Big Data Use Cases for Different Verticals and Adoption Patterns - Impetus Webinar

A review of multiple industry verticals
and how they are using Big Data

Anand Venugopal (AV)
Director – Business Development
Big Data Services

We will talk about….

• Definition
• Value
• Use-Cases and Patterns
• How to

Recorded version available at
© 2013 Impetus Technologies
http://www.impetus.com/webinar_registration?event=archived&eid=65

Definition
You have a Big Data situation…
When traditional information systems cannot store process
or analyze the volume, variety or velocity of data in a cost-
effective, timely manner

Store Volume
Process Velocity COST
Analyze Variety TIME


Where is the value?

How is Big Data monetized?


Where is the value of Big Data?
Example: Traditional DW/BI vs. Hadoop

CAPEX savings of
96% to 99.56%

$20,000 - $180,000 per TB
vs.
$800 per TB includes 24/7 support
$550 per TB per year after that.


Where is the value?

Quality of Management Deci sions
BIG DATA

• Accurate

• Relevant
Data
• Quick
Intuition

Amount of data analyzed


A powerful example
…that we all just
witnessed


Big Data Analytics won the election
"We are going to measure every single thing in this
campaign,” Campaign Manager Jim Messina said after
taking the job.

Analytics team = 5X of 2008

“Chief scientist" for the Chicago headquarters (Retail
Analytics guru)

The Obama campaign guarded what it believed to be its biggest
institutional advantage over Mitt Romney's campaign: its data.


Some facts from the Obama
campaign
The first 18 months – they aggregated ALL data silos

Modeled everything – sources, appeals, swing-voters, crowd-
pullers

George Clooney pulled in 40 – 49 age females: fundraiser
Millions

In the final weeks, simulation 66000 times every night – results
used to re-allocate resources

29000 sample from Ohio alone to model demographics
$ 1 Billion raised; Optimally deployed
8 out of 8 swing states swept


Big Data use cases?

Learning from others experiences


Analysis approach of use cases


Analysis approach of use cases
Company
(CEO)
BFSI Healthcare
Marketing Engineering
Sales (VP) Finance (VP) HR (VP)
(VP) (VP)
Telecom Hospitality
West region
(Director)
Retail Travel
East region
(Director)
Consumer durables IT
South region
(Director)
Automobiles Media
North region
(Director)


What are use case patterns?
Abstraction of use cases
across industry verticals
with a set of common
factors:

• Type of data analyzed
(3Vs)
• Nature of analysis or
processing
• Technology stack


Poll

Which patterns are you seeing?


Batch mode- Multi source Data
Analytics
• Flume, Scribe, Sqoop, Hiho
• Custom connectors
• HDFS/ NewSQL/ Hadapt/ MPP DBs
• Hive / PIG/ MR/ Impala/ Apache Drill

Analytics
Applications
Multiple Data
Sources-
Structured ETL/ ELTL-
Connector based
Big Data Store

Multiple Data
Sources-
Unstructured


Real Time or Near Real Time
Analytics
• Storm + Esper; Hstreaming
• StreamBase, StreamInsight, IBM Streams, SQL Stream
• JMS/ Messaging (e.g. Kafka)/ Streaming connectors
• NoSQL (fast ingest)
Alerts

Event Processing
Streams of real time Engine Big Data Store
EVENTS

Rules


Social Media and Natural
Language Processing
• NLTK, R (text)
• Apache Mahout, Apache Lucene
• Apache UIMA (entity extraction)
• GP Text, Madlib, Clarabridge, Text-miner (SAS, IBM)

NLP & ML

Multiple Data ETL/ ELTL
Sources- Big Data Store
Unstructured


Analysis approach: Value drivers
Functional areas of Big Data impact


Poll

Where do you see the most value?


Vertical: Telecommunication SPs
Value Driver: Cost reduce, capex optimize

Functional Area Use-case pattern Use-case
Correlate various devices logs.
Network optimization Batch Analytics and Reporting
Keep SLA, but lower cost of n/w.
Small % change =
Millions of $ DPI of Mobile apps data
Batch processing of multi-source
data Revenue share or throttle


Vertical: Telecom Media Entertainment
Value Driver: Increase revenue,
new products
Correlate data from:
Customer Behavior and New Real-time, Near real time
product analytics Analytics + Batch analytics TV viewing,
Internet,
Mobile app,
Telephone records,
Social Media updates and
Customer service records
Organizational All DATA ASSETS Consolidation DNS data,
Transformation! and Analytics – Real time, Mobile transaction,
Near Real time location, Geo-IP,
Telecom  Information Number-portability
Service


Vertical: Financial services
Value Driver: Increase revenue, new
products

Reducing False positives based on
Fraud Accuracy Real time + Batch customer’s individual transaction history

Monitoring and Management (Servers,
Personalization and AD Batch Analytics Channeling the right
targeting merchant offers to the right customer –
by individual transaction analytics and
profile scoring


Vertical: Financial services
Value Driver: Cost reduce, reduce risk


Evaluating credit-worthiness and risk-
Risk Batch Processing of multi-source profile of loan-targets using new data
data sets

Evaluate Auto-repair vendors and cost-
Claims (Insurance) Batch analytics of very large files, optimization and quality improvement of
unstructured data and social media claims processing
data


Vertical: Manufacturing/ Hi-tech
Value Driver: New product
development

Find new patterns from product testing
Product Engineering Batch Analytics and Reporting data from factories – failure analysis ?
and Process Analytics Multi-format flexible parsing, Root-cause ? Prediction of faults
search

2-3 TB / day – Statistical Process and
Real-time + Batch Quality control – real-time responses plus
pattern detection using batch analytics


Vertical: Manufacturing/ Hi-tech
Value Driver: Revenue++, up-sell/
cross-sell

To alert customers and for proactive quality
Field deployed Real time + Batch engineering and recommendations
product analytics
Up-sell/ New offering

(New revenue) Machine to Machine carrier
Sensor Data Analytics Real-time, Near real time – Cars, Wineries, Oil fields – Apply business
(Billing and Analytics) Analytics, Business rules engine + logic in real-time to alert and prevent failure
Batch analytics events;

Value-added services for consumers


Vertical: Retail
Value Driver: Revenue++, up-sell


Marketing: CLTV
Batch analytics of Mobile, POS, E-
Multi-channel commerce Propensity to buy - analytics
Consumer spend
modeling
Real-time analytics Access the customer before the retail
Location Analytics experience – NOT – after check-out
And early alerts (TELCO + RETAIL)


Vertical: Retail
Value Driver: Revenue++
Horizontal Area: Social Media Analytics

Retail staffing operations- based on
Operation and Real time Social Media Analytics reactions to promotions
Customer Service
Proactive customer complaints resolution


Poll

Which vertical do you see in 2013?


All Verticals
Horizontal use-cases which are identically
applicable to all or most industry verticals

BFSI Telecom Retail Consumer Automobile
Durables

Healthcare Hospitality Travel IT Media


Horizontal: IT
Value Driver: Cost-, Risk-, Capex-

IT Infrastructure Batch Analytics and Reporting DW Replacement with Hadoop for
Transformation storage, reporting and BI
Batch processing of multi-source ETL or ELT offload of DW with Hadoop
data
IT Operations Real Time, Near Real Time Analytics Monitoring and Management (Servers,
(logs, events, alarms) Networks, Applications, Storage,
Virtualization)

IT Systems Security Real Time Analytics Security event detection and prevention
(Application, in real time
Infrastructure, Database Batch Analytics Security breach pattern detection and
Network/ Firewall) machine learning to create rules-engine


Horizontal: Contact Center Analytics
Value Driver: Cost-, Revenue+, Risk
HR: Employee Batch Analytics and Reporting Find what type of employees will stay for
retention (Call-center) longer
Experience not as important as
personality
Contact-center Natural Language Processing + Plus Loss reduction: Customer Churn Analytics
Analytics multi- source batch analytics (email,
voice, web)
Revenue and Cost: Channel and
(voice transcripts= 150x of campaign management- outbound call
email/chat) analysis
Loss Control: Proactive fault isolation in
Telco networks using key-words as
leading indicators of N/W faults


Horizontal: Web Analytics/Digital Mktg
Value Driver: Revenue+
Sales from E- Event Pattern Analysis+ Batch Increase conversion to buy (Rapid A-B
commerce Analytics testing)
Increase revenue up-sell/ cross-sell
recommendation engine; (market basket
analysis – correlated products)
CLTV and Propensity to Buy analytics

Digital Marketing and Natural Language Processing + Plus Customer behavior and segmentation
Advertising multi- source batch analytics (email, Analytics
voice, web)
Personalization and Micro- Targeting

Advertising, Ad Effectiveness


Horizontal: Social Media Analytics
Value Driver: Revenue+, Cost, Risk

Marketing – Social Key-word search and Natural Competitive Analytics – Who and What are
Media Analytics Language Processing people talking about the most?

Sentiment Analysis using Natural Brand Analytics (sentiment)– What are they
Language processing saying – positive, negative? Impacts on
Brand perception, products

Graph Analytics + Social Media Influencers analytics – Who knows whom?
Analytics Who are the key influencers? What is the
revenue impact of friends-of-friends?

Operations and Real time text analytics Retail Staffing operations;
Customer service Proactive customer complaints resolution


We talked about

• Big Data-Definition 
• Value 
• Use-Cases 
• How to


Suggesting a pathway for Big Data
adoption

“Big data strategy is a journey, not a destination.
It’s not a product you’re going to buy; it’s not
something you’re going to stand up there and
be done with.”


Big Data roll-out strategy

Business Goals, PoV/ RoI
Strategy Locate Collate BIG
DATA
Budget
Technology

Identify best sub-set Design and
POC of use-cases Implement

Implement Operations
Production Integrate People
Optimize Add use-cases


Value Punch
$112M
New revenue from improving fraud accuracy

48 RDBMS licenses out
IT transformation to Big Data

11 million+ articles converted into PDF
Less than $300.00

New Business Launched for
Hispanic Consumer Data Analytics


Advisors.
Experience

Architects.
Expertise

Applications.
Excellence
bigdata.impetus.com bigdata@impetus.com

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• © 2013 Impetus Technologies. All rights reserved.

• You are prohibited from making a copy or modification of, or
from redistributing, rebroadcasting, or re-encoding of this
content without the prior written consent of Impetus
Technologies.

• This presentation includes images from other products and
services. These images are used for illustrative purposes only.
There is no explicit or implied endorsement or sponsorship of
these products by Impetus. All copyrights and trademarks are
property of their respective owners.


Big Data Use Cases for Different Verticals and Adoption Patterns - Impetus Webinar

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