2. Think about the positive !

3. Factors Surrounding everyone
Expense
cut
• How do I bring
down
Marketing cost
–OPEX
Focus on
Profitability
• Revenue
growth can
wait
Drive
Business
Insights
• Look into your
data – search
for patterns
Economic Slump

4. Challenges staring at us
How to use social media to talk to current customers
Controlling manufacturing SKUs and selecting the right SKUs
Effective and Optimized delivery
Getting the maximum talent out of the workers.
Effective use of marketing budget
Effective use of opex.

5. Barriers with existing Business Architectures
Longer time for analytical use case qualification
Technical expertise to architecture qualification
Too many technologies to chose from
System Legacy of years
Data not available at the right time and place
Realtime availability of data
Longer cycle to visualize business and operations

6. • Look at the right Analytics strategy which would help re evaluate your
business strategy by utilizing insights
• Reduce your CAPEX, invest in OPEX – Increase cashflow
• Adopt ready to use deployment frameworks for use cases like :
• Healthcare analytics
• Customer 360
• Churn Prediction
• IOT Hub and analytics
• Predictive Maintenance
• Self service Dashboards for quick visualization and insights
• Better insights = Quick Decisions
Look for more value- Fighting COVID-19 Slowdown

8. Pillars to recover
Digitization Automation
Derived
Analytics

9. Get rid of Tech debt

11. IOT the key to AUTOMATION

12. Do you have a Customer or Product 360 view

13. 81%
Have challenges
with single
customer view

14. Enabling data insights for any business
• Customer profitability analysis
• Personalize promotions and offers
• Proactively reduce customer churn
• Predict customer buying behaviour
• Gain actionable insights for better
sales and opportunity leads
• Holistic view of your ideal
customers’ purchasing behaviors
and patterns.
• Cross-selling and up-selling
opportunity identification
• Customer loyalty
• Predictive Maintenance
• Anomaly detection
• Product Manufacturing
Optimization
• Forecast demands for better
pricing and inventory
• Fraud Analytics
• Logistics route Optimization
Enabling Customer Sales Enabling Operations

15. Engaging your customer
Understanding the Analytics adoption process
Investigate Discover Plan Implement
Understand
business model
Market & technology
trends
Building knowledge
Define problem statement
Identify business use cases
Gather requirements
Develop success metrics
Identify high value,
high visibility use cases
Develop Data flow
reference architecture, scope
and pilot project
Develop expected ROI
Pilot the POC
Promote and extend the
POC result for other projects
Extend and enhance more
advanced analytic
capabilities
Devise a right engagement strategy based on end user Big Data adoption phase

16. The Journey – Laying the Foundation
• What are the key variables in our data, is it complete,
where are the gaps?
• Who are our customers, how do they shop, are they
engaged in our program
• What value is our program driving
• Who are our valuable customers, what is their potential?
• What do they buy and which direct marketing channels do
they use?
• What is our customers lifecycle
• how do we manage customers through their journey
• How do we priorities our marketing budget?
• Program dashboards and Post Campaign Analysis
• Loyalty Engagement Score
• Data driven decisions on Category,
Product and Promotions
Build the Data
Structure
1
Profiling
Segmentation
Personalization
Lifetime Value
2
Data
Monetization
3

17. Low SOW / High
Yield
High SOW / High
Yield
Low SOW / Low
Yield
High SOW / Low
Yield
Acquisition On-board & Lock In
Cross Sell
Personalisation Reward and Retain
Spend Stretch
Time
Focus our marketing
budget – Maximise ROI
Bespoke
Marketing
using
Behavioural
Segmentation
Customer Value Model
Needs Based Segmentation – 1-2-1 marketing
Churn Model
Lapsed - Reactivate
Highly Engaged – Reached full value potential
IMPLEMENT SEGMENTATION TO DRIVE PERSONALISED MARKETING CAMPAIGNS AND
STRATEGY
Automate Intelligent Segmentation

18. The Analytics Journey
Data Warehouses Data Lakes Machine Learning
Analysts Data Scientists
Centralized Cloud Multi-cloud
WORKLOADS
DEPLOYMENT
MODELS
AI
Targeted Offers
Fraud Detection
Smart Cars
Containerized
Targeted Offers
Security
Predictive
Maintenance
Security

19. Look at a single Platform to kickstart Analytics
Data Discovery
Data Preparation
Data Transformations
Data Analytics
Data Analyst – BI Team
This is great, I can find everything
here, no need to call IT J

20. Data is a resource as well
Hardware
DatawareMiddleware
Software

21. Dataware: Turning Data into a Manageable Resource
• Data Containerization
• Global Multi-Tenancy
• Data Portability
• Resource Isolation
• Workload independence
• Security
• Global Web-Scale Deployments
• Performance
• Universal Access

22. Machine Learning Process

23. Application Development and Deployment
Oracle
Bulk Load
Machine
Learning
Data
Lake
Predictive
Modeling
BI /
Reporting
Insights
DB
Events
(Kafka)
NoSQL
SQL
Server
Graph
DB
Microservice
(.NET)
Microservice
(NodeJS)
Microservice
(Java)
Customer Insights
SQL
Server
IIS, ASP.NET
Desktop
Browser
(Javascript, jQuery)
SQL
HTML, CSS, JS
Microsoft
Reporting
Service
2005 Today Desktop
Browser
(Javascript, 20+
Frameworks)
Tablet
Native
Android
Native
iOS
JSON
JSON, CSS,
HTML, JS
Backendfor
Frontend
(Java)

24. Extensibility -
Openess
Extensibilit
y
Use-cases

25. We are at an Inflection Point in Healthcare - Trends
Source: United Nations “Population Aging 2002”
25-
29%
30+ %
20-
24%
10-
19%
0-9%
% of population over
age 60
2050
WW Average Age 60+: 21%
Healthcare Costs are
Rising
Significant % of GDP
Global Aging
Average age 60+:
growing from 10% to
21% by 2050
Healthcare Big Data
Value
300 billion (USD) in
value/year
~0.7% annual
productivity growth*
25

26. Healthcare Data Silos
Clinical Data:
• EMR
• Medical
Images
Patient
Sentiment:
• Patient Behavior
• Social Networking
Claims Data:
• Utilization of
Care
• Cost Estimates
Life Sciences
Data:
• Clinical Trials
• High Throughput
Screening (HTS)
Libraries
McKinsey Global Institute Analysis

27. Insights for Healthcare
• Healthcare Examples:
• Provider: Clinical
Decision Support
• Payer/Government:
Claims Fraud Analysis
• Life Sciences:
Personalized Medicine
Life Sciences
Data
Clinical
Data
Patient
Sentiment
Claims
Data
Integration of data
required for major
opportunities
27

28. Analytical Solutions for Healthcare
28
Distributed
Platform
New Healthcare
Applications
Health Info
Services
Personal Health
Management
Analytics and
Visualization
Data Processing/
Management
Primary Care Aging Society
Clinical Decision
Support
Personalized
Medicine
Cancer Genomics
SQL-Like Query Machine Learning
Medical Imaging
Analytics
Medical Records Genome Data Medical Images
Storage
Optimization
Security & Privacy
Imaging
Acceleration

29. Retail Analytics

30. Insights Enabling right decisions for Retail
Put the right amount
of the products
customers want on
the shelf at the right
time
Get as many as
possible of the right
customers into their
store
Create a positive
buying experience
and offer value so
customers buy again
Offer products to
customers at prices
they are willing to pay
(e.g. price
discriminate)

31. Logistics Analysis
• Shipping Time
• Order Accuracy
• Delivery Time
• Transportation Costs
• Warehousing Costs
• Inventory Accuracy
• Inventory Turnover
• Inventory to Sales ratio
• Demand Forecasting
• Sensor based Tracking
• Route Optimization
• Location based Demand
Forecasting
• Cost optimization
Measuring Realtime KPIs Advanced Analytics

32. ✗
Access Logs
Authorization Logs
Access Classifications
Existing Customer Credit
Behavioral Score
+
Model
Training
✗
Trained Model
Sample
Customer Training
Set Labels
Training
Testing/
New
Good Customer:
Will not default within the
next 6 months
Bad Customer:
Will default within the
next 6 months
Source Data
Machine Learning
Feature
Extraction
BFSI Credit Risk: Binary Classification

33. • Situation: Person applies for a loan
• Task: Should a bank approve the loan?
• Note: People who have the best credit
don’t need the loans, and people with
worst credit are not likely to repay.
Bank’s best customers are in the middle
Assessing Credit Risk: Case Study
• Banks develop credit models using variety
of machine learning methods.
• Mortgage and credit card proliferation are
the results of being able to successfully
predict if a person is likely to default on a
loan
• Widely deployed in many countries
Credit Risk - Results

34. Focus: Client protection
§ Run statistical models against customer profiles
§ Detect anomalies with pattern-recognition algorithms
§ Identify and investigate identity theft
§ Detect and prevent fraud
§ Protect customer assets
§ Net result: protected customers
Security & Fraud

35. Intelligence into the future, smart cities
Smart Building
sensors
Smart Grid
sensors
Pollution
sensors
Smart
meters
Industrial
Automation
sensors
Portable medical
imaging services
Medical sensors
on ambulances
Sensors on
Smartphone
Meteorological
sensors
Inductive
sensors
Traffic cameras
INTELLIGENT CITY
INTELLIGENT
HOSPITAL
INTELLIGENT
FACTORY
INTELLIGENT
HIGHWAY
Sensors on
Vehicles
Embedded
Cloud
Dedicated
HPC
Transactional
Social
Location

36. End-uses & DR
Distribution System
Transmission System
Energy Storage
Fuel Supply System
Fuel Source/Storage
Power Plants
Renewable Plants
Data Collection and Processing
Predictive Analytics
Sensors
Controllers
End-to-End Power Delivery Chain Operation
Monitoring, Ingestion, Modeling, Analysis, Coordination & Control

37. Government - Smart Traffic Intelligent Transport System
Predictive Analytics
37
• Crime prevention, Info sharing,
• Predictive Traffic Analytics
• Machine Generated Data:
• Embedded HBase client in camera for real-time
inserts of structured/unstructured data
• 30000 + camera data collection points
• 2 billion HBase records
• Petabytes of traffic data
• Terabytes of images
• 1 week of Data mining
• Results:
• Automated queries for traffic violation
• Crime Prevention: ID fake
• licenses <1 minute
• Traffic Routing
App Servers
Regional Data Collection
Distributed Processing Across District Nodes
Derived Analytics Services
Crime Prevention Citizen Traffic Services

38. Thank You