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Business Intelligence Analytics and Data Science a Managerial Perspective 4th Edition Sharda Solutions Manual

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Business Intelligence Analytics and Data Science a Managerial Perspective 4th Edition Sharda Solutions Manual Full clear download (no error formatting) at: business intelligence, analytics, and data science: a managerial perspective pdf business intelligence analytics and data science a managerial perspective 4th edition pdf business intelligence a managerial perspective on analytics (3rd edition) pdf business intelligence a managerial perspective on analytics pdf download business intelligence sharda pdf business intelligence a managerial perspective on analytics ebook business intelligence, analytics, and data science, 4th edition pdf business intelligence, analytics, and data science: a managerial perspective pearson
    Business Intelligence Analytics and Data Science A Managerial Perspective 4th Edition Sharda Solutions Manual Full clear download (no error formatting) at: Business Intelligence Analytics and Data Science A Managerial Perspective 4th Edition Sharda Test Bank  Full clear download (no error formatting) at:  CHA PTE R     2   Descriptive Analytics I:   Nature of Data, St a tistic a l   Modeling, and   V is u al iza ti o n   Learning Objectives for Chapter 2   Understand the nature of data as it relates to business intelligence (BI) and analytics Learn the methods used to make real-world data analytics ready Describe statistical modeling and its relationship to business analytics Learn about descriptive and inferential statistics Define business reporting, and understand its historical evolution    Understand the importance of data/information visualization Learn different types of visualization techniques Appreciate the value that visual analytics brings to business analytics Know the capabilities and limitations of dashboards CHAPTER OVERVIEW   In the age of Big Data and business analytics in which we are living, the importance of data is undeniable. The newly coined phrases like “ data is the oi l,”   “ data is the    new bacon, ”   “ data is the new currency, ”  and “ data is the king ”  are further stressing the renewed importance of data. But what type of data are we talking about? Obviously, not  just any data. The “ garbage in garbage out  —  GIG O”  concept/principle applies to today ’ s “ Big Data ”  phenomenon more so than any data definition that we have had in the past. To live up to its promise, its value proposition, and its ability to turn into insight, data has to be carefully created/identified, collected, integrated, cleaned, transformed, and  properly contextualized for use in accurate and timely decision making. Data is the main theme of this chapter. Accordingly, the chapter starts with a description of the nature of data: what it is, what different types and forms it can come in, and how it can be  preprocessed and made ready for analytics. The first few sections of the chapter are dedicated to a deep yet necessary understanding and processing of data. The next few sections describe the statistical methods used to prepare data as input to produce both descriptive and inferential measures. Following the statistics sections are sections on reporting and visualization. A report is a communication artifact prepared with the specific intention of converting data into information and knowledge and relaying that information in an easily understandable/digestible format. Nowadays, these reports are more visually oriented, often using colors and graphical icons that collectively look like a dashboard to enhance the information content. Therefore, the latter part of the chapter is dedicated to subsections that present the design, implementation, and best  practices for information visualization, storytelling, and information dashboards. CHAPTER OUTLINE   2.1 Opening Vignette: SiriusXM Attracts and Engages a New Generation of Radio Consumers with Data-Driven Marketing 2.2 The Nature of Data 2.3 A Simple Taxonomy of Data 2.4 The Art and Science of Data Preprocessing 2.5 Statistical Modeling for Business Analytics 2.6 Regression Modeling For Inferential Statistics 2.7 Business Reporting 2.8 Data Visualization 2.9 Different Types of Charts and Graphs 2.10 The Emergence of Visual Analytics 2.11 Information Dashboards    ANSWERS TO END OF SECTION REVIEW QUESTIONS   Section 2.1 Review Questions   1. What does SiriusXM do? In what type of market does it conduct its business? SiriusXM is a provider of satellite radio. They primarily provide services in automobiles. 2. What were the challenges? Comment on both technology and data-related challenges. The company had several challenges. The first was the changing demographics of car owners. As cars were sold on the secondary market it was more difficult for them to identify new potential customers. Additionally, the company had a technical challenge because of an acquisition. There was uncertainty about their ability to use all of the technology available through the acquisition. 3. What were the proposed solutions? The company felt that it would be able to maintain a strategic advantage if it  began working towards being a data-driven marketing company. This would allow them to more precisely target current and potential customers. 4. How did they implement the proposed solutions? Did they face any implementation challenges? The company decided to bring all marketing work in-house. It was determined that it was important for them to clean the data and manage it in a central repository. To do this they partnered with Teradata. There were challenges with the implementation due to the variability in the data itself and the complexity of the task. 5. What were the results and benefits? Were they worth the effort/investment? The company has been able to progress significantly in its goal of becoming a data-driven marketing organization. With the new systems in place, it is possible to move campaigns faster with better visibility. 6. Can you think of other companies facing similar challenges that can potentially  benefit from similar data-driven marketing solutions? Most companies that market directly to end users could use a similar approach to managing and leveraging data in their marketing activities.
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