Quiz 1

Question 1

Nature of Big Data

Answer 1

Volume, Velocity, & Variety

Volume:Large-capacity data storage is not only the problem of data integration but also a critical challenge for analysis.

• Velocity: Data’s patency, availability, and liquidity become critical; velocity indicates the speed of data changes as well as the need for timely data access and processing.
• Variety: Firms have more new data for analysis, such as social media, mobile data, various databases that store hierarchical data, text records, e-mail, metering data, video, images, audio, stock ticker data, and financial transactions.

Question 2

Digital curation

Answer 2

Digital curation generally refers to the process of establishing and developing long-term repositories of digital assets for current and future reference by researchers, scientists, historians, and scholars.

Question 3

Role of IT in Organizations

Answer 3

Support
Enhance Effectiveness & Efficiency
Value Creation

Question 4

Data Pyramid

Answer 4

Bottom up, Data -> Information -> Knowledge

Question 5

Organization and Information Systems

Answer 5

A growing interdependence between a firm’s information systems and its business capabilities and operations:

Changes in strategy, rules, and business processes increasingly require changes in hardware, software, databases, and telecommunications; often, what an organization can do depends on what its information systems will permit it to do.

Question 6

IT Empowers…

Answer 6

Suppliers, Firm, Customers

Question 7

Identifying Valuable Customers: RFM Analysis

Answer 7

Recency, Frequency, & Monetary

Valuable customers” are more important to a firm

Question 8

Identifying Valuable Customer: Clustering Analysis in N-Dimensional Space

Answer 8

Demographic analysis
• Customer profiling
• Customer behavior analysis
->
Target marketing  • Medium choice
• Channel design
->
Facilitating transactions • Enhanced CRM
->
Computational algorithm for clustering analysis

Question 9

Data-Driven CRM: An Integrated Approach

Answer 9

Customer Life Cycle
Partial Functional Solutions
Complete Integrated Solution
Acquire
Direct marketing
Enhance
Cross-sell and up-sell
Retain
Proactive services
Sales force automation Customer support
Integrated CRM System and Applications
Cross-functional processes breaks down functional silos !!

Question 10

Data and Firm Performance

Answer 10

Data (Information) Visibility 
->
Data (Information) Accessibility
->
Data (Information) Analytics Capability
->
Information Velocity

Question 11

Data quality:

Answer 11

The totality of features and characteristics of data that bears on their ability to satisfy the given business purposes.

Question 12

Data Quality: Common Dimensions/Measures

Answer 12

• Accuracy
• Completeness • Consistency
• Timeliness
• Accessibility
• Believability
• Interpretability

Question 13

Data Governance

Answer 13

• Data governance establishes a formal structure and processes by which an organization manages all important issues surrounding data, including data quality as measured by accuracy, completeness, consistency, security, availability.

Question 14

Data Mining: A General Process

Answer 14

Selection
->
Preprocessing
->
Transformation
->
Mining
->
Interpretation/Evaluation

Question 15

System Development Projects: Key Deliverables

Answer 15

• Fully tested, operational system that meets business requirements, such as management reports, user queries, and business analyses.
• Documentations of essential system designs and data dictionary.
• User manuals; e.g., important management reports and common user queries.
• Documentations describing how the system relates to other relevant systems and how they can be integrated.
• Key contacts for system operations support and enhancement.

Question 16

Business-Driven Analysis of Technology Needs

Answer 16

Business Objectives
Business Strategies, Activities, and Operations
Information Requirements Core Systems Functionalities
Information Systems need prioritization and evaluation

Question 17

Key Challenges in IT Management

Answer 17

• A young technology (discipline).
• Rapid technological advancements and changes. • Deep penetrations in all aspect of organizations. • Widening business-technology gap.
• Increasing specialization and sub-specialization. • Shifting focuses, disruptive technology.

Question 18

IT and Modern Business

Organizations: Summary

Answer 18

• IT has enormous empowerment to firm and its employees
and managers !!
• Increasingly, IT deeply penetrates all aspects of an organization that now cannot operate and compete without IT.
• Effective investment, utilization and management of IT determine firm performance and competiveness.
• IT management is a management challenege, not a pure technology problem !!
• IT enhance firm’s business values and decision making.
• IT support and enable self-service models.
• Business managers must recognize and address the business-
technology gap in their organization; their commitments and
passion are critical to realizing the full business value of IT.

Question 19

IT and Modern Business

Organizations: Summary

Answer 19

• IT has enormous empowerment to firm and its employees
and managers !!
• Increasingly, IT deeply penetrates all aspects of an organization that now cannot operate and compete without IT.
• Effective investment, utilization and management of IT determine firm performance and competiveness.
• IT management is a management challenege, not a pure technology problem !!
• IT enhance firm’s business values and decision making.
• IT support and enable self-service models.
• Business managers must recognize and address the business-
technology gap in their organization; their commitments and
passion are critical to realizing the full business value of IT.

Question 20

Business Intelligence

Answer 20

(BI) refers to use of technology and statistical techniques to gather, analyze large amounts of data to support business decision making; i.e., discovering important patterns and phenomena for interpretation and business actions.

Question 21

Data mining:

Answer 21

A common technological/computational approach to extract business intelligence from vast amounts of (high-quality) data.

Question 22

Classification

Answer 22

Classification recognizes patterns that describe the group to which an item belongs by examining existing items that have been classified and by inferring a set of rules.

Question 23

Clustering

Answer 23

Clustering works in a manner similar to classification when no groups have yet been defined

Question 24

Association pattern/rule analysis

Answer 24

Association pattern/rule analysis (market basket analysis) discovers interesting co-occurrence of items from a set of transactions, each of which contains a collection of items.
• Analysis of retail transactions (e.g., items purchased in a transaction) helps vendors identify which products customers are likely to purchase together.

Question 25

Support of X→Y

Answer 25

Support of X→Y is obtained from dividing the number of transactions that contain X∩Y by the total number of transactions in the data

Question 26

Confidence of X→Y

Answer 26

Confidence of X→Y is obtained from dividing the number of transactions that contain X∩Y by the number of transactions that contain X

Question 27

Clustering analysis:

Answer 27

A process to partition (segment) a group of objects into multiple distinct clusters (subgroups) in a N-dimensional space, such that all the members in one cluster are similar to each other and distinctively different from the members of any other cluster, according to a particular similarity measure/metric; i.e., the opposite of distance between objects.

Question 28

Clustering Analysis for Market | Segmentation: Basic Steps

Answer 28

Formulate the segmentation problem and select the variables that we want to use as the basis for clustering. 2. Compute the distance customers along the selected variables. 3. Apply the clustering procedure to the chosen distance measure. 4. Decide the number of clusters. 5. Map and interpret clusters and draw conclusions; e.g., illustrative techniques include perceptual maps that are useful for firm’s interpreting the resulting clusters.

Question 29

Interval variables:

Answer 29

An interval variable contains continuous measurements that follow a linear scale (e.g., height, weight, temperature, cost, etc.); it is required that intervals keep the same importance throughout the scale.

Question 30

Ordinal variables:

Answer 30

An ordinal variable takes on more than two states; for example, you may ask someone to indicate his or her liking of a product according to the following categories: 1=detest, 2=dislike, 3=indifferent, 4=like and 5=admire; in an ordinal variable, the different states are ordered in a meaningful sequence but the interval between any two consecutive states may not be equally distanced.

Question 31

Nominal Variables:

Answer 31

A nominal variable can take on more than two states; for example, the eye color of a person can be blue, brown, green or grey eyes; these states may be coded as 1, 2, ..., M, but their order and the interval between any two states do not have any semantic meaning

Question 32

Binary Variables:

Answer 32

Binary variables have only two possible states; for example, a person’s gender is either female or male; binary variable can be viewed as a special case of nominal variable.

Question 33

k-Means Clustering: Example

Answer 33

Centroid of a cluster is the average of all the data points in that cluster.

Question 34

K-Means clustering: General Process

Answer 34

1. Choose the number of clusters, k. 2. Generate k random points as cluster centroids. 3. Assign each point to the nearest cluster centroid. 4. Re-compute the new cluster centroid. 5. Repeat steps 3-4 until some convergence criterion is satisfied. Typical convergence criterion is the assignment of customers to clusters has not changed in multiple iterations.

Question 35

k-Means Clustering Algorithm

Answer 35

Suppose that n objects described by the attribute vectors {x1, x2, ..., xn} be partitioned into k clusters, where k < n. Let mi be the mean of the vectors in cluster i. That is, object y is in cluster i if the distance between y and mi is the minimum. Randomly initialize the means m1, m2, ..., mk Repeat Use the means to classify all objects into clusters For i = 1 to k Replace mi with the mean of all objects in cluster i End-for Until there is no change in any mean

Question 36

Classification Analysis

Answer 36

* Classification is the process that establishes classes with attributes from a set of instances (called training examples). The class of an instance must be one from a finite set of pre-determined class values, while attributes of the instance are descriptors of the instance that are likely to affect its outcome class value. * Classification techniques: ID3 and its descendants (such as C4.5), CN2, AQ family, backpropagation neural network, etc.

Question 37

Neural Network Architecture

Answer 37

* Processing units are grouped into linear arrays (layers) * A neural network always has an input layer, an output layer, and may or may not have “hidden” layers. * Processing unit processes its inputs and produces a single output value; this processing is known as the unit’s activation function. * For an input node, the activation function simply passes its value to the output of the node. * For a non-input node, the activation function has two parts: a combination function and a transfer function.

Question 38

Confusion Matrix

Answer 38

Confusion matrix shows the number of correct and incorrect predictions made by the classification model compared to the actual outcomes A confusion matrix is a table that categorizes predictions according to whether they match the actual value. • The class of interest is known as the positive. • All others are known as negative • An spam classifier example: • Positive class is spam, as this is the outcome we hope to detect. • We can then imagine the confusion matrix as shown in the diagram to the right.

Question 39

True Positive, False Negative and Accuracy

Answer 39

``` • False Positive (FP): Incorrectly classified as the class of interest; a.k.a. error of omission, Type I error, or false alarm. • False Positive (FP): Incorrectly classified as not the class of interest; a.k.a. error of commission, Type II error. • Accuracy: Overall correctness of the model and is calculated as the sum of correct classifications divided by the total number of classifications. ```

Question 40

Precision and Recall

Answer 40

• Precision:Portionofinstancespredictedtobeaclass actually belong to this class • Howmanyinstancesarepredictedtobepositive? Predicted Positive = TP + FP • HowmanyofPredictedPositiveinstancesactually belong to the positive class? True Positive • Precision (positive) = TP / (TP + FP) • Precision (negative) = TN / (TN + FN) • Recall:Theabilitytocorrectlyclassifyinstances belonging to this class • Howmanyinstancesbelongtotheactualpositive class? Actual Positive = TP+FN • Howmanyofactualpositiveinstancesareclassified correctly? TP • Recall(positive)=TP/(TP+FN) • Recall(negative)=TN/(TN+FP)