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Database Systems, Data Warehouses, and Data Marts End of Chapter Solutions Reviews and Discussions 1. Define two types of data in a database. The two types of data in a database are: internal and external. Internal data is collected from within an organization and can include transaction records, sales records, personnel records, and so forth. External data comes from a variety of sources, such as tax records, competitors, customers, and suppliers. It is often stored in a data warehouse. 2. What is a DBMS? What are its major components? A DBMS is software for creating, storing, maintaining, and accessing database files. Its major components are: • Database engine • Data definition • Data manipulation • Application generation • Data administration 3. What is the relational model in the database environment? A relational model uses a two-dimensional table of rows and columns of data. Rows are records (also called tuples), and columns are fields (also referred to as attributes). 4. How many logical and physical views of data exists? For each database, there is only one physical view of data. However, there can be more than one logical view of data, depending on the user. 5. Which companies benefit the most from graph databases? In the healthcare industry, in particular, graph databases have proven to be very helpful as doctors may belong to multiple healthcare providers, diseases may have multiple symptoms, and there may be multiple relationships among organizations such as insurance companies, hospitals, and different employers. 6. What are three outputs from a data warehouse? Three outputs from a data warehouse are: • OLAP analysis • Data-mining analysis • Decision-making reports 7. What are three major types of business analytics? The major types of business analytics are descriptive, predictive, and prescriptive analytics. 8. What are five big data privacy risks? How could they be eliminated or minimized? The following are five big data privacy risks and ways to eliminate or at least minimize their impact: • Discrimination: Big data analytics may reveal information that gives a decision-maker a reason approve or decline an individual’s loan application. • Privacy breaches and embarrassments: Big data analytics may reveal that a customer of a retailer, for example, is pregnant, by sending out pregnancy promotional materials. • Unethical actions based on interpretations: Big data analytics may be misinterpreted and offer support for a decision that, while legal, may not be ethical. • Loss of anonymity: Big data analytics, by combining several datasets and cross referencing various data, could easily reveal the identity of individuals whose data were analyzed. • Few legal protections exist for the involved individuals: There are, to date, few (if any) legal requirements for protecting privacy while using big data analytics. Projects 1. After reading the information presented in this chapter and other sources, write a two-page paper that explains BI. Identify three companies (in addition to those mentioned in this chapter) that have been using BI, and explain the applications of BI in these companies. What are two differences between BI and information or data? Business Intelligence: Unveiling Insights for Informed Decisions Business Intelligence (BI) stands as a cornerstone in modern business operations, empowering organizations to transform raw data into meaningful insights for informed decision-making. At its essence, BI encompasses a set of tools, technologies, and methodologies designed to gather, store, analyze, and visualize data to support strategic and tactical decision-making processes within enterprises. Understanding Business Intelligence: BI facilitates the extraction of actionable insights from vast and complex datasets, enabling companies to gain a competitive edge in their respective industries. By leveraging BI, organizations can uncover patterns, trends, and correlations hidden within data, thus facilitating predictive and prescriptive analytics. Moreover, BI fosters a data-driven culture, wherein decision-makers rely on evidence-based insights rather than intuition or guesswork. Applications of BI: Numerous companies across various sectors have embraced BI to streamline operations, enhance performance, and drive innovation. For instance, Walmart, a retail giant, utilizes BI tools to analyze customer purchasing behavior, optimize inventory management, and forecast demand accurately. This enables Walmart to minimize stockouts, reduce carrying costs, and tailor promotions to individual customer preferences effectively. Similarly, Netflix harnesses BI to personalize content recommendations for its subscribers based on their viewing history, preferences, and demographic data. By leveraging advanced algorithms and machine learning models, Netflix delivers a highly curated viewing experience, thereby increasing user engagement and retention rates. Furthermore, American Express utilizes BI to detect fraudulent transactions in real-time by analyzing vast volumes of transactional data and identifying anomalous patterns indicative of fraudulent activity. This proactive approach not only safeguards the interests of cardholders but also minimizes financial losses for the company. Differences between BI and Information/Data: While BI, information, and data are interrelated concepts, they exhibit distinct characteristics and serve different purposes within organizational contexts. Two primary differences between BI and information/data are: 1. Purpose and Context: • BI focuses on transforming raw data into actionable insights tailored to support decision-making processes within organizations. It involves aggregating, analyzing, and visualizing data to derive meaningful conclusions and drive strategic initiatives. • Information refers to processed data that conveys meaning or context. It encompasses facts, figures, and statistics derived from data analysis, which are communicated to stakeholders to facilitate understanding and decision-making. • Data, on the other hand, comprises raw, unprocessed facts and figures devoid of context or meaning. It serves as the foundation for generating information and insights through analysis and interpretation. 2. Scope and Complexity: • BI encompasses a comprehensive suite of tools, technologies, and methodologies designed to collect, store, process, and analyze data from various sources. It involves sophisticated data modeling, visualization, and reporting capabilities to extract actionable insights from complex datasets. • Information encapsulates processed data that has undergone validation, interpretation, and contextualization to provide meaningful insights to end-users. It may include reports, dashboards, summaries, and presentations derived from BI processes. • Data represents the raw material from which information and insights are derived. It can encompass structured data from databases, semi-structured data from sources like XML or JSON, and unstructured data from documents, emails, or social media feeds. In conclusion, Business Intelligence serves as a vital enabler for organizations seeking to harness the power of data to drive strategic decision-making and gain a competitive edge in today's dynamic business landscape. By leveraging BI tools and methodologies, companies can unlock valuable insights, optimize performance, and capitalize on emerging opportunities to achieve sustainable growth and success. 2. After reading the information presented in this chapter and other sources, write a one-page paper that identifies three companies (in addition to those mentioned in this chapter) that are using data mining tools. Explain how data mining has helped these companies with their bottom lines. Are data-mining tools beneficial to service companies or manufacturing or both? Three companies that have effectively utilized data mining tools to enhance their bottom lines include Amazon, Netflix, and Walmart. 1. Amazon: Amazon is a prime example of a company leveraging data mining tools to improve its bottom line. Through the analysis of customer behavior, purchase history, and preferences, Amazon employs data mining algorithms to recommend personalized products to users, leading to increased sales and customer satisfaction. Additionally, Amazon utilizes data mining to optimize its supply chain management, forecast demand, and streamline inventory management, resulting in reduced costs and improved operational efficiency. 2. Netflix: Netflix utilizes data mining techniques to analyze user viewing patterns and preferences to recommend personalized content, improving customer engagement and retention. By understanding viewer preferences, Netflix can produce and acquire content that resonates with its audience, leading to increased subscription rates and revenue. Moreover, data mining helps Netflix in content categorization, allowing the platform to efficiently organize its vast library and enhance user experience. 3. Walmart: Walmart utilizes data mining tools to analyze sales data, customer demographics, and purchasing patterns to optimize product placement, pricing strategies, and inventory management. By mining transactional data, Walmart can identify trends, forecast demand, and tailor promotions to specific customer segments, ultimately driving sales and profitability. Additionally, Walmart employs data mining in fraud detection and prevention, enhancing security measures and minimizing losses. Data mining tools are beneficial to both service and manufacturing companies. In service industries, such as retail (e.g., Amazon and Walmart), data mining aids in understanding customer behavior, improving marketing strategies, and optimizing operations. For manufacturing companies, data mining enables predictive maintenance, quality control, and supply chain optimization, leading to cost reductions and improved productivity. Overall, data mining tools empower companies across various sectors to extract valuable insights from their data, driving informed decision-making and ultimately improving their bottom lines. 3. After reading the information presented in this chapter and other sources, write a one-page paper that identifies two companies that use mobile analytics. How has mobile analytics helped these companies achieve their sales goals? What are two differences between mobile analytics and traditional analytics? Two companies that have effectively utilized mobile analytics to achieve their sales goals are Starbucks and Airbnb. 1. Starbucks: Starbucks leverages mobile analytics through its mobile app, which collects data on customer preferences, purchasing behavior, and location-based information. By analyzing this data, Starbucks gains insights into customer trends, allowing them to personalize promotions, tailor product offerings, and optimize store layouts. Mobile analytics have helped Starbucks enhance customer engagement, increase loyalty, and drive sales by offering targeted incentives and rewards to app users. Additionally, Starbucks uses mobile analytics to track the effectiveness of marketing campaigns and optimize their mobile ordering and payment systems, resulting in improved operational efficiency and revenue growth. 2. Airbnb: Airbnb utilizes mobile analytics to analyze user interactions, booking patterns, and property listings to improve the overall user experience and drive bookings. Through its mobile app, Airbnb gathers data on user preferences, search behavior, and location data, allowing them to offer personalized recommendations and refine their search algorithms. Mobile analytics enable Airbnb to identify popular destinations, predict demand, and optimize pricing strategies, ultimately leading to increased bookings and revenue generation. Moreover, Airbnb uses mobile analytics to enhance host performance by providing insights into occupancy rates, pricing competitiveness, and guest reviews, empowering hosts to optimize their listings and maximize earnings. Two differences between mobile analytics and traditional analytics are: 1. Data Sources: Mobile analytics primarily rely on data collected from mobile devices, including app usage data, location information, and device-specific data such as screen size and operating system. In contrast, traditional analytics often involve data collected from various sources such as databases, CRM systems, and transaction records, which may not include mobile-specific data. 2. Real-Time Insights: Mobile analytics are often capable of providing real-time insights due to the immediate nature of mobile interactions. Companies can analyze user behavior as it happens, allowing for quick decision-making and personalized responses. Traditional analytics may involve batch processing of data and historical analysis, which may not offer real-time insights into customer behavior or market trends. In conclusion, companies like Starbucks and Airbnb have successfully leveraged mobile analytics to gain valuable insights into customer behavior, personalize offerings, and drive sales. Mobile analytics offer unique advantages such as real-time insights and access to mobile-specific data, distinguishing them from traditional analytics approaches. 4. After reading the information presented in this chapter and other sources, write a two-page paper that explains graph databases. Identify three companies (in addition to those mentioned in this chapter) that have been using graph databases and explain the applications of graph databases in these companies. Title: Exploring Graph Databases and Their Applications in Modern Enterprises Introduction to Graph Databases: Graph databases represent a specialized type of database management system (DBMS) that is designed to handle interconnected data with complex relationships. Unlike traditional relational databases, which store data in tables with predefined schemas, graph databases utilize graph structures consisting of nodes, edges, and properties to model and represent data. Nodes represent entities such as people, products, or locations, while edges represent the relationships between these entities. Properties provide additional information about nodes and edges. This unique architecture makes graph databases well-suited for scenarios where relationships between data entities are paramount, such as social networks, recommendation systems, and network analysis. Companies Utilizing Graph Databases: 1. Facebook: Facebook is one of the pioneers in utilizing graph databases to power its social networking platform. The company employs a graph database called Apache TinkerPop, which is part of the broader Apache Gremlin graph computing framework. Facebook's graph database model represents users as nodes and their connections (friendships) as edges. This allows Facebook to efficiently analyze social connections, recommend friends, and personalize content feeds based on users' social interactions. Additionally, Facebook utilizes graph databases for fraud detection, spam prevention, and content moderation by identifying suspicious patterns and relationships within its vast network. 2. LinkedIn: LinkedIn, the professional networking platform, relies on graph databases to manage and analyze its extensive network of professionals, companies, and job listings. LinkedIn's graph database model represents users, companies, job postings, and other entities as nodes, with connections (such as connections between users or job applications) represented as edges. By leveraging graph databases, LinkedIn enhances its recommendation algorithms for job seekers, suggests relevant connections, and optimizes its advertising targeting based on users' professional interests and interactions. 3. Uber: Uber, the ride-sharing and transportation company, utilizes graph databases to optimize its matching algorithms and improve the efficiency of its transportation network. Uber's graph database model represents drivers, riders, vehicles, and geographic locations as nodes, with ride requests, trips, and routes represented as edges. By analyzing the dynamic relationships between these entities, Uber can efficiently match riders with nearby drivers, optimize route planning, and minimize wait times. Additionally, Uber employs graph databases for fraud detection, driver-rider safety analysis, and market segmentation to better serve its diverse customer base. Applications of Graph Databases: Graph databases offer numerous applications across various industries, including: 1. Recommendation Systems: Graph databases enable personalized recommendations by analyzing connections between users, products, or content items. Companies like Amazon, Netflix, and Spotify leverage graph databases to suggest relevant products, movies, or music based on users' preferences and past interactions. 2. Fraud Detection and Risk Management: Graph databases facilitate the detection of fraudulent activities by identifying suspicious patterns and connections within networks. Financial institutions, e-commerce platforms, and insurance companies utilize graph databases to detect fraudulent transactions, mitigate risks, and enhance security measures. 3. Supply Chain Optimization: Graph databases help optimize supply chain management by modeling relationships between suppliers, manufacturers, distributors, and customers. Companies like Walmart, IBM, and FedEx utilize graph databases to improve inventory visibility, streamline logistics operations, and enhance demand forecasting. Conclusion: In conclusion, graph databases offer a powerful framework for managing interconnected data and extracting valuable insights from complex relationships. Companies across various industries, including Facebook, LinkedIn, and Uber, leverage graph databases to enhance their services, improve decision-making, and drive innovation. With their versatile applications and ability to handle dynamic, interconnected data, graph databases are becoming increasingly essential in the era of big data and interconnected systems. 5. After reading the information presented in this chapter and other sources, write a two-page paper that identifies three companies that have been using big data. Explain how big data is helping these companies improve the efficiency of their operations. How could big data privacy risks be eliminated or minimized? In today's data-driven landscape, companies across various sectors are harnessing the power of big data to drive operational efficiency and gain competitive advantages. Three notable companies at the forefront of leveraging big data are Amazon, Netflix, and Walmart. Each of these companies utilizes big data in unique ways to optimize operations and enhance customer experiences. Amazon, the e-commerce giant, employs big data analytics extensively to personalize recommendations, streamline supply chain management, and improve customer service. By analyzing vast amounts of customer data, including browsing history, purchase behavior, and demographic information, Amazon tailors product recommendations, resulting in increased sales and improved customer satisfaction. Additionally, Amazon utilizes predictive analytics to forecast demand, optimize inventory management, and minimize delivery times, thereby enhancing operational efficiency. Netflix, a leading streaming service provider, relies heavily on big data to enhance content recommendation algorithms and content production decisions. Through analyzing user interactions and preferences, Netflix customizes content recommendations, increasing user engagement and retention. Moreover, Netflix utilizes big data analytics to identify viewer trends and preferences, guiding content creation and acquisition strategies. This data-driven approach enables Netflix to produce high-quality content that resonates with its audience, thereby maintaining its competitive edge in the streaming industry. Walmart, a multinational retail corporation, harnesses big data to optimize various aspects of its operations, including inventory management, pricing strategies, and customer engagement. Walmart leverages data from point-of-sale transactions, online interactions, and supply chain operations to forecast demand, optimize product placement, and adjust pricing dynamically. By analyzing vast amounts of data in real-time, Walmart minimizes stockouts, reduces inventory costs, and enhances the overall shopping experience for its customers. While big data offers significant benefits in terms of operational efficiency and competitive advantage, it also raises concerns regarding data privacy and security. To mitigate privacy risks associated with big data, companies can implement several strategies: 1. Anonymization and pseudonymization techniques can be employed to protect personally identifiable information (PII) while still enabling data analysis. 2. Implementing robust data governance frameworks and compliance measures ensures that data usage adheres to legal and regulatory requirements, such as GDPR and CCPA. 3. Transparency and user consent mechanisms empower individuals to control how their data is collected, used, and shared, fostering trust and accountability. 4. Investing in advanced encryption technologies and secure data storage mechanisms safeguards sensitive data from unauthorized access or breaches. 5. Regular audits and assessments of data handling practices help identify potential privacy vulnerabilities and ensure ongoing compliance with privacy regulations. By adopting these measures, companies can harness the benefits of big data while mitigating privacy risks, thereby fostering trust among customers and stakeholders. In conclusion, companies such as Amazon, Netflix, and Walmart exemplify how big data analytics can drive operational efficiency and innovation across various industries. However, it is imperative for organizations to prioritize data privacy and security to maintain consumer trust and uphold ethical standards in the era of big data 6. The sample table below shows 11 of the students enrolled in an MIS course. Organize the data in a relational format, and use Microsoft Access to list all ACC majors, all ACC majors with a GPA higher than 3.7, all students who are MIS or ACC majors, and all students who aren’t ACC majors. Repeat this assignment, this time using Excel, and generate the same results. The procedure for this hands-on project is as follows: In MS Access, create the table first and then, by using Query Design View, generate the results. In Excel, data filter command should be used to generate the same results. Appendix C, available on CourseMate, explains Excel data filter in detail. Are You Ready to Move On? 1. Competitors, customers, and suppliers are examples of sources for internal data. Answer: False 2. The logical view involves how information appears to users and how it can be organized and retrieved. Answer: True 3. Normalization improves database efficiency by eliminating redundant data and ensuring that only related data is stored in a table. Answer: True 4. All of the following are outputs of a data warehouse except: a. big data b. OLAP analysis c. data-mining analysis d. decision-making reports Answer: a 5. Which of the following is not among the components of a DBMS? a. database engine b. magnetic tape c. application generation d. data administration Answer: b 6. All of the following are advantages of data marts over data warehouses except: a. Access to data is often slower because of their smaller size. b. They are less expensive. c. Response time for users is improved. d. They are easier to create because they are smaller and often less complex. Answer: a Case Studies Case Study 3-1: Data Mining Helps Students Enroll in Courses with Higher Chances of Success 1. Which other companies are using approaches similar to the one used by Austin Peay State? Netflix, eHarmony, and Amazon are using approaches similar to the one used by Austin Peay State. 2. Based on which data does the system makes a course recommendation to a student? It compares a student’s transcripts with those of past students who had similar grades and SAT scores. 3. How many courses are recommended to a student for possible selection? The program offers 10 “Course Suggestions for You.” 4. According to the case study, are any other Tennessee schools using this approach? Three other Tennessee colleges now use this software. Case Study 3-2: Data Mining Tools at Pandora Radio 1. How does Pandora Radio recommend music to its listeners? It applies data-mining tools to the Music Genome Project, which is a vast database of songs that a team of experts has broken down into their various components: melody, rhythm, vocals, lyrics, and so on. 2. How are listeners able to create their own customized stations? Listeners begin by entering their favorite songs, artists, or genres, creating customized “stations.” Then, Pandora Radio mines its database to find songs that are similar. 3. What are some variables that Pandora Radio uses to recommend a song? Some variables that Pandora Radio uses to recommend a song are the listeners’ past and current favorites. Solution Manual for MIS Hossein Bidgoli 9781305632004, 9781337625999, 9781337625982, 9781337406925
