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
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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
concepts.
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
problem
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
Framework
What is Decision making?
Simons 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)
Identification
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
Framework
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
Management
System
D S S database
Database
management
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
processor
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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