: Uses past data to predict future outcomes. Example: Forecasting sales for next month. D) Prescriptive Analytics: Suggests actions to take for the best results. Example: Recommending optimal pricing strategies. 6. What are Big Data Analytics? What are its characteristics? Answer: Big Data Analytics is the process of analysing huge amounts of data to uncover patterns, trends, and insights. Key Characteristics of Big Data are discussed as follows:  A) Volume: Deals with a massive amount of data. B) Velocity: Data is generated quickly and in real-time. C) Variety: Data comes in different formats like text, images, videos. D) Veracity: Ensures data quality and accuracy. 7. Why is Big Data Analytics important? Answer: Big Data refers to extremely large sets of data that are too complex for traditional data-processing tools. It includes information from various sources like social media, websites, sensors, and more. Here’s why Big Data Analytics is important: A) Better Decisions: It helps companies analyse huge amounts of data to make smarter choices. B) Improves Customer Experience: Businesses can understand customer preferences and offer personalized services. C) Gives Competitive Edge: Analysing big data helps companies stay ahead of competitors by predicting trends and spotting opportunities. D) Saves Money: It identifies ways to reduce costs by making business processes more efficient. E) Reduces Risks: By studying past data, companies can foresee and avoid potential problems. F) Boosts Innovation: Insights from big data can lead to new products and creative ideas. 8. Describe the framework for business intelligence? Answer: The framework for Business Intelligence (BI) refers to the process, technologies, and tools used to analyse data and support decision- making in an organization. A) Data Collection: Gathering raw data from different sources (e.g., databases, spreadsheets, websites). B) Data Integration: Combining and cleaning the data to ensure its accurate and usable for analysis. C) Data Storage (Data Warehouse): Storing the data in a centralized system like a data warehouse where it’s ready for analysis. D) Data Analysis: Using tools like dashboards, reports, or analytics software to process the data and find insights or trends. E) Reporting and Visualization: Presenting the analysed data using charts, graphs, or tables to make it easy to understand. F) Decision-Making: Using the insights to make informed business decisions and improve company performance. 9. What is the evolution of decision support systems (DSS)? Answer: The evolution of Decision Support Systems (DSS) is discussed as follows: A) Manual Systems (1960s): Early decision-making was manual, with people using paper reports and basic calculations to make business decisions. B) Model-Driven DSS (1970s): Computers were introduced to help make decisions based on models (mathematical formulas), mainly used for specific tasks like financial planning. C) Data-Driven DSS (1980s): With more data available, DSS focused on analyzing large sets of data to support decision-making. Data warehouses were used to store this data. D) Knowledge-Driven DSS (1990s): These systems used expert knowledge and artificial intelligence (AI) to give recommendations and insights. E) Web-Based DSS (2000s): The internet allowed decision-making systems to be accessed online, making it easier for multiple users to collaborate and make decisions. F) AI-Powered DSS (Present): Modern DSS uses advanced AI, machine learning, and big data to provide real-time insights and automate complex decision-making. Unit 2: Decision-Making Process 1.what is decision-making process? Answer: Decision-making is the process of choosing the best course of action from different options to solve a problem or achieve a goal. It involves thinking through the situation, gathering information, and selecting the best solution. 2. What are the phases of the decision-making process? Answer: The decision-making process typically involves four main phases: A) Intelligence Phase: Identifying the problem or opportunity. Example: Noticing low sales in a specific area. B) Design Phase: Developing possible solutions. Example: Brainstorming marketing strategies to increase sales. C) Choice Phase: Choosing the best solution. Example: Deciding to launch a social media campaign. D) Implementation Phase: Putting the solution into action. Example: Starting the social media campaign and tracking results. 3. What is a Model-Driven DSS? Answer: Model-Driven DSS (Decision Support System) is a type of computer system that helps in decision-making by using mathematical models. It analyses data to suggest the best actions for a specific problem or decision. 4. What is a Data-Driven DSS? Answer: A Data-Driven Decision Support System (DSS) is a tool that helps people make better decisions by analysing large amounts of data. It gathers, processes, and presents data in a way that makes it easier to understand and use for solving problems or making business decisions. 4. What is Sensitivity Analysis in Decision Support Systems? Answer: Sensitivity Analysis helps to understand how different inputs affect the outcomes in decision-making. It tests how sensitive a system is to changes in input variables. Steps in Sensitivity Analysis: A) Change Inputs: Adjust variables to see how they affect the decision outcome. B) Analyse Results: Understand which factors have the biggest impact. C) Make Decisions: Focus on the important variables when making decisions. 5. What are the capabilities of decision support systems? Answer: The capabilities of Decision Support Systems (DSS) discuss as follows: A) Data Collection: DSS can gather and organize large amounts of data from different sources. B) Data Analysis: It can analyse data to find patterns, trends, and relationships that help in decision-making. C) Scenario Analysis: DSS allows businesses to test different "what-if" scenarios to see the impact of different decisions. D) Support for Complex Decisions: It helps solve complicated problems that might be too difficult for humans to handle alone. E) Real-Time Decision-Making: DSS can give immediate results and insights for faster decision-making. F) Interactive Reports: It presents information in easy-to-understand formats, like graphs, charts, or dashboards. 6. Classify decision support systems? Answer: Decision Support Systems (DSS) can be classified into different types based on how they help in decision-making. A) Model-Driven DSS: Focuses on using mathematical models to analyse problems, like financial planning or scheduling. B) Data-Driven DSS: Uses large amounts of data to support decisions, often relying on databases and data warehouses. C) Knowledge-Driven DSS: Provides suggestions or recommendations based on expert knowledge, using artificial intelligence or expert systems. D) Communication-Driven DSS: Helps multiple people collaborate and make decisions, often using tools like video conferencing or group chats. E) Document-Driven DSS: Manages and retrieves documents to help in decision-making, like reports, manuals, or spreadsheets. F) Web-Based DSS: Accessible via the internet, allowing users to make decisions from anywhere, often integrating real-time data.