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business
business intelligence and analytics

Questions and Answers of Business Intelligence and Analytics

3. What are some of the major response activities that organizations take?
2. What are some of the major factors in today’s business environment?
1. List the components of and explain the Business Pressures–Responses–Support model.
5. Can the same data warehouse be used for business intelligence and optimization applications?
4. How does Norfolk Southern use the data warehouse for HR applications?
3. What type of information support is provided through accessNS?
2. What type of information is accessible through the visualization applications?
1. How are information systems used at Norfolk Southern to support decision making?
List and describe major ethical and legal issues of MSS implementation
Describe societal impacts of MSS
Learn the potential impacts of MSS on individuals
Describe organizational impacts of MSS
Understand Web 2.0 and its characteristics as related to MSS
Describe the potential of cloud computing in business intelligence
Describe how virtual-world applications can result in additional data for BI applications
Describe how virtual-world technologies can be used for decision support and the associated advantages and disadvantages
Know how RFID data analysis can help improve supply-chain management and other operations
Explore some of the emerging technologies that may impact MSS
Explore integrated intelligent support systems
Understand the concepts behind intelligent software agents and their use, capabilities, and limitations in developing advanced intelligent systems
Understand the concepts behind support vector machines and their applications in developing advanced intelligent systems
Understand fuzzy logic and its application in designing intelligent systems
Know the concepts behind and applications of genetic algorithms
Know the concepts behind and applications of case-based reasoning systems
Understand machine-learning concepts
Learn about tools and technologies for developing rule-based DSS
Identify proper applications of ES
Explain the benefits and limitations of rule-based systems for decision support
Learn the knowledge engineering process used to build ES
Understand the architecture of rule-based ES
Describe the concept and evolution of rulebased expert systems (ES)
Understand the importance of knowledge in decision support
Understand the concept and evolution of artificial intelligence
Define creativity and explain how it can be facilitated by computers
Describe the role of emerging technologies in supporting collaboration
Understand how the Web enables collaborative computing and group support of virtual meetings
Describe specifically how a GDSS uses parallelism and anonymity and how they lead to process/task gains and losses
Describe the three settings of GDSS
Understand the concept of GDSS and describe how to structure an electronic meeting in a decision room
Become familiar with the GSS products of the major vendors, including Lotus, Microsoft, WebEx, and Groove
Describe indirect support for decision making, especially in synchronous environments
Understand the concepts of process gain, process loss, task gain, and task loss and explain how GSS introduces, increases, or decreases each of them
Explain the underlying principles and capabilities of groupware, such as group support systems (GSS)
Explain the concepts and importance of the time/place framework
Describe how computer systems facilitate communication and collaboration in an enterprise
Understand the basic concepts and processes of groupwork, communication, and collaboration
Describe how knowledge management can revolutionize the way an organization functions
Describe the benefits and drawbacks of knowledge management initiatives
Describe the roles of people, process, and technology in knowledge management
Describe how KMS are implemented
Describe ways of evaluating intellectual capital in an organization
Describe the role of knowledge management in organizational activities
Describe the activities of the chief knowledge officer and others involved in knowledge management
Describe different approaches to knowledge management
Describe the technologies that can be used in a knowledge management system (KMS)
Describe organizational learning and its relationship to knowledge management
Describe the characteristics of knowledge management
Define knowledge and describe the different types of knowledge
Describe real-time (active) data warehousing
Explain data integration and the extraction, transformation, and load (ETL) processes
Understand data warehousing architectures
Understand the basic definitions and concepts of data warehouses
Understand some of the basics of dashboard design
Describe the differences between scorecards and dashboards
Describe the basic elements of the balanced scorecard and Six Sigma methodologies
Understand the role of methodologies in BPM
Appreciate the wide variety of applications of neural networks
Understand the step-by-step process of how to use neural networks
Understand how backpropagation learning works in feedforward neural networks
Learn the advantages and limitations of ANN
Learn the different types of neural network architectures
Know the similarities and differences between biological and artificial neural networks
Understand the concept and definitions of artificial neural networks (ANN)
Understand the different methods to introduce structure to text-based data
Know the process of carrying out a text mining project
Describe the key issues of model management
Explain what is meant by sensitivity analysis, what-if analysis, and goal seeking
Describe how to handle multiple goals
Explain the differences among algorithms, blind search, and heuristics
Understand how search methods are used to solve MSS models
Describe how to structure a linear programming model
Explain the basic concepts of optimization, simulation, and heuristics, and when to use them
Describe how spreadsheets can be used for MSS modeling and solution
Understand how to structure decision making with a few alternatives
Understand some different, well-known model classes
Describe how MSS models interact with data and the user
Understand the basic concepts of management support system (MSS) modeling
Define data mining as an enabling technology for business intelligence
Understand the systems approach
Learn how DSS support for decision making can be provided in practice
Differentiate between the concepts of making a choice and establishing a principle of choice
Recognize the concepts of rationality and bounded rationality and how they relate to decision making
Understand Simon’s four phases of decision making: intelligence, design, choice, and implementation
Understand the conceptual foundations of decision making
Understand current DSS issues
Become familiar with a DSS development language
Describe DSS hardware and software platforms
Explain the unique role of the user in DSS versus management information systems(MIS)
Explain Internet impacts on DSS and vice versa
Describe the components and structure of each DSS component: the data management subsystem, the model management subsystem, the user interface (dialog)subsystem, the knowledge-based management

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