1. Describe the four-step process of management decision-making:
a. Managers usually make decisions by following 4 steps
i. ONE: define the problem
ii. TWO: construct a model to describe real-world problem
iii. THREE: identify and evaluate possible solutions
iv. FOUR: compare, choose, and recommend solution
2. Describe the types of computer support that can be used for structured, semistructured, and
unstructured decisions:
a. Structured Decisions
i. Structured problems, which are encountered repeatedly, have a high level of
structure.
ii. It’s necessary to abstract, analyze, and classify them into specific categories
iii. It’s necessary to use a scientific approach for automating portions of managerial
decision making
b. Semistructured Decisions
i. Semistructured problems may involve a combo of standard solution procedures and
human judgement
ii. Management science can provide models for the portion of a decision-making
problem that is structured, but for unstructured a DSS can improve quality of info on
which decision is based by providing not only a single solution but alternative range
and impacts of each
c. Unstructured Decision
i. Unstructured problems can only partially be supported by standard computerized
quantitative methods.
ii. Usually necessary to develop custom solutions
1. But, custom solutions may already benefit from data and info generated
from corporate or ext sources
3. List and describe three levels or categories of analytics that are most often viewed as sequential
and independent, but also occasionally seen as overlapping:
a. Descriptive or reporting analytics
i. Refers to knowing what’s happening in the org and understanding some underlying
trends/causes of such occurrences
b. Predictive analytics
i. Aims to determine what is likely to happen in the future
ii. Is based on statistical techniques as well as other more recent techniques of data
mining
c. Prescriptive analytics
i. Recognizes what is going on as well as the likely forecast and make decisions to
achieve best performance possible
4. List and briefly discuss the major benefits of AI:
a. Significant reduction in cost of performing work
i. Reduction continues over time while cost of doing same work manually increases
with time
b. Work can be performed faster
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c. Work is consistent (or, more consistent than human work)
d. Increased productivity and profitability as well as competitive advantage drive AI in orgs
Describe and define Big Data. Why is a search engine a Big Data app?
a. Big Data is data that can’t be stored in a single storage unit.
b. Typically refers to data arriving in many different forms
i. Includes structured, unstructured, or streamed
c. Major sources include clickstreams from web sites, posts on social media, data from traffic
sensors/weather sensors, etc
d. Search Engines must search and index billions of web pages to provide relevant search
results in fractions of a second.
i. While not done in real time, generating an index of all web pages on the internet
isn’t easy.
Discuss the drivers of AI:
a. People’s interest in smart machines and artificial brains
b. Low cost of AI apps versus high cost of manual labor for same work
c. Desire of large tech companies to gain competitive advantage + market share
i. Willing to invest billions into AI
d. Pressure on mgmt to increase productivity and speed
e. Availability of quality data to contribute to AI
f. Increasing functions and reduced cost of computers in general
g. Development of new tech (particularly cloud computing)
Describe the Turing Test:
a. Test determines whether or not a computer can be considered smart only when a human
interviewer asks the same questions to both an unseen human and an unseen computer and
the interviewer can’t determine who is answering
b. Test is limited to question and answer mode
c. To pass, a computer needs to:
i. Understand a human language (NLP)
ii. Possess human intelligence (have a knowledgebase)
iii. Reason using stored knowledge
iv. Able to learn from experiences (machine learning)
Discuss some issues and factors that would drive the decision to successfully use AI:
a. Several issues determine justification and successfulness of AI:
i. Nature of the decision (ex. Routine, simple decisions are more likely to be fully
automated)
ii. Method of support and what technologies are used
1. In past, automated decision supports were rule-based
2. Expert systems created to generate solutions to specific decisions in welldefined domains
3. A “recommender” was used in the 1990s for e-commerce
iii. Today, machine learning is increasing as well as deep learning.
1. Example = pattern recognition and biometric recognition
Discuss how conversational bots can improve customer experience:
a. Use NLP for generating user documentation to improve customer-machine dialogue
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b. Use visual categorization to organize items
c. Provide personalized and segmented services by analyzing customer data
i. Includes improving shopping and CRM experiences
Describe the model for the use of intelligent technologies in manufacturing companies:
a. Five-component model:
i. Streamlining processes, minimizing waste, redesigning processes, using business
process mgmt. (BPM)
ii. Outsourcing certain business processes, including going offshore
iii. Using intelligence in decision making by deploying AI and analytics
iv. Replacing human tasks with intelligent automation
v. Digitizing customers’ experiences
List and describe the three layers of an ideal dashboard:
a. Monitoring: graphical, abstracted data to monitor key performance metrics
b. Analysis: summarized, dimensional data to analyze the root cause of problems
c. Management: detailed operational data identify what actions to take to resolve a problem
Describe the use of both Gantt and Pert charts:
a. GANTT: special case of horizontal bar charts to portray project timelines/tasks/durations
and overlaps.
i. Shows start and end times and relationships, it provides invaluable aid for mgmt
and control of projects
b. PERT: aka Network Diagram, developed primarily to simplify planning and scheduling of
large/complex problems.
i. Shows precedence of relationships among project tasks
ii. Composed of nodes (shown as circles or rectangles) and edges (shown as arrows)
iii. Based on selected convention, either nodes or edges can be used
Discuss how data can be classified:
a. At highest level of abstraction, one can classify data as structured and
unstructured/semistructured.
i. Unstructered/Semistructered
1. Composed of any combo of textual, imagery, voice, and web content
ii. Structured
1. What data mining algorithms use and can be classified as categorical or
numeric
2. Categorical data can be subdivided into nominal or ordinal data
3. Numerical data can be subdivided into intervals or ratios
What is the difference between correlation and regression?
a. Correlation makes no assumption of whether one variable is dependent on the other(s) and
is not concerned with the relationship bw variables
i. Instead, it gives estimate on degree of associations
b. Regression attempts to describe dependence of a response variable on one or more
explanatory variables
i. Implicitly assumes that there’s a one-way causal effect from the explanatory
variables to response var regardless of path being direct or indirect
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c. In summary, although correlation is interested in the low-level relationships between two
variables, regression is concerned with the relationships bw all explanatory variables and
the response var.
Describe the three main types of business reports:
a. METRIC MGMT REPORTS: used in many orgs bc business performance is managed through
outcome-oriented metrics.
i. For external groups, these are SLAs
ii. For internal mgmt., these are for KPIs
iii. Typically enterprise-wide agreed upon targets tracked over period
iv. Can be used as part of other mgmt strategies (Six Sigma, TQM, etc)
b. DASHBOARD TYPE REPORTS: Popular idea in business reporting recently in presenting a
range of diff perf indicators on one page like a car’s dashboard.
i. Dashboard Vendors provide set of predefined reports w/ static elements and
structure
1. BUT, they allow for customization of widgets, views, set targets
ii. Common to have color-coded traffic lights defined for performance
c. BALANCED SCORECARD REPORTS: method developed by Kaplan and Norton to present an
integrated view of success in an org.
i. In addition to financial perf, these reports include customer, business process, and
learning/growth perspectives.
List five reasons for the growing popularity of data mining in the business world:
a. More intense competition at global scale driven by customers’ changing needs/wants in an
increasingly saturated market
b. General recognition of the untapped value hidden in large data sources
c. Consolidation and integration of db records, which enables a single view of customers,
vendors, transactions, etc
d. The exponential increase in data processing and storage technologies
e. Significant reduction in the cost of hardware and software for data storage and processing
f. Movement toward the de-massification (conversion of info resources into non physical
form) of business practices
Describe the difference between quantitative and qualitative data. Include definitions for nominal
and ordinal data:
a. QUANT: measured using numeric values or numeric data
i. Can be readily represented by some sort of probability distribution
b. QUAL: aka categorical data, contains both nominal and ordinal data
i. Can be coded to numbers and then described by frequency distributions
c. NOMINAL DATA: have finite nonordered values
d. ORDINAL DATA: have finite ordered values
Why are support vector machines a popular machine-learning technique?
a. The provide superior predictive power and useful theoretical foundation
b. Are among the supervised learning techniques that produce input-output functions from a
set of labeled training data
Why have neural networks shown much promise in many forecasting and business classification
applications?
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a. Due to their ability to “learn” from the data
b. Their nonparametric nature (no rigid assumptions)
c. Ability to generalize
How is a general Hopfield network represented architecturally?
a. Represented as a single large layer of neurons with total interconnectivity
b. Each neuron is connected to every other neuron within the network
What are the three steps in the process-based approach to the use of support vector machines
(SVMs)?
a. Numericizing the data
b. Normalizing the data
c. Selecting the kernel type and kernel parameters
Describe the k-nearest neighbor (kNN) data mining algorithm:
a. It’s a prediction method for classification
b. It’s a prediction method for regression-type prediction problems
c. It’s a type of instance-based learning (or lazy learning) where the function is only
approximated locally
i. All computations are deferred until the actual prediction
What are the key attributes of cognitive computing systems?
a. Cognitive computing systems must have/be:
i. Adaptive
1. flexible enough to learn as info changes and goals evolve. Digest dynamic
data in real time and make changes accordingly
ii. Interactive
1. human computer interaction (HCI) is a critical component as users must be
able to interact with cog machines, define needs, etc. the tech must also be
able to interact with other processors, devices, and cloud platforms.
iii. Iterative and Stateful
1. Can identify problems by asking questions or pulling more data if question is
vague/incomplete
a. Done by maintaining info about similar situations
iv. Contextual
1. Must understand context in thought processes
2. Must understand/identify/mine contextual data including syntax, time,
location, domain, requirements, profiles, tasks, goals
3. May draw on multiple inf sources
How does cognitive computing differ from artificial intelligence?
a. TECH: both utilize machine learning, LP, neural networks, deep learning
i. But, cognitive computing adds text mining and sentiment analysis
b. CAPABILITIES: AI seeks to find hidden patterns in data sources
i. Cog computing attempts to stimulate the human thought process to find solutions
c. PURPOSE: AI seeks to automate complex processes
i. Cog computing seeks to augment human capabilities
d. INDUSTRIES: both have apps in most industries
i. Cog computing is currently customer service, marketing, healthcare, entertainment
25. Describe the framework of DeepQA used by IBM’s Watson:
a. MASSIVE PARALLELISM:
i. Watson needed to exploit massive parallelism in the consideration of multiple
interpretations and hypotheses
b. MANY EXPERTS:
i. Watson needed to be able to integrate, apply, and contextually evaluate a wide
range of loosely coupled probabilistic questions and content analytics
c. PERVASIVE CONFIDENCE ESTIMATION:
i. No component of Watson committed to an answer
ii. All components produced features and associated confidences, scoring diff question
and content interpretations.
iii. An underlying confidence-processing substrate learned how to stack and combine
the scores
d. INTEGRATION OF SHALLOW/DEEP KNOWLEDGE:
26. Define the term sensitivity analysis as it relates to ANNs:
a. It’s a method for extracting the cause-and-effect relationships among the inputs and the
outputs of a trained neural network model
27. Describe an MLP deep network. Why are these labeled as feedfordward networks?
a. AKA deep feedforward networks
b. The most general type of deep networks
c. Just large-scale neural networks that contain many layers of neurons and handle tensors as
their inputs
d. Types of characteristics of the network elements (weigh functions, transfer functions, etc)
are pretty much the same as in standard ANN models
e. Called feedforward because the flow of info is always forwarding through them and no
feedback connections are allowed
i. Feedback connections are connections in which outputs of a model are fed back to
itself
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