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

7/29/2019 Big Data Use Cases for Different Verticals and Adoption Patterns - Impetus Webinar

1/43

A review of multiple industry verticalsand how they are using Big Data

Anand Venugopal (AV)

Director Business Development

Big Data Services


2/43

Recorded version available athttp://www.impetus.com/webinar_registration?event=archived&eid=65

We will talk about.

2013 Impetus Technologies

Definition

Value

Use-Cases and Patterns

How to

Recorded version available athtt

p://www.impetus.com/webinar_registration?event=archived&eid=65
http://www.impetus.com/webinar_registration?event=archived&eid=65http://www.impetus.com/webinar_registration?event=archived&eid=65http://www.impetus.com/webinar_registration?event=archived&eid=65http://www.impetus.com/webinar_registration?event=archived&eid=65http://www.impetus.com/webinar_registration?event=archived&eid=65http://www.impetus.com/webinar_registration?event=archived&eid=65http://www.impetus.com/webinar_registration?event=archived&eid=65


3/43


Definition


You have a Big Data situation

When traditional information systems cannot store processor analyze the volume, variety or velocity of data in a cost-effective, timely manner

StoreProcessAnalyze

VolumeVelocityVariety

COSTTIME
http://www.impetus.com/webinar_registration?event=archived&eid=65http://www.impetus.com/webinar_registration?event=archived&eid=65http://www.impetus.com/webinar_registration?event=archived&eid=65


4/43


Where is the value?

How is Big Data monetized?



5/43


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.



6/43


Where is the value?


Intuition

Data

BIG

DATA

QualityofManagem

entDecisions

Amount of data analyzed

Accurate

Relevant

Quick


7/43


A powerful examplethat we all just

witnessed



8/43


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

Big Data Analytics won the election

"We are going to measure every single thing in thiscampaign, Campaign Manager Jim Messina said aftertaking the job.

Analytics team = 5X of 2008

Chief scientist" for the Chicago headquarters (RetailAnalytics guru)



9/43


Some facts from the Obamacampaign

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: fundraiserMillions

In the final weeks, simulation 66000 times every night resultsused to re-allocate resources

29000 sample from Ohio alone to model demographics

$ 1 Billion raised; Optimally deployed8 out of 8 swing states swept



10/43


Big Data use cases?

Learning from others experiences



11/43


Analysis approach of use cases



12/43



Analysis approach of use casesBFSI

Telecom

Retail

Consumer durables

Automobiles

Healthcare

Hospitality

Travel

IT

Media

Company(CEO)

Marketing(VP)

Sales (VP)

West region(Director)

East region(Director)

South region

(Director)

North region(Director)

Engineering(VP)

Finance (VP) HR (VP)


13/43


What are use case patterns?


Abstraction of use casesacross industry verticalswith a set of commonfactors:

Type of data analyzed(3Vs)

Nature of analysis or

processing Technology stack


14/43


Poll

Which patterns are you seeing?


15/43


Flume, Scribe, Sqoop, Hiho Custom connectors HDFS/ NewSQL/ Hadapt/ MPP DBs Hive / PIG/ MR/ Impala/ Apache Drill

Batch mode- Multi source DataAnalytics

Multiple DataSources-

StructuredETL/ ELTL-Connector based

Big Data Store

Multiple DataSources-

Unstructured

AnalyticsApplications



16/43


Real Time or Near Real TimeAnalytics

Storm + Esper; Hstreaming

StreamBase, StreamInsight, IBM Streams, SQL Stream

JMS/ Messaging (e.g. Kafka)/ Streaming connectors

NoSQL (fast ingest)

Rules

Alerts

Streams of real timeEVENTS

Event ProcessingEngine Big Data Store



17/43


NLTK, R (text) Apache Mahout, Apache Lucene Apache UIMA (entity extraction)

GP Text, Madlib, Clarabridge, Text-miner (SAS, IBM)

Social Media and NaturalLanguage Processing

ETL/ ELTL Big Data StoreMultiple Data

Sources-Unstructured

NLP & ML



18/43





19/43




Telecom

Retail

Consumer durables

Automobiles

Healthcare

Hospitality

Travel

IT

Media

Company(CEO)

Marketing(VP)

Sales (VP)



South region

(Director)


Engineering(VP)



20/43


Analysis approach: Value driversFunctional areas of Big Data impact



21/43


Poll

Where do you see the most value?


22/43





23/43




Telecom

Retail

Consumer durables

Automobiles

Healthcare

Hospitality

Travel

IT

Media

Company(CEO)

Marketing(VP)

Sales (VP)



South region

(Director)


Engineering(VP)



24/43


Vertical: Telecommunication SPs

Value Driver: Cost reduce, capex optimize

Functional Area Use-case pattern Use-case

Network optimization

Small % change =

Millions of $

Batch Analytics and Reporting

Correlate various devices logs.

Keep SLA, but lower cost of n/w.

Batch processing of multi-source

data

DPI of Mobile apps data

Revenue share or throttle



25/43


Vertical: Telecom Media EntertainmentValue Driver: Increase revenue,

new productsFunctional Area Use-case pattern Use-case

Customer Behavior and New

product analytics

Real-time, Near real time

Analytics + Batch analytics

Correlate data from:

TV viewing,Internet,

Mobile app,

Telephone records,

Social Media updates and

Customer service records

Organizational

Transformation!

Telecom Information

Service

All DATA ASSETS Consolidation

and Analytics

Real time,Near Real time

DNS data,

Mobile transaction,location, Geo-IP,

Number-portability



26/43


Vertical: Financial servicesValue Driver: Increase revenue, new

products

FunctionalArea Use-case pattern Use-case

Fraud Accuracy Real time + BatchReducing False positives based on

customers individual transaction history

Personalization and AD

targeting

Batch AnalyticsMonitoring and Management (Servers,

Channeling the right

merchant offers to the right customer

by individual transaction analytics and

profile scoring



27/43


Vertical: Financial servicesValue Driver: Cost reduce, reduce risk


Risk Batch Processing of multi-sourcedata

Evaluating credit-worthiness and risk-

profile of loan-targets using new datasets

Claims (Insurance) Batch analytics of very large files,

unstructured data and social media

data

Evaluate Auto-repair vendors and cost-

optimization and quality improvement of

claims processing



28/43


Vertical: Manufacturing/ Hi-techValue Driver: New product

development


Product Engineering

and Process AnalyticsBatch Analytics and Reporting

Multi-format flexible parsing,

search

Find new patterns from product testingdata from factories failure analysis ?

Root-cause ? Prediction of faults

Real-time + Batch

2-3 TB / day Statistical Process and

Quality control real-time responses plus

pattern detection using batch analytics



29/43


Vertical: Manufacturing/ Hi-techValue Driver: Revenue++, up-sell/

cross-sellFunctional Area Use-case pattern Use-case

Field deployed

product analytics

Real time + Batch

To alert customers and for proactive quality

engineering and recommendations

Up-sell/ New offering

Sensor Data Analytics

(Billing and Analytics)

Real-time, Near real time

Analytics, Business rules engine +

Batch analytics

(New revenue) Machine to Machine carrier

Cars, Wineries, Oil fields Apply business

logic in real-time to alert and prevent failure

events;

Value-added services for consumers



30/43


Vertical: RetailValue Driver: Revenue++, up-sell


Marketing:

Multi-channelConsumer spend

modeling

Batch analytics of Mobile, POS, E-

commerce

C L T V

Propensity to buy - analytics

Location Analytics

And early alerts

Real-time analytics

(TELCO + RETAIL)

Access the customer before the retail

experience NOT after check-out



31/43


Vertical: RetailValue Driver: Revenue++

Horizontal Area: Social Media Analytics


Operation and

Customer ServiceReal time Social Media Analytics

Retail staffing operations- based on

reactions to promotions

Proactive customer complaints resolution



32/43


Poll

Which vertical do you see in 2013?


33/43


All Verticals

Horizontal use-cases which are identicallyapplicable to all or most industry verticals


BFSI Telecom Retail ConsumerDurables

Healthcare Hospitality Travel IT Media

Automobile


34/43


Horizontal: ITValue Driver: Cost-, Risk-, Capex-


IT Infrastructure

TransformationBatch Analytics and Reporting DW Replacement with Hadoop for

storage, reporting and BI

Batch processing of multi-source

data

ETL or ELT offload of DW with Hadoop

IT Operations

(logs, events, alarms)Real Time, Near Real Time Analytics Monitoring and Management (Servers,

Networks, Applications, Storage,

Virtualization)

IT Systems Security

(Application,

Infrastructure, Database

Network/ Firewall)

Real Time Analytics Security event detection and preventionin real time

Batch Analytics Security breach pattern detection and

machine learning to create rules-engine



35/43


Horizontal: Contact Center AnalyticsValue Driver: Cost-, Revenue+, Risk


HR: Employee

retention (Call-center)Batch Analytics and Reporting Find what type of employees will stay for

longer

Experience not as important as

personality

Contact-centerAnalytics

Natural Language Processing + Plusmulti- source batch analytics (email,

voice, web)

(voice transcripts= 150x of

email/chat)

Loss reduction: Customer Churn Analytics

Revenue and Cost: Channel and

campaign management- outbound call

analysis

Loss Control: Proactive fault isolation in

Telco networks using key-words asleading indicators of N/W faults


H i l


36/43



Sales from E-

commerceEvent Pattern Analysis+ Batch

AnalyticsIncrease conversion to buy (Rapid A-B

testing)

Increase revenue up-sell/ cross-sell

recommendation engine; (market basket

analysis correlated products)

CLTV and Propensity to Buy analytics

Digital Marketing and

AdvertisingNatural Language Processing + Plus

multi- source batch analytics (email,

voice, web)

Customer behavior and segmentation

Analytics

Personalization and Micro- Targeting

Advertising, Ad Effectiveness

Horizontal: Web Analytics/Digital MktgValue Driver: Revenue+


H i t l S i l M di A l ti


37/43



Marketing Social

Media AnalyticsKey-word search and Natural

Language ProcessingCompetitive Analytics Who and What are

people talking about the most?

Sentiment Analysis using Natural

Language processing

Brand Analytics (sentiment) What are they

saying positive, negative? Impacts onBrand perception, products

Graph Analytics + Social Media

AnalyticsInfluencers analytics Who knows whom?

Who are the key influencers? What is the

revenue impact of friends-of-friends?

Operations and

Customer serviceRealtime text analytics Retail Staffing operations;

Proactive customer complaints resolution

Horizontal: Social Media AnalyticsValue Driver: Revenue+, Cost, Risk



38/43


We talked about

Big Data-Definition

Value

Use-Cases

How to



39/43



Suggesting a pathway for Big Dataadoption

Big data strategy is a journey, not a destination.Its not a product youre going to buy; its notsomething youre going to stand up there and

be done with.


40/43


Big Data roll-out strategy

StrategyBusiness Goals,

Locate Collate BIGDATA

PoV/ RoI

Budget

Technology

POC Identify best sub-setof use-casesDesign andImplement

Production

Implement

Integrate

Optimize

Operations

People

Add use-cases



41/43

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



42/43

Advisors.Experience

Architects.

Expertise

Applications.

Excellencebigdata.impetus.com [email protected]


43/43

Legal

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 ImpetusTechnologies.

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.

Documents

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