The most profitable insurance organizations will outperform competitors in key areas as personalized customer service, claims processing, subrogation recovery, fraud detection and product innovation. This requires thinking beyond the traditional data warehouse to the data fabric - an emerging data management architecture. In this webinar Andy Sohn, Senior Advisor at NewVantage Partners, and Bob Parker, Senior Director for Insurance at Cambridge Semantics, explore the role of the data discovery and integration layer in an enterprise data fabric for the Insurance industry. These are their slides.

1. Data and Analytics Driving
Insurance Disruption
Andrew Sohn
August 2019

2. Boston | New York | San Francisco | Austin | Charlotte | Santiago | Saõ Paulo2
About Me
› Chief Data and Analytics Officer – Crawford and Company
› Enterprise Data Management Executive – Bank of America
› Technology Strategy Executive – Bank of America
› CIO – Global Professional Services Organization
› Recent Conference Talks
• Forrester
• Gartner / Evanta
• Chief Data and Analytics Officers – Insurance
• Insurance Data Management Association (IDMA)
• Enterprise Data Warehouse (EDW)
AndrewSohn
ASohn@NewVantage.com

3. Boston | New York | San Francisco | Austin | Charlotte3
Major Societal and Technology Changes are Driving Disruption in Insurance
Source CXPartners

4. Boston | New York | San Francisco | Austin | Charlotte4
Legacy Carriers are Consolidating or Contracting
2842
2831
2794
2769
2743
2706
2666
2639
2620
2606 2600
1 2 3 4 5 6 7 8 9 10 11
Number of Property &
Casualty Filers (USA)
In 2018, Only 71 carriers have net
written premiums > $1B

5. Boston | New York | San Francisco | Austin | Charlotte5
The Barbarians at the Gate

6. Boston | New York | San Francisco | Austin | Charlotte6
Emerging Ecosystems Threaten Traditional Insurers

7. Boston | New York | San Francisco | Austin | Charlotte7
Insurance is increasing becoming about who can best leverage the eco-systems
Source: McKinsey: Digital disruption in insurance: Cutting through the noise

8. Boston | New York | San Francisco | Austin | Charlotte8
Old Models are Not Competitive
Day or Weeks
to Get Insured

9. Boston | New York | San Francisco | Austin | Charlotte9
Disruption Indicators and Responses

10. Boston | New York | San Francisco | Austin | Charlotte10
Common Insurance Industry Business Challenges
 Improve Customer Experience
 Reduce Claims Processing Time
 Reduce Fraud
 Reduce Claims Expense (Loss Adjusting Expense)
 Increase Subrogation Recovery
 Reduce Frequency and Cost of Litigation
 Improve Accuracy of Loss Reserves
 Personalize and Customize Products and Services
 Improve Underwriting and Pricing
 Reduce Distribution Costs

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Data Analytics benefits insurers and other stakeholders

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Data Analytics Translates into Profit for Insurance Companies
Source: Accenture
9-11%
2-3%
3-4%
2-3%
As increase of 16-21 percentage points in insurance profit can be captured by analytics programs

13. Boston | New York | San Francisco | Austin | Charlotte13
Data and Analytics is used in Many Ways

14. 14 Boston | New York | San Francisco | Austin | Charlotte | Santiago | Saõ Paulo
The New Reality of Data Sources
• Policy applications
• FNOL Documents
• ACORD Forms
• Adjuster Reports
• Paramedical exams
• Medical Information Bureau
Group Inc. (MIB)
• Motor vehicle records (MVRs)
• Prescription drug databases
• Property records—ownership,
tax appraisal and so on
• Genealogy records—births
and death
• Marriage licenses and divorce
decrees
• Criminal records, court
dockets, jail inmate records
• Voter registration
• Bankruptcy records
• Purchase Histories
• Government Databases
• Third-party marketing databases
• Electronic health records (EHRs)
• Photos
• Images
• Voice
• Biometric data from wearable devices
• Car Telematics
• Home automation and Sensors
• Behavioral and lifestyle data
• Facial Recognition Analysis
• Location Data
• Product Review
• Social Media Discussions
Traditional data sources
Traditional data sources that
are now easier to obtain
quickly, inexpensively and in
a digital format
New data sources

15. Boston | New York | San Francisco | Austin | Charlotte15
Challenges to Success

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Use the Right Tool and Pattern for the Appropriate Situation and Purpose
 Master Data Management
 Data Quality Management
 Relational Databases
 Document Databases
 Data Warehouses
 Data Lakes
 Hadoop / Spark
 Data Virtualization
 AI (Weak)
 NLP / Text Analytics
 Semantic Modeling
 Graph Databases
Data Discovery
Abstraction between Logical and Physical (Business and Technical)
Identifying Complex Relationships Between Entities
DataStrategyandArchitecture

17. Boston | New York | San Francisco | Austin | Charlotte17
Contact info
Andrew Sohn
AndrewSohn
@Molsonix
ASohn@NewVantage.com
www.NewVantage.com

18. Data Discovery and Integration in an
Insurance Data Fabric

19. “Unprecedented levels of data
scale and distribution are
making it almost impossible
for organizations to effectively
exploit their data assets”
Source: How To Use Semantics to Drive the Business Value of Your Data, Gartner Group, Guido De Simoni, 27 Nov. 2018
ENTERPRISE DATA MANAGEMENT TODAY REQUIRES
A MODERN DATA DISCOVERY AND INTEGRATION LAYER

20. RDBMS/OLTP Big Data / Hadoop Document Repositories
Traditional BI Cloud
CLAIM
CUSTOMER
PRODUCTS
POLICY
Semantics, Data Discovery, and
Integration in an Enterprise Data Fabric

21. ©2018 Cambridge Semantics Inc. All rights reserved.
Claims Admin
CRM
Policy Admin
Data Fabric: Catalog, Connect, Enrich and Expose Data
Use Cases:
Data Consumers In the Business
Data Sources:
Enterprise Data Landscape
Risk/Underwriting
Marketing
Billing
Digital Engagement
Customer 360
X-Sell, ‘Coverage Gap’
Increase Subrogation Recovery
Claim efficiency, automation
Litigation minimization
Data driven risk assessment
Associated Parties for Claims
Documents/
Unstructured

22. Question Build
Analytics
Prepare
Data
Semantic
Layer
Insights
Delivered
Locate & Load
(ETL) Data
Locate &
Ingest Data
TIME
TIME
The Semantic Layer dramatically improves Time-to-Value
Today
Anzo®
Insights
Delivered
Question

23. What parties are connected
to this claim?
Policy, claim, counter party, employer, experts,
law enforcement, co-insurer, MGA, reinsurer
What products should I suggest?
CRM, policy, social media, connections
Semantics help insurers connect, understand, and use
more enterprise data to answer business questions.
customer
policy
How can I price policies more
competitively and profitably?
Claims, risk, vehicle, property, medical, weather, crime
Is this a fraudulent claim?
Claim report, Incident data, police report, claim
history, customer’s connections, social media
Lifestyle, financial
And demographics
personal
profile
Is this an in market high value
prospective customer?
CRM, Financial, credit, vehicle,
property, marketing, web, social media
Social Media
Contact
info
connections
spouse
friend
claim
settlement
incident
Social Media
adjuster
counter party
vehicle
propertylost item
How can I personalize the
customer experience?
Marketing, CRM, social media, web

24. We apply semantics and graph to a data
fabric – so anyone can find, understand,
blend, and use enterprise data.
At a Glance:
• Based in Boston
• 100+ employees
• Origins in IBM and Netezza
• Anzo 4.0 GA 2017
• Added enterprise-scale OLAP
graph database engine in 2015

25. A modern data discovery and integration platform
for your enterprise data fabric.
Anzo lets business users find, connect, and blend
enterprise data into analytic ready datasets.
Map and Explore
Enterprise Data
Build Blended
Analytic-Ready
Datasets
Apply Enterprise-Ready
Data Management

26. 4 Step User Experience
Catalog and map your
existing data assets –
structured or unstructured.
Translate dataset into graph
models. Add business
definitions, object types, and
relationships with semantics.
Create blended analytic
ready datasets. Connect
graph models. Transform
data. Harmonize into
canonical models.
Analyze data using semantic
and graph models. Export
data for use with BI,
analytics, and machine
learning tools.
ON-BOARD MODEL BLEND ACCESS

27. Automated Deployment and Operations
Storage and Compute Integration
MODEL
Graph Data Model
• Lift Data into
Data Fabric
• Design Ontologies
• Connect Data
Models
ON-BOARD
Ingest & Map
• Automated ETL
• Collaborative
Mapping
• Metadata
Capture
Enterprise
Data Sources
Machine
Learning and AI
Enterprise
Search
“Last Mile”
Analytics Tools
Metadata Catalog
Semantic-based Metadata Management, Governance and Lineage
Cloud or On-Prem Data Storage Infrastructure
Data Storage Layer
Ingest
BLEND
GraphMarts
• Combine and Align
Related Data Sets
• In-memory MPP
Query Engine
• Data Layers
ACCESS
Hi-Res Analytics
• Analyze All
Data Together
• Fast, Iterative Queries
Ad Hoc, What if
• Code Free or API
Graphical Application Interface

28. Begin your journey.
Identify your
initial use case
Define the
IT/business
partnership
Quick start
deployment
4 - 8 weeks
Leverage
CSI technical
expertise /staff

29. Watch the full webinar on-demand
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