This webinar discusses how data virtualization can help insurance companies address challenges from trends like digital disruption and mergers and acquisitions. It provides examples of how Denodo has helped customers like AXA XL and Prudential Financial improve data access, consistency, and governance through a data fabric architecture built on data virtualization. Key benefits highlighted include reduced data replication, increased data accuracy and reliability, role-based access controls, and more agile use of data to support initiatives like analytics and regulatory reporting.

1. EMEA WEBINAR
A Successful Data Strategy for
Insurers in Volatile Times

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

3. Agenda
1. Top Trends for Insurance in 2020
2. COVID-19 – Accelerating Changes
3. Data Virtualization – Agile Data…Fast
4. Customer Case Study
5. Q&A
6. Next Steps

4. Key Trends for Insurance in 2020

5. 5
Shift to Services and New Products
Source: A demanding future – The four trends that define insurance in 2020, Deloitte LLC

6. 6
Mergers & Acquisitions
Source: A demanding future – The four trends that define insurance in 2020, Deloitte LLC

7. 7
M&A Poor Success Rate
Source: A demanding future – The four trends that define insurance in 2020, Deloitte LLC

8. 8
Digital Disruption
Source: A demanding future – The four trends that define insurance in 2020, Deloitte LLC

9. 9
Coronavirus Pandemic – Accelerating Changes
• 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

10. 10
Common Factor in These Trends?

11. 11
Data Integration – A Journey Through Time…
S
Data
Sources
Data Ingestion
Staging S
Data Transformation
External
Data
Consumers
Web Logs,
Click stream
GEO location
data
Social
Networks
Sensor
data
Machine
Generated
New Data Sources

12. 12
Typical Data Architecture

13. 14
Adaptive Data Architectures
• Organizations need an adaptive data architecture
• An architecture that can flex and adapt to new technologies, new data sources, new formats,
new protocols, new data uses, etc. while minimizing the impact on the consumers
• Future-proofs the architecture
• We can’t predict what technologies will emerge in next 3-5 years (or 5-10 years), but we can
build architectures that will accommodate them
• Allows users to access new data, new technologies using existing, familiar tools
• e.g. read data from a Parquet file using Excel (via the Data Virtualization Platform)
• A Data Fabric – built on data virtualization – provides this adaptability and protects your
existing technology investments and de-risks the adoption of new, emerging technologies

14. 15
Gartner – Technology Trends

15. 16
Data Fabric in an Adaptive Architecture
Source: Financial Services Technology 2020 and Beyond: Embracing Disruption, PwC
DATA FABRIC

16. 17
Data Fabric Architecture
Reporting
Analytics
Data Science
Data Market Place
Data Monetization
AI/ML
iPaaS
Kafka
ETL
CDC
Sqoop
Flume
RawDataZoneStagingArea
CuratedDataZoneCoreDWHmodel
Data Warehouse
Data Lake
Data Virtualization Platform
Analytical Views
Data Science Views
λ Views
Real-Time Views
DWH Views
Hybrid Views
Cloud Views
UniversalCatalogofDataServices
CentralizedAccessControl
Enterprise Data Fabric

17. Customer Case Study

18. 19
AXA XL – Untangling the Data Mess
• AXA XL is the Property & Casualty (P&C) and Specialty Risk Division of AXA
• Serving clients in over 200 countries
• AXA acquired XL Group in Sept. 2018
• #1 global P&C commercial lines insurance platform
• Over 30 lines-of-business including Property, Casualty, Cyber, Construction,
Professional Liability, Financial Lines, Accident & Health, and Environmental
Webinar: AXA XL: Data Virtualization in the Cloud – Paco Hernandez, Director, CoE Lead - Semantic & Data Modeling at AXA XL

19. 20
AXA XL Before Data Virtualization
Source
System A
Data Mart
X
Source
System B
Source
System C
Data Mart
Y
Operational DS 1
Staging DS 1
Operational DS 2
BI Tool
A
BI Tool
B
BI Tool
C
BI Tool
D
Interim DB 2
Data Mart
1
Data Mart
2
Data Mart
3
CSV
Interim DB 1
Semantic
Layer A
Semantic
Layer C
User-generated File
User-generated File
Legacy
System
CSV

20. 21
AXA XL Before Data Virtualization
• Inconsistency in data
▪ Multiple versions of the same truth
▪ Unreliable figures presented to stakeholders
▪ Lack of understanding of data deficiencies
• Lack of data access control
▪ No tracing of who access what data and when
▪ No role-based access to information
• Loose representation of business terminology
▪ Inconsistent naming conventions in multiple systems
▪ Limitations of field naming options from systems (e.g. column names with no spaces, acronyms, contractions)
• Excessive data replication
▪ Multiple data jumps
▪ Replication leading to inconsistencies
• High data latency
▪ Outdated information presented to users
▪ Changes to data definitions slow to reach all users

21. 22
AXA XL – Data Ecosystem & Engagement Platform (DEEP)
Source
System A
Data Mart
X
Source
System B
Source
System C
Data Mart
Y
BI Tool
A
BI Tool
B
BI Tool
C
BI Tool
D
User-generated File
Legacy
System
Engagement Platform
Data
Virtualization
Landing Zone
Standardized
Zone
Analytical Zone
Data Ecosystem
Data Governance & Data Quality
Data Lake
Workbench
(Self Service)
Operational
Zone

22. 23
AXA XL with Data Virtualization
• Higher level of consistency in data
▪ One leap closer to “One version of the truth”
▪ Much more reliable calculations and traceability
• Complete control on access to data
▪ Role Based Access Control (RBAC) to every data structure and element through LDAP
▪ Full traceability of queries
• Improved representation of business terminology
▪ High integration with Data Governance
▪ Flexibility in naming of structures and fields to represent Business terms
• Minimize data replication
▪ Do not move data; access it from the source
▪ Architecture based on reusable data assets
▪ Only high-cost operations performed during ETL
• Increased Agility
▪ Changes in data definitions seamlessly performed and quickly implemented

23. 24
Prudential Financial – Data Democratization with a Data Fabric
Webinar: Data Democratization at Prudential with Logical Data Fabric – Ralph Aloe, Director, Enterprise Information Management at Prudential Financial

24. 25
Prudential Financial – Data Challenges

25. 26
Prudential Financial – Architecture

26. 27
Prudential Financial – Benefits

27. 1. Data is a critical asset to any organization – and
insurance companies are no different.
2. Traditional technologies and data replication don’t
cut it anymore.
3. Data virtualization makes it quick and easy to
expose data from multiple source to your users
while still maintaining governance and security
4. Data virtualization is core to a Data Fabric and
accelerates a wide range of initiatives; from self-
service analytics to data marketplaces to
regulatory reporting and compliance.
Key Takeaways

28. Q&A

29. 30
Next Steps
Access Denodo Platform in the Cloud!
Take a Test Drive today!
www.denodo.com/TestDrive
GET STARTED TODAY

30. 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.