CSUF – Acct 302Special Project, Non-ProfitsWhen you begin to un

CSUF – Acct 302Special Project, Non-Profits
When you begin to understand non-profits, donations are
extremely important to enable the nonprofit to continue and
expand its mission. Just as it happens in business, nonprofits
look to expand their donor base and their program reach.
Oftentimes, nonprofits that are funded by individual donations
feel that if they reach out for the “large donations” like through
grants or corporate sponsors, their life will be easier. The
business issues with adding a new avenue of donations is the
same as it is in a for profit entity: What are the costs involved
in making this happen versus the eventual return. Furthermore,
corporate donors and/or grants can come and go with changes in
the economy or environment – which adds a large risk
dimension to this source of funds.
Additionally, charities need to allocate expenses to either
Program Service Expenses, Administrative expenses and Fund-
Raising expenses. Charity Navigator and many contributors
look to see how much and what % of total expenses are spent on
Program Service Expenses versus Administrative or Fund-
Raising Expenses. The higher the % of expenses spent on
program expenses, generally the better the charity is viewed.
You can find summary information on Human Options Inc.
(extracted from the Form 990) by searching for Human Options
Inc. (a California not for profit) on the website
CharityNavigator.org. Scroll down the page to view summary
information about the charity and other charities performing
similar work. The actual 990 can be found by visiting Human
Options’ website. (humanoptions.org)
When you review the information about Human Options, answer
the following questions:

1. What is Human Options’ mission?
2. Did total contributions increase or decrease for 2019? What
was the main source of contributions for Human Options for
2019?
3. What comments do you have about the amount of money paid
to the executive officer relative to the size of the charity and
relative to other similar charities?
4. For the most recent year, what percent of total expenses were
allocated to each of the categories: Program Services,
Administrative Services, and Fundraising? Did total Program
expenses increase or decrease from the prior year and does that
seem reasonable to you?
5. Based on Human Options’ mission, if you were auditing
Human Options what kind of expenses would you expect to see
classified as program expenses (ie, expenses that directly relate
to the mission).
Please submit your answers to me via Titanium. Please be sure
to answer each of these questions in order. Points will be
deducted for Grammar and spelling errors.
(From the Syllabus: I will deduct 10% of the points (***UP TO
THE ENTIRE POINTS FOR THE ASSIGNMENT***) for each
spelling, grammar or math errors)
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
Copyright © 2020, 2015, 2011 Pearson Education, Inc. All
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1
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.
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Slide 2 is list of textbook LO numbers and statements
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.
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Slide 2 is a list of textbook LO numbers and statements
3
Opening Vignette (1 of 2)
How Intelligent Systems Work for KONE Elevators and
Escalators Company
The problem…
The solution…
The results…
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How Intelligent Systems Work for KONE Elevators and
Escalators Company
Questions For The Opening Vignette
It is said that K O N E is embedding intelligence across its
supply chain and enables smarter buildings. Explain.
Describe the role of I o T in this case.
What makes I B M Watson a necessity in this case?
Check I B M Advanced Analytics. What tools were included

that relate to this case?
Check I B M cognitive buildings. How do they relate to this
case?
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Changing Business Environments And Evolving Needs For
Decision Support And Analytics
Big-bet, high-risk decisions.
Cross-cutting decisions, which are repetitive but high risk that
require group work.
Ad hoc decisions that arise episodically.
Delegated decisions to individuals or small groups.
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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
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Decision Making Process (2 of 2)
A more detailed process is offered by Quain (2018):
Understand the decision you have to make.
Collect all the information.
Identify the alternatives.
Evaluate the pros and cons.
Select the best alternative.
Make the decision.
Evaluate the impact of your decision.
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The Influence of the External and Internal Environments on the
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, …
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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
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Decision-making Processes And Computerized Decision Support
Framework
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:
Intelligence
Design
Choice
[+] Implementation
[+] Monitoring
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The Decision-Making Process

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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
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Application Case 1.1
Making Elevators Go Faster!
Questions for Discussion:
Why this is an example relevant to decision making?
Relate this situation to the intelligence phase of decision
making.
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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
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The Classical Decision Support System Framework
Degree of structuredness
Structured, unstructured, semistructured problems
Type of control
Operational, managerial, strategic
The decision Support matrix
Computer support for …
Structured decisions
Unstructured decisions
Semistructured problems
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Decision Support Framework
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Key Characteristics and Capabilities of Decision Support
System (D S S)
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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
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Components of a D S S (2 of 2)
The Model Management Subsystem
Model base
M B M S
Modeling language
Model directory
Model execution, integration, and command processor
The User Interface Subsystem
The Knowledge-Based Subsystem

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Evolution of Computerized Decision Support to Business
Intelligence, Analytics, Data Science
Figure 1.5 Evolution of Decision Support, Business
Intelligence, Analytics, and A I.
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A Framework for Business Intelligence
Definitions of business intelligence (B I)
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)
Transaction processing versus analytics processing
Appropriate planning and alignment of B I with the business
strategy
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Evolution of Business Intelligence (B I)

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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
Solution
s Seattle, W A: The Data Warehousing Institute, p. 32,
Illustration 5.
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Data Warehouse Framework

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A Multimedia Exercise in B I
Teradata University Network (T U N)
B S I (Business Scenario Investigations) [like C S I]
Go to
https://www.teradatauniversitynetwork.com/Library/Items/BSI-
The-Case-of-the-Misconnecting-Passengers/ or
www.youtube.com/watch?v=NXEL5F4_aKA
Watch the video
Analyze the video - www.slideshare.net/teradata/bsi-how-we-
did-itthe-case-of-the-misconnecting-passengers
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Analytics Overview (1 of 2)

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Analytics Overview (2 of 2)
Three types of analytics
Descriptive (or reporting) analytics …
Predictive analytics …
Prescriptive analytics …
Analytics applied to different domains
Analytics or data science?
What is Big Data?
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Silvaris Increases Business with Visual Analysis and Real -Time
Reporting Capabilities

What was the challenge faced by Silvaris?
How did Silvaris solve its problem using data visualization with
Tableau?
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Siemens Reduces Cost with the Use of Data Visualization
What challenges were faced by Siemens visual analytics group?
How did the data visualization tool Dundas B I help Siemens in
reducing cost?
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Analyzing Athletic Injuries
What types of analytics are applied in the injury analysis?
How do visualizations aid in understanding the data and
delivering insights into the data?
What is a classification problem?
What can be derived by performing sequence analysis?
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A Specialty Steel Bar Company Uses Analytics to Determine
Available-to-Promise Dates
Why would reallocation of inventory from one customer to
another be a major issue for discussion?
How could a D S S help make these decisions?
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Analytics Examples in Selected Domains (1 of 2)
Sports Analytics—An Exciting Frontier for Learning and
Understanding Applications of Analytics
Example 1: Business office
Example 2: The Coach
Healthcare—Humana Examples
Example 1: Preventing Falls in a Senior Population
Example 2: Define the Right Metrics
Example 3: Predictive Models to Identify the Highest Risk
Membership in a Health Insurer
Retail—Retail Value Chain …
Image Analytics
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Analytics Examples in Selected Domains (2 of 2)
Retail …
Figure 1.15 Example of Analytics Applications in a Retail

Value Chain.
Source: Contributed by Abhishek Rathi, C E O, vCreaTek.com.
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34
Image Analysis Helps Estimate Plant Cover
What is the purpose of knowing how much ground is covered by
green foliage on a farm? In a forest?
Why would image analysis of foliage through an app be better
than a visual check?
Explore research papers to understand the underlying
algorithmic logic of image analysis. What did you learn?
What other applications of image analysis can you think of?
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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, …
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The Landscape of A I
Major technologies
Knowledge-based technologies
Biometric related technologies
Tools and platforms …
A I applications …

Narrow (weak) versus general (strong) A I
The three flavors of A I decisions
Assisted intelligence
Autonomous A I
Augmented Intelligence
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A I Increases Passengers’ Comfort and Security in Airports and
Borders
List the benefits of A I devices to travelers.
List the benefits to governments and airline companies.
Relate this case to machine vision and other A I tools that deal
with people’s biometrics.
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Societal Impacts of A I
Impact on agriculture
Contribution to health and medical care
Other societal applications
Transportation
Utilities
Education
Social services
Also see Chapter 13 for smart cities
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Robots Took the Job of Camel-Racing Jockeys for Societal
Benefits
It is said that the robots eradicated the child slavery. Explain.
Why do the owners need to drive by their camels while they are
racing?

Why not duplicate the technology for horse racing?
Summarize ethical aspects of this case (Read Boddington,
2017). Do this exercise after you have read about ethics in
Chapter 14.
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Convergence of Analytics and A I
Major differences between analytics and a i
Why combine intelligent systems?
How convergence can help?
Big Data Is empowering A I technologies
The convergence of A I and the IoT
The convergence with blockchain and other technologies
I B M and Microsoft support for intelligent systems
convergence
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Amazon Go Is Open for Business
Watch the video. What did you like and/or dislike?
Compare the process described here to a selfcheck available
today in many supermarkets and “big box” stores (Home Depot,
etc.).
The store was opened in downtown Seattle. Why was the
downtown location selected?
What are the benefits to customers? To Amazon?
Will customers be ready to trade privacy for convenience?
Discuss.
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Overview of Analytics Ecosystem

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Plan of the Book
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Copyright
This work is protected by United States copyright laws and is
provided solely for the use of instructors in teaching their
courses and assessing student learning. Dissemination or sale of
any part of this work (including on the World Wide Web) will
destroy the integrity of the work and is not permitted. The work
and materials from it should never be made available to students
except by instructors using the accompanying text in their
classes. All recipients of this work are expected to abide by
these restrictions and to honor the intended pedagogical

purposes and the needs of other instructors who rely on these
materials.
Rights Reserved
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Analytics, Data Science and A I: Systems for Decision Support
Eleventh Edition
Chapter 2
Artificial Intelligence Concepts, Drivers, Major Technologies,
and Business Applications
Rights Reserved
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should be able to get a list of links by using INSERT+F7
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equations, you may need to check that your computer has the
following installed:
1) Math Type Plugin
2) Math Player (free versions available)
3) NVDA Reader (free versions available)
1
2.1Understand the concepts of artificial intelligence (A I).
2.2 Become familiar with the drivers, capabilities, and benefits
of A I.
2.3 Describe human and machine intelligence.
2.4 Describe the major A I technologies and some derivatives.
2.5 Discuss the manner in which A I supports decision making.
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Slide 2 is list of textbook LO numbers and statements

2
2.6 Describe A I applications in accounting.
2.7 Describe A I applications in banking and financial
services.
2.8 Describe A I in human resource management.
2.9 Describe A I in marketing.
2.10 Describe A I in production-operation management.
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Slide 2 is a list of textbook LO numbers and statements
3
I N R I X Solves Transportation Problems
http://www.inrix.com
The problem…
The solution…
The results…

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Questions for the Opening Vignette:
Explain why traffic may be down while congestion is up (see
the London case at inrix.com/uk-highways-agency/).
How does this case relate to decision support?
Identify the A I elements in this system.
Identify developments related to A I by viewing the company’s
press releases from the most recent four months at
inrix.com/press-releases. Write a report.
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5

Questions for the opening vignette (cont.):
According to Gitlin (2016), I N R I X’s new mobile traffic app
is a threat to Waze. Explain why.
Go to sitezeus.com/data/inrix and describe the relationship
between I N R I X and Zeus. View the 2:07 min. video at
sitezeus.com/data/inrix/. Why is the system in the video called a
“decision helper”?
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Introduction to Artificial Intelligence
Definitions for artificial intelligence (A I)
Many definitions of A I
Relationship between A I and logic
plato.stanford.edu/entries/logic-ai
Major characteristics of A I machines
Smarter computers/machines
Major elements of A I …
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The Functionalities and Applications of A I
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Artificial Intelligence (A I) (1 of 8)
Many application of A I exists
Example: Pitney Bowes Is Getting Smarter with A I
Major goals of A I
Perceive and properly react to changes in the environment that
influence specific business processes and operations.
Introduce creativity in business processes and decision making.
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Drivers of A I
Interest in smart machines and artificial brains
The low cost of A I applications
The desire of large tech companies
The pressure on management to increase productivity
The availability of quality data
The increasing functionalities and reduced cost of computers in
general
The development of new information technologies, particularly
the cloud computing
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Benefits of A I
A I has the ability to complete certain tasks much faster
The consistency of the work
A I machines do not make arbitrary mistakes

A I systems allow for continuous improvement projects
A I can be used for predictive analysis via its capability of
pattern recognition
A I can manage delays and blockages in business processes
A I machines do not stop to rest or sleep
Many more…
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Figure 2.2 Cost of Human Work versus the Cost of A I Work.
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Examples of A I Benefits

I S D A uses A I to eliminate tedious activities
A I revolutionizing business recruitment
A I is redefining management
Help blind people experience the world around them
Identify overlooked borrowers
Predict customer expectation
Startup A I companies are emerging in large numbers
Most impactful: customer experience and enjoyment.
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Some limitations of A I Machines
Lack human touch and feel
Lack attention to non-task surroundings
Can lead people to rely on A I machines too much
Can be programmed to create destruction
Can cause many people to lose their jobs
Can start to think by themselves, causing significant damage
Hypothetically … no evidence of that!
These limitations are diminishing over time

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What A I can and cannot do?
Three flavors of A I decisions
Assisted intelligence
Autonomous intelligence
Augmented intelligence
Artificial brain
A people made machine “as intelligent, creative, and self-aware
as humans”
To date, no one has created such a machine
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Technology Insight – Augmented Intelligence
Combining the performance of people and machines [combining
Augmented machines extend human abilities
Examples
Cybercrime fighting
E-commerce decisions
High-frequency stock market trading
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Human and Computer Intelligence (1 of 4)
What is intelligence?
Types of intelligence:
Linguistic and verbal, logical, spatial, body/movement, musical,
interpersonal, intrapersonal, naturalist
Intelligence is not a simple concept!
Content of intelligence
Reasoning, learning, logic, problem-solving, perception, and
linguistic ability

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Capabilities of intelligence
Learning or understanding from experience
Making sense out of ambiguous, incomplete, or even
contradictory messages and information
Responding quickly and successfully to a new situation (i.e.,
using the most correct responses)
Understanding and inferring in a rational way, solving
problems, and directing conduct effectively
Applying knowledge to manipulate environments
Recognizing and judging the relative importance of different
elements in a situation
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How intelligent is A I?
Comparing human intelligence with A I
Table 2.1 Artificial Intelligence versus Human
Intelligence.AreaAIHumanExecutionVery fastCan be
slowEmotionsNot yetCan be positive or negativeComputation
speedVery fastSlow, may have troubleImaginationOnly what is
programmed forCan expand existing knowledgeAnswers to
questionsWhat is in the programCan be
innovativeFlexibilityRigidLarge, flexible
Many more, in the book…
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Measuring A I: The Turing Test
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How Smart Can a Vacuum Cleaner Be?
How did the Korean researchers determine the performance of
the vacuum cleaners?
If you own (or have seen) the Roomba, how intelligent do you
think it is?
What capability can be generated by the deep learning feature?
(You need to do some research.)
Find recent information about L G’s Roboking. Specifically,
what are the newest improvements to the product?
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Major A I Technologies & Drivers (1 of 3)
Intelligent agents
Intelligent? Autonomous? Mobile? …
Machine learning

“Human learning embedded into machines”
Deep learning
A part of machine learning (see Chapter 6)
Computer vision (machine vision)
Video analytics
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Major A I Technologies
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How Machine Learning Is Improving Work in Business
Discuss the benefits of combining machine learning with other

A I technologies.
How can machine learning improve marketing?
Discuss the opportunities of improving human resource
management.
Discuss the benefits for customer service.
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Robotic systems
Industrial robots [for manufacturing]
Service robots
Example: Walmart is using robots to properly stock shelves
Use of robots (or bots) in eComemrce
Many are being used at Amazon.com
Online shopping robots (shopbots)
SoftBank – a cellphone store in Tokyo entirely staffed by robots
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Natural language processing
Natural language understanding
Natural language generation
Speech (voice) understanding
An interesting application cs.cmu.edu/~./listen
Machine translation of human languages
Balel fish (babelfish.com)
Google translator (translate.google.com)
Example: Sogou’s travel translator
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Knowledge and Expert Systems (1 of 2)
Knowledge sourced intelligent systems
Knowledge acquisition
Identifying experts
Knowledge representation

Reasoning from knowledge
Chatbots
Emerging A I technologies
Effective computing
Biometric analysis
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Knowledge and Expert Systems (2 of 2)
Cognitive computing
Knowledge derived from cognitive science
Self learning algorithms
I B M Watson
More on this is covered in Chapter 6
Augmented reality
Augmentation: integration of digital information within the user
environment in real time
Real + virtual combined
Virtual reality

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Automated Decision Making Process
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A I Support for Decision Making
Jeff Bezos, the C E O of Amazon.com, said in May 2017 that A
I is in a golden age …
A I can …
Solve complex problems that people have not been able to
solve.
Make much faster decisions.
Find relevant information, even in large data sources, very fast.
Make complex calculations rapidly.
Conduct complex comparisons and evaluations in real time.
Watch “A I Will Be Making Decisions for You” at

https://www.youtube.com/watch?v=Dr9jeRy9whQ
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Using A I in Decision Making
Issues & factors:
The nature of the decision [routine vs non-routine]
The method of support / technologies used
Expert systems, recommender systems
Deep learning, pattern recognition, biometrics recognition
Cos-benefit and risk analysis
Using business rules
A I algorithms
Speed
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A I Support for Decision-Making Process
As it relates to Simon’s decision making process (see Chapter 1
for the background information)
A I support in problem identification
A I support in generating or finding alternative solutions
A I support in selecting a solution
A I support in implementing the solution
A I can (and should) play a role in each and every step in the
decision making process
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How Companies Solve Real-World Problems Using Google’s
Machine-Learning Tools
Why use machine learning for predictions?
Why use machine learning for detections?
What specific decisions were supported in the five cases?

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Intelligent & Automated Decision Support
Automated decision making (since 1970s)
Common examples:
Small loan approvals
Initial screening of job applicants
Simple restocking
Prices of products and services (when and how to change them)
Product recommendation (e.g., at Amazon.com)
Example: Supporting Nurses Diagnosis Decisions
An experiment conducted in a Taiwanese hospital (in 2015)
87% agreement between A I and human experts
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Technology Insight 2.2
Schrage’s Models for Using A I to Make Decisions

The autonomous advisor
The autonomous outsource
People-machine collaboration
Complete machine autonomy
Implementing these four models require appropriate
management leadership and collaboration with data scientists.
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A I Applications in Accounting
A I in big accounting firms (see application case 2.4)
A I in small accounting firms
Solve complex billing problems (especially in healthcare)
Claim processing and reimbursement
Real estate contracts, risk analysis …
A I provides cheaper and better data-driven support
Generates needed insights from data analysis
Frees time of accountants for more complex tasks
Machine learning is often used for prediction
A I will improve and automate accounting tasks but at the same
time will take away some accounting jobs.

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How E Y, Deloitte, and P w C Are Using A I
What are the characteristics of the tasks for which A I is used?
Why do the big accounting firms use different implementation
strategies?
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A I Applications in Financial Services (1 of 2)
Diverse use of A I, in banking and insurance.
Examples of A I use in general financial services:
Extreme personalization (e.g., chatbots, personal assistants,
etc.)

Shifting customer behavior both online and in branches
Facilitating trust in digital identity, revolutionizing payments
Sharing economic activities (e.g., person-to-person loans)
Offering financial services 24/7 and globally
Banking can also uses A I for …
Face recognition (safer online banking), help customer with
smart investment decisions, prevent money laundering, …
Insurance – mostly in issuing policies and handling claims
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A I Applications in Financial Services (2 of 2)
Application of A I uses in Banking
Employee surveillance (A I machines, e.g., I B M Watson).
Tax preparation/filing (H&R block uses I B M Watson).
Automated customer service; answering customer inquiries in
real-time.
See Rainbird Co. ar rainbirf.ai as a company that provides such
services (using I B M Watson).
Automated online support for paying bills and account inquiries
using Amazon Alexa (e.g., Capital One).

Fraud detection and anti–money-laundering activities; also
improving customer experience (Bank Danamon).
Victual banking assistant, Olivia at H S B C, learn from
experience and helps customer better.
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U S Bank Customer Recognition and Services
What are Einstein’s advantages to U S Bank?
What are its advantages to customers?
What are the benefits of voice communication?
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A I in Human Resource Management (1 of 2)

Recruitment – talent acquisition
See Application Case 2.6 for an example
Training – A I facilitates training
Performance assessment (evaluation)
Retention –eliminating attrition
Predicting attrition way ahead of time to eliminate loss of talent
Using chatbots for supporting H R M
See olivia.paradox.ai.
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A I in Human Resource Management (2 of 2)
Introducing A I to H R M operations:
Experiment with a variety of chatbots
Develop a team approach involving other functional areas
Properly plan a technology roadmap for both the short and long
term, including shared vision with other functional areas
Identify new job roles and modifications in existing job roles in
the transformed environment
Train and educate the H R M team to understand A I and gain
expertise in it.

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How Alexander Mann

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