Watch full webinar here: [https://buff.ly/2R4JjBX] Organizations today are data rich and insights poor. There is data everywhere. ERP systems, CRM systems, external data, data lakes and ponds. The real question to ask is “Are the users getting the insights they need when they need where they need to drive successful business outcomes”. Data Integration is a core pillar of the “Data to Value” journey. In this session you will hear how enterprises across industries are grappling with data, insights challenges and how organizations have adopted data virtualization to accelerate their "data to value" journeys. Watch this Denodo DataFest 2018 session to learn: How to reduce effort to get from data to value Hope to gain faster time to Insights How to reduce overall cost of ownership

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Accelerating
Data to Value
Journey
Krishna Gubili
Head – Data ,Analytics and AI
Nov 1, 2018

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Wipro today
IT Services Revenue
$8.4Bn
Sustained
Growth
Active
global clients
1254*
Strong
Partnership
Workforce
166,790
Attracts the
Best Talent
Employee presence
in 65 countries
65
Global
Footprint
Figures based on Q1, FY17-18 for Global IT Services business
Leader in
strategic design and
design thinking
Leader in the Cloud, CX –
Salesforce, Workday, Google
Wipro’s Artificial
Intelligence and Hyper-
automation Platform
Strategic Investment arm
of Wipro – $100m fund

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Data, Analytics and Artificial Intelligence practice overview

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Context
IT Challenges
• Exponential data growth
• Complex technologies
• Shorter project cycles
Business Needs
• Agility
• Faster time to market
• Right information to right
user at right time
Common Issues across organizations
Continously
Changing business
environment
Shorter turn
around times
Data and
application silos
Performance
& scalability
Data latency

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The legacy approach
DeployTestBuild
Ready
to
Build
❖ Profile data
❖ Logical Model
❖ Physical Model
❖ Create tables
❖ Build ETL
❖ Unit Test
❖ Integrate test
❖ System Test
❖ UAT
❖ Build Deployment
❖ Validate
❖ Promote to
production
Sources
ERP
Mainframe
Dimensional
OLAP Unstructured
Semi
Structure
ETL Tools Connectors Big Data tools Big Data tools
Physical Data
warehouse
Physical
Data Lakes
Are we solving the problem??
• Significant delay in Business transformation programs
• Users need to wait for data (12 hours to 48 hours)
• 3 to 6 months waiting time to bring new functionality
• Long duration to implement small changes

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The new approach

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The new approach – what does it mean?
Faster time to
market
• Subscription Model
• New sources can be configured
in days
• 100’s of application entities can
be integrated in weeks
• New business functionality can
be added in weeks
• Existing functionality allows to
bring various types of sources
• Pre-built in functionality to
support Agile development
• Approximately 40% to 60% of
cost savings from development
• Approximately 30% to 40% of
test cycle reduction results cost
saving
• Operational cost reduction by
40% by eliminating possible
physical copies
• Zero cost to remove physical
copies
• Automatic query re-writing
• Dynamic query optimizer
• Query pushdown
• On-The-Fly-Data-Movement
• Caching
ExecutionOptimized cost

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Data Virtualization Market Adoption
Across
Industries
65% of
fortune 500
15,000
organizations
worldwide
Top vendor
Denodo
Agile BI &
Analytics
Logical Data
Warehouse
Logical Data
Lake and Cloud
Modernization
Data as a
Service(s)
By Implementations By Use Cases
35% increase in its adoption by 2019

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Success story – Improve customer experience at Mfg. leader
Context
• Improve customer experience by
• Proactive & preventive maintenance
• Reduce time to repair (optimize spare part stocking in service
centers)
• Personalized customer communication & offers
• Data, application silos and complex integration
Solution
• Data virtualization layer significantly reducing data movement
• Business glossary / semantic layer enabling insights
Impact
• Significant improvement in customer experience measured by
MTTR, out of stock events and campaign response

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Success story - Improving customer Service for Finance leader
KANA Agent
Desktop
CAPITA
CCM Desktop
SOA Services
Data Services
KANA CCM
CCM
Archive
Access
One
OCDB
Unified view of Case and contact
history
Context
• Improve customer service process by
• Enabling a unified case and contact history
• Reducing time taken to resolve customer issue
• Reducing TCO across 18+ markets globally
Solution
• Data virtualization layer enabling agents to view historical
data (case history)
• Improved performance accessing data from multiple sources
Impact
• Significant improvement in agent productivity & customer
service levels

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Alliance and Partner Ecosystem
Alliance Strategy Aimed
at bringing Greater Value to the
Customer through Joint
Solutions & Delivery Geared to
Delight our Customers.
Collaborative Innovation: Provide unique solutions
addressing Industry needs combining joint expertise
Best of Technology & Business Expertise:
Partners in building new solutions on leading edge
technology platforms.
Industry Specific GTM Alignments to facilitate
knowledge sharing
Technology Sharing: Priority access to vendors
premier products, education and technical services
Joint Centers of Excellence to Build New Solutions:
competency development, Beta testing and customer
showcase

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..Leverage wipro’s in-house accelerators and
frameworks for speed up delivery
Unified framework
Customer 360
• Leveraged Banking
Customer Data Model
• Leverage Communication
Customer Data Model
Asset 360
• Leverage Asset Data Model
NextGen DI™
Wipro’s transformation tool
• Identification of Integration
patterns to automate
Integration creation in bulk
• Provides UI to configure &
operate
• Design pattern based approach
to automate the development
• Analysis & migration
Cook Book
• Reference Architecture
• Implementation approaches
• Review check list
• Learnings, Possible mitigation
options
• Common problems, solutions
• Re-usable frameworks
Smart Data Validation
• Extensive usage for
Regression test execution
facilitating DevOps, CICD
methodologies
• 100% Data validation with
complete coverage on Test
cases, test execution, data
• Batch execution – Scheduler to
trigger test execution with no
manual intervention

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Krishna Gubili
Data, Analytics and AI Practice
Thank you
for your time