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Discussion 1

Discussion (Chapter 1): Compare and contrast predictive analytics with prescriptive and descriptive analytics. Use examples.

· Your response should be 250-300 words.  Respond to two postings provided by your classmates.
· There must be at least one APA formatted reference (and APA in-text citation) to support the thoughts in the post.  Do not use direct quotes, rather rephrase the author’s words and continue to use in-text citations.

Analytics, Data Science and A I:
Systems for Decision Support
Eleventh Edition

Chapter 1
Overview of Business Intelligence, Analytics, Data

Science, and Artificial Intelligence: Systems for

Decision Support

Slide in this Presentation Contain Hyperlinks. JAWS users should be

able to get a list of links by using INSERT+F7

ITS 531: Business Intelligence

Professor: Miguel Buleje, Ph.D

Learning Objectives (1 of 2)

1.1 Understand the need for computerized support of

managerial decision making.

1.2 Understand the development of systems for providing

decision-making support.

1.3 Recognize the evolution of such computerized support

to the current state of analytics/data science and

artificial intelligence.

1.4 Describe the business intelligence (B I) methodology and


1.5 Understand the different types of analytics and review

selected applications.

Learning Objectives (2 of 2)

1.6 Understand the basic concepts of artificial intelligence

(A I) and see selected applications.

1.7 Understand the analytics ecosystem to identify various

key players and career opportunities.

Decision Making Process (1 of 2)

The four step managerial process:

• Define the problem

• Construct a model

• Identify and evaluate possible solutions

• Compare, choose, and recommend a solution to the


Decision Making Process (2 of 2)

A more detailed process is offered by Quain (2018):

1. Understand the decision you have to make.

2. Collect all the information.

3. Identify the alternatives.

4. Evaluate the pros and cons.

5. Select the best alternative.

6. Make the decision.

7. Evaluate the impact of your decision.

The Influence of the External and Internal Environments

on the Process (As part of the decision making process)

• Technology, I S, Internet, globalization, …

• Government regulations, compliance, …

– Political factors

– Economic factors

– Social and psychological factors

– Environment factors

• Need to make rapid decision, changing market conditions,

Technologies for Data Analysis and Decision Support

• Group communication and collaboration

• Improved data management

• Managing giant data warehouses and Big Data

• Analytical support

• Overcoming cognitive limits

• Knowledge management

• Anywhere, anytime support

• Innovation and artificial intelligence

Decision-making Processes And Computerized Decision Support


• What is “Decision making”?

• Simon’s Decision Making Process

– Proposed in 1977 by Herbert Alexander Simon (an

American economist and political scientist)

– Includes three phases:

1. Intelligence

2. Design

3. Choice

4. [+] Implementation

5. [+] Monitoring

The Decision-Making Process

Decision-making Processes (1 of 2)

Phase 1 – The Intelligence Phase: Problem (or Opportunity)


• Issues in data collection

• Problem classification

• Problem decomposition

• Problem ownership

Decision-Making Processes (2 of 2)

Phase 2 – The Design Phase

– Models

Phase 3 – The Choice Phase

– Evaluating alternatives

Phase 4 – The Implementation Phase

– Implementing the solution

Phase 5 – Monitoring

• Phase 4 and 5 were not part of Simons’ original model

The Classical Decision Support System


• Degree of structuredness / Type of decision

– Structured, unstructured, semistructured problems

• Type of control

– Operational, managerial, strategic

• The decision Support matrix

• Computer support for …

– Structured decisions

– Unstructured decisions

– Semistructured problems

Decision Support Framework

Key Characteristics and Capabilities of Decision Support System (D S S)

Components of a D S S (1 of 2)

• The Data



– D S S database

– Database


system (D B M S)

– Data directory

– Query facility

Components of a D S S (2 of 2)

• The Model Management Subsystem

– Model base

– Model Base Management System (MBMS)

– Modeling language

– Model directory

– Model execution, integration, and command


• The User Interface Subsystem

• The Knowledge-Based Subsystem

Evolution of Computerized Decision Support to Business

Intelligence, Analytics, Data Science

Figure 1.5 Evolution of Decision Support, Business Intelligence, Analytics, and A I.

A Framework for Business Intelligence

• Definitions of business intelligence (B I)

– A conceptual framework for managerial decision

support. Combines architecture, databases (or any

data warehouse), analytical tools, and applications.

• A brief history of B I

• The architecture of B I

– Data warehousing (D W) [as a foundation of B I]

– Business Performance Management (B P M)

– User interface (dashboard)

• Appropriate planning and alignment of B I with the

business strategy

Evolution of Business Intelligence (B I)

The Origins and Drivers of B I

Figure 1.7 A High-Level Architecture of B I.

Source: Based on W. Eckerson. (2003). Smart Companies in the 21st Century: The Secrets of Creating Successful Business Intelligent Solutions

Seattle, W A: The Data Warehousing Institute, p. 32, Illustration 5.

Data Warehouse Framework

Analytics Overview (1 of 2)

• Three types of analytics

– Descriptive (or reporting) analytics …

– Predictive analytics …

– Prescriptive analytics …

Analytics Overview (2 of 2)

Artificial Intelligence Overview

• What Is artificial intelligence (A I)?

– Technology that can learn to do things better over time.

– Technology that can understand human language.

– Technology that can answer questions.

• The major benefits of A I

– Reduction in the cost of performing work.

– Work can be performed much faster.

– Work is more consistent than human work.

– Increased productivity, profitability, …

Societal Impacts of A I

• Impact on agriculture

• Contribution to health and medical care

• Other societal applications

– Transportation

– Utilities

– Education

– Social services

– Smart cities / Transit & Others

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