Acord 2016 Data Governance & Analyitcs - Perfect Together

ACORD2016

Data Governance & Analytics: Perfect Together!

Pat Saporito, SAP

Sandi Perillo-Simmons, The Hartford

© 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


What’s HappeningNew Business Models, Customer Experience & Value Paradigms

UberThe world’s largest taxi

company, owns no

vehicles.

AlibabaThe most valuable

retailer, has no inventory

AirbnbThe 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!

LemonadeWants 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


Living in a Digital Economy

The need to

provide

analytics to

everyone

Reduce cost

and complexityAutomate

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


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 InsurersData & 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


BI/Analytics Projects Failure CausesTechnology 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.”


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


Enterprise Analytics: Strategy to Execution

Execution

Feedback to

Strategy

Strategy

Link & AlignStrategy 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


Building Blocks of a Rock Solid Analytics Strategy

SAP Analytics Strategy Framework

ObjectivesBusiness

Needs

Business

BenefitTechnology 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

© 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


These are the top business issues

where you need better information governance

metadataefficiency

legal-hold SFAapplication-integration

SE

C

content-management regulation

business-processSOX business-user roadmap

big-data qualityaudit HANAworkflowshared-services

enterprise-standardtraceability

compliance

CRMcostorganizational-change

acquisitionanalytics

marketingsecurity

strategy

Dodd-FrankFCC

ERP

multi-

use

-technolo

gy

co

ntr

ols

mig

rati

on

risk

em

bed

ded

-UI

breach HCM conversion


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

ProcessPeople

Policies &

StandardsMetrics

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

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


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:

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

En

terp

rise

Do

ma

inP

roje

cts

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


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


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


· Constructs data assets

· Ensures system needs are met

· Implements physical model

· Optimizes performance


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?

20

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 ReportsDatabasesTables

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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http://www.google.com/url?url=http://www.envisn.com/cognos-data-management&rct=j&frm=1&q=&esrc=s&sa=U&ei=t0pbVcLBH7OwsASv4IB4&ved=0CCQQ9QEwBzgU&usg=AFQjCNEeJG0Yc_iCXBFpM7gMSdQ9-UK4KQ

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

21

The Goal: Manage data as a trusted source of information and accelerate our usage of data and information for

competitive advantage.


SAP Analytics Maturity ModelLevels of performance along the best practice framework

Level 1

Level 2Level 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


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)

© 2015 SAP SE or an SAP affiliate company. All rights reserved.

Thank you

Pat Saporito, CPCU, FIDMSr. Dir., Global COE for Analytics

SAP Labs

+1 (201) 681-9671

[email protected]

Twitter: @PatSaporito

Author Applied Insurance Analytics

Amazon http://amzn.to/1mfmYiC

Sandi Perillo-Simmons, MBA, AIDM, ACEDirector & Data Governance Lead

Enterprise Data Office

The Hartford Financial Services Group

860-547-3461

[email protected]

mailto:[email protected]

http://amzn.to/1mfmYiC

mailto:[email protected]