Essentials of Management

Information Systems
Fifteenth Edition

Chapter 11
Improving Decision Making and
Managing Artificial Intelligence

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Learning Objectives
11.1 Identify the different types of decisions and how the
decision-making process works.
11.2 Describe how business intelligence and business
analytics support decision making.
11.3 Define artificial intelligence (AI) and explain how it
differs from human intelligence.
11.4 Identify the major types of AI techniques and how they
benefit organizations.
11.5 Understand how MIS can help your career.

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Video Case
• Predictive Tech Can Save $3B-$4B A Year: Tom Seibel

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Big Data Analytics: A New Way to
Fight Wildfires
• Problem
– Severe, unpredictable wildfires
– Opportunities from new technology
• Information System: Data-driven Analytics
– Model wildfire scenarios
– Predict wildfire behavior
• Illustrates how information systems can dramatically
improve decision making

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Business Value of Improved Decision
Making
• Possible to measure value of improved decision making
• Decisions made at all levels of the firm
– Some are common, routine, and numerous
– Although value of improving any single decision may be
small, improving hundreds of thousands of “small” decisions
adds up to large annual value for the business

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Types of Decisions
• Unstructured
– Decision maker must provide judgment to solve problem
– Novel, important, nonroutine
– No well-understood or agreed-upon procedure for making
them
• Structured
– Repetitive and routine
– Involve definite procedure for handling them so do not have
to be treated as new
• Semistructured
– Only part of problem has clear-cut answer provided by
accepted procedure
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Figure 11.1 Information Requirements
of Key Decision-Making Groups in a
Firm

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The Decision-Making Process
1. Intelligence
– Discovering, identifying, and understanding the problems
occurring in the organization
2. Design
– Identifying and exploring various solutions
3. Choice
– Choosing among solution alternatives

4. Implementation
– Making chosen alternative work and monitoring how well
solution is working

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Figure 11.2 Stages in Decision
Making

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High-Velocity Automated Decision
Making
• Made possible through computer algorithms precisely
defining steps for a highly structured decision
– Humans eliminated from the decision-making process
• For example: High-speed computer trading programs
– Trades executed in nanoseconds
• Require safeguards to ensure proper operation and
regulation

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Quality of Decisions and Decision
Making
• Accuracy
• Comprehensiveness
• Fairness
• Speed (efficiency)
• Coherence
• Due process

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What Is Business Intelligence?
• Business intelligence: a vendor-defined term used to
describe infrastructure for managing data from business
environment. Includes:
– Warehousing
– Integrating
– Reporting
– Analyzing
• Business analytics: focuses more on tools and techniques
for analyzing data; also a vendor-defined term
– Hadoop, O LA P, analytics
• Major vendors: SAP, Oracle, IBM, SAS, Microsoft

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The Business Intelligence
Environment
• Six elements in the BI environment:
– Data from the business environment
– Business intelligence infrastructure
– Business analytics toolset
– Managerial users and methods
– Delivery platform—MIS, DSS, ESS
– User interface
• Tableau a leading data visualization and business
intelligence tool
• Virtual reality (VR) systems and augmented reality (AR)
systems are also data visualization tools.

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Figure 11.3 Business Intelligence and
Analytics for Decision Support

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Business Intelligence and Analytics
Capabilities (1 of 5)
• Goal is to deliver accurate real-time information to decision
makers
• Six major analytic functionalities
– Production reports
– Parameterized reports
– Dashboards/scorecards
– Ad-hoc query/search/report creation
– Drill down
– Forecasts, scenarios, models
 Linear forecasting, what-if scenario analysis, data analysis

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Business Intelligence and Analytics
Capabilities (2 of 5)
• Predictive analytics
– Statistical analytics, data mining, historical data;
assumptions of future conditions
– Extracts information from data to predict future trends and
behavior patterns
 Responses to direct marketing campaigns
 Best potential customers for credit cards
 At-risk customers
 Customer response to price changes and new services
– Example: Accuracies range from 65 to 90 percent for FedEx
predictive analytics system

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Business Intelligence and Analytics
Capabilities (3 of 5)
• Big Data analytics
– Predictive analytics can use Big Data generated from social
media, consumer transactions, sensor and machine output.
– Big data analytics driving move toward “smart cities”
 Utility management
 Transportation operation
 Healthcare delivery
 Public safety

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Business Intelligence and Analytics
Capabilities (4 of 5)
• Operational intelligence and analytics
– Involves real-time day-to-day monitoring of business
decisions and activity
• Internet of Things (IoT)
– Creating huge streams of data from web activities, sensors,
and other monitoring devices
• Example: Schneider National truckload logistics services
provider
– Data developed from sensors in trucks, trains, industrial
systems

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Business Intelligence and Analytics
Capabilities (5 of 5)
• Location analytics
– Big Data analytics that uses location data from mobile
phones, sensors, and maps
 Example: Helping a utility company view customer costs as
related to location

• Geographic information systems (GIS)

– Help decision makers visualize problems with mapping
– Tie location data about resources to map

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Business Intelligence Users (1 of 2)
• Support for semistructured decisions
– Decision-support systems (DSS)
 BI delivery platform for “super-users” who want to create own
reports, use more sophisticated analytics and models
 What-if analysis
 Sensitivity analysis
 Backward sensitivity analysis
 Pivot tables: Spreadsheet function for multidimensional
analysis
 Intensive modeling techniques

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Figure 11.4 Business Intelligence
Users

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Figure 11.5 Sensitivity Analysis

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Figure 11.6 A Pivot Table That Examines
Customer Regional Distribution and Advertising
Source

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Business Intelligence Users (2 of 2)
• Decision support for senior management
– Executive support systems
 Combine internal data with external data
 Drill-down capabilities
– Balanced scorecard method: Measures four dimensions of
firm performance
– Financial
– Business process
– Customer
– Learning and growth
 Considered balanced because it focuses on more than
financial performance
 Key performance indicators used to measure each
dimension
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Figure 11.7 The Balanced Scorecard
Framework

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Evolution of AI
• Artificial intelligence: Grand vision vs. narrow definition
• Evolution of AI
– Big Data databases
– Reduction in the price of processors
– Expansion in capacity of processors
– Refinement and explosion of algorithms
– Large investments in information technology and AI
• Progress in image recognition and natural language
– Examples: Siri, Alexa, facial recognition

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Expert Systems (1 of 2)
• Capture human expertise in a limited domain of knowledge
– Model human knowledge as a set of rules called knowledge
base
– Inference engine searches through the collection of rules
and formulates conclusion
• Typically used for structured decision making
– Credit granting applications
– Diagnosing a malfunctioning machine

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Expert Systems (2 of 2)
• Benefits:
– Improve decisions
– Reduce errors
– Reduce costs and training time
– Enable better quality and service
• Limitations:
– Experts often cannot explain how they make decisions
– Knowledge base can become chaotic
– Rules change and need to be continually updated
– Not useful for dealing with unstructured problems
– Do not scale well to very large datasets

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Figure 11.8 Rules in an Expert
System

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Machine Learning (1 of 2)
• Main focus is on finding patterns in data and classifying
data into known and unknown outputs
• Different paradigm than expert systems
• Many of today’s Big Data analytics use machine learning
technology
• Examples: Facebook; other large e-commerce firms

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Machine Learning (2 of 2)
• Supervised learning
– System “trained” by providing examples of desired inputs
and outputs identified by humans in advance
– One technique used to develop autonomous vehicles
• Unsupervised learning
– Same procedures as used with supervised learning, but
humans do not provide examples
– “Cat Paper”

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Neural Networks (1 of 2)
• Use machine learning algorithms loosely based on the
biological human brain
• Find patterns and relationships in massive amounts of data
too complicated for humans to analyze
• “Learn” patterns by searching for relationships, building
models, and correcting over and over again
• Humans “train” network by feeding it data inputs for which
outputs are known, to help neural network learn solution by
example from human experts
• Used in medicine, science, and business for problems in
pattern classification, prediction, financial analysis, and
control and optimization
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Figure 11.9 How a Neural Network
Works

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Neural Networks (2 of 2)
• Deep learning neural networks
– More complex, with many layers of transformations of input data to
produce target output
– Used almost exclusively for pattern detection on unlabeled data
(unsupervised learning)
– Some believe these come closest to “grand vision” of AI

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Figure 11.10 A Deep Learning
Network

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Genetic Algorithms
• Form of machine learning
• Useful for finding optimal solution for a specific problem by
examining a very large number of alternative solutions
• Method of solving problems based on ideas inspired by
evolutionary biology
• Example: GE engineers used genetic algorithms to help
optimize the design for jet turbine aircraft engines

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Figure 11.11 The Components of a
Genetic Algorithm

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Natural Language Processing,
Computer Vision Systems, and
Robotics (I of 3)
• Natural language processing (NLP)
– Makes it possible for a computer to analyze natural human
language
– Based on machine learning, including deep learning
– Example: IBM Watson

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Spotlight on Technology: What
Happened to Watson Health?
• Class discussion
– One critic has described Watson Health as “a hammer
looking for a nail” and said it is more effective to define and
understand a problem before building an AI application.
Discuss.
– How could IBM Watson Health have benefited from using
the four-step problem-solving method introduced in Chapter
1?
– To what extent was Watson Health a technology problem? A
people problem? An organizational problem? Explain your
answer.
– How can organizations using AI in healthcare avoid the
mistakes IBM made?
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Natural Language Processing,
Computer Vision Systems, and
Robotics (2 of 3)
• Computer vision systems
– Emulate human visual system to view and extract
information from real-world images
 Incorporate image processing, pattern recognition, and image
understanding
 Use neural networks and deep learning
 Example: GumGum

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Spotlight on People: Do You Know
Who Is Using Your Face?
• Class discussion
– Explain the key technologies used in facial recognition
systems.
– What are the benefits of using facial recognition systems?
How do they help organizations improve operations and
decision making? What problems can they help solve?
– Identify and describe the disadvantages of using facial
recognition systems and facial databases.

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Natural Language Processing,
Computer Vision Systems, and
Robotics (3 of 3)
• Robotics
– Design, construction, operation, and use of movable
machines that can substitute for humans
– Robots are programmed to perform specific actions
automatically
– Most widespread use in manufacturing and logistics
 Example: Amazon Robotic Fulfillment Centers

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Intelligent Agents

• Intelligent Agents
– Software programs that work in the background without
direct human intervention to carry out specific tasks
 The agent uses a limited built-in or learned knowledge base
 Some are capable of learning from experience and adjusting
their behavior using machine learning and natural language
processing.
 Example: Siri
– Chatbots: software agents designed to simulate a
conversation with human users
 Example: Evri’s Holly
 Large language models have more sophisticated
conversational capabilities
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How M IS Can Help Your Career
• The Company: SeeandHear AI Group
• Position Description
• Job Requirements
• Interview Questions
• Author Tips

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Copyright

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