Week 10
•
Chapter 12
•
LeBow Course Evaluation / Break
•
Chapter 13
•
Graduating Seniors
•
Homework Assignment
• Course/Professor Evaluation
• Study for Final
• Final Exam Next week – June 11, 2024
• In class – same place; same time - 6:00pm
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Week 10 - June 4, 2024
Week 10
2
Week 10 - June 4, 2024
How Data Analytics Help an Organization
(McKinsey and Co.)
• Making data more transparent and usable more quickly
• Exposing variability and boosting performance
• Tailoring products & services; improving customer service
• Improving products
• Improving decision making
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Data Analytics Video
• What Is Data Analytics? An Introduction (Full Guide)
- Bing video
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Terminology
• Knowledge management:
• The processes to generate, capture, codify and transfer knowledge
across the organization to achieve competitive advantage
• Business intelligence:
• The set of technologies and processes that use data to understand
and analyze business performance
• Business analytics:
• The use of quantitative and predictive models, algorithms, and
evidence-based management to drive decisions
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Data, Information, and Knowledge
What product(s) does
Walmart increase shipments
of to Florida when
hurricanes are forecasted?
Beer
&
Strawberry Pop-Tarts
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Data, Information, and Knowledge
Analytics indicates this priority of items:
Beer
Strawberry Pop-Tarts
Bottled Water
Batteries
Fuel Containers
Bread
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The Value of Managing Knowledge
Value
Sources of Value
Sharing best practices
•
•
Avoid reinventing the wheel
Build on valuable work and expertise
Sustainable competitive advantage
•
•
Shorten innovation life cycle
Promote long term results and returns
Managing overload
•
•
Filter data to find relevant knowledge
Organize and store for easy retrieval
Rapid change
•
•
•
Build on/customize previous work for agility
Streamline and build dynamic processes
Quick response to changes
Embedded knowledge from
products
•
•
•
Smart products can gather information
Blur distinction between manufacturing/service
Add value to products
Globalization
•
•
•
Decrease cycle times by sharing knowledge globally
Manage global competitive pressures
Adapt to local conditions
Insurance for downsizing
•
•
•
Protect against loss of knowledge when departures occur
Provide portability for workers who change roles
Reduce time to acquire knowledge
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Dimensions of Knowledge
Explicit
 Teachable
 Articulable
 Observable in use
 Scripted
 Simple
 Documented
Tacit
 Not easily teachable
 Not easily articulated
 Not easily observable
 Rich
 Complex
 Undocumented
Examples:
• Procedure manuals
• Instructions w/ explicit steps
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Examples:
• Estimating work
• Deciding best action
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Four Modes of Knowledge Conversion
(and examples)
Transferring by
mentoring,
apprenticeship
Transferring by
models,
metaphors
Learning by doing; Obtaining and
studying manuals following manuals
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Knowledge Management – Four Processes
1) Generate
• discover “new” knowledge
2) Capture
• scan, organize, and package it
3) Codify
• represent it for easy access, consumption and transfer
• even as simple as using hash tags to create a folksonomy
4) Transfer
• transmit from one person to another to absorb it
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Components of Business Analytics
Component
Definition
Example
Data Sources
Data streams and repositories
Applications and processes for
statistical analysis, forecasting,
predictive modeling, and
optimization
Organizational environment that
creates and sustains the use of
analytics tools
Data warehouses; weather data
Data mining process; forecasting
software package
Software Tools
Data-Driven
Environment
Skilled Workforce
Workforce that has the training,
experience, and capability to use
the analytics tools
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Reward system that encourages
the use of the analytics tools;
willingness to test or
experiment
Data scientists, chief data
officers, chief analytics officers,
analysts, etc. Netflix, Caesars
and Capital One have these
skills
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Data Sources for Analytics
• Structured (customer info., weather patterns)
• Unstructured (Tweets, YouTube videos)
• Internal (employee surveys)
• External (vendor info.)
• Data Lakes / Data Warehouses
• full of a variety of information
• Real-time information such as stock market prices
• Resulting in “Big Data” sources
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Data Mining
• Combing through massive amounts of data
• Customer data example, usually focused on:
• Buying patterns/habits (for cross-selling)
• Preferences (to help identify new products/
features/enhancements to products)
• Unusual purchases (spotting theft)
• Identifies previously unknown relationships among data
• Complex statistics can uncover clusters on many dimensions
not known previously
• People who like movie “x” will probably like movie “y”
• People who like product “x” will probably like product “y”
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Four Categories of Data Mining Tools
• Statistical analysis:
• Answers questions such as “Why is this happening?”
• Forecasting/Extrapolation:
• Answers questions such as “What if these trends continue?”
• Predictive modeling:
• Answers questions such as “What will happen next?”
• Optimization:
• Answers questions such as “What is the best that can happen?”
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How to be Successful
• Achieve a data driven culture
• Create an analytical environment
• Develop skills for data mining
• Personnel with appropriate skill sets
• Have the right software tools available
• Identify the right sources of data
• Use a Chief Analytics Officer (CAO) / Chief Data Officer (CDO)
• Shoot for high maturity level (see next slide)
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Five Maturity Levels of Analytical Capabilities
Level
Description
Source of Business Value
1 – Reporting
What happened?
Reduce costs of summarizing,
printing
2 – Analyzing
Why did it happen? Understanding root causes
3 – Describing
What is happening
now?
Real-time understanding &
corrective action
4 – Predicting
What will happen?
Can take best action
5 – Prescribing
How should we
respond?
Dynamic correction
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Business Intelligence and
Competitive Advantage
• There is a very large amount of data in databases
• Big data: techniques and technologies that make it
economical to deal with very large datasets at the extreme
end of the scale: e.g., 1021 data items
• Large datasets can uncover potential trends and causal issues
• Specialized computers and tools are needed to mine the data
• Big data emerged because of the rich, unstructured data
streams created by social IT
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Practical Example
• Asthma outbreaks can be predicted by U. of Arizona
researchers with 70% accuracy
• They examine tweets and Google searches for words and
phrases like
• “wheezing” “sneezing” “inhaler” “can’t breathe”
• Relatively rare words (1% of tweets) but 15,000/day
• They examine the context of the words:
• “It was so romantic I couldn’t catch my breath” vs
• “After a run I couldn’t catch my breath”
• Helps hospitals make work scheduling decisions
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Sentiment Analysis
• Can analyze tweets and Facebook ‘likes’ for
• Real-time customer reactions to products
• Spotting trends in reactions
• Useful for politicians, advertisers, software versions,
sales opportunities
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Dark Side of Big Data
• False discoveries
• Omitting hidden causal factors (e.g., ice cream sales
and child drownings)
• Invasion of privacy
• Fake news
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Google Analytics and Salesforce.com
• Listening to the community:
• Identifying and monitoring all conversations in the social Web on a
particular topic or brand
• Learning who is in the community:
• Identifying demographics such as age, gender, location, and other trends
to foster closer relationships
• Engaging people in the community:
• Communicating directly with customers on social platforms such as
Facebook, YouTube, LinkedIn, and Twitter using a single app
• Tracking what is being said:
• Measuring and tracking demographics, conversations, sentiment, status,
and customer voice using a dashboard and other reporting tools
• Building an audience:
• Using algorithms to analyze data from internal and external sources to
understand customer attributes, behaviors, and profiles, then to find new
similar customers
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Google Analytics
• Web site testing and optimizing:
• Understanding traffic to Web sites and optimizing a site’s content and
design for increasing traffic
• Search optimization:
• Understanding how Google sees an organization’s Web site, how
other sites link to it, and how specific search queries drive traffic to it
• Search term interest and insights:
• Understanding interests in particular search terms globally, as well as
regionally, top searches for similar terms, and popularity over time
• Advertising support and management:
• Identifying best ways to spend advertising resources for online media
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Internet of Things (IoT)
• Much big data comes from IoT
• Sensor data in products can allow the products to:
• Call for service (elevators, heart monitors)
• Parallel park, identify location/speed (cars)
• Alert you to the age of food (refrigerator)
• Waters the lawn when soil is dry (sprinklers)
• Self-driving cars find best route (Google)
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Current Tools
• Artificial intelligence (AI)
• Machines perform tasks formerly done by humans
• Google Home and Amazon Echo devices almost seem human
• Machine learning
• Specific type of AI
• Example: machine can “learn” what a problem appears to be,
such as charges in two distant countries at the same time
• Deep learning – the system learns to categorize
• Feed photos of x-rays and indicate which were fractures and
which were not
• Do not feed any instructions about how to tell
• The system then performs well in categorizing new photos
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Intellectual Capital vs Intellectual Property
• Intellectual Capital: the process for managing knowledge
• Intellectual Property: the output of creating information
• Intellectual Property rights differ remarkably by country
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Closing Caveats
• These are emerging concepts and disciplines
• Sometimes knowledge should remain hidden (tacit) for
protection
• We should remain focused on future events, not just look
over the past
• A supportive culture is needed in a firm to enable effective
Knowledge Management and Business Intelligence
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LeBow Evaluation
and
Break
Students can access their evaluations at:
https://lebow.drexel.edu/Evals
Current Status:
MIS-346-001
22.86%
MIS Strategy
35 students
completion
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Week 10 - June 4, 2024
Three Breaches: TJX, Target, Home Depot
• TJX Company:
• largest security breach of its computer system in the history
of retailing: 90 million customer records were stolen
• Target: 40 million; Home Depot: 56 million
• All had to decide between notifying their customers
immediately or waiting the 45 days allowed by the law
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Outcomes
• Target:
• Stock fell 9% a few days after disclosure
• Profits fell 46% in the following quarter
• TJX:
• Stock fell 8%
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Breaches can lead to Laws
• 2017: Credit firm Equifax suffered a breach revealing
private information for 147.7 million USA residents (the
majority of the adult population)
• Enough information to enable identity theft
• Sept 21, 2018: New bill requiring consumers to order
“freezes” on new credit applications at no charge
• That was a former revenue source for the three large
credit agencies in the USA
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U.S. Cybersecurity Legislation
21 Jun 2022
U.S. Passes New Cybersecurity Legislation in June 2022
The bills, signed into law on June 21, 2022 aim to strengthen the federal
cyber workforce and foster collaboration across all levels of government.
On June 21, 2022, U.S. President Joe Biden signed two cybersecurity bills into law.
The latest in a series of efforts to improve the nation’s cybersecurity, the new legislation is
intended to build skills and experience among the federal cyber workforce and promote
coordination on security issues at all levels of government.
The State and Local Government Cybersecurity Act of 2021 is designed to improve
coordination between the Cybersecurity and Infrastructure Security Agency (CISA) and
state, local, tribal, and territorial governments. Under the new law, these bodies will be
able to share security tools, procedures, and information more easily.
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Three Theories of Business Ethics
Stockholder / Stakeholder / Social Contract
• Managers must assess initiatives from an
ethical point of view
• Most managers are not trained in ethics,
philosophy, and moral reasoning
• Difficult to determine or discuss social norms
• Three theories of business ethics are useful
for assessing an initiative
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Business Ethics
Figure 12.1 Three normative theories of business ethics.
Theory
Definition
Metrics
Stockholder
Maximize stockholder wealth Will this action maximize stockholder
in legal and non-fraudulent value? Can goals be accomplished
manners.
without compromising company
standards and without breaking laws?
Stakeholder
Maximize benefits to all
stakeholders while weighing
costs to competing
interests.
Does the proposed action maximize
collective benefits to the company?
Does this action treat one of the
corporate stakeholders unfairly?
Social contract
Create value for society in a
manner that is just and
nondiscriminatory.
Does this action create a “net” benefit
for society? Does the proposed action
discriminate against any group in
particular, and is its implementation
socially just?
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Stockholder Theory
• Stockholders advance capital to corporate managers
who act as agents in advancing their ends
• Managers are bound to the interests of the
shareholders (maximize shareholder value)
• Manager’s duties:
• Bound to employ legal, non-fraudulent means
• Must take long view of shareholder interest
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Stakeholder Theory
• Stakeholders are:
• Any group that vitally affects corporate survival and success
• Any group whose interests the corporation vitally affects
• Management must balance the rights of all stakeholders
without impinging upon the rights of any one particular
stakeholder
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Social Contract Theory
• Corporations are expected to create more value to society
than it consumes
• Social contract:
• Social welfare – corporations must produce greater benefits
than their associated costs
• Justice – corporations must pursue profits legally, without
fraud or deception, and avoid actions that harm society
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The Three Normative Theories
•
•
•
Stockholder Theory
Stakeholder Theory
Social Contract Theory
• How do they apply to TJX, Target, Home Depot?
• What are the advantages of notifying customers early?
• What are the advantages of waiting as long as legally
permitted?
• What are the advantages of finding a way to avoid
notifying customers?
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Big Data
• Can guess income from zip code
• Therefore, can identify targets from zip codes
• Should you pass up the opportunity to alert
potential customers of your products?
• If not, your competitors will get there first!
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Targeting/Scraping
Snapchat Employees Abused
Data Access to Spy on Users
Multiple sources and emails also describe SnapLion, an internal
tool used by various departments to access Snapchat user data.
By Joseph Cox
May 23, 2019, 3:44pm
•Several departments inside social media giant Snap have
dedicated tools for accessing user data, and multiple employees
have abused their privileged access to spy on Snapchat users,
Motherboard has learned.
How your personal data is being scraped from social media
16 July 2021
By Joe Tidy, Cyber security reporter, BBC News
How much personal information do you share on your social media profile pages?
Name, location, age, job role, marital status, headshot? The amount of information people are comfortable with posting online varies.
But most people accept that whatever we put on our public profile page is out in the public domain.
So, how would you feel if all your information was catalogued by a hacker and put into a monster spreadsheet with millions
of entries, to be sold online to the highest paying cyber-criminal?
That's what a hacker calling himself Tom Liner did last month "for fun" when he compiled a database of 700 million LinkedIn
users from all over the world, which he is selling for around $5,000 (£3,600; €4,200).
In 2014, Cambridge Analytica
“scraped” personal data from
Facebook to target 50 million
individuals for particular messages
that would be of interest
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Targeting/Scraping
• Are these examples breaches?
• It was not a “break in” but many call it a breach
• What are the Privacy and Ethical implications?
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Study in Science
• Take a file from a credit card agency, with
disguised credit card numbers: 1.1 million records
• 90% of the identities can be found by connecting
three pieces of information
• Information easily found on Instagram, Facebook,
FourSquare
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Ethical Tensions with Governments
• UAE tried to require Research In Motion (Blackberry) to
disclose confidential information for national security
• Sony Pictures had a project ruined by North Korean threats
• Google’s use is restricted in China
• GDPR in Europe provides strict privacy laws that impact
firms doing business in the European Union
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Mason’s areas of managerial concern
“PAPA”
Area
Critical Questions
Privacy
What information must a person reveal about one’s self to others?
What information should others be able to access about you – with
or without your permission? What safeguards exist for your
protection?
Accuracy
Who is responsible for the reliability and accuracy of information?
Who will be accountable for errors?
Property
Who owns information? Who owns the channels of distribution, and
how should they be regulated?
Accessibility
What information does a person or an organization have a right to
obtain, under what conditions, and with what safeguards?
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Privacy
“PAPA”
Privacy
Accuracy
Property
Accessibility
• The right to be left alone
• Possessing and using the “best” information helps
an organization win
• High priority: Keeping it safe and secure
• Regulations cover the authorized collection,
disclosure and use of personal information
• But is it clear enough?
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Privacy Paradox
• Convenience vs privacy
• Make it harder for criminals to steal information, it will be less
convenient for genuine users
• 15,000 customers in 15 countries:
• Overall, 51% said they wouldn’t trade off privacy for
convenience; 27% said they would.
• India: 40% wouldn’t; 48% would
• Germany: 70% wouldn’t; 12% would
• Privacy laws can vary by country and even by state in the US
• No one global privacy law
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What about Actual Behavior?
• Teens repeatedly demonstrate a lack of concern about
privacy, often regretting their decisions later
• 70% of recruiters have rejected candidates for postings
they found online
• But only 20% strengthened their privacy settings when
Facebook began allowing it
• Privacy seems to be valued more in Europe than in the US
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Software or Site Terms of Service
• Ignored widely, often due to length and legal language
• Pen Pal’s Terms of Service are longer than Hamlet
• Fewer than 2% read the terms
• A UK site included selling a person’s immortal soul and
thousands accepted it
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Cookies
• Can access only the cookie it created!
• So, what’s the concern?
• Easy. Have a third party place content on your page
• Widespread practice: DoubleClick has content on
thousands of sites
• But back to convenience: Without cookies, you could
not have a “shopping cart”
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Accuracy
“PAPA”
Privacy
Accuracy
Property
Accessibility
• Controls are needed to ensure accuracy
• Data entry errors must be controlled and managed carefully
• Data must also be kept up to date
• Removing data after needed or when legally mandated is
not easy
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Bank of America Example
Accuracy or inaccuracy?
• What did Bank of America do to the couple near Christmas?
• Just from checking out refinancing rates, appearance of risk
rose
• BoA admitted error but neglected to report this to credit
agencies
ANY PERSONAL EXAMPLES ????
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Property
“PAPA”
Privacy
Accuracy
Property
Accessibility
• Mass quantities of data are stored
• Who owns the data?
• Who has rights to it?
• Who owns the images that are posted in cyberspace?
Photographer? Subject? Facebook?
• Proper ownership implies legal rights but duties too
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Accessibility
“PAPA”
Privacy
Accuracy
Property
Accessibility
• Access to systems and their data is paramount
• Users must be able to access this data from any
location (if legal and it can be properly secured)
• Major issue – how to create and maintain access to
information for society at large
• This access needs to be limited to those who have a
right to see and use it (to limit identity theft)
• Also, adequate security measures must be in place on
their business partners’ end
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What Should a Manager Do?
• Create a culture of responsibility
• Post policies
• Implement governance processes for information control
• Avoid decoupling responsibility
• i.e., make Managers responsible for their decisions that lead
to privacy problems
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Other Ethical Concerns……The Planet
Green Computing
• The digital economy uses 10% of the world’s energy
• In 2007, the 5 largest search companies used 2.4 gigawatts
• Hoover Dam only generates 2.0
• Since then, it has reduced thanks to “green” efforts in data
centers
• Virtualization
• Relocation for more natural cooling
• Google in Finland which uses seawater from the Bay of Finland as
part of its advanced cooling system
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Other Ethical Concerns
Triple Bottom Line Impact
• TBL (3BL) – People, Planet, Profit
• People: Being socially responsible
• Planet: Saving the environment
• Profit: Saving/making money
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https://youtu.b
e/Kw-_Ew5bVxs
Congratulations!!
Graduating Seniors
Homework Assignment
Professor / Course Evaluation – use template in Blackboard
Purpose:
• Improving my performance as a professor moving forward
• Improving future student’s experience taking this course
Due:
• by June 11, 2024 - Week #10 folder
Note:
This is a required submission although not graded and will not impact your grade
UNLESS,
It is not completed and then it will negatively impact your grade
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Reminder
Homework Due by:
June 11, 2024 by noon in Blackboard Week #10
Final Exam Next Week – Regular Class day/time
Tuesday June 11, 2024 @ 6:00pm
Same Time; Same Place