Watch full webinar here: https://bit.ly/3c6v8K7 Banking, Financial Services and Insurance (BFSI) organizations are globally accelerating their digital journey, making rapid strides with their digitization efforts, and adding key capabilities to adapt and innovate in the new normal. Many companies find digital transformation challenging as they rely on established systems that are often not only poorly integrated but also highly resistant to modernization without downtime. Hear how the BFSI industry is leveraging data virtualization that facilitates digital transformation via a modern data integration/data delivery approach to gain greater agility, flexibility, and efficiency. In this session from Denodo, you will learn: - Industry key trends and challenges driving the digital transformation mandate and platform modernization initiatives - Key concepts of Data Virtualization, and how it can enable BFSI customers to develop critical capabilities for real-time / near real-time data integration - Success Stories on organizations who already use data virtualization to differentiate themselves from the competition.

1. Accelerating Data-Driven Enterprise
Transformation in Banking, Financial
Services and Insurance

2. Speaker
Paul Moxon
SVP Data Architectures & Chief
Evangelist, Denodo

3. 3
THE LEADER IN DATA VIRTUALIZATION
LEADERSHIP
Longest continuous focus on data virtualization – since 1999
Leader – Gartner Data Integration MQ 2020
Leader in Forrester 2020 Wave – Enterprise Data Fabric
Leader in Forrester 2017 Wave – Data Virtualization
#2 in Gartner Peer Insights for Data Integration Tools
Winner of numerous awards
DENODO OFFICES and EMPLOYEES
• 24 offices across 18 countries
• New offices in 2019 – Canada, Mexico, China
• New offices in 2020 – UAE, Saudi, Brazil,
Russia
• 30% growth in employees in 2019
CUSTOMERS and PARTNERS
• 800+ customers; 100+ new in 2019
• 250+ active and engaged partners
FINANCIALS
• Backed by $4B+ private equity firm; $0 debt
• 50 – 60+% annual growth; Profitable.

4. 4
COVID-19 Forced Change on BFSI Organizations

5. 5
Online and Digital Payments

6. 6
COVID-19 – Accelerating the Trends
Digital transactions increased by 20%
• Mobile and online
• Accelerating ongoing changes in
banking distribution channels
Physical branches – Essential
services, but…
• Reduced hours and visits by
appointment
Multichannel integration and
cross-channel CX is critical
• Customers need and use all channels
• Mobile, online, branch, call center, etc.

7. 7
COVID-19 – Accelerating Changes in Insurance
• Disruption of the distribution chain
• Brokers and agents cannot meet clients face-to-face
• Acceleration of distribution disruption – move towards
aggregators and direct
• Less auto travel, but premiums are still rising
• Accelerate move to UBI or on-demand auto insurance
• Life insurance medical exams under lockdown?
• Life Insurance firms are using other data and AI to set life
insurance rates in absence of medical exams

8. 8
• Digital Transformation initiative accelerated
• 3-5 year initiatives ==> 2-3 year initiatives
• Omnichannel customer engagement
• In-person, online, mobile, contact centers
• More investment in data and analytics
• Advanced analytics and AI
• Smart process automation using RPA
• Augment insights with external data
Changing Priorities

9. 9
Post-COVID Priorities for BFSI Organizations
Customer Experience
• Omnichannel integration for
360° view of customer
• Frictionless digital on-boarding
• Open Banking for all-encompassing
approach to customer engagement
Cost Management
• Increased use of AI for
decision making
• Operational efficiencies
through digitalization
Workforce Enablement
• Remote vs. Office-based working
• Upskilling for digital economy
• Agility to adapt to new working
practices

10. 10
Customer Experience

11. 11
Cost Management

12. 12
Workforce Enablement

13. 13
The Common Need for These Initiatives?

14. 14
Typical Data Ecosystems
Streaming
Analytics
Product Customer
Asset
Data Warehouse
Data Warehouse
Appliance
Master Data
Management
Real-time analytics
and decision
management
Data exploration
and investigation
Data mining, machine learning and
Deep Learning model development
Graph analysis
Traditional reporting
and analysis
Data mining and
model development
Advanced Analytics (Multi-Structured Data)

15. 15
…Leading to Data Silos
Streaming
Analytics Data Warehouse
Data Warehouse
Appliance
Product Customer
Asset
Master Data
Management
Advanced
Analytics
Graph Database
Advanced
Analytics
Analytical
Tools/Apps
Analytical
Tools/Apps
Analytical
Tools/Apps
Analytical
Tools/Apps
Analytical
Tools/Apps
Analytical
Tools/Apps
Analytical Models
Tools/Apps
Data Integration
Tools and Scripts
Data Integration
Tools and Scripts
Data Integration
Tools and Scripts
Data Integration
Tools and Scripts
Data Integration
Tools and Scripts
Data Integration
Tools and Scripts
Silo Silo Silo Silo Silo Silo Silo
Streaming Data Structured and
Multi-Structured
Data
Multi-Structured
Data
Multi-Structured
Data
CRM
ERP
SCM CRM
ERP
SCM CRM
ERP
SCM
Structured Data Structured Data Master Data
Management

16. 16
Where is My Data?
Web Content
Big Data Application
Graph Database
Relational
Databases
Cloud Storage
(S3, ADLS, GCS)
JSON/XML Files
SaaS Applications
Flat Files
Data Warehouse
Excel Spreadsheets
?

17. 17
The Evolution of Data Architectures
This is a Second Major Cycle of Analytical Consolidation
Operational Application
Operational Application
Operational Application
IoT Data
Other NewData
Operational
Application
Operational
Application
Cube
Operational
Application
Cube
? Operational Application
Operational Application
Operational Application
IoT Data
Other NewData
1980s
Pre EDW
1990s
EDW
2010s
2000s
Post EDW
Time
Logical Data
Architectures
Operational
Application
Operational
Application
Operational
Application
Data
Warehouse
Data
Warehouse
Data
Lake
?
Logical Data
Warehouse
Data Warehouse
Data Lake
Marts
ODS
Staging/Ingest
Unified analysis
› Consolidated data
› "Collect the data"
› Single server, multiple nodes
› More analysis than any
one server can provide
Unified analysis
› Logically consolidated view of all data
› "Connect and collect"
› Multiple servers, of multiple nodes
› More analysis than any one system can provide
Fragmented/
nonexistent analysis
› Multiple sources
› Multiple structured sources
Fragmented analysis
› "Collect the data" (Into
› different repositories)
› New data types,
› processing, requirements
› Uncoordinated views

18. 18
Logical Data Warehouse Architecture
DATA VIRTUALIZATION

19. 20
What is a Logical Data Architecture?
Data Fabric
Location
Customer
Products
RDBMS/OLTP Traditional Analytics/BI Data Lakes Cloud Data Stores Apps and Document
Repositories
Flat Files
Third Party
Legacy
Mart
Data Warehouse
Mart
ETL ETL
XML • JSON • PDF
DOC • WEB
Applications/APIs
REST OData
SOAP/XML GraphQL
Supplier
Data Integration Services
Data Fabric Services Data Compute Services
Data Marketplace Data Access Services
Management
Services

20. 21
What is a Logical Data Architecture?
RDBMS/OLTP Traditional Analytics/BI Data Lakes Cloud Data Stores Apps and Document
Repositories
Flat Files
Third Party
Legacy
Mart
Data Warehouse
Mart
ETL ETL
XML • JSON • PDF
DOC • WEB
Applications/APIs
REST OData
SOAP/XML GraphQL
Data Integration Services
Data Fabric Services Data Compute Services
Data Marketplace Data Access Services
Management
Services
Data Steward
Sys Admin
Data Fabric
Admin

21. 22
Data Virtualization – A Logical Data Layer
“Data virtualization
integrates
disparate data
sources in real
time or near-real
time to meet
demands for
analytics and
transactional
data.”
– Create a Road Map
For A Real-time, Agile,
Self-Service Data Platform,
Forrester Research,
Dec 16, 2015
Consume
in business applications
Enterprise Applications, Reporting, BI, Portals, ESB, Mobile, Web, Users
DATA CONSUMERS
Analytical Operational
Multiple Protocols,
Formats
Query, Search,
Browse
Request/Reply,
Event Driven
Secure
Delivery
Combine
related data into views
CONSUME
Share, Deliver,
Publish, Govern,
Collaborate
COMBINE
Discover, Transform, Prepare,
Improve Quality, Integrate
CONNECT
Normalized views
of disparate data
SQL,
MDX
Web
Services
Big Data
APIs
Web Automation
and Indexing
Connect
to disparate data sources
Databases & Warehouses, Cloud/Saas Applications, Big Data, NoSQL, Web, XML, Excel, PDF, Word...
DISPARATE DATA SOURCES
More Structured Less Structured
3
2
1

22. 23
How Does It Work?
Development
Lifecycle Mgmt
Monitoring
& Audit
Governance
Security
Development Tools
and SDK
Scheduled Tasks
Data Caching
Query Optimizer
Mobile, Web, Users
Enterprise application, ESB Reporting, BI, Portals
Databases &
warehouses
Enterprise
applications
Cloud/SaaS
applications
XML, Excel,
Flat Files
Big data
NoSQL
Collaboration
Web 2.0
PDF, Docs,
Index, Email
Data Source Layer
Derived View Derived View
Unified View Unified View
Unified View
Unified View
Customer360
View
Data Mart
View Application Layer
Business Layer
Transformation
& Cleansing
J
J J
S J
A
J
U
JDBC/ODBC/ADO.Net SOAP/REST WS
Base
View
Base
View
Base
View
Base
View
Base
View
Base
View
Abstraction
Base
View

23. 24
Data Virtualization Connects the Users to the Data That They Need
Data Virtualization allows you to connect to (almost) any data source
You can combine and transform that data into the format needed by the consumer
The data can be exposed to the consumers in a format and interface that is usable by them
• Typically consumers use the tools that they already use – they don’t have to learn new tools
and skills to access the data
All of this can be done without copying or moving the data
• The data stays in the original sources (databases, applications, files, etc.) and is retrieved,
in real-time, on demand

24. 25
Example Use Cases
Data Consumption
Self-Service
Analytics
Real-time
Decisions
Data Science
(ML & AI)
Data
Marketplace
K.Y.C.
(Customer 360)
Compliance
(IFRS17, GRC)
Mergers &
Acquisitions
Apps
(Mobile & web)
Data Governance, Manipulation & Access
Agility
& Simplicity
Semantic
Layer
Data
Abstraction
Real-time
Delivery
Zero
Replication
Sophisticated
Optimizations
Data
Governance
Data
Security
Data Storage & Management
Data
Integration
Logical Data
Warehouse
Hybrid
Data Fabric
Enterprise
Data Fabric
Cloud
Modernization
APIfication
(& SQLification)
Refactoring &
Replatforming
Apps/API
Sales HR
Marketing
Data Science
AI/ML
Executive
DATA VIRTUALIZATION
From Data Storage & Management, to Data Consumers, going through Data Governance & Security

25. 26
Prudential Financial – Data Democratization with Data Virtualization
Webinar: Data Democratization at Prudential with Logical Data Fabric – Ralph Aloe, Director, Enterprise Information Management at Prudential Financial

26. 27
Prudential Financial – Data Challenges

27. 28
Prudential Financial – Architecture

28. 29
Prudential Financial – Benefits

29. 30
Leading Global Bank – Pain Points
Supporting
Multiple Data
Access Tools
Changing
Technologies
Data Lifecycle
Management
Data
Discovery

30. 31
Leading Global Bank – Data Marketplace
Data Virtualization Platform
Data Marketplace
Client & Account
Active Clients Client
Accounts
Party Summary
Positions & Holdings Securities & Pricing Market Data Hub Index & Benchmark
Systems of Record Data Lake Data Warehouse
with Business Semantic Layer
Virtual Data Lake

31. 33
CIT Group – U.S. Commercial Bank
• Large commercial bank grew through acquisitions
• One West Bank, Direct Capital Corporation (DCC)
• Breached SIFI threshold in 2013
• ‘Too big to fail’ financial institution
• Subjected to more scrutiny from federal regulators
• Participate in CCAR (‘stress tests’)
• Needs to provide a complete view of risk across complete organization
• Market, credit, third-party, …
• Used Data Virtualization to expose data to downstream applications and reporting

32. 34
CIT Group – Data Platform

33. 1. The ‘new normal’ post-pandemic world will require
banks and insurance companies to become more agile.
2. BFSI organizations need to provide integrated multi-
channel delivery to customers.
3. An agile ‘integration fabric’ – a Data Fabric – is needed
to support these changes.
4. Data Virtualization is core to a Data Fabric.
• Accelerates wide range of projects; self-service analytics,
regulatory reporting, M&A integration, rapid
deployment of agile solutions.
Key Takeaways

34. Q&A

35. Next Steps

36. 38
denodo.link/Drive2105

37. 39
D E N O D O V I R T U A L L U N C H & L E A R N A S E A N :
Agile Data Management
with Enterprise Data Fabric
25 May, Tuesday | 1.00pm – 2.15pm SGT
REGISTER YOUR INTEREST
denodo.link/DLL2105
Elaine Chan
Regional Vice President,
Sales, ASEAN & Korea
Chris Day
Director,
APAC Sales Engineering

38. 40
denodo.link/dvbook2105

39. 41
Closing
1. Upon exiting this session, kindly help us complete a short survey - denodo.link/survey2105
2. You will be receiving an email with your MYR 15 GrabFood e-voucher within 3 days after this session, if not
please email me.
3. If your question was not answered during the session, we will follow up with you via email. If you did not
receive a reply from us within 3 days, kindly reach out to me via email.
4. If you have any other questions after this session, do feel free to reach out to me at: mgoh@denodo.com
Thank you for your time and have a good rest of the day!

40. Thanks!
www.denodo.com info@denodo.com
© Copyright Denodo Technologies. All rights reserved
Unless otherwise specified, no part of this PDF file may be reproduced or utilized in any for or by any means, electronic or mechanical, including photocopying and microfilm,
without prior the written authorization from Denodo Technologies.