Overview: Big Data Use Cases in Telecom, Retail, Insurance, Automotive, Media & Banking & Finances Industry Segments. How can we map these business challenges to Solutions on AWS Cloud? Let's Find Out! Big Data is Growing Bigger & Bigger with a prediction of 40 Zeta Bytes of Data by 2020. > What are the 4 Vs of Big Data? > Big Data Industry Use Cases: - Telecommunications - Retail - Insurance - Automotive - Media - Banking Which AWS Components can be mapped to each stage of the Big Data Life Cycle: AWS S3, AWS EC2, AWS EMR, AWS Redshift, Data Pipelines & many more.

2. The Big Data Story
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Big Data is getting
Bigger and Bigger !
Why is Cloud Big
Data’s Best Friend ?
Identifying Big Data
Industry Use cases
Figuring Out the
Big Data Life Cycle
How AWS Building Blocks
can Help Tame Big Data!
Cloudlytics – A Big
Data Use Case

3. So What is Big Data ?
Simply put, Big Data is
data which cannot be
processed by the current
tools or technologies. Big
Data is too Big, too Fast,
too Varied & Too
Unpredictable!
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* Twitter & Flickr Visualizations in North America

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The 4Vs - Volume
VOLUME

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2.5 quintillion
bytes of Data is
generated
everyday!
40 Zeta bytes
of Data Will be created
by 2020!
With 2.4 Trillion GBs
of data created
everyday!
Most Companies
in the U.S. Have
100TBs of
Data Stored
The 4Vs - Volume

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The 4Vs - Variety

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The 4Vs - Velocity
VELOCITY

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The NY Stock
Exchange
Captures 1TB of
Trade Information
Every Day
Modern Cars have
close to
100 Sensors
measuring Fuel Level
to Tire Pressure
The 4Vs - Velocity

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The 4Vs - Veracity
VERACITY

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27% Respondents
in a Survey were
unsure of How much
of their Data was
inaccurate.
Poor Data Quality
Costs the U.S. $3.1
Trillion per Year!
The 4Vs - Veracity

11. Big Data is Getting Bigger and BIGGER!
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“ More data
crosses the
internet EVERY
SECOND than
were stored in
the entire
internet just
20 years ago!“
“ Zuckerberg noted that 1
billion pieces of content are
shared via Facebook’s Open
Graph DAILY ! “
“ It is estimated that Walmart collects
more than 2.5 petabytes of data EVERY
HOUR from its customer transactions ”

12. IMPACT of Big Data Play In Your Industry?
This McKinsey Report says How Big
Data Will Impact Different
Industries, But Whatever be Your
Industry, you can’t survive Without
Big Data after the Next 10-15 Years!
So Be the Early Bird!
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13. Ring Ring? Anyone There?
The Telecom Industry
You have Your Networks Spread over 1000 Cities, some with Over a Million
Connections, Challenges such as:
• Customer Churning & Retention
• Understanding Payment Details for new Schemes
• Providing Customized Payment & Service models to the Right Customers
• Understand Competitor Pricing Models & faster innovation
• Checking Which Offers & Schemes are popular in which Geographies
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14. Telecom - Opportunities Using Big Data:
Innovative Business Models
Operational Efficiency: (10-15%
Open
Reduction)
Precise Business
Models
Real Time Analysis & Decision
Making:
(Revenue Potential Inc. 5-10%)
Create Data Driven API models
for improved customer service.
Creating World Class customer
care, by tracking in depth
subscriber activity, tracking issues
and reducing call center iterations
& time.
Optimizing Offers based on
Subscriber Network usage
patterns & Traffic to come up with
newer offers which is critical on
driving value added service
adoption.
Controlling RAN(Radio Access
Network) Congestion by dividing
Subscribers to Individual Sub Cell
Levels & by assimilating data of
past geographic positions & real
time data on current locations can
provide priority to certain
subscribers over others.
Using Payment Data from Retail
Chains & Outlets to create
Coupons & Offers, Combining
them with NFC(Near Field
Communication) to increase
Buying frequency of
Customer
Anticipating & Implementing
Network Planning even before
the demand & predict Network
stress points & Under utilized
Network areas.
Help Service providers understand
what behaviors will trigger churn
events & what actions will
prevent churning,
by dynamic offers created by
complaint triggers in real time.
Reducing Churn Rates by 8-12%
Cyber Cop Initiatives where
pattern matches of subscriber
activities can be used to detect
malicious activities and determine
traffic changing abnormal
consumptions
to predict Fraud activities.
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15. What are You Buying Today?
The Retail Industry
Imagine Your Retail Outlets over a 100s of Cities,
with more than 10 outlets per city. Even if you
have a 10000 Customers per month buying at a
frequency of 5. You will end up With 5,00,00,000
unique records! This Does not even take into
Consideration the Age, Gender, Geographic Trend,
Whether, Time of the month & more!
Challenges:
The Number
Jumble :
• Buying Patterns • Shopping Offers
• Cross Selling Success
• Loyalty &
Retention
• Effective Marketing
Campaigns • Predictive Demands
• Dynamic Price
Optimization
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16. Retail - Opportunities Using Big Data:
Personalized
Recommendations
Dynamic Pricing In Store Experience
Micro segmentation &
inventory management
Data Collected based on previous
online & offline purchases, even
online clicks, likes & wish lists are
recorded to generate
recommendations in real time.
Online Shoppers are given
reduced prices based on data on
time of the day, Festive offers
validity period, loyalty of
customers and more.
Geo-Fencing which allows retailers
to provide real time offers to
customers on their cell
phones(based on their previous
shopping sprees) as they enter a
geo fenced area.
Segmenting Customers have been
taken to the next level, with social
media interactions, marketing
campaign results, wish lists.
Targeted offers are now made to
granular customer segments with
promo codes & coupons!
Online Shoppers are given
recommendations at reduced
prices based on their previous
purchase trends. Amazon.com has
increased their sales volumes by
25% on this.
Offline shoppers use these data
driven approaches to map
shopping patterns and based on
proximity of customers allow price
variations with RFID price tags.
Check out Our Blogs for MORE
Optimized Product Placement is
done Scientifically where
algorithms check buyer
tendencies & have products
placed in the right geography, this
becomes very important for
bigger players like Wal-Mart &
Macy's.
With Big Data Retailers can get
predictive analytics on prices as
they fluctuate through the supply
chain. This allows them to set
prices, and also react proactively
to demand spikes to avoid over
stock-outs.
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17. Will My Policy cover this Accident ?
Image Courtesy: blairingle
Challenges - Insurance Industry
$80 Billion LOSS in U.S. per Year due to Frauds!
15% of premium costs in South Africa are Frauds!
Claims on Automobiles have a 25-33% Fraud !
Are Your
Claims
Fraud?
• What Policies are best
Suited for Customers?
• How to tackle Increasing
Diseases & ailments?
• How to reduce
customer Hassles?
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18. Insurance - Opportunities Using Big Data:
Fraud Detection Turning the Claim Centric Approach Person Centric.
Using Cohort analytics to track Social activities of beneficiary &
associated parties.
Integration of these Data streams of Information to detect fraud patterns for Future (predictive
analysis)
Delighting the
Customer
Variety of Customer records can be stored in No SQL databases, and real time integration to
multiple sources to optimize the process of the insurance validation & reimbursements.
Customer Call Logs & interaction with staff can be checked for Sentiment analysis, to optimize
insurance processes & reduce iterations base on negative customer calls.
Predictive Analysis Understand Customer Lifestyle by integrating feeds of social networks to determine Disease
Patterns so that insurance schemes 10-15 years into the future insurance companies can identify
these degenerating lifestyles & offer schemes at higher premiums.
Improving Product
Opportunities
Checking out the Success of Insurance Schemes & Which are most popular, to drive similar scheme
models & understand why other schemes are not popular. This can be done by mapping the
successful customer base lifestyle trends.
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19. I Want My Car to Drive on its OWN!
Connected Cars is No Longer a Concept!
Number of Internet Capable Vehicles
in Europe 48Mil by 2016!
There are more than 74 Sensors
in Ford’s Connected Cars!
These Hybrid Cars can generate
25GBs/Hr of Data!!
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20. Automotive - Opportunities Using Big Data
Vehicle
Insurance
Using "Telematics", driver's driving patterns can be analyzed. These can be used by insurance companies
to give out alerts, warnings in real time & even give personalized pricing.
Integrating
with Geo
Fencing &
Social Media
GPS trackers can provide customized alerts as vehicles pass a particular location. These alerts are real time
& based on your social media likes & shared combined with discount coupons & offers!
Self Repair &
Maintenance
Your Car's intelligence system will keep a track of all parts & liquids to be changed or repaired for periodic
maintenance, giving you real time alerts as you pass repair shops, which will also bid for discounted
pricing!
Learning
from The
mistakes
Using The Black Box mechanism similar to airplanes, product engineers can understand if any vehicle part
was the cause & how parts can be improved in design to reduce future accidents.
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21. Don’t Watch that Movie! It’s Pathetic!
Challenges – The Media Industry
• What Content
is popular?
Did You Know?
You Tube Users Upload 48 Hrs of Video Every Minute!
• Where are my viewers
coming from?
• What are my viewers
opinions about my content?
• How Do I monetize
my Content?
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22. Media - Opportunities Using Big Data
Predictive
analysis
Using Big Data Tools to analyze current content viewed, the storylines, characters etc. to determine
which type of movies and or soaps are going to be a success in the future.
Log Analysis Analyzing Viewer demographics, the popular content, devices used to view and download data,
detecting spams, browsers and OS used to generate actionable reports driving business decisions.
Sentiment
Analysis
Tracking user comments, likes, shares, tweets & other user interactions with media content on social
networks to track popularity & promote content similar to the hits.
Website
Optimization
Based on Navigational pattern analysis that are popular among users, website builders can optimize
the ease of reach of the content.
Ad Targeting &
Scope to
Monetize
Ad servers based on Visitor Cookie analysis & bid values generated sometimes even in real time,
deliver ads to visitors in real time & continuously update for successful clicks & failures. Learn more on
Ad serving with AWS Cloud.
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23. When will my Loan Get Sanctioned ?
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Challenges – The Banking Industry
• Detecting Frauds
• Which Schemes for
which customers?
• When is my
Customer Not Happy?
• How Do I Segment
my customer base?

24. Banking - Opportunities Using Big Data
Creating
Customer
Segmentation
Banks are now pooling in all types of customer buying patterns, lifestyle habits & interests to create
segmentations. With this 360 degree view from Big Data Analytics integration, banks can now customize
product offerings & re distribute spending from non profitable to profitable customers.
Fraud
Detection
Financial & Banking Institutes are using credit/debit card purchases to understand spending habits and
detect suspicious patterns of buying to detect frauds.
Customer
Sentiment
Analysis
Banks and Financial Institutions can now track the the success or failure of their product or schemes as they
integrate social sentiments of their products and track user complaints.
Sales And
Marketing
Campaigns
Using 360 Degree Customer Insights banks are generating smarter marketing & sales campaigns integrating
them with offers & schemes that are more successful to the different customer segments.
Analyzing
Voice
Sentiments
Many Banks are now using highly unstructured data, such as customer voices, and using complex data
analysis to track customer complaints . They are also trying to integrate the information with the
transactional data warehouse to reduce attrition, drive up sales & even detect frauds.
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25. Let us Figure out the Big Data Life Cycle
In order to make the entire process of
Big Data more tangible, it is divided
into 4 stages:
Generation
Collection
& Store
Analyze &
Computation
Data
Collaboration
& Sharing
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26. Generation
Generating the Data
Structured Data –
Employee Records
Semi Structured Data –
End User Logs
Unstructured Data –
Social User Profile images
Data Mining Log file analysis
Machine learning
Web indexing
Financial
analysisScientific
simulations
Data
warehousing
Bioinformatics
research
Web based APIs can be used
to access this data and Store it.
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27. Fitting AWS Cloud Components
AWS Direct Connect
AWS Storage Gateway
AWS Import/Export
Establish a dedicated network connection
from your premises to AWS
Secure Integration between an On-premises
IT & AWS’s storage infrastructure
Move large amounts of data into and out of AWS
using portable storage devices for transport
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28. Transferring Your Data to AWS Cloud
AWS Direct Connect
AWS Storage Gateway
AWS Import/Export
Establish a dedicated network connection
from your premises to AWS
Secure Integration between an On-premises
IT & AWS’s storage infrastructure
Move large amounts of data into and
out of AWS using portable storage
devices for transport
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29. Collecting & Storing Data on AWS Cloud
Write, read, and delete objects
containing from 1 byte to 5 terabytes
of data each.
A full featured relational databases giving
you access to capabilities of a MySQL,
Oracle, SQL Server, or PostgreSQL
databases engines.
Relational Database
Service (RDS)
A fast, fully managed NoSQL database service
making it simple & cost-effective to store & retrieve
any amount of data, and serve any level of request traffic.
AWS DynamoDB
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Simple Storage Service (S3)

30. Data Analysis, Retrieval & Automation
Amazon Elastic
Map Reduce (EMR)
A managed Hadoop distribution
by Amazon Web Services using
customized Apache Hadoop
framework. It integrates with
AWS S3 & EC2.
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Amazon Redshift is a fast, fully
managed, petabyte-scale data
warehouse service making it simple
& cost-effective to efficiently analyze
all your data using your existing
business intelligence tools.
AWS Redshift
This allows users to define a
dependent chain of data
sources and destinations with
an option to create data
processing activities called
pipeline.
AWS Data Pipelines
ANALYSIS RETRIEVAL AUTOMATION

31. AWS Kinesis (Big Data in Real Time)
Amazon Kinesis is a fully managed service for real-time processing of streaming data at
massive scale. Amazon Kinesis can collect and process hundreds of TBs of data/hr from hundreds of
thousands of sources.
• Real Time Processing allowing you to answer
questions about the current state
of your data.
• Amazon Kinesis automatically provisions &
manages the storage required to reliably &
durably collect your data stream.
• Your Kinesis Streams are connected to your Kinesis
App from which you can use DynamoDB or Redshift
to process complex queries at real Time.
Image courtesy: https://static.gosquared.com/images/liquidicity/kinesis/
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32. The Big Data Life cycle - Compiled
AWS S3
AWS RDS
AWS DynamoDB
AWS EMR
AWS Data Pipeline
Generation
Collection
& Store
Analyze &
Computation
Data
Collaboration
& Sharing
AWS S3
AWS RDS
AWS DynamoDB
AWS Redshift
AWS Data Pipeline
AWS Data Pipeline
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33. Use Case - Cloudlytics
Cloudlytics is a Pay-as-you-Go, SaaS based Log Analytics Tool powered by AWS. It
Takes the Big Data Approach using AWS Components such as EMR & Redshift.
Customer Log Files
Stored in S3
Processing Processed
Data
Customer
Reports
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34. Let’s Discuss Your Specific Business Use Case.
Contact Us
www.blazeclan.com
Follow Us On :
Our Blog : http://blog.blazeclan.com/
Contact us : info@blazeclan.com
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