Joint Analysis Design
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Axis facilitated joint analysis design (JAD) workshops between IT and business groups at a client organization to enhance their global reporting systems. Through interviews and collaborative workgroups, Axis helped surface gaps, interdependencies, and critical issues that had not previously been identified. This process cultivated mutual understanding and consensus across groups. The outcome of the JAD workshops provided an effective basis for the client to make cost-effective decisions on how to move their global reporting systems forward.
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Enterprise Information Management Strategy - a proven approach
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Sharda_dss11_im_01.docChapter 1An Overview of Analy.docx
Sharda_dss11_im_01.doc
Chapter 1:
An Overview of Analytics, and AI
Learning Objectives for Chapter 1
· Understand the need for computerized support of managerial decision making
· Understand the development of systems for providing decision-making support
· Recognize the evolution of such computerized support to the current state of analytics/data science and artificial intelligence
· Describe the business intelligence (BI) methodology and concepts
· Understand the different types of analytics and review selected applications
· Understand the basic concepts of artificial intelligence (AI) and see selected applications
· Understand the analytics ecosystem to identify various key players and career opportunities
CHAPTER OVERVIEW
The business environment (climate) is constantly changing, and it is becoming more and more complex. Organizations, both private and public, are under pressures that force them to respond quickly to changing conditions and to be innovative in the way they operate. Such activities require organizations to be agile and to make frequent and quick strategic, tactical, and operational decisions, some of which are very complex. Making such decisions may require considerable amounts of relevant data, information, and knowledge. Processing these in the framework of the needed decisions must be done quickly, frequently in real time, and usually requires some computerized support. As technologies are evolving, many decisions are being automated, leading to a major impact on knowledge work and workers in many ways. This book is about using business analytics and artificial intelligence (AI) as a computerized support portfolio for managerial decision making. It concentrates on the theoretical and conceptual foundations of decision support as well as on the commercial tools and techniques that are available. The book presents the fundamentals of the techniques and the manner in which these systems are constructed and used. We follow an EEE (exposure, experience, and exploration) approach to introducing these topics. The book primarily provides exposure to various analytics/AI techniques and their applications. The idea is that students will be inspired to learn from how various organizations have employed these technologies to make decisions or to gain a competitive edge. We believe that such exposure to what is being accomplished with analytics and that how it can be achieved is the key component of learning about analytics. In describing the techniques, we also give examples of specific software tools that can be used for developing such applications. However, the book is not limited to any one software tool, so students can experience these techniques using any number of available software tools. We hope that this exposure and experience enable and motivate readers to explore the potential of these techniques in their own domain. To facilitate such exploration, we include exercises that direct the reader to Teradata.
Sharda_dss11_im_01.docChapter 1An Overview of Analy.docx
This chapter introduces analytics and artificial intelligence as tools to support managerial decision making. It discusses how the business environment has become more complex, requiring quick strategic decisions based on large amounts of data. The chapter then provides an overview of the evolution of decision support systems from MIS to business intelligence to today's analytics. It defines different types of analytics including descriptive, predictive, and prescriptive and provides examples. Finally, it introduces artificial intelligence and discusses how analytics and AI are converging to automate more decision making.
Making your strategy work
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