Presentation on Big Data Analytics at Vellore Institute of Technology, Chennai

1. Big Data Analytics
!
Industry Perspective
Shankar Radhakrishnan

2. Topics
• Market Research
• Market Trends
• Big Data Analytics in
• Banking and Financial Services
• Insurance
• Travel and Hospitality
• Retail
• Life Sciences
• Manufacturing
• Telecommunications
• Challenges vs. Opportunities
• Q & A

3. 3

4. Market Research
4

5. Key Trends driving Big Data AnalyticsIndustry
Financial services
▪ Customer Insights – Integrating Transactional data (CRM/Payments) and unstructured Social feeds
▪ Regulatory Compliance – Risk exposures across asset classes, LOBs and firms
▪ Fraud Detection in Credit Cards & Financial Crimes (AML) in Banks
Travel, Hospitality & Retail
▪ Customer centricity – Customer behavior analysis from Omni channel retailing & Social feeds
▪ Markdown Optimization – Improve markdown based on actual customer buying patters
▪ Market basket analysis – Narrow down market basket analysis by demographics
Life Science
▪ Improve targeting & predictions – Automatic Detection of Adverse Drug Effects (ADEs)
▪ Patient data analysis – Longitudinal Patient Data (LPD) analysis
▪ Predictive Sciences – Analyze Preclinical Side Effect Profiles of Marketed Drugs
Healthcare
(Payers & Providers)
▪ Cost of Care – Drug effectiveness & Cost of Care Analysis based on electronic Health Records (EMR)
▪ Self Service Healthcare – Increase in mHealth & eHealth to allow consumer access to health information
▪ Claims Analytics – Analyze insurance claims data for fraud detection & preferred treatment plans
Communication, Media &
Entertainment
▪ Discover churn patterns based on Call data records (CDRs) and activity in subscribers’ networks
▪ Digital Asset Management (DAM) – Analyze & capitalize digital data assets
Manufacturing
▪ Proactive Maintenance & Recommendation – Sensor Monitoring for automobile, buildings & machinery
▪ Energy Efficiency – Leveraging Smart meters for utility energy consumption
▪ Location or Proximity Tracking – Location based analytics using GPS Data
Hi-Tech ▪ Extend and complement conventional information supply chain with big data path
▪ Predictive analysis and real time decision support
Trends
5

6. John calls a customer care
executive at the bank.
!
He is irritated with the services
offered to him and is expressing
signs of making a switch
Executive validates the customer’s
identify and pulls up an application
powered by Big Data that presents
all relevant information to make a
decision.
!
Big Data Application converts his
speech to text in real time and
identifies his propensity to churn.
Based on John’s tonal sentiment
the application immediately pulls
up top 5 offers or decisions to
take based on the Customer
Persona information which
contains likes/dislikes, past
experiences, which channels he
prefer, CLV(Customer Life-time
Value) etc.
Well Informed Customer Service Executive
6

7. Social media
Depositions
Complaints
Voice Data
Unstructured Data Speech to Text
Conversion
Decision Engine
Analytical System
Customer Persona
•Customer Persona
•Demographics,
•Top interactions
•Channel Preferences,
•Dis-satisfiers
•Customer Lifetime Value
•Recent Contact History
•Customer Sentiment
•Trend during the call
Customer’s state of mind
Sentimental
Analysis
Other Channel
information
(ATM, Branch)
Big Data Warehouse
Traditional Warehouse
•Customer Executive Dashboard
presents all intelligence required
to make a decision
•The decision engine also presents
important decisions to be taken
for the particular customer issue
Well Informed Customer Service Executive
7

8. Fraud Pattern Analysis & Detection
Envisaged Benefits
▪New fraud patterns can be identified by building ‘analytical models’ to run against X yrs. of
History data
▪‘Web crawling’, ‘Contextual text analysis’, ‘Natural Language Processing’ allows fraud behavior
identification from social media. It may increase Fraud detection success rate
▪‘Real time’ models to capture behavioral patters and do pattern analysis against History data
to evaluate Fraud case validity. The model learns by self and updates ‘Fraud pattern master
sets. This brings ‘artificial intelligent’ fraud pattern detection and analysis
▪‘Real time’ (in the order of .5-1 minute refresh rate) alerts to Fraud analysts about ‘self
learned’ fraud patterns based on new customer behavior patterns
Process
▪Formation of key value groups to the order of XcY (where X no. of attributes that are relevant
to Fraud and Y is no. of attributes that should be combined to identify patterns)
▪High speed history data loading from source systems
▪Efficient Real time fraud detection by identifying patterns through customer behavioral events
and processing them over X yrs. of history data
Scenario
▪Formation of Fraud patterns using
•Real time data coming from different departments like IVR, WEB, Customer profile,
Transactions etc
•Real time Mining and analysis of history data to form prior patterns
Fraud Pattern Analysis & Detection
8

9. Legacy
Fraud Data
Customer
Profile Data
Social Media
Data
Card
Transaction
Data
Decision
Engine
Approval/
Denial
Decision
History Data
Processing to
find Fraud Patterns
over years
Real-time Customer
Behavior Analysis for
Fraud Detection
Real time Analysis of
behavior patterns
Real time update to
Decision Engine
Self Learning Fraud Detection
9

10. Cross Channel Analytics
John exhibits a specific pattern when
he avails services.
!
He always visits the bank when he
wants to deposit a check.
!
He prefers most other operations to be
online.
!
He has recently started paying his
utility bill payments through mobile.
• Analytical Solution integrates
Customer transactions through
different channels and reveals
insights on customer’s channel
preferences and activities.
• It also integrates data from call
centers, surveys and complaints and
measures Customer Experience.
• It reveals customer activities across
channels which is normally not
available for a customer touch-point
to deliver superior service
• I t r e v e a l s o p p o r t u n i t i e s t o
consolidate channels and optimize
cost of operations by incentivizing
customers to choose one medium
over other
Analytical Solution produces
!
• Dominant Path Analysis specifying
which channel is used by John for
which events
• Service Behavior Segmentation
• Customer Journey analysis
• Root Cause & Repeat Issue analysis
• Longitudinal analysis on customer
preference changes
!
Helps bank deliver superior service
and also optimize cost on specific
channels
10

11. Analytics
Cross Channel Analytics
Big Data
Warehouse
Dominant Path Analysis- channel
usage info
Service Behavior Segmentation
Repeat Issue analysis
Query Drill-down
Ad hoc Reports
Predictive Modeling
Statistical Analysis & Text Mining
Optimization
Root cause analysis
Call Reasons analysis
Customer Journey analysis
Structured data
Web & Mobile
ATM / Branch
IVR, Call Records, Notes
CRM Data
ACH / Wire Transfer /
Other channels
Unstructured
Content / Logs
DW
Transactions
Mailings, Offers, Lists
Other Channels
Survey
Complaints
11

12. Analytics
Data Mart
Member profiling based on
profile, demographic, social
media and history data.
Identification of key
predictor variable for
customer churn
Member profiling
and variable
identification
Termination
prediction
modeling
Termination prediction
modeling engine to
determine “probability
of termination” at each
member level
Member Prioritization
Matrix
List of members with
high likelihood of
termination
Retention Target
list generation
Alternate product
recommendation
engine
List of suitable
products for each
customer
Analyze profitability of
each of the
recommended product
Create most optimal and
effective Retention
campaigns
Personalized Retention
Plan
Churn and Retention Analytics
1212

13. Analyze customer’s search
pattern by doing the weblog
analysis using big data.
!
e.g. Rate or amenity which
customer prefers
!
Step 1: Customer Starts the
search on website
Drill down into specific search patterns and
analyze customer’s rate preference or amenity
expectation on a particular rate e.g.
!
Step 2: Customer selects some destination.
!
Search displays all the hotels and then refine
the search by selecting a price range or sort the
search based on price and then he leaves and
doesn’t book.
!
It concludes that customer didn’t find the
hotels at hisher expected rate.
!
Step 3: Customer selects some destination.
!
Search displays all the hotels and then refine
the search by selecting preferred amenity e.g.
swimming pool,wifi etc. and then he leaves and
doesn’t book.
!
It concludes that customer didn’t find the
hotels with expected amenities.
Popup right offers to the customer when
they search which in return increase
customer attraction and sales as well.
!
Revenue management team to use this
data and come up with ideal rate.
!
The search pattern can be used for
individual property amenity improvisation.
!
Step 4: This data can be forwarded to
revenue management team to setup the
rightcompetitive rates in right geography
!
Step 5: This data can be forwarded to
properties as well for amenity
improvisation
Look to Book Ratio Analytics
13

14. Planogram – created by
planners and buyers
Actual view of the shelf
arranged by store associates
Compliance dashboard as well as
compliance score by
Dept./Category/Subclass
▪ Planogram compliance is the process of verifying if the
products arrangement and the manner in which they are
displayed on the shelf in each store match the planogram
that is strategically created and collaboratively developed
between planners and trading partners
▪ Usually this verification and compliance check is a time
consuming process and done on a sample basis. When the
execution of planogram is compromised or if there are
assortment void, it is a lost opportunity
▪ To accelerate this compliance check – take picture of the
actual shelf by product facing, position and systematically
compare for compliance
The Need
▪ Storing the planogram’s created at corporate
location for each store/dept./category combination
▪ Storing the actual photo of the shelf
▪ Comparing this unstructured data for matching
▪ Integrate this matching score with planogram
planning data in Data warehouse to produce various
dashboards and metrics that will influence
sell-thru, profitability and customer satisfaction
Big Data Analytics
Planogram’s Compliance
14

15. ▪ eCommerce retailer needs to analyze graphic images depicting
items for sale over the Internet
▪ When a consumer wants to buy a red dress, their search may not
match the tags used to identify each item’s search terms.
▪ Manufacturers do not always label their goods clearly for the
distributors or identify keywords with which users are likely to
search.
The Need
▪ Analyze thousands of dress images, detecting the red
prominence of the primary object in the graphic (JPGs,
GIFs and PNGs)
▪ This requires enormously complex logic for the computer
to “see” the dress and its primary colors as humans do.
▪ Millions of images are tagged with additional information
to assist consumers with their search
▪ Increases the chances that they find the item they were
looking for and make a purchase
Big Data Analytics
Intelligent Item Search
15

16. ProcessInput Benefits
Predictive Biology
External and Internal Literature
sources
Text mining used for linking molecule with
metabolic processes such as glucose
uptake, fatty acid synthesis, metabolic
stress etc.
!
Manual curation can be done to extract
assertions and relationships with respect to
effect, drug treatment, experiment type
etc.
Vital evidence collected on the effect
and relationships on species, tissues
and linkages to canonical pathways and
RNA expression data
Business Goal
Rapid extraction of key information from literature sources to collect evidence on biological processes
!
Assessing the incidence of Nausea in development compounds by analyzing the preclinical side effect
profiles of marketed drugs
Statistical models created to find out the
relation between various preclinical
observations and occurrence of nausea
!
Model shows clustering of compounds
associated with nausea having higher
gastrointestinal preclinical observations
Model helps in identifying the risk of
nausea early on during development
!
Running this model during compound
selection can minimize the risk of
seeing nausea in follow up compounds
Predictive Pre-Clinical Safety
Gastrointestinal preclinical findings
of marketed drugs
16
Predictive Sciences - Predictive Biology & Predictive Pre-Clinical Safety

17. Data Processing StepsInput Benefits
EMR data
!
Prescription data
!
Promotion data
(eMail, Sales Calls etc.)
!
Identify key themes in the EMR data
for a particular disease type
Use the prescription data to validate
the patterns / themes
!
Merge the findings with the promotion
data to uncover any relationships
between promotion and treatment
Refining targeting / promotional
strategies
!
Cost Reductions
!
Uncover potential reasons for
choosing a particular therapy
Business Goal
Linkage of EMR/Prescriber/Promotion data in order to understand the relationship between
prescriber promotions and treatment patterns
Improving Diagnosis by EHR/EMR Data Analysis
17

18. Challenge Analytics Benefits
Wastage of energy and resources
!
Under utilized room’ temperature and
lighting settings
!
Huge Energy bills
Historic Sensor Data
!
Blue-prints of the building and room
layouts
!
Realtime Sensor Data
!
Temperate settings in the room and
building
!
Usage patterns of the room
Green Energy, Smart Energy Management
Optimize consumption of energy in
business environments
!
Networked sensors and a new
generation analytics tools play a
huge role in gaining insights and to
implement the most efficient and
sustained energy strategies.
18

19. Case
• Typically contact center channel data is analyzed typically from SLA perspective: TAT, Average wait time.
• However, the actual transcript of the conversation can yield powerful insights regarding telecom infrastructure usage
Customer call-center
Text Mining
Collocation Analysis from
Cell Phone Towers
• Collocation analysis by an investigation team finds out if there were multiple phones with the same person.
• Examination involves Terabytes of CDR/Tower records from the switch, one can triangulate on a few
collocation events
Multi-device Event
Stream Analysis
co-relating Firewall & IDS
& Switch activity
• Most telecom infrastructures IDS (Intrusion detection systems) sit at the periphery, with network monitoring , Firewalls and application logs
being captured in silo
• Deploy Central Log File repository with events streaming from multiple devices that are ingested and collated centrally
• Channels into intelligence, network infrastructure and security of the telecom assets
• Optimizes significantly to detect everything from malware and spear phishing attempts to breach security
Optimizing cost of
Telecom Tower
Maintenance
• Big Data platform manages fuel consumption data in the telecom tower business
• Each of the telecom towers has a generator and one of the biggest components of cost is diesel cost
• Sensors/energy meters which constantly emit large data streams of operational data
• Machine learning algorithms crawls through operational data stored over years to predict and optimize cost and revenue
User Behavior Analysis
• Operational systems at each telecom service provider generates huge data volumes in the form of
Call Data Records(CDR) for each call/SMS handled
• Signaling data between various switches, nodes, and terminals within the network
• Mining of this data leads to insights for improving marketing operations, network and service optimization
Planning Sales Approach
• Large-scale data analysis boosts the ability to pinpoint exactly where ongoing sales approach could make further gains
• Study the behavior of customers to see what factors motivated them to choose one brand or product over another.
• This involves analyzing online search data and real-time information, shared by consumers across social networks and
other Web-based channel - about the company’s products and services
• Brand affinity and customer sentiments are measured using Sentiment Analysis algorithms
Big Data Analytics In Telecom
Big Data Analytics
19

20. ✓ Hype, Buzz & Myth
✓ “How?” vs. “Why?”
✓ Big Data Analytics for Business, than just for IT
✓ Business Case Justification
✓ Right Partner’s For Your Big Data Analytics Journey
✓ Evangelization and Alignment
✓ Business Onboarding
✓ Execution Plan And Course Corrections
✓ Talent and Knowledge Management
✓ Right math for ROI
20
Big Data Analytics : Challenges vs. Opportunities

21. Source: The Evolving Role of the Enterprise Data Warehouse in the Era of Big Data Analytics , By Ralph Kimball
Vector, matrix, or
complex structure
Free text
Image or
Binary data
Data “bags”
Iterative logic
or complex
branching
Advanced
analytic
routines
Rapidly
repeated
measuremen
ts
Extreme
low
latency
Access to all
data
required
Search Ranking X X X X X X
Ad Tracking X X X X X X X X
Location or Proximity Tracking X X X X X
Social CRM X X X X X X X
Document Similarity Testing X X X X X X X X
Genomic Analysis X X X X X
Customer Cohort groups X X X X X X
Fraud Detection X X X X X X X X X
Smart Utility Metering X X X X X X
Churn Analysis X X X X X X X
Satellite Image Analysis X X X X
Game Gesture Analysis X X X X X X X X
Data Bag Exploration X X X X X X
Ad Tracking / Click stream analytics Location or Proximity Tracking
Social Media Analytics
/ Social CRM
Document Similarity Testing / Match Making
Customer Cohort Groups
Sensor Monitoring (Flights / Building Smart Utility
Metering
Call Center Voice Analytics Log Analytics
Satellite / CAT Image Comparisons Fraud Detection Game Online Gesture Analysis
Big Science (Astronomy, weather, atom smashers, Genome
decoding)
Search Ranking Risk Management Churn Analysis Data “Bag” Exploration / Causal Factor Analysis
Design Challenge
21

22. Thanks Much !