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Chapter 1: An Overview of Business Intelligence, Analytics, and Decision Support

Introduction to Business Intelligence and Analytics

  • Role of Business Intelligence (BI) and analytics systems in decision making.

  • Key focus on supporting organizational decision-making processes.

Plan of the Book

  • Part I: Decision Making and Analytics

    • Chapters 1 & 2

  • Part II: Descriptive Analytics

    • Chapters 3 & 4

  • Part III: Predictive Analytics

    • Chapters 5 - 8

  • Part IV: Prescriptive Analytics

    • Chapters 9 - 12

  • Part V: Big Data and Future Directions

    • Business analytics in a rapidly evolving landscape.

Learning Objectives

  1. Understand the business landscape and organization resilience.

  2. Identify the necessity of computerized support for managerial decisions.

  3. Familiarize with a framework for managerial decision-making.

  4. Grasp foundational concepts of Decision Support Systems (DSS) methodology.

  5. Explore BI concepts and their relationship to DSS.

  6. Differentiate various types of analytics.

  7. Recognize tools used for computerized decision support.

Opening Vignette: Magpie Sensing Case Study

  • Overview of Magpie Sensing's use of analytics to manage a vaccine supply chain.

  • Discussion points on the effectiveness of analytics in decision-making processes.

Questions for Consideration

  1. What insights does descriptive analytics provide?

  2. How does predictive analytics support decision-making?

  3. The role of prescriptive analytics in business strategy.

  4. Methods for real-time information reporting.

  5. Other contexts requiring real-time monitoring.

Changing Business Environment

  • Increasing reliance on computerized operations.

  • Business Pressures–Responses–Support Model:

    • Identifies competitive pressures in the business environment.

    • Illustrates company responses to counteract these pressures.

    • Highlights the support structures necessary for effective decision-making.

Business Environment Factors

  • Markets:

    • Competitive dynamics, global expansion, e-commerce opportunities.

  • Consumer Demands:

    • Increasing customization, quality, and delivery expectations from customers.

  • Technology:

    • Fast-paced innovation, obsolescence, and overwhelm of information.

  • Societal Factors:

    • Regulatory environments, workforce demographics, social responsibilities.

Organizational Responses

  • Strategies for organizational adaptation:

    • Reactive, anticipative, adaptive, proactive approaches to management.

    • Development of strategic plans and innovative business models.

Closing the Strategy Gap

  • Aim of computerized decision support: to minimize the disparity between existing and desired performance outcomes.

Managerial Decision Making

  • Concept of management as an organized approach to achieving goals through resource utilization.

  • Decision-making focuses on solving problems by assessing alternatives.

Nature of Managers’ Work

  • Based on Mintzberg's 10 Managerial Roles, including interpersonal, informational, and decisional roles.

Decision-Making Process

  • Four-Step Process:

    1. Define the problem or opportunity.

    2. Construct a model to analyze the problem context.

    3. Identify and evaluate possible solutions.

    4. Recommend a potential solution based on the evaluation.

Information Systems Support

  • Tools for facilitating decision-making:

    • Enhanced group communication, data management, and knowledge management.

    • Support for information processing and cognitive limitations.

Early Decision Support Framework

  • The structure of decision-making systems as outlined by Gory and Scott-Morten (1971).

  • Types of decisions: structured, semi-structured, and unstructured.

The Concept of Decision Support Systems (DSS)

  • Definition and purpose of DSS in unstructured problem scenarios.

  • Evolution of decision systems into comprehensive Business Intelligence frameworks.

Framework for Business Intelligence (BI)

  • BI represents an evolution in decision support systems enhancing data access and visualization.

  • Importance of BI in analytical capabilities and organizational performance measurement.

Definition of BI

  • BI as a comprehensive integration of tools and methodologies aimed at transforming data into actionable insights for managerial decision-making.

A Brief History of BI

  • Evolution from traditional reporting and Executive Information Systems to dynamic systems incorporating AI and Big Data analytics.

Evolution of BI Capabilities

  • Development from querying and reporting to advanced dashboard analytics and management systems.

The Architecture of BI

  • Components of a BI system:

    • Data warehouses, analytics tools, performance management, and user interfaces.

Business Value of BI Analytical Applications

  • Applications include:

    • Customer segmentation, fraud detection, procurement optimization, and customer attrition analysis.

Application Cases in Analytics

  • Various case studies demonstrating the practical application of business intelligence and analytics in decision-making contexts.

End-of-Chapter Thoughts

  • Emphasis on the importance of BI in enhancing customer service and operational efficiency within organizations, exemplified through case studies.