Final Project Description: The goal of this assignment is again to apply concepts from the textbook to real-world phenomena, the only difference is that you will be compiling information from a range of sources.  Notice that in the other assignments, the information was all contained (effectively) within a single source (either the case study or the business periodical report).  This time, you need to dig deeper and compile the information you need to complete the project as requested.  Find an example of a firm that is in a turbulent environment, performing badly and/or in need of a new strategy, preferably a well-known firm about which you can find information online. You can see a few on the Fortune website. Search the Fortune 500’s Worst-Performing Stocks. Format: 1. the first paragraph should explain the issue you want to discuss. 2. An overview of the company (e.g., industry, business model, major competitors and the firm’s competitive advantage over them, etc.). 3. Then you need to do a systematic analysis, which has three parts: 1. five forces analysis. Separate each of the five in different bullets and explain the strength of that threat (weak, medium, high). 2. SWOT (Strength, Weakness, Opportunity, Threat) analysis, you can explain each dimension (e.g., Strengths) in a separate subsection. A very important thing is that you clearly mention what you want to discuss (e.g., Strengths). While you better cover all of them, focus on those that are specific and related to your solution. 3. PESTEL analysis. Again, try to separate sections to make it easy to follow. And Also, don’t forget to focus on main factors that can impact the company. 4. After doing a summary of the issue, following by analysis, you need to suggest a solution. It is very important that your solution be related to your analysis. For example, if you suggest the company to expand its operation, merge with another firm, engage in social activities, and so on, you need to explain how you think it is a good suggestion based on the Strenght and Weakness of firms and/or the Opportunity and Threat that exist in the external environment. One big weakness of essays is the lack of connection between analysis and suggestion. I suggest you this about the factors in this industry define success (key success factors) and the firm should focus on. 5. You can conclude with a summary if you have space. It is an individual assignment and should be 3 pages with “Time New Roman”, the double-spaced, and standard margin in Words. Citations and additional data to support your analysis can be added as appendix and are not counted in the page limit. 1 Data Breaches Chapter Extension 14 ce14-2 Study Questions C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c . Q1: What is a data breach? Q2: How do data breaches happen? Q3: How should organizations respond to data breaches? Q4: What are the legal consequences of a data breach? Q5: How can data breaches be ...

1. Final Project Description:
The goal of this assignment is again to apply concepts from the
textbook to real-world phenomena, the only difference is that
you will be compiling information from a range of sources.
Notice that in the other assignments, the information was all
contained (effectively) within a single source (either the case
study or the business periodical report). This time, you need to
dig deeper and compile the information you need to complete
the project as requested. Find an example of a firm that is in a
turbulent environment, performing badly and/or in need of a
new strategy, preferably a well-known firm about which you can
find information online. You can see a few on
the Fortune website. Search the Fortune 500’s Worst-
Performing Stocks.
Format:
1. the first paragraph should explain the issue you want to
discuss.
2. An overview of the company (e.g., industry, business model,
major competitors and the firm’s competitive advantage over
them, etc.).
3. Then you need to do a systematic analysis, which has three
parts:
1. five forces analysis. Separate each of the five in different
bullets and explain the strength of that threat (weak, medium,
high).
2. SWOT (Strength, Weakness, Opportunity, Threat) analysis,
you can explain each dimension (e.g., Strengths) in a separate
subsection. A very important thing is that you clearly mention
what you want to discuss (e.g., Strengths). While you better
cover all of them, focus on those that are specific and related to
your solution.
3. PESTEL analysis. Again, try to separate sections to make it
easy to follow. And Also, don’t forget to focus on main factors

2. that can impact the company.
4. After doing a summary of the issue, following by analysis,
you need to suggest a solution. It is very important that your
solution be related to your analysis. For example, if you suggest
the company to expand its operation, merge with another firm,
engage in social activities, and so on, you need to explain how
you think it is a good suggestion based on the Strenght and
Weakness of firms and/or the Opportunity and Threat that exist
in the external environment. One big weakness of essays is the
lack of connection between analysis and suggestion. I suggest
you this about the factors in this industry define success (key
success factors) and the firm should focus on.
5. You can conclude with a summary if you have space.
It is an individual assignment and should be 3 pages with “Time
New Roman”, the double-spaced, and standard margin in Words.
Citations and additional data to support your analysis can be
added as appendix and are not counted in the page limit.
1
Data Breaches
Chapter
Extension 14
ce14-2
Study Questions
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
Q1: What is a data breach?

3. Q2: How do data breaches happen?
Q3: How should organizations respond to data breaches?
Q4: What are the legal consequences of a data breach?
Q5: How can data breaches be prevented?
2
ce14-3
Q1: What is A Data Breach?
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
• Data breach
– Unauthorized person views, alters, or steals secured data
• 1+ billion people affected in past 5 years, 75% of breaches
happened in US
• Average cost of a single data breach $3.5 million
• Average costs per stolen record
Healthcare ($359), Pharmaceutical ($227 Communications
industries ($177)
Education ($294) Financial ($206)
ce14-4

4. Costs of Handling a Data Breach
Direct Costs
• Notification
• Detection
• Escalation
• Remediation
• Legal fees and
consultation
Indirect Costs
• Loss of reputation
• Abnormal customer
turnover
• Increased customer
acquisition activities
• Additional $3.3 million
per incident in US
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
3
ce14-5

5. What Are the Odds?
• More likely to lose smaller amounts of data than larger
amounts
of data
-month
period
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
ce14-6
Well-known Data Breaches
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
4
ce14-7
Why Do Data Breaches Happen?
• 67% are hackers trying to make money from:
– Personally identifiable information (PII)
numbers, credit card numbers, health records, bank
account numbers, PINs, email addresses
• Rogue internal employees

6. • Credit card fraud, identity theft, extortion, industrial
espionage
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
ce14-8
Q2: How Do Data Breaches Happen?
• Attack vectors
– Phishing scam
– Trick users into donating funds for a natural disaster
– Exploit new software vulnerability
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
5
ce14-9
Hitting Target
• Lost 40 million credit and debit card numbers to attackers
(Dec.
18, 2013)
• Less than a month later, announced additional 70 million
customer names, emails, addresses, phone numbers stolen
– Total 98 million customers affected
• Stolen from point-of-sale (POS) systems at Target retail stores

7. C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
ce14-10
How Did They Do It?
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
6
ce14-11
The Damage
• Attackers sold about 2 million credit card numbers and PINs
for
about $26.85 each (total $53.7 million)
• Sold in batches of 100,000 card numbers
• Cost Target $450 million
– Upgraded POS terminals to support chip-and-PIN enabled
cards
– Increased insurance premiums, legal fees, credit card
processors
settlement, pay for consumer credit monitoring, regulatory fines
– Lost sales, 46% drop in next quarter revenues
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.

8. ce14-12
Collateral Damage
• Credit unions and banks
– Spent more than $200 million issuing new cards
• Consumers
– Enrolled in credit monitoring, continually watch their credit,
and fill out paperwork if fraudulent charges appear on
statements
• Increased insurance premiums, stricter controls, and more
system auditing for organizations similar to Target
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
7
ce14-13
Q3: How Should Organizations Respond
To Data Breaches?
• Respond Quickly
– Stop hackers from doing more damage
– Immediately notify affected users
• Plan for a Data Breach

9. – Walkthroughs, business continuity planning, computer
security incident response team (CSIRT)
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
ce14-14
Q3: How Should Organizations Respond
To Data Breaches? (cont'd)
• Get experts to perform an effective forensic investigation
• Identify additional technical and law enforcement
professionals
needed
• Be honest about the breach
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
8
ce14-15
Best Practices for Notifying Users of a Data
Breach
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
ce14-16

10. Q4: What Are The Legal Consequences of a Data
Breach?
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
9
ce14-17
Regulatory Laws Govern the Secure Storage
of Data in Certain Industries
• Federal Information Security Management Act (FISMA)
– Requires security precautions for government agencies
• Gramm-Leach-Bliley Act (GLBA), a.k.a., Financial Services
Modernization Act
– Requires data protection for financial institutions
• Health Information Portability and Accountability Act
(HIPAA)
– Requires data protection for healthcare institutions
• Payment Card Industry Data Security Standard (PCI DSS)
– Governs secure storage of cardholder data
• Family Educational Rights and Privacy Act (FERPA)
– Provides protection for student education records
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.

11. ce14-18
Q5: How Can Data Breaches Be Prevented?
• Use countermeasures software or procedures to prevent an
attack
• Better phishing detection software
• Better authentication (i.e., multifactor authentication
• Network intrusion detection system (NIDS) to examine traffic
passing through internal network
• Data loss prevention systems (DLP) to prevent sensitive data
from being released to unauthorized persons
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
10
ce14-19
Q5: How Can Data Breaches Be Prevented?
(cont'd)
• Appoint a chief information security officer (CISO) to ensure
sufficient executive support and resources
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.

12. 1
Database Marketing
Chapter
Extension 12
ce12-2
Study Questions
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
Q1: What is a database marketing opportunity?
Q2: How does RFM analysis classify customers?
Q3: How does market-basket analysis identify cross-selling
opportunities?
Q4: How do decision trees identify market segments?
2
ce12-3
Owner of Carbon Creek Gardens Needs Database
Marketing
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.

13. • As business grew, lost track of customers
• Lost valuable customer and didn't know it
• Has lot of sales data, but needs system to:
– Store and track customers
– Store and track services provided to customers
– Store and report future scheduled services
ce12-4
Database Marketing
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
• Application of business intelligence systems to:
– Planning marketing programs
– Executing marketing programs
– Assessing marketing programs
• Databases and data mining techniques key components
3
ce12-5
Q2: How Does RFM Analysis Classify Customers?
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
• Recently

14. • Frequently
• Money
ce12-6
RFM Analysis Classifies Customers
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
4
ce12-7
Q3: How Does Market-Basket Analysis Identify
Cross-Selling Opportunities?
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
• Unsupervised data mining method
– Statistical methods to identify sales patterns in large volumes
of data
– Products customers tend to buy together
– Probabilities of customer purchases
– Identify cross-selling opportunities
ce12-8

15. Market-Basket Example: Transactions = 400
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
5
ce12-9
Support: Probability that Two Items Will Be Bought
Together
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
• P(Fins and Masks) = 250/400, or 62%
• P(Fins and Weights) = 20/400, or 5%
ce12-10
Confidence = Conditional Probability Estimate
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
• Probability of buying Fins = 250 -- Probability of buying
Mask = 270
• P(After buying Mask, then will buy Fins) -- Confidence =
250/270 or 92.6%
6

16. ce12-11
Lift = Confidence ÷ Base Probability
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
• Lift = Confidence of Mask/Base P(Fins)
• Lift = .926/.625 = 1.32
ce12-12
Warning!
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
• Analysis only shows shopping carts with two items
• Must analyze large number of shopping carts with three or
more
items
• Know what problem you are solving before mining the data
– Know what question you want to answer
7
ce12-13
Q4: How Do Decision Trees Identify Market
Segments?

17. C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
• Hierarchical arrangement of criteria to predict a classification
or
value
• If/Then hierarchy
• Unsupervised data mining technique
• Basic idea of a decision tree
– Select attributes most useful for classifying something on
some criterion to create “pure groups”
ce12-14
Decision Tree
for Student
Performance
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
If Junior = Yes
Lower-level groups
more similar than
higher-level groups
8

18. ce12-15
Transforming a Set of Decision Rules
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
• If student is a junior and works in a restaurant,
– Then predict grade =>3.0
• If student is a senior and is a nonbusiness major,
– Then predict grade <3.0
• If student is a junior and does not work in a restaurant,
– Then predict grade <3.0
• If student is a senior and is a business major,
– Then make no prediction
ce12-16
Decision Tree for Loan Evaluation
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
• Classify loan applications by likelihood of default
• Rules identify loans for bank approval
• Identify market segment
• Structure marketing campaign
• Predict problems
9

19. ce12-17
Credit Score
Decision Tree
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
ce12-18
Ethics Guide: Data Mining in the Real World
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
Problems with data:
• Dirty data
• Missing values
• Lack of knowledge at start of project
• Overfitting – too many variables
• Probabilistic—good model may have unlucky first uses
• Seasonality influences
• High risk – uncovering something self-defeating to reveal
10
ce12-19
Ethics Guide: Data Mining in the Real World
(cont’d)
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.

20. • When you start a data mining project, you never know how it
will turn out
• Decision trees can be used to select variables for other types
of
data mining analysis
1
Reporting Systems and OLAP
Chapter
Extension 13
ce13-2
Study Questions
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
Q1: How do reporting systems enable people to create
information?
Q2: What are the components and characteristics of reporting
systems?
Q3: How are reports authored, managed, and delivered?
Q4: How are OLAP reports dynamic?

21. 2
ce13-3
Q1: How Do Reporting Systems Enable People to
Create Information?
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
• Reporting systems
– Create meaningful information from disparate data sources
– Deliver information to user on time
• Generate information by:
– Filtering data
– Sorting data
– Grouping data
– Making simple calculations
ce13-4
Raw Sales Data: List Contains Little or No
Information
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
3
ce13-5
Sales Data Sorted by Customer Name

22. C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
Create
Information
by Sorting
ce13-6
Sales Data Sorted by Customer Name and Grouped by Number
of Orders and Showing Purchase Amounts
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
Create Information by
Grouping & Summing
4
ce13-7
Sales Data Filtered to Show Repeat Customers
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
Create Information by
Filtering & Formatting
ce13-8
Q2: What Are the Components and Characteristics of
Reporting Systems?

23. C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
• Reads and combines data from disparate data sources
• Filters, sorts, groups, simple calculations to produce
meaningful
contexts
• Metadata to prepare and deliver reports
• Report characteristics
– Type
– Media
– Mode
5
ce13-9
Components of a Reporting System
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
ce13-10
Summary of Report Characteristics
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.

24. 6
ce13-11
Report Media
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
• Printed on paper
• PDFs—Portable Document Format
• Delivered to computer screens and mobile devices
Applications for CRM and ERP systems
• Digital dashboards
• Alerts
• Exported from report generator to another program
• Published via Web service
ce13-12
Digital Dashboard Example
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
7
ce13-13
Report Mode
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c

25. .
Types of reports
• Push report
– Sent or e-mailed to users according to a preset schedule
• Pull report
– Users download report from web portal or digital dashboard
ce13-14
Q3: How Are Reports Authored, Managed and
Delivered?
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
Report authoring
• Connecting to data sources, create report structure, format
report
• Examples: MS Access, Visual Studio
Report management
• Defines who receives what reports, when, what format, and
by what means
• Defines user accounts and user groups
8
ce13-15

26. Connecting to a Report Data Source Using Visual
Studio
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
ce13-16
How Are Reports Delivered?
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
Report delivery function
• Pushes or allows pulling reports based on metadata
• Delivered via an email server, Web site, SOA services, or
other program-specific means
• Security components ensure only authorized users receive
reports
• For queries, serves as intermediary between users and report
generator
9
ce13-17
Q4: How Are OLAP Reports Dynamic?
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.

27. • OLAP provides ability to sum, count, average, perform other
simple arithmetic operations on groups of data
• OLAP reports
– Dynamic
– Measure: item summed, averaged or processed in OLAP
report.
– Dimension: category or class
– Cube: measure with associated dimensions
– OLAP vendors and products
ce13-18
OLAP CUBE: Typical OLAP Report
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
OLAP Product Family by Store Type
10
ce13-19
OLAP Sales Report by Product Family, Store
Country, State, Store Type
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.

28. ce13-20
OLAP Sales Report by Country, State, City and
Store Type
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
11
ce13-21
Role of OLAP Server and Dimensional Database
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
1
Business Intelligence Systems
Chapter 9
9-2
Study Questions
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
Q1: How do organizations use business intelligence (BI)

29. systems?
Q2: What are the three primary activities in the BI process?
Q3: How do organizations use data warehouses and data marts
to acquire data?
Q4: What are three techniques for processing BI Data?
Q5: What are the alternatives for publishing BI?
2
9-3
Q1: How Do Organizations Use Business
Intelligence (BI) Systems?
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
Structure of Business
Intelligence System
9-4
Sample of Uses of Business Intelligence
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.

30. 3
9-5
What Are Typical Uses for BI?
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
• Identifying changes in purchasing patterns
– Important life events change what customers buy
• BI for Entertainment
– Netflix has data on watching, listening, and rental habits
– Classify customers by viewing patterns
• Just-in-Time Medical Reporting
– Real-time injection notification services to doctors during
exams
9-6
Q2: What Are the Three Primary Activities in the BI
Process?
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
4
9-7

31. Using Business Intelligence to Find Candidate
Parts at Falcon Security
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
• Candidate parts
– Provided by vendors who agree to make part design files
available for sale
– Purchased by larger customers
– Frequently ordered parts
– Ordered in small quantities
– Simple in design (easier to 3D print)
• Part weight and price surrogates for simplicity
9-8
Acquire Data: Extracted Order Data
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
• Query
Sales (CustomerName, Contact, Title, Bill Year, Number
Orders, Units, Revenue, Source, PartNumber)
Part (PartNumber, Shipping Weight, Vendor)
5
9-9
Sample Extracted Data: Part Table

32. C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
9-10
Joining Order Extract and Filtered Parts Tables
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
6
9-11
Sample Orders and Parts View Data
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
9-12
Creating Customer Summary Query
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
7
9-13
Customer Summary

33. C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
9-14
Qualifying Parts Query Design
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
8
9-15
Publish Results: Qualifying Parts Query Results
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
9-16
Publish Results: Sales History for Selected
Parts
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
9
9-17

34. Q3: How Do Organizations Use Data Warehouses
and Data Marts to Acquire Data?
C o p y r i g h t © 2 0 1 7 P e a r s o n E d u c a t i o n , I n c
.
• Data Warehouse Functions
– Obtain data from operational, internal and external databases
– Cleanse data
– Organize and relate data
– Catalog data using metadata
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Components of a Data Warehouse
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10
9-19
Examples of Consumer Data That Can Be
Purchased
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Possible Problems with Source Data
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35. .
Curse of
dimensionality
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Data Warehouses Versus Data Marts
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Q4: What are Three Techniques for Processing BI
Data?
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Reporting Analysis
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• Process of sorting, grouping, summing, filtering, and

36. formatting
structured data
• Structured data
– Data in rows and columns like tables
• Exception reports
– Produced when something out of predefined bounds occurs
• Printed and dynamic reports
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Data Mining
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• Application of statistical techniques to find patterns and
relationships among data for classification and prediction
• Combined discipline of statistics, mathematics, artificial
intelligence, and machine-learning
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Two Broad Categories of Data Mining
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1. Unsupervised procedures

37. – Does not start with a priori hypothesis or model
– Hypothesized model created afterward based on analytical
results to explain patterns found
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Supervised Data Mining
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• Uses a priori model to compute outcome of model
• Prediction, such as multiple linear regression
• Ex: CellPhoneWeekendMinutes
= (12 + (17.5*CustomerAge)+(23.7*NumberMo nthsOfAccount)
= 12 + 17.5*21 + 23.7*6 = 521.7
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BigData
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• Huge volume
– Petabyte and larger
• Rapid velocity

38. – Generated rapidly
• Great variety
– Structured data, free-form text, log files, graphics, audio, and
video
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MapReduce Processing Summary
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Google search
log broken
into pieces
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Google Trends on the Term Web 2.0
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Hadoop
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39. • Open-source program supported by Apache Foundation
• Manages thousands of computers
• Implements MapReduce
– Written in Java
• Amazon.com supports Hadoop as part of EC3 cloud offering
• Pig query language platform for large dataset analysis
– Easy to master
– Extensible
– Automatically optimizes queries on map-reduce level
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Q5: What Are the Alternatives for Publishing BI?
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What Are the Two Functions of a BI Server?
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40. How Does the Knowledge in This Chapter Help
You?
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• Business intelligence a critical skill
• Enables imagining innovative uses for data your employer
generates and know some of constraints of such use
• Digital marketing #1 priority for technology investment
• At PRIDE, knowledge of this chapter helps understand
possible
uses for exercise data
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Ethics Guide: Unseen Cyberazzi
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• Data broker or Data aggregator
– Acquires and purchases consumer and other data from
public records, retailers, Internet cookie vendors, social
media trackers, and other sources
– Used to create business intelligence to sell to companies and
governments
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Ethics Guide: Unseen Cyberazzi (cont'd)
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• Cheap cloud processing makes processing consumer data
easier, less expensive
• Processing happens in secret
• Data brokers enable you to view data stored about you, but:
– Difficult to learn how to request your data
– Torturous process to file for it
– Limited data usefulness
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Ethics Guide: Unseen Cyberazzi (cont'd)
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• Do you know what data is gathered about you? What is done
with it?
• Have you thought about conclusions data aggregators, or their
clients, could make based on your use of frequent buyer cards?
• Concerned about what federal government might do with data
it
gets from data aggregators?
• Where does all of this end?

42. • What will life be like for your children or grandchildren?
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Guide: Semantic Security
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1. Unauthorized access to protected data and information
• Physical security
2. Unintended release of protected information through reports
and documents
3. What, if anything, can be done to prevent what Megan did?