1. Big Data Technical Overview
31 Jan 2015
Big Data : Crystal Ball of
Success
National Conference - Information of Technical Infrastructure at
Hiraben Nanavati Institute of Management and Research for Women

2. Big Data Technical Overview
Presenter
 Over 16 years of IT experience
 Help business in designing their data architecture and information
roadmap
 Help business in identifying their performance metrics and
designing it through data and winning business.
 Worked globally and delivered key businesses to the Retail, Banking
and Manufacturing.
 Key Big Data Implementation : Sears & Roebuck Co., Bank of
America etc.
 A technical expert, Sr. Data Architect/Data Scientist with strong
Banking, Retail and Telecom domain experience
 Responsible for Big Data management and leading the Pioneer
project using Hadoop
 Engaged with Technology stack like Teradata, Netezza,
Microstrategy, Cognos, Big Data etc.
 MBA in Marketing giving edge to the business governance and
business objectives
 Business Community focus with Technology help
 Expert in building and Leading skilled resources to enable business
success.
1
______________________________________
Sanjiv Kumar,
Technology Evangelist, Big Data & Data
Science,
Zensar Technologies Limited
Mobile: +91 9028837503
Landline : +91-20- 4071 3274
Email : sanjiv.kumar@zensar.com |
Website: www.zensar.com |
LinkedIn: http://in.linkedin.com/in/sanjiv123/
______________________________________
Big Data Video

3. Big Data Technical Overview
What is Big Data
2
Big
Data for
Dummies
Big Data is a tool that allows any company the ability
to accurately analyze its data.

4. Big Data Technical Overview 3
New Sources of Data

5. Big Data Technical Overview 4
How data is born and growing?

6. Big Data Technical Overview 5
Magic mirror of data

7. Big Data Technical Overview 6
Big data is data that exceeds the processing
capacity of conventional database systems. The
data is too big, moves too fast, or doesn’t fit the
structures of your database architectures. To gain
value from this data, one must choose an
alternative way to process it.
Defnition of Big Data

8. Big Data Technical Overview
Market opportunity
 Walmart handles more than 1 million customer transactions every hour.
 Facebook handles 40 billion photos from its user base.
 Decoding the human genome originally took 10years to process; now it can be
achieved in one week.

9. Big Data Technical Overview 8
The Data Explosion

10. Big Data Technical Overview 9

11. Big Data Technical Overview
Who uses BIG DATA ANALYTICS
Manufacturing Trading AnalyticsFraud and RiskRetail: Churn, NBO
Finance Smarter HealthcareMulti-channel salesLog Analysis
Homeland Security TelecomTraffic Control Search Quality
12
3 4

12. Big Data Technical Overview
BUSINESS VALUE : Retail Industry
Retailer are flooded with data from sources such as web logs , social media , Point Of Sale (PoS) and online sales data (credit card and
reward card purchases) , in-store video footage , search data , foot traffic , RFID etc.
BenefitstoretailerfromBigData
Enhance customer profiling and segmentation for
better targeted sales, enabling better customer
retention rates
Real time analysis to market responses to price –
product changes, enabling optimal pricing and
stocking
Enable calculation of cross elasticity of demand
Real time analysis of customer behavior and
purchase patterns resulting in accurate appraisal
of customer lifetime value
RetailFunctions
• Distribution and logistic optimization
• Management supplier negotiations
Supply Chain &
Procurement
• Assortment & Price Optimization
• Store Layout
Merchandising
• Performance transparency
• Labor Inputs
Operations
• Cross Selling
• Sentimental Analysis
Sales & Marketing
• Customer behavior analysisCustomer Service
1
Indian service providers like Infosys , Fractal are providing
retailers Big Data Implementation and analytics services
for campaign management , sentimental analysis ,
inventory management etc…

13. Big Data Technical Overview
BUSINESS VALUE: Manufacturing Industry
The manufacturing sector has witnessed many advancements in streamlining processes and improvements in quality. Big data
provides it the next wave of enhancement particularly in supply chain responsiveness , monitoring equipment and R&D.
BigDataApplication-Manufacturingvaluechain
2
• Product Development
• Customer Collaboration
• Integrating datasets
R & D
• Improve responsiveness of supply
chain management
• Inventory optimization
Procurement
• Enterprise asset management
• Digital shop floor control
• Operation cost forecasting
Production
• Customer Relationship Management
• Marketing Campaigns
• Customer segmentation
Sales & Distribution
• Real –time analysis of after sales and
feedback data
• Warranty analysis
After Sales Service
• Improves demand forecasting and supply
planning through real time planning
• Enables collaboration engineering through
crowd sourcing that significantly cuts time
–to – market and improves quality
• Enables design to value , improvement in
output quantity and facilitates mass
customization
• Better product lifecycle management, job
scheduling and service levels
• Real –time detection

14. Big Data Technical Overview
BUSINESS VALUE: Finance Industry
Customer and transaction data from multiple channels like branch, kiosks, mobile, web , social media, emails , credit cards data,
insurance claims data , stock market data , statistical data , PDF & excel files , news , videos, government filings,… etc. are key Big Data
sources ……
BigDataApplication-FinancialServicesSub-sector
3
Risk Management / Assessment
Trading
Surveillance
Banking
Capital Markets
Trading
Insurance
Credit Line
Optimization
Credit reward
analysis
Pre –trade decision support analysis
Fraud Detection
Portfolio Analytics
Compliance & Regulatory Reporting
Customer Relationship Management
Consumer Behavioral Patterns
Predict client
longevity
Trading Pattern
Analysis
Intra- day
Analysis
Using weather &
calamity
information for
managing losses
• Detect , prevent and remedy financial fraud in
real – time
• Accurate risk assessment of customers
credibility and financial position
• Effective trade monitoring and analysis
• Adhering to stringent compliances and provide
granular reporting to regulators
• Enhance customer experience through
personalized products and services
• Execute high value marketing campaigns
• Reduce cost and discover new revenue
opportunities
BenefitstoFinancesectorfromBigData

15. Big Data Technical Overview
BUSINESS VALUE: Healthcare Industry
Healthcare players create terabytes of structured and unstructured data through growing digitalization of healthcare, the monitoring
of in-patient and out-patent through sensors , epidemic data and the mass sequencing of the genome is generating huge amount of
data.
BigDataApplication-FinancialServicesSub-sector
4
• Improved quality of patient care,
proactive care
• Lower cost of healthcare services
and patient care
• Enhanced fraud detection and
efficient hospital operations
• Big Data Implementation in
healthcare is expected to gain
traction
BenefitsfromBigDatatoHealthcaresector
• Drug Discovery, data annotations and
validity analysis
• Study of gene expression for next
generation sequencing
R & D , Life Sciences
/ Bio Medicines
• Patient monitoring and assessment
• Patient care personalization
• Provide effective value added services
Patient Care
• Influence consumer behavior
• Optimize physician interactions
• Clinical decision support
Healthcare
Operations
• Health issues trend analysis across
geographies , tracking spread of diseases
Epidemiology
• Fraud detectionHealthcare Security

16. Big Data Technical Overview 15
What’s diiferent is this time

17. Big Data Technical Overview 16
Why we use Hadoop
Hadoop Use Cases
Reducing infrastructure costs
New analytics on existing data
Expanding data for existing applications
Combining different data sources
New application from new data sources
Power of distributed computing
Power of parrallel processing
Power of comodity hardware
How do Hadoop does it?

18. Big Data Technical Overview 17
Big Data can create value in a broad range of
industries Long list of opportunity identified
17
Vertical Application (industry specific)
Financial
Services
 Risk mgmt
 Fraud detection
 Improve debt
 recovery rates
 Personalize
 banking
products
 Mktng
effectiveness
 Micropayments
 Improve
customer service
 Churn
prevention
 Customer
segmentation
 Smarter trading
 Improve
underwriting
 Gamification of
savingss
Retail /
Consumer
 Near real-time
pricing
 Stock keeping &
replenishment
optimization
 Store layout
optimization
 In-store
behavior
analysis
 Cross selling
 Optimize
pricing,
placement,
design
 Performance
transparency
 Customer
service
 Inventory
mgmt.
TMT
 Customer
insight based
targeting
(offers,
advertising)
 Social network
analysis
 Churn
management
in elcos
 Reduce
downtime for
systems
Public Sector
 Reduce
fraud
 Segment
populations,
customize
action
 Support
open data
initiatives
 Automate
decision
making,
improve
event
response
Healthcare
 Clinical
decision
support
 Outcomes
benchmarking
 Life sciences
research
 Optimal
treatment
pathways
 Remote
patient
monitoring
 Predictive
modelling for
new drugs
 Personalized
medication
 Improved
diagnostics,
hospital ops
Insurance
 Usage based
insurance
(PAYD1)
 Fraud
detection
 Niche
products and
insurance
bundles
 Customer
knowledge
 Intelligent
insurance
renewa
 Big Data to
calculate
insurance
premium
Energy
 Natural
resource
exploration
 Demand
response
optimization
 Maintenance
and drilling
optimization
 Micro
weather
forecasting
 Optimize
asset
location
Transportati
on
 Traffic &
congestion
manageme
nt
 Travel
assistance
 Fleet/netwo
rk
optimizatio
n
 Preventive
maintenanc
e of fleets
Industrial
Goods
 Data driven
product
design
 Shop-floor
optimization
 Agile supply
chain mgmt.
 Design to
value
 Crowd-
sourcing
 "Digital
factory" for
lean
manufacturin
g
 Improve
service via
product
sensor data
Location-based marketing
Social Segmentation
Social Media / Other Application
Sentiment Analysis
Price Comparison Service

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20. Big Data Technical Overview 19
Smart Cities

21. Big Data Technical Overview
Thank You