Expert answer:Need paper about IoT and Big Data

Answer & Explanation:On the theme of IoT and Big Data, write a 3 page (minimum) report. You can use the resources I uploaded but feel free to find additional information for this assignment. Also be sure to cite sources in APA format. For the report, there should be three parts:  1. A summary / explanation of “Big Data” and why it is important. 2. A summary / explanation of IoT and its importance and 3. Describe how IoT and Big data are relatedBig.Data.pdf big-data-spectrum.pdf
big.data.pdf

big_data_spectrum.pdf

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Why is BIG Data Important? | March 2012 | 1
Why is BIG Data
Important?
A Navint Partners White Paper
May 2012
www.navint.com
Why is BIG Data Important? | March 2012 | 2
What is Big Data?
Big data is a term that refers to data sets or
combinations of data sets whose size (volume),
complexity (variability), and rate of growth
(velocity) make them difficult to be captured,
managed, processed or analyzed by conventional
technologies and tools, such as relational
databases and desktop statistics or visualization
packages, within the time necessary to make
them useful. While the size used to determine
whether a particular data set is considered big
data is not firmly defined and continues to change
over time, most analysts and practitioners
currently refer to data sets from 30-50
terabytes(10 12 or 1000 gigabytes per terabyte)
to multiple petabytes (1015 or 1000 terabytes per
petabyte) as big data.
The complex nature of big data is primarily
driven by the unstructured nature of much of the
data that is generated by modern technologies,
such as that from web logs, radio frequency Id
(RFID),
sensors
embedded
in
devices,
machinery, vehicles, Internet searches, social
networks such as Facebook, portable computers,
smart phones and other cell phones, GPS
devices, and call center records. In most cases,
in order to effectively utilize big data, it must be
combined with structured data (typically from a
relational database) from a more conventional
business application, such as Enterprise
Resource Planning (ERP) or Customer
Relationship Management (CRM).
Similar to the complexity, or variability, aspect
of big data, its rate of growth, or velocity aspect,
is largely due to the ubiquitous nature of modern
on-line, real-time data capture devices, systems,
and networks. It is expected that the rate of
growth of big data will continue to increase for the
foreseeable future.
Specific new big data technologies and tools
have been and continue to be developed. Much
of the new big data technology relies heavily on
massively parallel processing (MPP) databases,
which can concurrently distribute the processing
of very large sets of data across many servers.
As another example, specific database query
tools have been developed for working with the
massive amounts of unstructured data that are
being generated in big data environments.
BIG Data – Growth and Size Facts
(*MGI Estimates)
 There were 5 billion mobile phones in use in
2010.
 There are 30 billion pieces of content shared on
Facebook each month.
 There is a 40% projected growth in global data
generated per year vs. 5% growth in global IT
spending.
 There were 235 terabytes of data collected by
the US Library of Congress in April 2011.
 15 out of 17 major business sectors in the
United States have more data stored per
company that the US Library of Congress.
Big Data – Value Potential(*)
 $300 billion annual value to US healthcare –
more than twice the total annual healthcare
spending in Spain.
 $600 billion – potential annual consumer surplus
from using personal location data globally.
 60% – potential increase in retailers’ operating
margins possible via use of big data.
Big Data – Industry Examples
 Major utility company integrates usage data
recorded from smart meters in semi real-time
into their analysis of the national energy grid.
 Pay television providers have begun to
customize ads based on individual household
demographics and viewing patterns.
 A major entertainment company is able to
analyze its data and customer patterns across
its many and varied enterprises – e.g. using park
attendance, on-line purchase, and television
viewership data.
 The security arm of a financial services firm
detects fraud by correlating activities across
multiple data sets. As new fraud methods are
detected and understood, they are used to
encode new algorithms into the fraud detection
system.
www.navint.com
contactus@navint.com
888.607.6575
This message and any attachment are confidential and may be privileged or otherwise protected from disclosure and solely for the use of the person(s) or
entity to which it is intended. If you are not the intended recipient, be advised that any use of this message is prohibited and may be unlawful, and you must
not copy this message or attachment or disclose the contents to any other person.
Why is BIG Data Important? | March 2012 | 3
Why is Big Data Important?
When big data is effectively and efficiently captured, processed, and analyzed, companies are able to
gain a more complete understanding of their business, customers, products, competitors, etc. which
can lead to efficiency improvements, increased sales, lower costs, better customer service, and/or
improved products and services.
For example:
–
Manufacturing companies deploy sensors in their products to return a stream of telemetry.
Sometimes this is used to deliver services like OnStar, that delivers communications, security
and navigation services. Perhaps more importantly, this telemetry also reveals usage patterns,
failure rates and other opportunities for product improvement that can reduce development and
assembly costs.(**Oracle)
–
The proliferation of smart phones and other GPS devices offers advertisers an opportunity to
target consumers when they are in close proximity to a store, a coffee shop or a restaurant. This
opens up new revenue for service providers and offers many businesses a chance to target new
customers.(**)
–
Retailers usually know who buys their products. Use of social media and web log files from their
ecommerce sites can help them understand who didn’t buy and why they chose not to,
information not available to them today. This can enable much more effective micro customer
segmentation and targeted marketing campaigns, as well as improve supply chain
efficiencies.(**)
–
Other widely-cited examples of the effective use of big data exist in the following areas:
 Using information technology (IT) logs to improve IT troubleshooting and security breach
detection, speed, effectiveness, and future occurrence prevention.
 Use of voluminous historical call center information more quickly, in order to improve
customer interaction and satisfaction.
 Use of social media content in order to better and more quickly understand customer
sentiment about you/your customers, and improve products, services, and customer
interaction.
 Fraud detection and prevention in any industry that processes financial transactions online, such as shopping, banking, investing, insurance and health care claims.
 Use of financial market transaction information to more quickly assess risk and take
corrective action.
www.navint.com
contactus@navint.com
888.607.6575
This message and any attachment are confidential and may be privileged or otherwise protected from disclosure and solely for the use of the person(s) or
entity to which it is intended. If you are not the intended recipient, be advised that any use of this message is prohibited and may be unlawful, and you must
not copy this message or attachment or disclose the contents to any other person.
Why is BIG Data Important? | March 2012 | 4
Key Big Data Challenges
 Understanding and Utilizing Big Data – It is
a daunting task in most industries and
companies that deal with big data just to
understand the data that is available to be
used, determining the best use of that data
based on the companies’ industry, strategy,
and tactics. Also, these types of analyses
need to be performed on an ongoing basis as
the data landscape changes at an everincreasing rate, and as executives develop
more and more of an appetite for analytics
based on all available information.
 New, Complex, and Continuously Emerging
Technologies – Since much of the technology
that is required in order to utilize big data is
new to most organizations, it will be necessary
for these organizations to learn about these
new technologies at an ever-accelerating
pace, and potentially engage with different
technology providers and partners than they
have used in the past.
Like with all
technology, firms entering into the world of big
data will need to balance the business needs
associated with big data with the associated
costs of entering into and remaining engaged
in big data capture, storage, processing, and
analysis.
 Cloud Based Solutions – A new class of
business software applications has emerged
whereby company data is managed and
stored in data centers around the globe.
While these solutions range from ERP, CRM,
Document Management, Data Warehouses
and Business Intelligence to many others, the
common issue remains the safe keeping and
management of confidential company data.
These solutions often offer companies
tremendous flexibility and cost savings
opportunities compared to more traditional on
premise solutions but it raises a new
dimension related to data security and the
overall management of an enterprise’s Big
Data paradigm.
 Privacy,
Security,
and
Regulatory
Considerations – Given the volume and
complexity of big data, it is challenging for
most firms to obtain a reliable grasp on the
content of all of their data and to capture and
secure it adequately, so that confidential
and/or private business and customer data are
not accessed by and/or disclosed to
unauthorized parties.
The costs of a data
privacy breach can be enormous.
For
instance, in the health care field, class action
lawsuits have been filed, where the plaintiff
has sought $1000 per patient record that has
been inappropriately accessed or lost. In the
regulatory area, for instance, the proper
storage and transmission of personally
identifiable information (PII), including that
contained in unstructured data such as emails
can be problematic and necessitate new and
improved
security
measures
and
technologies. For companies doing business
globally there are significant differences in
privacy laws between the U.S. and other
countries. Lastly, it will be very important for
most forms to tightly integrate their big data,
data
security/privacy,
and
regulatory
functions.
 Archiving and Disposal of Big Data – Since
big data will lose its value to current decisionmaking over time, and since it is voluminous
and varied in content and structure, it is
necessary to utilize new tools, technologies,
and methods to archive and delete big data,
without sacrificing the effectiveness of using
your big data for current business needs.
 The Need for IT, Data Analyst, and
Management Resources – It is estimated that
there is a need for approximately 140,000 to
190,000 more workers with “deep analytical”
expertise and 1.5 million more data-literate
managers, either retrained or hired. Therefore,
it is likely that any firm that undertakes a big
data initiative will need to either retrain
existing people, or engage new people in
order for their initiative to be successful.
www.navint.com
contactus@navint.com
888.607.6575
This message and any attachment are confidential and may be privileged or otherwise protected from disclosure and solely for the use of the person(s) or
entity to which it is intended. If you are not the intended recipient, be advised that any use of this message is prohibited and may be unlawful, and you must
not copy this message or attachment or disclose the contents to any other person.
Why is BIG Data Important? | March 2012 | 5
Developing a Big Data Strategy
Big Data Basics
Business Systems
Big Data Assessment
Big Data Strategy
Sources and Uses
Organization Impacts
Social Data
Unstructured Data
Volumes & Metrics
Opportunity Analysis
Estimated Growth
Methods and Tools
Process Data
Privacy & Regulatory
Compliance
Impact/ Value Potential
Business Case & ROI
About Navint Partners
Navint is a different kind of management consulting firm, excelling in large scale business process
change. With offices in New York, Chicago, Boston, Pittsburgh, Philadelphia and Rochester, Navint’s
consultants specialize in managing the alignment of people, processes and technology when
organizations face operational restructuring and IT transformation. A unique blend of experience and
innovative thinking allows Navint consultants to address clients’ business challenges in imaginative
ways. http://www.navint.com/
www.navint.com
contactus@navint.com
888.607.6575
This message and any attachment are confidential and may be privileged or otherwise protected from disclosure and solely for the use of the person(s) or
entity to which it is intended. If you are not the intended recipient, be advised that any use of this message is prohibited and may be unlawful, and you must
not copy this message or attachment or disclose the contents to any other person.
1
Contents
Introduction . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1
View Point – Phil Shelley, CTO, Sears Holdings
Making it Real – Industry Use Cases
Retail – Extreme Personalization. . . . . . . . . . . . . . . . . . . . . . . . . .
6
Airlines – Smart Pricing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
Auto – Warranty and Insurance Efficiency . . . . . . . . . . . . . . . . . . . . 12
Financial Services – Fraud Detection. . . . . . . . . . . . . . . . . . . . . . . 16
Energy – Tapping Intelligence in Smart Grid / Meters. . . . . . . . . . . . . . . 19
Data warehousing – Faster and Cost effective. . . . . . . . . . . . . . . . . . 22
View Point – Doug Cutting, Co-founder, Apache Hadoop
Making it Real – Key Challenges
Protecting Privacy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
Integrating with Enterprise Systems. . . . . . . . . . . . . . . . . . . . . . . . 30
Handling Real Time Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
Leveraging Cloud Computing. . . . . . . . . . . . . . . . . . . . . . . . . . . 37
View Point – S. Gopalakrishnan (Kris), Co-Chairman, Infosys
Making it Real – Infosys Adoption Enablers
Accelerators – Solution and Expertise. . . . . . . . . . . . . . . . . . . . . . . 43
Services – Extreme Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
Product – Voice of Customer Analytics . . . . . . . . . . . . . . . . . . . . . . 51
Platform – Social Edge for Big Data. . . . . . . . . . . . . . . . . . . . . . . . 53
Introduction
What is Big Data?
Today we live in the digital world. With increased digitization the amount of
structured and unstructured data being created and stored is exploding. The data
is being generated from various sources – transactions, social media, sensors, digital
images, videos, audios and clickstreams for domains including healthcare, retail,
energy and utilities. In addition to business and organizations, individuals contribute
to the data volume. For instance, 30 billion content are being shared on Facebook
every month; the photos viewed every 16 seconds in Picasa could cover a football
field.
It gets more interesting. IDC terms this as the ‘Digital Universe’ and predicts that this
digital universe is set to explode to an unimaginable 8 Zeta bytes by the year 2015.
This would roughly be a stack of DVD’s from Earth all the way to Mars. The term
“Big Data” was coined to address this massive volume of data storage and processing.
a
Dat
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Da
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Big Data
(Volume, Velocity, Variety)
Locatio
n
/
G
eo
Da
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n Data
ctio
Me
d
ia
Cli
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am
re
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S e ns or Da ta
It is increasingly becoming imperative
for organizations to mine this data to stay
Competitive. Analyzing data can provide
significant competitive advantage for an
enterprise. The data when analyzed properly
leads to a wealth of information which helps
the businesses to redefine strategies. However
the current volume of big data sets are too
complicated to be managed and processed
by conventional relational databases & data
warehousing technologies.
The volume, variety and velocity of Big Data causes performance problems when being
created, managed and analyzed using the conventional data processing techniques.
Using conventional techniques for Big Data storage and analysis is less efficient as
memory access is slower. The data collection is also challenging as the volume and
variety of data has to be derived from sources of different types. The other major
challenge in using the existing techniques is they require high end hardware to handle
the data with a huge volume, velocity and variety.
Big Data is a relatively new phenomenon. As with any new adoption, the adoption
of Big Data depends on the tangible benefits it provides to Business. Large data
sets which are considered as information overload are invariably treasure troves
for business insights. The volume of data sets has immense value that can improve
the business forecast, help in decision making, deciding business strategies over the
competitors. For instance, Facebook, blogs and twitter data gives insights on current
business trends.
1
Data
Mining
Faster Business
Decisions
Reports
Aggregated
Intelligence
Forecasting
Growth of
Information Assets
Real Time
Analytics
Innovative Business
Value
Storage
The data sets are beyond the capability of humans to analyze manually. Big data tools
have the ability to run ad-hoc queries against the large data sets in less time with a
reasonable performance. For instance, in retail domain understanding what makes
a buyer to look into a product online, sentiment analysis of a product based on the
Facebook, tweet and blogs are of great value to the business. This will enable the
business to improve their services for customers.
Big Data analysis enables the executives to get the relevant data in less time for making
decisions. Big Data can pave way for fraudulent analysis, customer segmentation
based on the store behavior analysis, loyalty programs that identifies and targets the
customers. This enables us to perform innovative analysis which indeed changes the
way we think about data.
Exploring Big Data Spectrum
With unstructured data dominating the world of data, the way to exploit it is just
becoming clearer. Information proliferation is playing a vital role in leveraging the
opportunities and is also presenting a plethora of challenges.
The industry opportunities presented by the plethora of data are plenty. To
understand how to leverage Big Data opportunities is a clear need to the business.Big
Data spectrum covers use case from five different industries Retail, Airlines, Auto,
Financial Services and Energy.
All opportunities come with a set of challenges. The way to know and address these
challenges is discussed in the Key Challenges section. To name a few: Data Privacy,
2
Data Security, Integrating various technologies, catering to real time flow of data and
leveraging cloud computing.
To dive deep into Big Data technology with the goal of having a quick, managed and
quality implementation, a set of enablers were designed by the Architects at Infosys.
The section on Adoption Enablers gives the insights into these enablers.
The sections are interleaved with the viewpoints from Phil Shelly, CTO, Sears
Holdings Corporation, Doug Cutting, Co-founder of Apache Hadoop (popularly
known as father of Big Data) and Kris Gopalakrishnan, Co-Chairman, Infosys Ltd.
3
Phil Shelley
CTO, Sears Holdings Corporation
Dr. Shelley is a member of CIO forum, Big Data Chicago forum
Phil, Sears is one of early adopter of Big Data. What are the sweet spot use
cases in the retail industry?
Transactional data such as POS, Web-based activ …
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