MIS470 1

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

Learning Objectives

• Understand today’s turbulent business environment and describe how organizations survive and even excel in such an environment (solving problems and exploiting opportunities)

• Understand the need for computerized support of managerial decision making

• Understand an early framework for managerial decision making

• ...

(Continued…) MIS470: Decision Support & Business Intelligence 2

Learning Objectives

• Learn the conceptual foundations of the DSS methodology

• Describe the BI methodology and concepts and relate them to DSS

• Understand the various types of analytics

• List the major tools of computerized decision support

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Changing Business Environment & Computerized Decision Support • Companies are moving aggressively to computerized support of their

operations  Business Intelligence

• Business Pressures–Responses–Support Model • Business pressures result of today's competitive business climate

• Responses to counter the pressures

• Support to better facilitate the process

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Business Pressures–Responses–Support Model

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The Business Environment

• The environment in which organizations operate today is becoming more and more complex, creating • opportunities, and

• problems.

• Example: globalization.

• Business environment factors: • markets, consumer demands, technology, and societal…

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Business Environment Factors FACTOR DESCRIPTION

Markets Strong competition

Expanding global markets

Blooming electronic markets on the Internet

Innovative marketing methods

Opportunities for outsourcing with IT support

Need for real-time, on-demand transactions

Consumer Desire for customization

demand Desire for quality, diversity of products, and speed of delivery

Customers getting powerful and less loyal

Technology More innovations, new products, and new services

Increasing obsolescence rate

Increasing information overload

Social networking, Web 2.0 and beyond

Societal Growing government regulations and deregulation

Workforce more diversified, older, and composed of more women

Prime concerns of homeland security and terrorist attacks

Necessity of Sarbanes-Oxley Act and other reporting-related legislation

Increasing social responsibility of companies

Greater emphasis on sustainability

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Organizational Responses

• Be Reactive, Anticipative, Adaptive, and Proactive

• Managers may take actions, such as • Employ strategic planning.

• Use new and innovative business models.

• Restructure business processes.

• Participate in business alliances.

• Improve corporate information systems.

• … more [in your book]

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Closing the Strategy Gap

• One of the major objectives of computerized decision support is to facilitate closing the gap between the current performance of an organization and its desired performance, as expressed in its mission, objectives, and goals, and the strategy to achieve them.

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Managerial Decision Making • Management is a process by which organizational

goals are achieved by using resources. • Inputs: resources

• Output: attainment of goals

• Measure of success: outputs / inputs

• Management  Decision Making

• Decision making: selecting the best solution from two or more alternatives

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The Nature of Managers’ Work Mintzberg's 10 Managerial Roles

Interpersonal 1. Figurehead

2. Leader

3. Liaison

Informational 4. Monitor

5. Disseminator

6. Spokesperson

Decisional 7. Entrepreneur

8. Disturbance handler

9. Resource allocator

10. Negotiator

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Decision-Making Process • Managers usually make decisions by following a

four-step process (a.k.a. the scientific approach) 1. Define the problem (or opportunity)

2. Construct a model that describes the real-world problem.

3. Identify possible solutions to the modeled problem and evaluate the solutions.

4. Compare, choose, and recommend a potential solution to the problem.

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Information Systems Support for Decision Making

• Group communication and collaboration

• Improved data management

• Managing data warehouses and Big Data

• Analytical support

• Overcoming cognitive limits in processing and storing information

• Knowledge management

• Anywhere, anytime support

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An Early Decision Support Framework (by Gory and Scott-Morten, 1971)

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An Early Decision Support Framework

• Degree of Structuredness (Simon, 1977) • Decisions are classified as

• Highly structured (a.k.a. programmed)

• Semi-structured

• Highly unstructured (i.e., nonprogrammed)

• Types of Control (Anthony, 1965) • Strategic planning (top-level, long-range)

• Management control (tactical planning)

• Operational control

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The Concept of DSS

• DSS - interactive computer-based systems, which help decision makers utilize data and models to solve unstructured problems

(Gorry and Scott-Morton, 1971)

• Decision support systems couple the intellectual resources of individuals with the capabilities of the computer to improve the quality of decisions.

• DS as an Umbrella Term

• Evolution of DS into Business Intelligence

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A Framework for Business Intelligence (BI)

• BI is an evolution of decision support concepts over time

• Then: Executive Information System

• Now: Everybody’s Information System (BI)

• BI systems are enhanced with additional visualizations, alerts, and performance measurement capabilities

• The term BI emerged from industry

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Definition of BI

• BI is an umbrella term that combines architectures, tools, databases, analytical tools, applications, and methodologies

• BI is a content-free expression, so it means different things to different people

• BI's major objective is to enable easy access to data (and models) to provide business managers with the ability to conduct analysis

• BI helps transform data, to information (and knowledge), to decisions, and finally to action

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A Brief History of BI • The term BI was coined by the Gartner Group in the

mid-1990s

• However, the concept is much older • 1970s - MIS reporting - static/periodic reports

• 1980s - Executive Information Systems (EIS)

• 1990s - OLAP, dynamic, multidimensional, ad-hoc reporting -> coining of the term “BI”

• 2010s - Inclusion of AI and Data/Text Mining capabilities; Web-based Portals/Dashboards, Big Data, Social Media, Analytics

• 2020s - yet to be seen

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The Evolution of BI Capabilities

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The Architecture of BI

• A BI system has four major components • a data warehouse, with its source data

• business analytics, a collection of tools for manipulating, mining, and analyzing the data in the data warehouse

• business performance management (BPM) for monitoring and analyzing performance

• a user interface (e.g., dashboard)

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A High-Level Architecture of BI

Data Warehouse

Technical staff

Data Warehouse Environment

Data Sources

Business Analytics Environment

Performance and Strategy

Business users Managers / executives

Built the data warehouse Access

Manipulation Results

BPM strategy ü Organizing ü Summarizing ü Standardizing

Future component intelligent systems

User Interface - browser - portal - dashboard

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Business Value of BI Analytical Applications

• Customer segmentation

• Propensity to buy

• Customer profitability

• Fraud detection

• Customer attrition

• Channel optimization

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DSS-BI Connections

• Similarities and differences? • Similar architectures, data focus, …

• Direct vs. indirect support

• Different target audiences

• Commercially available systems versus in-house development of solutions

• Origination – Industry vs. Academia

• So, is DSS = BI ?

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Analytics Overview

• Analytics? • Something new or just a new name for …

• A Simple Taxonomy of Analytics (proposed by INFORMS) • Descriptive Analytics

• Predictive Analytics

• Prescriptive Analytics

• Analytics or Data Science?

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Analytics Overview

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Introduction to Big Data Analytics

• Big Data? • Not just big! • Volume • Variety • Velocity

• More of Big Data and related analytics tools and techniques are covered in Chapter 13.

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Plan of the Book • Part I - Decision Making and Analytics: An Overview

• (Chapters 1 & 2)

• Part II - Descriptive Analytics • (Chapters 3 & 4)

• Part III - Predictive Analytics • Chapters 5 - 8

• Part IV - Prescriptive Analytics • Chapter 9 - 12

• Part V - Big Data and Future Directions for Business Analytics • Chapters 13 & 14

• PLUS - Online Supplements • …

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Application Case 1.4 (p. 53)

Moneyball: Analytics in Sports and Movies

Questions for Discussion

1. How is predictive analytics applied in Moneyball?

2. What is the difference between objective and subjective approaches in decision making?

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End of the Chapter

• Questions / Comments…

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