First Writing Assignment - Instruction
Motivation Theories Assignment
Using the motivation theories, explain either your best, or your worst workplace experience in terms of motivation. Go in detail and provide information on your boss, work, context, environment etc. that affected your motivation. How and why did the situation lead you to perform at your best? Alternatively, how and why did you chose to not be performing according to expectations?
If you have little or no prior work experience, or don't feel comfortable sharing, please watch the movie “Office Space” ( http://www.imdb.com/title/tt0151804/ (Links to an external site.) ) and analyze the situation Peter Gibbons and his colleagues are experiencing (you may chose either character, however, I recommend you address Peter Gibbon’s situation). If you chose to analyze the movie, your analysis has to go beyond the two short videos posted on the course website. Using the theories discussed, explain why he is motivated, or not so motivated, to perform highly.
Theory 1:
Theory 2:
Theory 3:
As a reminder, the eight motivation theories discussed in the book and in class were:
1. Maslow's Hierarchy of Needs
2. Alderfer's ERG Theory
3. Herzberg's Two-factor Theory
4. McClelland's Motive Dispositions Theory
5. Equity Theory
6. Expectancy Theory
7. Reinforcement Theory
8. Goal Setting Theory
Length & Structure: Your assignment should not exceed three to three and a half (double spaced) pages in length (Times New Roman font size 12, double spaced) that is, about one (1) page (double spaced) for each theory (required), 0.5 page introduction (optional). Please make sure to use headlines, introductions, and other elements of structure. An introduction might help if you are referencing your own work experience. It is less important if you opt to analyze the movie.
Grading: This assignment will be graded 70% on your ability to correctly apply motivation theories and 30 % on grammar and spelling. It is therefore important that you carefully edit your answers before turning them in. Please make sure you reference three different theories. If you analyze the same theory three times you will receive full points. If you chose your own work experience, you may analyze the same or different work situations/jobs.
File Format: You are required to upload your assignment in the form of a WORD or PDF document. If your document uses a different file format it will not be read and graded you will receive ZERO points. Please be aware that you cannot re-submit your document in the proper file format after the deadline has passed, even if you submit a different (unreadable) file format in time. If your file format is NEITHER PDF NOR WORD your assignment cannot be read and graded and you will receive ZERO points. The link will close after the due date that is posted. There is going to be a zero tolerance for late submissions.
Where to submit: You are required to upload your assignment on the separate link provided in this module right underneath the project instruction.
Only assignments submitted in due time will receive consideration.
Only assignments in proper file format will be graded.
Chapter 13:
Big Data Analytics
Business Intelligence and Analytics: Systems for Decision Support
(10th Edition)
Business Intelligence and Analytics: Systems for Decision Support
(10th Edition)
Copyright © 2014 Pearson Education, Inc.
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1
Learning Objectives
Learn what Big Data is and how it is changing the world of analytics
Understand the motivation for and business drivers of Big Data analytics
Become familiar with the wide range of enabling technologies for Big Data analytics
Learn about Hadoop, MapReduce, and NoSQL as they relate to Big Data analytics
Understand the role of and capabilities/ skills for data scientist as a new analytics profession
(Continued…)
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2
Learning Objectives
Compare and contrast the complementary uses of data warehousing and Big Data
Become familiar with the vendors of Big Data tools and services
Understand the need for and appreciate the capabilities of stream analytics
Learn about the applications of stream analytics
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Opening Vignette…
Big Data Meets Big Science at CERN
Situation
Problem
Solution
Results
Answer & discuss the case questions.
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Questions for the Opening Vignette
What is CERN, and why is it important to the world of science?
How does the Large Hadron Collider work? What does it produce?
What is the essence of the data challenge at CERN? How significant is it?
What was the solution? How were the Big Data challenges addressed with this solution?
What were the results? Do you think the current solution is sufficient?
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Big Data - Definition and Concepts
Big [volume] Data is not new!
Big Data means different things to people with different backgrounds and interests
Traditionally, “Big Data” = massive volumes of data
E.g., volume of data at CERN, NASA, Google, …
Where does the Big Data come from?
Everywhere! Web logs, RFID, GPS systems, sensor networks, social networks, Internet-based text documents, Internet search indexes, detail call records, astronomy, atmospheric science, biology, genomics, nuclear physics, biochemical experiments, medical records, scientific research, military surveillance, multimedia archives, …
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6
Technology Insights 6.1 The Data Size Is Getting Big, Bigger…
Hadron Collider - 1 PB/sec
Boeing jet - 20 TB/hr
Facebook - 500 TB/day.
YouTube – 1 TB/4 min.
The proposed Square Kilometer Array telescope (the world’s proposed biggest telescope) – 1 EB/day
Names for Big Data Sizes
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Big Data - Definition and Concepts
Big Data is a misnomer!
Big Data is more than just “big”
The Vs that define Big Data
Volume
Variety
Velocity
Veracity
Variability
Value
…
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8
A High-level Conceptual Architecture for Big Data Solutions
(by AsterData / Teradata)
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Application Case 13.1
BigData Analytics Helps Luxottica Improvement its Marketing Effectiveness
Questions for Discussion
What does “big data” mean to Luxottica?
What were their main challenges?
What were the proposed solution and the obtained results?
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Fundamentals of Big Data Analytics
Big Data by itself, regardless of the size, type, or speed, is worthless
Big Data + “big” analytics = value
With the value proposition, Big Data also brought about big challenges
Effectively and efficiently capturing, storing, and analyzing Big Data
New breed of technologies needed (developed (or purchased or hired or outsourced …)
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Big Data Considerations
You can’t process the amount of data that you want to because of the limitations of your current platform.
You can’t include new/contemporary data sources (e.g., social media, RFID, Sensory, Web, GPS, textual data) because it does not comply with the data schema.
You need to (or want to) integrate data as quickly as possible to be current on your analysis.
You want to work with a schema-on-demand data storage paradigm because the variety of data types.
The data is arriving so fast at your organization’s doorstep that your analytics platform cannot handle it.
…
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Critical Success Factors for Big Data Analytics
A clear business need (alignment with the vision and the strategy)
Strong, committed sponsorship (executive champion)
Alignment between the business and IT strategy
A fact-based decision-making culture
A strong data infrastructure
The right analytics tools
Right people with right skills
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Critical Success Factors for Big Data Analytics
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Enablers of Big Data Analytics
In-memory analytics
Storing and processing the complete data set in RAM
In-database analytics
Placing analytic procedures close to where data is stored
Grid computing & MPP
Use of many machines and processors in parallel (MPP- massively parallel processing)
Appliances
Combining hardware, software and storage in a single unit for performance and scalability
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Challenges of Big Data Analytics
Data volume
The ability to capture, store, and process the huge volume of data in a timely manner
Data integration
The ability to combine data quickly/cost effectively
Processing capabilities
The ability to process the data quickly, as it is captured (i.e., stream analytics)
Data governance (… security, privacy, access)
Skill availability (… data scientist)
Solution cost (ROI)
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Business Problems Addressed by Big Data Analytics
Process efficiency and cost reduction
Brand management
Revenue maximization, cross-selling/up-selling
Enhanced customer experience
Churn identification, customer recruiting
Improved customer service
Identifying new products and market opportunities
Risk management
Regulatory compliance
Enhanced security capabilities
…
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Application Case 13.2
Top 5 Investment Bank Achieves Single Source of the Truth
Questions for Discussion
How can Big Data benefit large-scale trading banks?
How did MarkLogic infrastructure help ease the leveraging of Big Data?
What were the challenges, the proposed solution, and the obtained results?
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Application Case 13.2
Moving from many old systems to a unified new system
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Big Data Technologies
MapReduce …
Hadoop …
Hive
Pig
Hbase
Flume
Oozie
Ambari
Avro
Mahout, Sqoop, Hcatalog, ….
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Big Data Technologies - MapReduce
MapReduce distributes the processing of very large multi-structured data files across a large cluster of ordinary machines/processors
Goal - achieving high performance with “simple” computers
Developed and popularized by Google
Good at processing and analyzing large volumes of multi-structured data in a timely manner
Example tasks: indexing the Web for seearch, graph analysis, text analysis, machine learning, …
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Big Data Technologies - MapReduce
How does
MapReduce
work?
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Big Data Technologies - Hadoop
Hadoop is an open source framework for storing and analyzing massive amounts of distributed, unstructured data
Originally created by Doug Cutting at Yahoo!
Hadoop clusters run on inexpensive commodity hardware so projects can scale-out inexpensively
Hadoop is now part of Apache Software Foundation
Open source - hundreds of contributors continuously improve the core technology
MapReduce + Hadoop = Big Data core technology
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Big Data Technologies - Hadoop
How Does Hadoop Work?
Access unstructured and semi-structured data (e.g., log files, social media feeds, other data sources)
Break the data up into “parts,” which are then loaded into a file system made up of multiple nodes running on commodity hardware using HDFS
Each “part” is replicated multiple times and loaded into the file system for replication and failsafe processing
A node acts as the Facilitator and another as Job Tracker
Jobs are distributed to the clients, and once completed the results are collected and aggregated using MapReduce
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Big Data Technologies - Hadoop
Hadoop Technical Components
Hadoop Distributed File System (HDFS)
Name Node (primary facilitator)
Secondary Node (backup to Name Node)
Job Tracker
Slave Nodes (the grunts of any Hadoop cluster)
Additionally, Hadoop ecosystem is made-up of a number of complementary sub-projects: NoSQL (Cassandra, Hbase), DW (Hive), …
NoSQL = not only SQL
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Big Data Technologies Hadoop - Demystifying Facts
Hadoop consists of multiple products
Hadoop is open source but available from vendors, too
Hadoop is an ecosystem, not a single product
HDFS is a file system, not a DBMS
Hive resembles SQL but is not standard SQL
Hadoop and MapReduce are related but not the same
MapReduce provides control for analytics, not analytics
Hadoop is about data diversity, not just data volume.
Hadoop complements a DW; it’s rarely a replacement.
Hadoop enables many types of analytics, not just Web analytics.
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Application Case 13.3
eBay’s
Big Data
Solution
Questions for Discussion
Why did eBay need a Big Data solution?
What were the challenges, the proposed solution, and the obtained results?
EBay’s Multi Data-Center Deployment
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Data Scientist
“The Sexiest Job of the 21st Century”
Thomas H. Davenport and D. J. Patil
Harvard Business Review, October 2012
Data Scientist = Big Data guru
One with skills to investigate Big Data
Very high salaries, very high expectations
Where do Data Scientist come from?
M.S./Ph.D. in MIS, CS, IE,… and/or Analytics
There is not a specific degree program for DS!
PE, PML, … DSP (Data Sceice Professional)
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Skills That Define a Data Scientist
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A Typical Job Post for Data Scientist
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Application Case 13.4
Big Data and Analytics in Politics
Questions for Discussion
What is the role of analytics and Big Data in modern day politics?
Do you think Big Data analytics could change the outcome of an election?
What do you think are the challenges, the potential solution, and the probable results of the use of Big Data analytics in politics?
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Application Case 13.4 Big Data and Analytics in Politics
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Big Data And Data Warehousing
What is the impact of Big Data on DW?
Big Data and RDBMS do not go nicely together
Will Hadoop replace data warehousing/RDBMS?
Use Cases for Hadoop
Hadoop as the repository and refinery
Hadoop as the active archive
Use Cases for Data Warehousing
Data warehouse performance
Integrating data that provides business value
Interactive BI tools
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Hadoop versus Data Warehouse When to Use Which Platform
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Coexistence of Hadoop and DW
Use Hadoop for storing and archiving multi-structured data
Use Hadoop for filtering, transforming, and/or consolidating multi-structured data
Use Hadoop to analyze large volumes of multi-structured data and publish the analytical results
Use a relational DBMS that provides MapReduce capabilities as an investigative computing platform
Use a front-end query tool to access and analyze data
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Coexistence of Hadoop and DW
Source: Teradata
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Big Data Vendors
Big Data vendor landscape is developing very rapidly
A representative list would include
Cloudera - cloudera.com
MapR – mapr.com
Hortonworks - hortonworks.com
Also, IBM (Netezza, InfoSphere), Oracle (Exadata, Exalogic), Microsoft, Amazon, Google, …
Software,
Hardware,
Service, …
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Top 10 Big Data Vendors with Primary Focus on Hadoop
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Application Case 13.5
Dublin City Council Is Leveraging Big Data to Reduce Traffic Congestion
Questions for Discussion
Is there a strong case to make for large cities to use Big Data Analytics and related information technologies? Identify and discuss examples of what can be done with analytics beyond what is portrayed in this application case.
How can a big data analytics help ease the traffic problem in large cities?
What were the challenges Dublin City was facing; what were the proposed solution, initial results, and future plans?
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Technology Insights 13.4 How to Succeed with Big Data
Simplify
Coexist
Visualize
Empower
Integrate
Govern
Evangelize
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Application Case 13.6
Creditreform Boosts Credit Rating Quality with Big Data Visual Analytics
Questions for Discussion
How did Creditreform boost credit rating quality with Big Data and visual analytics?
What were the challenges, proposed solution, and initial results?
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Big Data And Stream Analytics
Data-in-motion analytics and real-time data analytics
One of the Vs in Big Data = Velocity
Analytic process of extracting actionable information from continuously flowing/streaming data
Why Stream Analytics?
It may not be feasible to store the data
It may loose its value if not processed immediately
Stream Analytics Versus Perpetual Analytics
Critical Event Processing?
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Stream Analytics A Use Case in Energy Industry
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Stream Analytics Applications
e-Commerce
Telecommunication
Law Enforcement and Cyber Security
Power Industry
Financial Services
Health Services
Government
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Application Case 13.7
Turning Machine-Generated Streaming Data into Valuable Business Insights
Questions for Discussion
Why is stream analytics becoming more popular?
How did the telecommunication company in this case use stream analytics for better business outcomes? What additional benefits can you foresee?
What were the challenges, proposed solution, and initial results?
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End of the Chapter
Questions, comments
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46
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher. Printed in the United States of America.
Copyright © 2014 Pearson Education, Inc.
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47
Math and StatsDataMiningBusinessIntelligenceApplicationsLanguagesMarketing
ANALYTIC TOOLS & APPSUSERS
DISCOVERY PLATFORMINTEGRATED DATA WAREHOUSEDATAPLATFORM ACCESSMANAGEMOVE
UNIFIED DATA ARCHITECTURE
System Conceptual View
MarketingExecutivesOperationalSystemsFrontlineWorkersCustomersPartnersEngineersDataScientistsBusinessAnalysts
EVENT PROCESSING
ERPERPSCMCRMImagesAudio and VideoMachine LogsTextWeb and Social
BIG DATA SOURCES
ERP
Keys to Success
with Big Data
Analytics
A Clear
business need
Strong,
committed
sponsorship
Alignment
between the
business and IT
strategy
A fact-based
decision-making
culture
A strong data
infrastructure
The right
analytics tools
Personnel with
advanced
analytical skills
BeforeAfter
Before it was difficult to identify financial
exposure across many systems (separate
copies of derivatives trade store)
After it was possible to analyze all contracts in
single database (MarkLogic Server eliminates
the need for 20 database copies)
4
3
3
3
3
Raw DataMap FunctionReduce Function
Curiosity and
Creativity
Internet and Social
Media/Social Networking
Technologies
Programming,
Scripting and Hacking
Data Access and
Management
(both traditional and
new data systems)
Domain Expertise,
Problem Definition and
Decision Modeling
Communication and
Interpersonal
DATA
SCIENTIST
$0$10$20$30$40$50$60$70
Sensor Data
(Energy Production
System Status)
Meteorological Data
(Wind, Light,
Temperature, etc.)
Usage Data
(Smart Meters,
Smart Grid Devises)
Permanent
Storage Area
Streaming Analytics
(Predicting Usage,
Production and
Anomalies)
Energy Production System
(Traditional and Renewable)
Energy Consumption System
(Residential and Commercial)
Data Integration
and Temporary
Staging
Capacity Decisions
Pricing Decisions
Chapter 14:
Business Analytics: Emerging Trends and Future Impacts
Business Intelligence and Analytics: Systems for Decision Support
(10th Edition)
Business Intelligence and Analytics: Systems for Decision Support
(10th Edition)
Copyright © 2014 Pearson Education, Inc.
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1
Learning Objectives
Explore some of the emerging technologies that may impact analytics, BI, and decision support
Describe how geospatial and location-based analytics are assisting organizations
Describe how analytics are powering consumer applications and creating a new opportunity for entrepreneurship for analytics
Describe the potential of cloud computing in business intelligence
(Continued…)
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Learning Objectives
Understand Web 2.0 and its characteristics as related to analytics
Describe the organizational impacts of analytics applications
List and describe the major ethical and legal issues of analytics implementation
Understand the analytics ecosystem to get a sense of the various types of players in the analytics industry and how one can work in a variety of roles
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Opening Vignette…
Oklahoma Gas and Electric Employs Analytics to Promote Smart Energy Use
Company background
Problem description
Proposed solution
Results
Answer & discuss the case questions...
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4
Questions for the Opening Vignette
Why perform consumer analytics?
What is meant by dynamic segmentation?
How does geospatial mapping help OG&E?
What types of incentives might the consumers respond to in changing their energy use?
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Geospatial Analytics
Geocoding
Visual maps
Postal codes
Latitude & Longitude
Enables aggregate view of a large geographic area
Integrate “where” into customer view
Location-Based Analytics
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6
Location-Based Analytics
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Location-based databases
Geographic Information System (GIS)
Used to capture, store, analyze, and manage the data linked to a location
Combined with integrated sensor technologies and global positioning systems (GPS)
Location Intelligence (LI)?
Interactive maps that further drill down to details about any location
Location-Based Analytics
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8
Retailers – location + demographic details combined with other transactional data can help …
determine how sales vary by population level
assess locational proximity to other competitors and their offerings
assess the demand variations and efficiency of supply chain operations
analyze customer needs and complaints
better target different customer segments
Use of Location-Based Analytics
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Global Intelligence
U.S. Transportation Command (USTRANSCOM)
track the information about the type of aircraft
maintenance history
complete list of crew
equipment and supplies on the aircraft
location of the aircraft
well-informed decisions for global operations
Overlaying weather and environmental data
Teradata, NAVTEQ, Tele Atlas …
Use of Location-Based Analytics
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10
Application Case 14.1
Great Clips Employs Spatial Analytics to Shave Time in Location Decisions
Questions for Discussion
How is geospatial analytics employed at Great Clips?
What criteria should a company consider in evaluating sites for future locations?
Can you think of other applications where such geospatial data might be useful?
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Geospatial Analytics Examples
Sabre Airline Solutions’ application
Traveler Security
Geospatial-enabled dashboard
Assess risks across global hotspots
Interactive maps
Find current travelers
Respond quickly in the event of any travel disruption
Telecommunication companies
Analysis of failed connections
See the Multimedia Exercise, next
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A Multimedia Exercise in Analytics Employing Geospatial Analytics
Go To Teradata University Network (TUN)
Find the BSI Case video on “The Case of the Dropped Mobile Calls”
Watch the video via TUN or at YouTube youtube.com/watch?v=4WJR_Z3exw4
Also, look at the slides at
slideshare.net/teradata/bsi-teradata-the-case-of-the-dropped-mobile-calls
Discuss the case
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Real-Time Location Intelligence
Many devices are constantly sending out their location information
Cars, airplanes, ships, mobile phones, cameras, navigation systems, …
GPS, Wi-Fi, RFID, cell tower triangulation
Reality mining?
Real-time location information = real-time insight
Path Intelligence (pathintelligence.com)
Footpath – movement patterns within a city or store
How to use such movement information
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Application Case 14.2
Quiznos Targets Customers for Its Sandwiches
Questions for Discussion
How can location-based analytics help retailers in targeting customers?
Research similar applications of location-based analytics in the retail domain.
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Real-Time Location Intelligence
Targeting right customer based on their behavior over geographic locations
Example Radii app
Collects information about the user’s favorite locations, habits, interests, spending patterns, …
Radii uses the Gimbal Context Awareness SDK
Combines time + place + duration + action + …
Assigns Location Personality Recommendation
New members receive 10 “Radii” to spend
Radii can be earned and spent on those locations
For more info, search for radii app on the Internet
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Real-Time Location Intelligence
Augmented reality
Cachetown - augmented reality-based game
Encourage users to claim offers from select geographic locations
User can start anywhere in a city and follow markers on the Cachetown app to reach a coupon, discount, or offer from a business
User can point a phone’s camera toward the virtual item through the Cachetown app to claim it
Claims free good/discount/offer from a nearby business
For more info, go to cachetown.com/press
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Explosive growth of the apps industry
iOS, Android, Windows, Blackberry, Amazon, …
Directly used by consumers (not businesses)
Enabling consumers to become more efficient
Interesting Examples
CabSense – finding a taxi in New York City
Rating of street corners; interactive maps, …
ParkPGH – finding a parking spot
Downtown Pittsburgh, Pennsylvania
For a related example, see Application Case 14.3, next
Analytics Applications for Consumers
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Application Case 14.3
A Life Coach in Your Pocket
Questions for Discussion
Search online for other applications of consumer-oriented analytical applications.
How can location-based analytics help individual consumers?
How can smartphone data be used to predict medical conditions?
How is ParkPGH different from a “parking space–reporting” app?
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Other Analytics-Based Applications
In addition to fun and health...
Productivity
Cloze – email in-box management
Intelligently prioritizes and categorizes emails
The demand and the supply for consumer-oriented analytic apps are increasing
The Wall Street Journal (wsj.com/apps) estimates that the app industry has already become a $25 billion industry
Privacy concerns?
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20
Recommendation Engines
People rely on recommendations by others
Success for retailer line Amazon.com
Recommender systems
Web-based information filtering system that takes the inputs from users and then aggregates the inputs to provide recommendations for other users in their product or service selection choices
Data
Structured ratings/rankings
Unstructured textual comments
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Recommendation Engines
Two main approaches for recommendation systems
Collaborative filtering
Based on previous users’ purchase/view/rating data
Collectively deriving user item profiling
Use this knowledge for item recommendations
Techniques include user-item rating matrix, kNN, correlation, …
Disadvantage – requires huge amount of historic data
Content filtering
Based on specifications/characteristics of items (not just ratings)
First, characteristics of an item are profiled, and then the content-based individual user profiles are built
Recommendations are made if there are similarities found in the item characteristics
Techniques include decision trees, ANN, Bayesian classifiers
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The Web 2.0 Revolution and Online Social Networking
Web 2.0?
Advanced Web - blogs, wikis, RSS, mashups, user-generated content, and social networks
Objective – enhance creativity, information sharing, and collaboration
Changing the Web from passive to active
Consumer is the one that creates the content
Redefining what is on the Web as well as how it works
Companies are adopting and benefiting from it
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Representative Characteristics of Web 2.0
Allows tapping into the collective intelligence of users
Data is made available in new or never-intended ways
Relies on user-generated/user-controlled content/data
Lightweight programming tools for wider access
The virtual elimination of software-upgrade cycles
Users can access applications entirely through a browser
An architecture of participation and digital democracy
A major emphasis is on social networks and computing
Strong support for information sharing and collaboration
Fosters rapid and continuous creation of new business models
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Social Networking
Social networking gives people the power to share, making the world open/connected
Facebook, LinkedIn, Google+, Orkut, …
Wikipedia, YouTube, …
A social network is a place where people create their own space, or homepage, on which they write blogs (Web logs); post pictures, videos, or music; share ideas; and link to other Web locations they find interesting
Mobile social networking
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Enhancing marketing and sales in public social networks
Using Twitter to Get a Pulse of the Market
Listening to the public for opinions/sentiments
Product/service brand management
Text mining, sentiment analysis
How – built in-house or outsource
reputation.com
Share content in a messaging ecosystem
WhatsApp, Draw Something, SnapChat, …
Social Networks - Implications of Business and Enterprise
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Cloud Computing and BI
A style of computing in which dynamically scalable and often virtualized resources are provided over the Internet.
Users need not have knowledge of, experience in, or control over the technology infrastructures in the cloud that supports them.
Cloud computing = utility computing, application service provider grid computing, on-demand computing, software-as-a-service (SaaS), …
Cloud = Internet
Related “-as-a-services”: infrastructure-as-a-service (IaaS), platforms-as-a-service (PaaS)
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Cloud Computing Example
Web-based email cloud computing application
Stores the data (e-mail messages)
Stores the software (e-mail programs)
Centralized hardware/software/infrastructure
Centralized updates/upgrades
Access from anywhere via a Web browser
e.g., Gmail
Web-based general application = cloud application
Google Docs, Google Spreadsheets, Google Drive,…
Amazon.com’s Web Services
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Cloud Computing Example
Cloud computing is used in
e-commerce, BI, CRM, SCM, …
Business model
Pay-per-use
Subscribe/pay-as-you-go
Companies that offer cloud-computing services
Google, Yahoo!, Salesforce.com
IBM, Microsoft (Azure)
Sun Microsystems/Oracle
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Cloud Computing and BI
Cloud-based data warehouse
1010data, LogiXML, Lucid Era
Cloud-based ERP+DW+BI
SAP, Oracle
Elastra and Rightscale
Amazon.com and Go Grid
SaaS
DaaS
SaaS
DaaS
+ IaaS
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Cloud Computing and Service-Oriented Thinking
Service-oriented thinking is one of the fastest-growing paradigms today
Toward building agile data, information, and analytics capabilities as services
Service orientation + DSS/BI
Component-based service orientation fosters
Reusability, Substitutability, Extensibility, Scalability, Customizability, Reliability, Low Cost of Ownership, Economy of Scale,…
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Service-Oriented DSS/BI
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Major Components of Service-Oriented DSS/BI
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Major Components of Service-Oriented DSS/BI
Data-as-a-Service (DaaS)
Accessing data “where it lives”
Enriching data quality with centralization
Better MDM, CDI
Access the data via open standards such as SQL, XQuery, and XML
NoSQL type data storage and processing
Amazon’s SimpleDB
Google’s BigTable
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Major Components of Service-Oriented DSS/BI
Information-as-a-Service (IaaS)
“Information on Demand”
Goal is to make information available quickly to people, processes, and applications across the business (agility)
Provides a “single version of the truth,” make it available 24/7, and by doing so, reduce proliferating redundant data and the time it takes to build and deploy new information services
SOA, flexible data integration, MDM, …
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Major Components of Service-Oriented DSS/BI
Analytics-as-a-Service (AaaS)
“Agile Analytics”
AaaS in the cloud has economies of scale, better scalability, and higher cost savings
Data/Text Mining + Big Data Cloud Computing
Storage and access to Big Data
Massively Parallel Processing
In-memory processing
In-database processing
Resource polling, scaling, cost and time saving, …
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Impacts of Analytics in Organizations: An Overview
New Organizational Units
Analytics departments
Chief Analytics Officer, Chief Knowledge Officer
Restructuring Business Processes and Virtual Teams
Reengineering and BPR
Job Satisfaction
Job Stress and Anxiety
Impact on Managers’ Activities/Performance
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Issues of Legality, Privacy, and Ethics
Legal issues to consider
What is the value of an expert opinion in court when the expertise is encoded in a computer?
Who is liable for wrong advice (or information) provided by an intelligent application?
What happens if a manager enters an incorrect judgment value into an analytic application?
Who owns the knowledge in a knowledge base?
Can management force experts to contribute their expertise?
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Issues of Legality, Privacy, and Ethics
Privacy
“the right to be left alone and the right to be free from unreasonable personal intrusions”
Collecting Information About Individuals
How much is too much?
Mobile User Privacy
Location-based analysis/profiling
Homeland Security and Individual Privacy
Recent Issues in Privacy and Analytics
“What They Know” about you (wsj.com/wtk)
Rapleaf (rapleaf.com), X + 1 (xplusone.com), Bluecava (bluecava.com), reputation.com, sociometric.com...
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Issues of Legality, Privacy, and Ethics
Ethics in Decision Making and Support
Electronic surveillance
Software piracy
Invasion of individuals’ privacy
Use of proprietary databases
Use of knowledge and expertise
Accessibility for workers with disabilities
Accuracy of data, information, and knowledge
Protection of the rights of users
Accessibility to information
Personal use of corporate computing resources
… more in the book
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Analytics Industry Clusters
Data Infrastructure Data Warehouse Providers
Middleware/BI Platform Industry
Data Aggregators/Distributors
Analytics-Focused Software Developers
Application Developers or System Integrators
Analytics User Organizations
Analytics Industry Analysts and Influencers
Academic Providers and Certification Agencies
An Overview of The Analytics Ecosystem
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Analytics Ecosystem
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Analytics Ecosystem - Titles of Analytics Program Graduates
Masters Degrees
UG Degrees
Certificate Programs
…
Data Scientist
…
Decision Science
Marketing Analytics
Management Science
…
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End-of-Chapter Application Case
Southern States Cooperative Optimizes its Catalog Campaign
Questions for Discussion
What is the main business problem faced by Southern States Cooperative?
How was predictive analytics applied in the application case?
What problems were solved by the optimization techniques employed by Southern States Cooperative?
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End of the Chapter
Questions, comments
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