First_Writing_Assignment_-_InstructionMotivation_Theories

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)

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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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4

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?

13-‹#›

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

13-‹#›

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?

13-‹#›

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.

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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

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)

14-‹#›

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…)

14-‹#›

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

14-‹#›

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?

14-‹#›

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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9

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

14-‹#›

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.

14-‹#›

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?

14-‹#›

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

14-‹#›

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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