Slides from a webinar featuring Pekin Insurnace's Kim Wienzierl, ValueMomentum's Atanu Sarkar and Novarica's Jeff Goldberg. To view the webinar on demand visit: https://hubs.ly/H0fr5Fs0 Learn how Pekin Insurance undertook a bold and forward looking approach to ensure that their modernization initiative not only accounts for today’s data needs but also emerging needs. They did so by incorporating tools to process large sets of disparate data, gaining better insights into customers, operations, opportunities and risks. Today, they are well positioned to access new sources of data and provide improved capabilities to enable better decision making. You will learn: 1. From Novarica about industry trends and the drive for data modernization initiatives 2. Approaches for harnessing data to prepare for current and emerging needs from ValueMomentum 3. Pekin Insurance’s data transformation journey To view the webinar on demand visit: https://hubs.ly/H0fr5Fs0

2. Proprietary and Confidential to Novarica, Inc.
May not be used or distributed without our written permission.
Data Strategy for Insurers:
Key Issues and Best Practices
Jeff Goldberg
SVP, Research & Consulting​
jgoldberg@novarica.com
October 25, 2018

3. Proprietary and Confidential to Novarica, Inc.
We serve clients in life/annuity/retirement,
property/casualty, workers’ compensation,
and reinsurance. Clients range from
Fortune 100 companies to small regionals.
Although most of our clients prefer we
keep their names confidential, a partial
client roster includes Amica, GenRe, Penn
Mutual, ProSight, SECURA, SunLife, and
XL Catlin.
Our senior team has direct experience as
senior IT executives at firms including AIG,
Arbella, AXA, Guardian, Liberty Mutual,
MassMutual, Marsh, Progressive,
Prudential, Voya, and others.
Our research covers trends, best practices,
and vendors, leveraging relationships with
more than 300 insurer CIO members of our
Research Council.
Our advisory services provide on-demand
phone and email consultations on any topic
for a fixed annual fee.
Our consulting services include vendor
selection, benchmarking, project assurance,
and IT strategy development, providing
rapid, actionable insights and guidance,
delivered directly by our senior team.
More information at www.novarica.com
Introduction
Novarica helps more than 100 insurers make better decisions about technology projects
and strategy through research, advisory services, and consulting.
About Novarica

4. Proprietary and Confidential to Novarica, Inc. 3
• Defining Data
• Challenges
• Benefits
• Future of Data
• Conclusions
Overview
Today’s Webinar

5. Proprietary and Confidential to Novarica, Inc.
Defining Data
4

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• Data is the foundation for achieving innovation with digital and core initiatives.
• These key insurance process areas cannot run without data:
• Product Development
• Marketing
• Distribution
• Underwriting
• Customer Engagement
• Claims
• IT Operations
Defining Data
Data as an Asset

7. Proprietary and Confidential to Novarica, Inc. 6
Defining Data
The Physical Data Environment

8. Proprietary and Confidential to Novarica, Inc.
Challenges
7

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Limited integration can manifest in two ways:
1. Diverse Core Systems
• Often, data testing is limited or inconsistent due to these disparate data sources, and
automated test harnesses are expensive to establish.
• Automated ETL jobs are often unreliable when multiple sources of the same data are
involved.
• In addition, there is frequently no 360-degree view of the agent or policyholder.
2. Limited Agreement on What Data Assets Mean
• Many times, a data glossary or dictionary is missing. This leads to a lack of
consistency on how data is treated across systems.
Challenges
Limited Integration

10. Proprietary and Confidential to Novarica, Inc. 9
• Some of the struggles with data quality can include:
• Poor documentation around existing data architecture
• External events such as infrastructure
• Quality of legacy data rearing its head
• Lack of purpose-built analytics environments, making it impossible to analyze
unstructured content and third-party data
• The need to quality check reports before they are submitted to senior management,
limiting the effectiveness of self-service reporting
Challenges
Struggles with Data Quality and Completeness

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Challenges
Most insurer core systems and warehouses maintain data as a set of policies, limiting an
insurer’s ability to look at a 360-degree picture of its customer base.
Policy View vs. Customer View
Policies Claims Customers
POLICY VIEW CUSTOMER VIEW
Policies Claims Account
Info
Customer

12. Proprietary and Confidential to Novarica, Inc.
Benefits
11

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• Several attributes make today’s predictive analytics distinct from other BI and analytics:
• Increased complexity and sophistication of modeling techniques
• Breadth and depth of both internal and external data sources
• The use of a single “score” as the output of the analysis that can be used to guide
decision-making
Benefits
Support for Predictive Modeling and Analytics

14. Proprietary and Confidential to Novarica, Inc. 13
• Carriers have organization and process gaps on top of technology infrastructure gaps,
which can prevent an effective execution of a data strategy.
• Data strategies often recommend establishing the role of a Chief Data Officer (CDO)
who does not report to IT.
• If a carrier has a CDO, he or she often reports to the CEO or COO and owns the
execution of the roadmap and deployment of the blueprint specified in the data
strategy.
• The CDO or other data owner interacts with senior executives of the company including
the Chief Underwriter, Chief Actuary, Chief Claims Officer, and CIO to coordinate
investments and make sure projects align with the data strategy.
• Other key roles identified in a data strategy include data scientists, data architects,
project managers with data project experience, and ETL experts.
Benefits
Alignment of People, Process, and Tech

15. Proprietary and Confidential to Novarica, Inc. 14
• Elements of a data testing strategy include:
• Establishing a data test environment,
• Creating an automated test harness for data output and report testing, and
• Validation of the ETL processes around data.
• Enterprise tools such as a data glossary and data dictionary help with achieving an
understanding of the data elements, their definition, as well as constraints on their
usage from regulations.
Benefits
Resolution of Issues with Data Completeness and Accuracy

16. Proprietary and Confidential to Novarica, Inc.
Future of Data
15

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Emerging Technologies Are Data Technologies
Most emerging technologies are about data, whether capturing, storing, or insight. But
all of these technologies are evolving simultaneously.
Future of Data

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Emerging Technologies Have Multiple Phases of Maturity
Insurers need to lay a groundwork now in order to be able to capture full value in the
future. But this doesn’t mean instant mastery of any particular technology.
Future of Data

19. Proprietary and Confidential to Novarica, Inc. 18
• Significant gaps exist between current carrier data capabilities and business users’
expectations of how and where data can be used.
• The key to successfully using data is to have an owner of the data strategy responsible
for its execution and reevaluation as technology and business challenges evolve.
• Data strategies are necessary to prioritize investments and align organizations.
• Data strategies require both functional and IT personnel to assess current capabilities
needs.
• While carriers do need to plan for the long term, they should periodically reevaluate
their data strategy.
• Insurers should be looking at how emerging technologies will influence and change
today’s data strategy investments.
Concluding Thoughts

20. Proprietary and Confidential to Novarica, Inc. 19
Novarica New Normal 100: Digital, Data,
and Core Capabilities for
Property/Casualty Insurers
Novarica New Normal 100: Digital, Data,
and Core Capabilities for Life/Annuity
Insurers
Master Data Management in a Big Data
World
Data Science and Actuarial: Managing
Potential Conflict
Contact Jeff:
Jeff Goldberg
jgoldberg@novarica.com
833-668-2742 Ext. 117
Contact Novarica:
inquiry@novarica.com
617-342-8100
www.novarica.com
www.linkedin.com/company/novarica/
For further reading: For further discussion:
Appendix
Additional Information

21. novarica.com

22. Data Platform
Modernization:
A Point of View
Data Platform
Modernization:
A Point of View

23. VALUEMOMENTUM – KEY FACTS
Fastest Growing P/C IT
Services Provider in
North America
Top 10
NA P/C IT Svs Provider
by # of customers
15 > 5
year client relationships
90+
Customers Served
2150+ Employees
23%
CAGR since inception
Digital & Cloud
IT Ops
Launch,refresh products
Update, upkeep Biz Apps
Future / Strategic Focus
Day to Day Operational Focus
EmployeesCustomers
Quality Engineering
Quality Engineering
Data&InsightsData&Insights
OUR CUSTOMERS’ GOALS

24. 23
Harnessing data has a transformational value for driving smart
actions
Level 1 –
Reactive
 Only internal
structured dataset is
available for the
business to make
decisions
 Data is used
reactively
 No data lake
Level 2 -
Informative
 Centralized
Structured data
management &
Analysis
 Informs business
 Collect
unstructured data
in a repository for
future analysis
Level 3-
Predictive
 Data capture is
comprehensive and
provides ability to
run business based
on datasets
 Leads business
decisions based on
advanced analytics
 Transform
unstructured data in
batch for reporting
& analysis
Level 4 -
Transformative
 Data transforms
business to drive
desired outcomes
 Any data, any
source, anywhere at
scale
 Establish
enterprise-wide
data lake and run
analytics on
unstructured data
that arrives in real
time
Maturity
Timeline
Source: Microsoft

25. Key steps for a data platform modernization initiative
1 Identify Major
Components
2 Prioritizing the Capabilities
for Continuous ROI
3 Realizing the Business
Value
4 Solution Realization

26. Major Components
DATA SOURCES
POLICY
ENTERPRISE DATA
CLAIMS
UNDER WRITING
PRODUCTS CUSTOMER
ACCOUNTING
D&B,
LEXUS NEXUS
EDUCATION
GOVERNAMENT
AGENCIES
CREDIT HISTORY
PERMITS, LICENSES
LOCATIONS
CRIME
EXTERNAL DATA
WEATHER
SOCIAL MEDIA
TELEMATICS
TRAFFIC DENSITY
BIG, IOT DATA
LOGS, CLICKSTREAM
OPEN SOURCE
ADVANCED ANALYTICS
Prescriptiv
e
PredictiveDiagnosticDescriptive Augmente
d
Real-time
POLICY, PROCEDURES, AND PROCESS
(BUSINESS & IT)
BI SERVICES
DW TRANSFOR
M
REPORT
PORTAL, INTRANET AND
MOBILE ACCESS
ALERTS / NOTIFICATION
BUSINESS
ACCESS
DATA ANALYSIS AND
VISUALIZATION
BI, REPORTS, DASHBOARDS,
BUREAU REPORTING
TECHNOLOGYFile System Relational RPYTHON
INFORMATION MANAGMENT
DATA
STORE
DATA
IDENTIFICATION
DATA
GOVERNANCE
DATA
PROCESS
DATA
PROVISION
BIG
DATA
SMALL
DATA
STRUCTURED, SEMI-STRUCTURED,
UNSTRUCTURED - INTERNAL / EXTERNAL
DATASETS
DATA
INGESTION
ENTERPRISE DATA HUB

27. Modularize
Repeatable
processes
Automate
Identify
Support
Model
• Organize deliverables into logical
capabilities
• Deploy frequently, iterate, harvest gains
• Leverage Agile and DevOps
• Identify repeatable processes
• Standardize them
• Review with business and
make sure they are essential
• Study internal and external
best practices to identify a
feasible support model.
• Leverage standardized
support model, that are
proven to improve output over
time
• Leverage AI and metadata data to
automate the repeatable processes
• Created Augmented Data Processing
Prioritizing Capabilities For Continuous ROI

28. Realizing Business Value and Outcomes
TARGET BUSINESS
OUTCOMES
KEY ACTIONS KEY SUCCESS
FACTORS
Excellent Data
Quality and Data
accuracy
Business owns the
data and tracks data
as an asset
Complete alignment
of stakeholder
Democratized Data
for the business use
Complete frontline
adoption and removal
of unwanted
platforms
Trusted data
available to the
business when
making decisions
Reduced time to
market and rapid
digital solution
deployments
Integrate analytics
solutions into
workflow
Ability to articulate
analytics inference
and business value
Data platform modernization is hard work. However,
with a razor sharp focus on key success factors and
deliberate actions, insurers can realize target
business outcomes consistently and rapidly

29. Hallmark of Successful Implementations
KEY FACTORS APPROACH
Strategy • Make the business your strongest champion by defining the
future business capabilities.
• Define and plan milestones so that the organization sees
frequent visible results
Architecture • Leverage proven architecture, frameworks, methodologies and
delivery processes to accelerate the architecture deliverables
Platform and
Technology
• Identify and select technologies taking into account:
• maturity of the technology infrastructure and its ecosystem
• strong user community and availability of ample resources on
the chosen technology stack
• incremental approach to the desired target platform
Data Governance Perhaps the strongest measure of a successful implementation – no
compromises here
Source Data Choose the data sets that map to the business capability road map

30. Atanu Sarkar,
Vice President, DataLeverage
ValueMomentum
www.valuemomentum.com
(925) 984-3254
atanu.sarkar@valuemomentum.com

31. Data Modernization
Kim Wienzierl
October 2018

32. Kim Wienzierl
• Title: Assistant Vice President – Data Management, Program Management and
Infrastructure @ Pekin Insurance
• Speaker bio: Kim Wienzierl has been at Pekin Insurance for the past 2
years Prior to that she was the Data Champion and Enterprise Information
Management (EIM) Division Manager at Caterpillar. She has 32 years of
experience in all aspects of IT and holds a CPBI (Certified Professional of Business
Intelligence) from Villanova University. She has worked with some of the best
data leaders known to the industry and has successfully executed their concepts.
I am a practitioner of the theories
and approaches from world renowned
data enthusiasts and celebrities.

33. Pekin Insurance
• Life, Health, Auto, Home, and Business insurance carrier
located in Pekin, IL
• Sell core lines in 6 states and Life in 15 states
• Started in 1921
• 900,000+ policyholders through an Independent Agency
system of over 8,500 independent agents
• 900+ employees in 2 main locations

34. A P/C Insurance Data Modernization Journey:
Learn from Pekin Insurance's success
 How to approach the task of modernizing companies that have
neglected their focus and funding on data advancements.
 Develop plans to transition from a legacy environment into a
modern data platform at the same time covering the 8 elements
of data governance.
 Execute!

35. What does your data look like?
Unclaimed library somewhere in the world Peabody Library @ John Hopkins University
Measure your Data Quality and Maturity Level!
Define your future state business capability.
Measure It

36.  What is data?
 What is the burning platform? Why now?
 What are some specific current examples
with ROI?
 How are we going to do this?
 How are others doing this?
 What does it cost?
 How long will it take?
 When will we be DONE?
 How will you measure success?
Frame It

37. Sell It
1. Find friends it’s free, sort of ~ Establish a Data Governance Board
(DGB)
2. Grow excitement  develop a LOGO and make buttons, decals, cups, t-
shirts and table tents and hit the internal mass media ~ Set the tone
3. Educate the DGB on data quality build data evangelists
4. Brainstorm & affinitize data quality issues bottom up which leads to
some urgent top down
5. Glom onto an existing transformational program or build your own
data program
6. Play with stuff  spin up a free Hadoop cluster, test drive metadata
tools, look at universal data models, attend Novarica webinars with
your new data evangelists
7. Put deep thought into how long, how much, and what you need then
convert to $$$$$ (data celebrities & Novarica can help with this)
8. Build an executive level presentation with your Measure It/Frame
It/Sell It all the while dripping on your supporters

38. Option #1
------------
Do It Yourself
Option #2
------------
Seek Help
Big company ~ small
names
Or
Big names ~ small
company
Or
Big names ~ big
company
- Do you have support?
- Do you have the
knowledge?
- Do you have the
resources to assist?

39. Develop a Data Management Framework

40. Develop an Elevator Speech

41. Create a placemat

42. Create a placemat (cont.)

43. Create a roadmap – business capability

44. Build the Data Management Team
Data Sheriff
Scrum Master
Solution Architect
ETL Developer
Quality Analyst
ETL Developer
ETL Developer
ETL Developer
Data Analyst
Data Base Administrator
Business Analyst
ETL Developer
ETL Developer
Data Marshall
Scrum Master
ETL Developer
Data Analyst
System Administrator
Business Analyst
Quality Analyst
Data Analyst
Data Judge
Scrum Master
Solution Architect
Business Analyst
Data Modeler
Data Engineer
Data Ranger
Scrum Master
Business Intelligence Lead
System Administrator
Data Architect
Data Analyst
ETL Developer
Data Warden (Kanban)
Kanban Master
ETL Developer
Data Analyst
ETL Developer
ETL Developer

45. Data Model

47. Next Steps
• Continually mature and grow the data platforms
• Move to the cloud
• Measure data as an asset

49. Questions
Join our next webinar:
Learn how Pekin & NJM Insurance drove quality with speed in
their core implementations
Register at: resources.valuemomentum.com/getquality