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ACORD2016
Data Governance & Analytics: Perfect Together!
Pat Saporito, SAP
Sandi Perillo-Simmons, The Hartford

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© 2015 SAP SE or an SAP affiliate company. All rights reserved. 2Public
Data Trends & Growth
Analytics & Data Strategy Framework
Business Value of Data & Data Governance
The Hartford Case Study
Key Takeaways
Agenda

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© 2015 SAP SE or an SAP affiliate company. All rights reserved. 3Public
What’s Happening
New Business Models, Customer Experience & Value Paradigms
Uber
The world’s largest taxi
company, owns no
vehicles.
Alibaba
The most valuable
retailer, has no inventory
Airbnb
The world’s largest
accommodation provider,
owns no real estate.
LEADERS REQUIRE A VISION TO LEAD IN THE DIGITAL ECONOMY
DATA & ANALYTICS ARE THE OIL OF THE DIGITAL ECONOMY!
Lemonade
Wants to become the
world’s first peer-to-
peer property and
casualty insurance
provider
Meteo Protect
Offers a new type of insurance
to meet the needs of those
affected by climate change
and adverse weather
conditions
Discovery
Uses thousands of analytic
models to classify and
underwrite risk, adjust claims
and help improve member’s
health and wellness

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© 2015 SAP SE or an SAP affiliate company. All rights reserved. 4Public
Living in a Digital Economy
The need to
provide
analytics to
everyone
Reduce cost
and complexity
Automate
analytics in the
age of the
algorithm
Travel in light
years with
enterprise
speed and
scalability
HYPERCONNECIVITY IS DRIVING NEW CHANNELS AND CONNECTED ECOSYSTEMS
DATA AND ANALYTICS ARE THE OIL FOR THE DIGITAL ECONOMY
90%
of the world’s data
has been
generated.1
72%
of insurers are
forming new
distribution
partnerships2
212 Billion
“Things” will be connected 3
43%
of insurers plan to or have
acquired
innovators/startups for new
innovation capabilities2

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© 2015 SAP SE or an SAP affiliate company. All rights reserved. 5Public
Digital core
Omnichannel
network
The Internet of Things
network
Service and supplier
network
Workforce network
Front
office
Middle office Back office
SAP HANA platform
Property Life Health
Real Time Data & Analytics
Digital Framework for Insurers
Data & Analytics is the life blood of the Digital Insurer
Personalized experience
Reduce FTE costs
Engaged partners & suppliers
Optimized risk management
Operational AgilityDigital Orchestration Full analytic transparency
Transactional & Analytic Data Platform

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© 2015 SAP SE or an SAP affiliate company. All rights reserved. 7Public
BI/Analytics Projects Failure Causes
Technology is only One Consideration
Between 70% to 80% of corporate business intelligence/analytics projects fail (to meet
expectations), according to research by analyst firm, Gartner.
“Organizations tend to throw
technology at BI problems. You could
have the right tool, but it could be
doomed to failure because of political
and cultural issues, an absence of
executive support so the message
doesn't get out, and poor
communication and training.”

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© 2015 SAP SE or an SAP affiliate company. All rights reserved. 8Public
Data Challenges
Challenges
• Data is everywhere
• Multiple data types
• Data integration
• Analytic tools
• Analytic skills
Impact
• Data silos
• Data credibility
• Data access
• Lack of data use
• GIGO – “Junk” analytics

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© 2015 SAP SE or an SAP affiliate company. All rights reserved. 9Public
Enterprise Analytics: Strategy to Execution
Execution
Feedback to
Strategy
Strategy
Link & Align
Strategy to ExecutionBusiness Driven
Strategically Aligned
User Access & Usability
Improved Decisioning
Analytics
Strategy &
Roadmap
Corporate
Strategy
New Data /
Capabilities
Real Time
Analytics
Operational
Reporting
Exploration &
Visualization
Predictive
Analytics
Ad Hoc
Reporting
Corporate Data
Strategy &
Metrics
Performance
Management
Governance, Risk
& Compliance
Business
Strategy
Analytics
Strategy
Data
Strategy

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© 2015 SAP SE or an SAP affiliate company. All rights reserved. 10Public
Building Blocks of a Rock Solid Analytics Strategy
SAP Analytics Strategy Framework
Objectives
Business
Needs
Business
Benefit
Technology Organization
Background and
Purpose
Current State and
History
Analytics
Objectives and
Scope
Summary of
Analytics needs
Envisioned To-Be
State
Priorities and
Alignment
Value Proposition
of Analytics
Expected Benefits
– Future State KPI
Business Case
Information
Categories
Architecture and
Standards
Applications &
Tools
Governance
Structure
Program
Management
Roadmap and
Milestones
Measurement
Education /
Training
Support
Critical Success Factors:
• Business driven analytics strategy, supported by a data strategy
• Executive sponsorship

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© 2014 SAP AG or an SAP affiliate company. All rights reserved. 11
Why is it important to manage your company’s data
effectively?
Poor data quality
is a primary reason for
40%
of all business initiatives failing
to achieve their targeted benefits
Gartner, Inc., Measuring the Business Value of Data Quality, October 2011

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© 2014 SAP AG or an SAP affiliate company. All rights reserved. 12
These are the top business issues
where you need better information governance
metadata
efficiency
legal-hold SFA
application-integration
SEC
content-management regulation
business-process
SOX
business-user roadmap
big-data qualityaudit HANAworkflow
shared-services
enterprise-standard
traceability
compliance
CRMcost
organizational-change
acquisition
analyticsmarketing
security
strategy
Dodd-Frank
FCC
ERP
multi-use-technology
controls
migration
risk
embedded-UI
breach HCM conversion

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© 2014 SAP AG or an SAP affiliate company. All rights reserved. 13
What is Information Governance?
Information Governance
A discipline that includes people, processes, policies, and metrics for the oversight
of enterprise information to improve the business value
High Value Information
Optimized Business Processes
Smarter Business Analytics
Timely Mergers and Acquisitions
Compliance with Laws and
Regulations
Process
People
Policies &
Standards
Metrics

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Copyright © 2016 by The Hartford. All rights reserved. No part of this document may be reproduced, published or posted without the permission of The Hartford.
DATA GOVERNANCE: The Hartford Case Study

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Copyright © 2016 by The Hartford. All rights reserved. No part of this document may be reproduced, published or posted without the permission of The Hartford. 15
Recognition of Need for Enterprise Data Management
Premise: Data is an important strategic asset that must be effectively leveraged to support growth strategies and
expense management objectives. The complexity of our data is high due to siloed legacy systems and data
repositories.
Internal Forces
• Business strategies & investments with significant
data requirements
• “Siloed” legacy systems and data repositories
• Cost/time to complete projects within the current
environment
External Forces
• Regulatory reporting requirements
• Heightened compliance environment
• Data driven companies continue to use data to enter
markets and dominate
• Customers & producers expect high-quality service and
increased value
Enterprise Data Management must be a multi-faceted approach to address our data challenges which includes:
 Understanding business information needs (strategies, competition, data pain points)
 Understanding and improving the deep information (ever-changing) landscape (quality, tools, governance)
 Supporting information-centered business strategies
Targeted Outcomes:
 Common data definitions
 Easy access to consistent, high-quality data
 Enterprise view of data
 Standards and guiding principles
 Enterprise planning of data initiatives
Benefits Expected:
 Increase revenue
 Manage Cost and Complexity
• Improved portfolio implementation
• Eliminate data reconciliation efforts and work-arounds
• Reduce operational and defect management costs
 Reduce Risk and Support Corporate Compliance

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Copyright © 2016 by The Hartford. All rights reserved. No part of this document may be reproduced, published or posted without the permission of The Hartford. 16
Data Management Policy
Scope: The policy applies to “structured data” only meeting the following criteria:
Policy Topic Description of Policy
Identification & Definition of New Business Data • New data must be named and defined according to standards
Enterprise Critical Data Requirements: • Critical data must be identified, defined, and named
• Employees must ensure that data integrity is maintained
Data Governance & Compliance • Business Data Standards must be followed
• Data Certification will be required
Documentation of Data Issues & Problems • Data issues must be documented
• Data Stewards will be the primary contact to remediate and escalate documented data
issues
Data Artifacts • Projects and maintenance efforts must complete the appropriate artifacts as defined by the
engagement model and weDeliver methodology
Enterprise Metadata Repository • Metadata must be captured and uploaded into the enterprise tool
• Metadata must be maintained for high quality
Data Type Data Type Description Compliance Timeframe
Enterprise
Critical Data
Data that is integral to The Hartford’s most critical business
functions
3 months from identification
Domain
Critical Data
Data that is identified as being critical to a domain’s primary
business functions
6 months from identification
New Data Data that is being introduced to Hartford systems for the first time At implementation when new data is being introduced to
Hartford systems for the first time
Existing Data Data that exists in a current Hartford system that will now be stored
on a new system or a rewritten version of the existing system.
At implementation when existing data is stored on a new
system or a rewritten version of the existing system.
The policy includes roles, responsibilities and the following policy topics:

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DATA GOVERNANCE FRAMEWORK
Executive Leadership Team (ELT)
Enterprise Data Governance Council (DGC)
Third Party Data (3PD) Enterprise Data Stewardship
• MANDATE & AUTHORIZATION
• CORPORATE OWNERSHIP
• FIDUCIARY RESPONSIBILITY
• REGULATORY RESPONSIBILITY
Chief Data Officer
Business Domain Owners
• AUTHORITY & ACCOUNTABILITY
• DIRECTION SETTING
• STANDARDS COMPLIANCE
• POLICY SETTING
• DATA OWNERSHIP
• PROCUREMENT
& DELIVERY
• RELATIONSHIP
MANAGEMENT
• STANDARDS
COMPLIANCE
• POLICY
DEVELOPMENT
3PD Review Team
3PD Relationship
Managers
• OVERSIGHT OF
DATA DOMAINS
• STRATEGIC
LEVEL
• STEWARDSHIP
INITIATIVES &
DECISION
MAKING
Data Stewardship Council
(DSC)
Domain Data Stewardship
Group (DDSG)
Architecture Review Board (ARB)
Project Teams
• STANDARDS
COMPLIANCE
• PROJECT
ACCOUNTABILITY
• PROJECT
ACCOUNTABILITY
• DATA-CENTRIC PROJECT
EXECUTION
• DEVELOPMENT &
DELIVERY OF CORE
ARTIFACTS
Collective Membership Project Architects
Data Stewards
Business SMEs
Database Admins
App Developers
Business Analysts
Data Modelers
Data Architects
Business & Technical Leaders
IT Consultation
Domain Data Governance Council (DDGC)• DOMAIN DATA
ACCOUNTABILITY
• DAY TO DAY DIRECTION
• OVERSIGHT TO DATA
GOVERNANCE FUNCTIONS
Cross-Functional Domain Leadership
Domain Data Stewards
Data Owners
Lead Data Stewards
EnterpriseDomainProjects
Project Governance
ENTERPRISE
STRATEGIC
DATA SERVICES
(ESDS)
Program
Management for:
• Data Governance
• Data Stewardship
• Data Quality
• Metadata
Advisory
Project Scores
Advisory
Advisory /
Chair
Support
Advisory /
Chair
Support
-----------------
Support for:
• Data Management
Policy
• Enterprise Business
Data Standards
• Variance Request
Process &
Repository
• Data Management
Scorecard
• Critical Data
Element Lists
• Decision-Making
Models
• Integration with
SDLC

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Copyright © 2016 by The Hartford. All rights reserved. No part of this document may be reproduced, published or posted without the permission of The Hartford. 18
Metrics
Strategy
Business
Factory
How
Is data
impacting
results ?
How is business leveraging
data?
How is our Data
Factory operating?
• Improvements in UW results
• Increased customer retention and
up-sell
• Reduced claim fraud
• Sales force effectiveness
• Improved producer results
• Faster executive reporting
• Data Confidence
• Business Ready
• Data Protection
• Data Consumption
• Information Usage
• Time to deliver
• Throughput
• Availability
• Data Quality
• Regulatory &
Compliance

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Copyright © 2016 by The Hartford. All rights reserved. No part of this document may be reproduced, published or posted without the permission of The Hartford. 19
Data Management Operating Model
· Data Mapping
· Overall Requirements
· Consults on all artifacts
· Sets and champions broad
policies
· Enforces execution
· Approves exceptions
· Publishes score results
· Has long-term ownership
· Grows steward community
· Scores data assets
· Reports to governance
· Certifies business data
· Maintains business
metadata
· Constructs data assets
· Connects to enterprise
· Solves system data needs
· Follows stewardship guidance
· Maintains technical metadata
· Designs data movement patterns
· Constructs logical model
· Profiles and maps data elements
· Follows design standards
· Maintains technical metadata
· Constructs data assets
· Ensures system needs are met
· Implements physical model
· Optimizes performance
· Maintains technical metadata

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Copyright © 2016 by The Hartford. Confidential. For internal distribution only. All rights reserved.
No part of this document may be reproduced, published or posted without the permission of The Hartford.
What is Metadata?
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Type Who uses it? Metadata is used to:
Business Definitions / Glossary Everyone Build a common language
Data Ownership Everyone Know which people make decisions and answer questions
Databases and Tables Analysts, IT Find data
Data Lineage Analysts, IT Understand movement/transformations and for impact analysis
Valid Values Everyone Understand what data should be
Data Models Analysts, IT Understand data design
Business and Data Quality Rules Analysts, IT Create confidence in data
Known Issues Analysts Avoid and/or correct problems
Definitions /
Glossary
Owners ReportsDatabases
Tables
Data
Models
Valid
Values
Data Quality
Rules
Metadata Repository
Data
Lineage
Metadata is data about data. It summarizes basic information about data, which can make
finding and working with particular instances of data easier.
Known Issues

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Copyright © 2014 by The Hartford. Confidential. For internal distribution only. All rights reserved.
No part of this document may be reproduced, published or posted without the permission of The Hartford.
Current Focus: Enhanced Governance from
Source to Use with Improved Business
Ownership and Accountability
Outcomes Initiatives
Data-Driven Culture • Shifted focus from project-driven to data asset-driven
• Improved business ownership and accountability
Data Strategy /
Roadmap
• Creation of a data strategy and roadmap / Clear sense of purpose
• Determination of key data initiatives that can transform our company
• Coordination of capital projects and alignment/compliance to a reference architecture
• Alignment of Data Governance
Authority • Improved authority to govern
• More local governance
Resources • The right people with the right skills (including data owners)
• Funding
Improved Execution • End-to-end application and right-sized from source through use
• Faster, improved execution / agility
• A scalable governance & stewardship model (Gold, Silver, Bronze)
Quality Data and
Information
• Well-defined data quality framework
• Next generation data quality practices: Tools, automation, continuous monitoring for critical data
• Resolved data issues
Recognized Data Value • Communication to “sell” the value of governance & stewardship
• Training and education to improve data literacy
Measurable Success • Metrics that show how our data efforts help us achieve our business goals.
• Data Quality dashboard
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The Goal: Manage data as a trusted
source of information and accelerate
our usage of data and information for
competitive advantage.

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© 2015 SAP SE or an SAP affiliate company. All rights reserved. 22Public
SAP Analytics Maturity Model
Levels of performance along the best practice framework
Level 1
Level 2
Level 3
Level 4
IT Driven Analytics
Governance
Requirements driven
from a limited
executive group
KPIs/Analytics are
identified, but not
well used
KPIs/Analytics are
identified and
effectively used
KPIs/Analytics
used to manage the
full Value Chain
Business Driven
Analytics
Governance
Evolving
Business Governance
with Competency
Center Developing
Enterprise-wide
Analytics Governance
with Business
Leadership
Do not exist or are
not uniform
Exist and are
not uniform
Uniform, followed
and audited
Analytics “Silos”
for each
Business
Some Shared
Analytics
Applications
Consolidating and
Upgrading
Robust and flexible
Analytics
architecture
Evolving effort to
formalize
Information and
Analytics
Governance
Standards and
Processes
Application
Architecture
Information
Chaos
Information
Oversight
Information
Democracy
Information
Empowerment

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© 2015 SAP SE or an SAP affiliate company. All rights reserved. 23Public
IDMA Workshops
www.idma.org
IDMA offers the following One-Day Training Workshops for insurance industry
professionals:
 Claims Data Management (ClaimsDM)
 Data and Analytics Strategy (D&AS)
 Data Management for Insurance Professionals (DMIP)
 Data Governance and Stewardship (DG&S)
 Enterprise Data Management (EnterpriseDM)
 Insurance Data Quality (IDQ)
 Strategic Data Management (StrategicDM)
 Tools for Managing Data Effectively (TMDE)

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© 2015 SAP SE or an SAP affiliate company. All rights reserved.
Thank you
Pat Saporito, CPCU, FIDM
Sr. Dir., Global COE for Analytics
SAP Labs
+1 (201) 681-9671
pat.saporito@sap.com
Twitter: @PatSaporito
Author Applied Insurance Analytics
Amazon http://amzn.to/1mfmYiC
Sandi Perillo-Simmons, MBA, AIDM, ACE
Director & Data Governance Lead
Enterprise Data Office
The Hartford Financial Services Group
860-547-3461
sandi.perillo-simmons@thehartford.com