MIS Chapter 6: Foundations of Business Intelligence PDF

**Chapter 6: Foundations of Business Intelligence: Databases and Information Management** Students: Sara Trifunović 77381, Selma Mavrić 77522 Potential Exam Questions **1. Define the difference between data and information. Why is this distinction important in business intelligence?** By informa...

**Chapter 6: Foundations of Business Intelligence: Databases and Information Management** Students: Sara Trifunović 77381, Selma Mavrić 77522 Potential Exam Questions **1. Define the difference between data and information. Why is this distinction important in business intelligence?** By information we mean data that have been shaped into a form that is meaningful and useful to human beings. Data, in contrast, are streams of raw facts representing events occurring in organizations or the physical environment before they have been organized and arranged into a form that people can understand and use. **2. Explain the purpose of a Database Management System (DBMS) and its key functions.** A database management system is software that enables an organization to centralize data, manage them efficiently and provide access to the stored data by application programs. The DBMS acts as an interface between application programs and the physical data files. The DBSM reveals the programmer or end users where and how the data are actually stored by separating the logical and physical view of the data. *The logical* view presents data as they would be perceived by end users or businesses specialists where the *physical view* shows how data are actually organized and structured on physical storage media. **3. Compare and contrast relational databases and NosQL databases. In what scenarios might each be preferable?** For more than 30 years relational databases technology has been the gold standard. Cloud computing, massive workloads for web services, and the need to store new types of data require database alternatives to the traditional relational model of organizing data in the form of tables, columns, and rows. Companies are turning to NosQL - Nonrelational database technologies for this purpose. Non relational database management system use more flexible data model and are designed for managing large data sets across many distributed machines and for easily scaling up or down. They are useful for accelerating simple queries against large volumes of structured and unstructured data including web social media graphics and other forms of data that are difficult to analyze with traditional SQL -based tools. **4. What is data normalization, and why is it significant in database design?** The process of creating small, stable, yet flexible and adaptive data structures from complex groups of data is called normalization figure 6.9 and 6.10 illustrate this process.  **5. Describe the concept of a data warehouse. How does it contribute to business intelligence?** A data warehouse is a database that store\'s current and historical data of potential interest to decision markers throughout the company that data originate in many core operational transactional systems such as systems for sales, customer accounts, and manufacturing, it may include data from website transactions. The data warehouse extracts current and historical data from multiple operational systems inside the organization. These data are combined with data from external sources and transformed by correcting inaccurate and incomplete data and restructuring the data for management reporting and analyzing before being loaded into the data warehouse. **6. What are some common techniques used in data mining? Provide examples of how businesses can use data mining to their advantage.** A data mining tool can discover different groupings within data, such as finding affinity groups for bank cards or partitioning a database into group of customers based on demographics and types of personal investments. One popular use for data mining is to provide detailed analysis of patterns and customer data for one to one marketing campaigns or for identifying profitable customers. Ostalo nisam nasla **7. Discuss the ethical considerations businesses must address when managing and analyzing data.** Businesses must address ethical considerations in data management, including safeguarding privacy, ensuring security, and using data transparently and for its intended purpose. They should mitigate biases in algorithms and datasets to promote fairness and avoid harm to stakeholders. Compliance with regulations like GDPR and practices such as informed consent and accountability are essential. Additionally, organizations should minimize the environmental impact of data operations by adopting sustainable technologies. Ovo je sa chata jbg 8**. Explain the importance of data quality and integrity in business intelligence applications.** With today\'s organizations relying so heavily on data to drive operations and decision making, data quality assurance he is especially important. If a database is properly designed and enterprise wide data standards are established, duplicate or inconsistent data elements should be minimal. Most data quality problems, however, such as misspelled names, transposed numbers, or incorrect or missing codes, steam from errors during data input. The incidence of such errors is rising as companies move their businesses to the web and allow customers and suppliers to enter data into their websites that directly update internal systems. When faulty data go unnoticed, they often lead to incorrect decisions, product recalls, and even financial losses some of those data quality problems are faulty inventory descriptions erroneous financial data, incorrect supplier information, and incorrect employee data. Some of these data quality problems are caused by redundant and inconsistent data produced by multiple systems. **9.** **How do business intelligence tools aid in decision-making? Provide specific examples of Bl tools and their functions.** Business intelligence (BI) tools aid decision-making by analyzing and visualizing data to uncover trends, patterns, and insights. They enable businesses to make data-driven decisions quickly and effectively. For example, Tableau provides interactive dashboards and visualizations to identify key performance metrics, while Power BI integrates data from various sources for comprehensive reporting. SAP BusinessObjects supports advanced analytics for large enterprises, helping forecast sales or monitor supply chain efficiency. Tools like Qlik Sense allow users to explore data dynamically, enhancing flexibility in decision-making. Overall, BI tools streamline data interpretation, improving strategic and operational choices. **10.** **What role does metadata play in database management and business intelligence?** Metadata plays a crucial role in database management and business intelligence by providing descriptive information about data, such as its structure, source, and usage. It helps organize and locate data efficiently, ensuring accuracy and consistency across systems. In business intelligence, metadata supports data integration, enables advanced analytics, and enhances reporting by offering context about the data. For example, it defines relationships between tables in a database or specifies data formats in a report. Overall, metadata ensures data is meaningful, accessible, and reliable for decision-making.