Data and Analytics Journey Discussion

Document for Nationwide

February 2017

Draft for discussion

Agenda

THE IMPETUS FOR DATA & ANALYTICS IN INSURANCE

2

Copyright © 2016 Accenture All rights reserved.

The Insurance landscape is evolving

More informed, tech-savvy and

price-sensitive customers with

high service expectations

New, innovative products are

disrupting established markets

New distribution models are

forcing the reassessment of

traditional agent models

72% Connected

48% Social

52% Online

64% Price-sensitive

68% Self-directed

Usage-based

Pricing

Innovative Products/Services Evolving Customers New Channels

Behavior-

based Pricing

Cyber-threat

Insurance

Loss Mitigation

Products

Real-time Life

Pricing….

Direct to Customer

P2P Insurance?

Insurance Auction?

3

1990s 2000s Future Trends

Internal – few

external sources

Structured

Very large,

unstructured, multi-

source e.g. sensors

Seamless combination

of internal and external

data

Months (batch) Weeks and DaysNear real-time

insights

Structuring internal

data for analysis

Structuring data for

descriptive &

predictive analysis

Systematically integrate

signals and insights into

our processes

Static reporting Dynamic Drill-down Role-based, contextually

relevant actionable insight

Big DataTraditional Analytics Data Economy

Data

Latency

Consumption

Analysis

Improve speed to

capability

Prove value

incrementally

Improve business

outcomes quickly

Empower innovation

and experimentation

Why It Matters?

The possibilities with data and analytics has changed dramatically

4

Analytics is at the core of the disruption in the industry from new

entrants as well as responses from incumbents

Pricing/

Negotiation

Coverage and

Product

Source: Accenture Research

Peer-to-Peer broker based on a

share-economy approach;

policyholders with the same

insurance type form small

groups and earn rebates

Bought By Many allows

customers to join a group and

purchase discounted insurance

from large carriers

A smartphone app to receive

bids and auto repair

estimates from local auto-

body shops

Risk

EvaluationClaimsDistribution Servicing

Pay as you go

insurance

Examples of

FinTech/

Digital Player

disruption

Examples of

Incumbent

initiatives

Predictive analytics with

machine learning to identify

inter-relationships between

various risk attributes

Used by carriers to engage

with prospects via websites,

email campaigns, and social

networks (currently used by

NY Life, Farmers, AXA,

Pacific Life)

Customers manage policies,

certificates, as well as get rate

comparisons and claims support.

App based on-demand insurance

platform providing advice and

products for short-term coverage for

household appliances

Risk and insurance

management platform using

predictive analytics (XL

Innovate investment)

Spixii is an automated insurance

agent, powered by artificial

intelligence, that engages

Clients via mobile/text

Using ecosystem of

partners (telecoms,

retailers) to offer

microinsurance product

solutions (accident,

disability, credit life) for

low-income families

Fully digital online

insurance broker using data

driven analytics to develop

tailor made products for

individual SMEsSimply Business

Carrier investments in

technologies to personalize

the claims process from

FNOL to payment5

How Insurance Carriers are responding to these trends in Analytics

Organizations are exploring new, creative ways to organize, source, and deploy analytics

capabilities to accelerate speed to capability, pace of adoption and speed to value.

Enterprise Analytics

Operating Models Chief Analytics Officers

Innovation Labs

Multi-Pronged Approach to

Talent

Data Discovery & Data

Science Capabilities

Immersive Data

Environments

Flexible Analytics Capacity

Models

Analytics Services

@ Scale

6

Investments are paying off in the form of the business impact from

leveraging data-driven insights

Auto InsurerUsage-based Pricing

10+ billion driving miles, more

captured every second

Competitive rates

Low claims ratio

Life InsurerCross-sell

Leveraging cross-business-unit

view of households across the

insurance & asset businesses

Higher household penetration

Life InsurerUnderwriting

More precise risk estimates

using big data (e.g. clinical,

drug & customer history)

Optimal rates

P&C InsurerUnderwriting

Combining live weather data

with historical patterns to

optimize catastrophe loss rates

Optimal rates

Medical InsurerFraud

Leveraging extensive data

(demographics, policy, surveyor,

provider etc.) to identify fraud in

accident portfolio

5% reduction in loss

7

Agenda

PRIORITY TOPICS FOR NATIONWIDE

8

Our understanding

1. Establish a common vision and strategy for analytics

among the leadership team

2. Current Business Model Opportunities – assess gaps

and opportunities for Nationwide to utilize analytics for

value

3. New Business Model Opportunities – explore new

business models and data monetization opportunities

analytics could open up for Nationwide

4. Operating Model and Talent Strategy – design the

operating model to support the vision with a view of the

evolution as the maturity increases

5. Data Architecture – determine the right architecture,

governance and data approach to enable the

capabilities

• The insurance industry is in a state of evolution as new

disruptors are challenging traditional business models,

new data sources are emerging and the sophistication

of analytic tools is dramatically improving

• Internal data sources at Nationwide are not being fully

utilized and many external data sources are still

untapped

• Nationwide is seeking to align as a leadership team

around a clear vision for its analytics capabilities

• Understanding the Data strategy and Operating model

to execute the vision is critical for Nationwide to

understand as the continue on the journey to become

a leading analytics company

The context Nationwide objectives

9

Observations from the Industry on these five primary components

Current Business Model

Opportunities

Future Business Model

Opportunities

Operating Model and

Talent Strategy

Analytics/Data

Architecture &

Information Mgmt

• Intelligent Automation (using RPA + Machine-learning + AI) applied to Underwriting, Claims and Servicing

• Prescriptive Customer Management enabled through Customer 360 (external and internal), micro-segmentation and

curated experimentation specially in the digital channels

• Lead and Distribution optimization (focused on 360 views, external data and propensity scoring)

• Superior Fraud and Premium Mgmt (leveraging unstructured data and complex algorithms)

• New Products/Services (e.g. supporting sharing economy, digital risks/threats, connected lives, autonomous/AI-based

services, life-style based offerings, etc.) – directly or through investments in start-ups

• Insuring personal items through cognitive recognition with real time pricing

• Data Monetization through targeted Cross-Industry Platforms/Capabilities

• Appointment of CDOs/CAOs to drive Enterprise-level Agenda

• Hub-and-Spoke deployment; New roles (Bridge role, Visualization Engineer, etc.); Rapid up-skilling

• Multi-pronged approach to Talent Mgmt – organic, vendor partner and academia

• New “Business-Data Science-IT” engagement models reflecting iterative value identification and capture

• Innovation Labs/Data Discovery environments to rapidly test new ideas; Immersive environments

• Hybrid Architectures (Big Data + Legacy) to support traditional and emerging (e.g. real-time, IoT ,Drone-based) use-cases

• Tiered Data Governance to support multiple “classes” of data; Business-driven Governance models

• Machine Learning-based Data Cleansing; Use-case driven Data Mgmt Uplift (vs large data programs)

• Cloud-based Environments to support external/internal data, enable scale and to deploy AI-based capabilities

• Democratized but Controlled Data Access through Insights marketplace

Vision and Strategy

• Embedding Data and Analytics as a key component of the overall company strategy

• Making key strategic tradeoffs on what you want to be in data and analytics (e.g. Innovator vs. fast follower)

• Creating momentum by piloting with the intent to scale rather than making big bets on specific ideas

• Embedding the vision into the culture of the organization to drive the behavior change in front-line decision making

10

Data & Analytics FrameworkBased on Nationwide’s priorities for their Data and Analytics Strategy, we suggest starting with defining

the vision and strategy….and then drive the journey forward

Vision & Strategy

Identifying and prioritizing use cases for analytics

to improve the existing businesses across the

value chain and defining the capabilities required

to execute

New business models enabled through

analytics to respond to disruptive models in

the industry or New Data Monetization

opportunities to create new revenue sources

The people, their skills and experience, and the

organizational structure needed to support

analytic transformation

Current Business Model Opportunities

2

Future Business Model Opportunities

3

1

Defining the overall vision, priorities and journey for analytics across the enterprise along with associated

value proposition

The capabilities to identify and prioritize the

specific data elements to be extracted,

integrated, processed, and managed

Operating Model & Talent Strategy4 Data Architecture and Information

Management

5

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11

Goals

Philosophy

Well-defined Business Case Required

Adopt a prototyping to industrialization approach.

Prove the value before industrialization

Pilot with Intent to Scale

Target capabilities/ use cases for advanced analytics

well defined. Goal is to ‘be the best at a few things”

Focused on a Few Areas of Value

Broad focus of potential value across the company, goal

would be to embed analytics across the entire org

Focus across the Entire Value Chain

Initial focus on building the right capabilities not on

making he return numbers right away

Focused on Capability over Return

Significant upfront planning, design and development

Difficult to pivot to new ideas

Big bets on Analytics Uses Cases

Making some strategic tradeoffs

Areas of Value

Analytics will be a differentiator for us in the market and

we will invest to be the best

Competitive Differentiator

Strategic PriorityBeing “as good as” competitors is the goal

rather than fall behind

Priority, but not a Differentiator

Embed analytics in fact based decision making and

see it as a component of the culture

Data-driven Decisions as a CultureCultureData and analytics better understood in the

organization but not core part of the culture

Incremental Change, not Cultural Shift

Investment will be made with a clear expectations

of return in the short term

Define the strategic tradeoffs for Data and Analytics at Nationwide making some explicit tradeoffs of

what Nationwide wants to become

1 Vision & Strategy

12

The potential impact from analytics in the existing business is

significant

$107-210M(10-21%)

Lower Customer Service

Expenses

$14-17M(1.3-1.6%)

$14-17M(1.3-1.6%)

Underwriting and Pricing

Lower Claims Costs

$17-25M(1.6–2.4%)

Anti Fraud

$25-28M(2.3–2.6%)

Higher Cross Sell and Up Sell Rates

$21-29M(2–2.7%)

Increased Customer Retention

$36-47M(3.4-4.4%)

Higher Product Penetration

$41-55M(3.8–5.2%)

Lower Customer Acquisition

Costs

Total

$7-11M(0.7-1%)

Premium Growth

Operational Efficiency

LR improvement

• Prospective Customer Segmentation

• Design and Offer Customised Products

• Chanel Expansion and Optimization

• Reduce Attrition

• Improved Lead Management

• Customer Experience & Service

• Fraud Analytics

• Fraud Identification & Mitigation

• Improved Internal Audit Mechanisms

• Cross-Sell / Upsell Campaign Management

• Sales Force Effectiveness and Optimization

• Campaign Effectiveness

• Improve Producer Effectiveness

• Better Risk Monitoring

• Portfolio Management

• Improved Underwriting & Pricing

• Claims processing analytics

• Improve Subrogation / Recovery

• Compliance: Solvency II

• Improve Assignment of Claims

• Reduce G&A Support Costs

• Optimise Service Cost

• Campaign Mgmt. & Optimization

• Channel Optimization

Overall impact

reduced by overlap

between levers

Illustrative use case

2 Current Business Models

13

Assessing the current business model opportunities Using different value levers to assess current processes/capabilities can help identify current and future business model opportunities

Underwriting Improvement

Growth

Increase Submission Flow

Improve Hit Ratio

Improve Retention Ratio

Quality / Profitability

Improve Risk Identification

Improve Consistency

Improve UW Insight

Efficiency

Process Improvement

Resource Assignment

Automation

Value Levers E.g. Solutions

• Early Declination

• Intelligent Targeting

• Win Probability

• Speed to Quote

• Low Touch Renewals

• Retention Probability

• Exposure Identification

• Satellite Assessment

• LoA Enforcement

• Data Movements

• Comparative Analytics

• Data Visualization

• Lean Improvements

• Early data collection

• Intelligent Submission

routing

• Task automation

• Intelligent Assessment

Each of these enablers have

different strengths and needs.

Evaluating them holistically

ensures we identify the right

solutions to move the needle for

a given business.

Early results using just some

of these tools show real

business value to SME and

larger commercial lines

Process Analytics RPA AI/ML

Illustrative use case

2 Current Business Models

14

COMPETITIVE DIFFERENTIATIONLow High

Use Case Gaps & OpportunitiesU

RG

EN

CY

/ P

RIO

RIT

YH

igh

Lo

w

12 6

7

151

2

21

UNDERWRITING, RISK CONTROL & AUDIT

ANALYTICS

6

7

8

UW real-time risk quality recommendations using

3rd party data & submission text data

Premium Audit reporting

Risk Appetite Trending/Variance to Plan

10 Customer propensity to quote/bind predictive

model

9 Risk Control Survey selection predictive model

PRODUCT ANALYTICS

11

12

13

IOT/Telematics based Pricing

methods

Product Rules Analytics

Prefill Submission data with 3PD to improve Agent

Experience

CROSS FUNCTIONAL

ANALYTICS

1

2

3

Profitability granularity including premium/loss matching

Front End data quality Control (e.g. VIN)

Consistent calculation rules for retention, pass thru rate

& loss ratio

4

5

Predictive Modeling & Visualization Tools

Improve Speed of IM data

requests

15

16

17

Claim Handling data integration &

reporting

Claims notes/medical records text

mining for fraud, nursing, return to

work & subrogation models

CLAIMS ANALYTICS

DISTRIBUTION

ANALYTICS

22

ACTUARIAL ANALYTICS

19

20

21

Agent-Broker 360 intelligence/3rd

Party Data

Agent-Broker Segmentation

Customer Claims Review Cost

Containment

14 Rate adequacy metric automation

Fraud/SIU Predictive Modeling

18

11

1622

5

Claim frequency/severity

granularity

Incentive comp & profit sharing

Reporting with financial controls

4

18

2014

3

17

1319

8

10

9

Prioritizing potential opportunities for data and analytics to improve the current business.

Example use cases

2 Current Business Models

15

Disruptors will force carriers to pivot to new business models

requiring new capabilities

Future Business Models3

16

Carriers are also turning to data monetization new business modelsAlignment on Data Monetization Strategy and Framework

Insurance client was interested in monetizing its existing and emerging sources of data (e.g. Telematics).

We drove alignment on a data monetization strategy and sources of value for a major insurance client,

building on our cross-industry point of view

Future Business Models3

17

Why are

organisations

evolving this way?

Cross functional

Pace of innovation

Scarcity of talent

Centralised

Decentralised

Hub & Spoke

Stage 1

Novice

Stage 2

Localised

Stage 3

Amateurs

Stage 4

Practicioner

Stage 5

Competitors

Operating Model PerspectivesLeading Analytics Companies are moving toward a Hub & Spoke Model.

4 Operating Model & Talent Strategy

18

The Hub & Spoke model can evolve as maturity increases

Thick Hub Balanced Thick Spokes

Wh

ere

Ap

plic

ab

le

• Starting to broadly increase

the use of analytics

• Too few analysts to justify

spoke-aligned resources

• Capabilities somewhat

immature

• Relatively simple business

model

• Growing demand and critical

mass of analytics analysts

• Allocation of these scarce

resources is a priority

• Analytical competitor

• High demand for analytics

across organization

Data

Management

LOBs/

FunctionsCoE

Advanced

Analytics

Technology &

Processes

Value

Realisation

Ad-Hoc

Analysis

The models typically vary based on the organizational complexity and analytics maturity.

4 Operating Model & Talent Strategy

19

Enterprise roles to enable Advanced AnalyticsThere are a few leadership roles that are critical to the advanced analytics organization.

Core Leadership

Roles

Lead Data Scientist

Lead Data Architect

Business Domain Expert

Chief Analytics Officer leads analytics

for the enterprise and defines analytics

and big data needs of the CNA in

alignment with enterprise priorities and

ensures these needs are met

Business Domain

Experts provide the

bridge between

analytics hub and

spokes; has deep

expertise in a specific

domain

Lead Data Scientist is

responsible for data

discovery and analytical

efforts; identifies

potential innovation and

big data solutions

Lead Data Architect

designs data

environments, tools &

technology solutions to

meet business, technical

and user requirements

Analytics Delivery

Lead

Analytics Delivery

Lead oversees end-

to-end delivery of

analytics

capabilities

4 Operating Model & Talent Strategy

20

Analytics Execution Roles

Cross functional teams work together in a pod structure to develop new capabilities or use

cases

Analytics Pod Model

Business Domain Expert

Business Analyst

Data Scientist

Modeler develops

statistical and

predictive models

(e.g. cross-sell

propensity,

sentiment analysis)

Data Scientist leads

data discovery and

analytical efforts;

identifies potential

innovation and big

data solutions

Business Domain

Expert provides bridge

between analytics hub

and spokes; has deep

expertise in a specific

domain (e.g. Claims)

Visualization Expert

develops visualization

tools to understand

and manipulate

complex datasets in

an intuitive format

ModelerVisualiza-tion Expert

Data Engineer

Data Engineer

source, transforms

and produces

analytical data sets

from both internal and

external data sources

Business Analyst

connects business

needs to analytics

requirements and

defines business

deployment strategies

4 Operating Model & Talent Strategy

21

#

The Five A’s of Change

Act AdjustAlign Adopt Adapt

Initiate the journey Course

correct

Adopt analytics

technologies and

expertise

Adapt decision making

processes

powered by analytics

Align Leaders

and Organization

Think Big

Clear Vision and

Direction

Get Support

Clear Sponsorship and

Stakeholder Network

Start Small

Focus on key areas

and test through Pilots

Be Value-Driven

Define success and

priorities, track value

Refine Refine Refine

Measure outcomes and

adjust op. model

Seed a Team

Mobilize an initial

analytics organization

Scale

Expand the program

and operating model

Strengthen Team

Internally by hiring/

training or externally by

partnering

Empower Decisions

Provide contextually

relevant insights

Make it Simple

Simplify the insight

delivery mechanism

Agile in DiscoveryIndustrialized

in Execution

Sustaining

the Changes

6 Insight Driven Decisions

Driving the insights into decision making at the front line often requires these five steps to

deliver and sustain the change

22

Getting Started

We recommend interviewing the set of key executives to understand where Nationwide currently

stands in its analytics journey. The interviews work best when they are targeted and yet flexible in

terms of focus. Some of the relevant topics/questions for these interviews will include:

Strategy, vision, and size of the prize:

• What is your strategy, and how do you envision analytics supporting that strategy?

• Where do you feel you stand compared to what competitors are doing with analytics?

• What are your current pain points in applying analytics more broadly?

• What would you do differently if you had instant and easy access to data? What would you do differently if you had access to

better analytics?

Target capabilities:

• What business domains do you think need better analytics first? Why?

• What use cases would you currently like to enable but cannot?

• How easy is it for you to access business intelligence? How easy is it to access analytic insights?

• What are your aspirations in Big Data, and why?

• How convinced are you that text mining or vision machine will be an important part of getting your job done in the next 3 years?

What other advanced capabilities are you tracking?

Operating model:

• Who do you turn to for analytic insights today? How well does that process work?

• What analytic services can you request today? Who balances supply and demand for these services?

• What technology roadmaps are currently on your personal radar?

1

2

3

23

Agenda

DATA & ANALYTICS JOURNEYS – CASE EXAMPLES

24

Lessons learned from successful Analytics journeysThere are lots of learnings from the journeys with our clients; advantage for Nationwide would

be to recognize these learnings in their journey design, and avoid the pitfalls of early adopters.

• Develop proof points, then

industrialize and scale initiatives

based on experiences and use

cases

• Scale across the business in a

way that will drive value fastest

• Maximize all available data sets

to drive the highest value

• Partner for innovation by

identifying external resources for

growth (e.g. academia)

• Value creation led by focusing

on what matters to business

decision making

• Adopt an iterative approach to

analytics by embracing failure

and learning by doing

• Enable efficient access to data

for discovery

• Encourage frequent and close

engagement with business

stakeholders

• Bring analytics to the point of

decision so that insights can

drive impacts

• Answer the “what?” and the

“why?” before the “who?” and the

“how?”

• Identify champions to help

overcome internal change

barriers

• Build the bridge role for

building relationships between

the business and the analyst

communities

Sustain

the Change“Do-Learn-Do” Approach

Industrialize

and Scale

25

Developed a Comprehensive Analytics Roadmap for a

Top 3 P&C Insurer in US, Providing a Roadmap to Close

“Analytics Deficit” and Gain “Analytics Differentiation”

Identified Analytic DeficitsIdentified specific areas of analytic capability gaps

where the client was lagging behind the emerging

practices

Business Capability RoadmapDeveloped a 3 year, quarterly, roadmap to develop

the needed business capabilities

Technical RoadmapDeveloped the corresponding technical capabilities

roadmap that was needed to enable the Business

Capabilities

People and Processes RoadmapFinally, also prepared the roadmap to develop the

talent, organization and the processes to sustain the

analytics differentiation in long term

Analytics Roadmap

Business Capabilities

Technical Capabilities

People & Talent

Processes

Case Study #1

26

Case Study #1: Developing an Analytics Roadmap for a Top 3 P&C

Insurer

Business Capability Gap

Assessment

Future State Technical Architecture

Talent and Processes Roadmap3 Year Integrated Roadmap Combining

Capabilities, Technology, People and

Processes

27

Large Insurer Data/Analytics Journey (page 1 of 3)

Identified Business Capabilities and Data/Tech Enablers To Support Those Capabilities

Business Area & Initiatives Value Delivered (Issues Outcomes) Technology Enablers

Distribution Analytics

• Agent 360 Intelligence

• Agent Segmentation

• Agent Performance Management

Issues: Limited ability to understand agent-broker

performance, behavior and product affinity

Outcomes: Target the right agents with the right

message; Optimize agent incentives

• Accelerate internal/external integration

via Data Lake & Data Marketplace

• Identify segment clusters rapidly through

discovery toolsets

• Replace AMT with collaborative BI tools

Product & Pricing Analytics

• Product Rules Effectiveness

Issues: No insights into how product rules impact

agent experience and underwriting performance

Outcomes: Optimize Agent-Broker experience and

underwriting effectiveness

• Rapidly discover integration rules

with Data Munging tools

• Prototype visualizations

• Master geospatial data in Data Lake

• Co-locate unstructured rules data in

Data Lake

• Discovery test and learn environment

Actuarial & X-Functional Analytics

• Profitability Database – Legacy CM

• Pre-Issuance/Production Database

• Geospatial Analysis

Issues: Insight generation is largely limited to one

LOB

Outcomes: Accelerate rate-making and profitability

analytics

Claims Analytics

• Fraud/SIU models enhanced with text

mining and 3rd party data

• Integrated Claims Reporting Platform

Issues: The majority of claims fraud is detected

manually by referrals.

Outcomes: Close the gap between the actual 1-2%

of claim fraud detected and estimated 7% of claims

• Co-locate claims notes, 3rd party

data and losses in Data Lake

• Text mining using Discovery tool’s

machine learning libraries

Data/Technology

Enablers

Business Objectives:

• Developed a broad-based

analytics strategy that would be

able to serve the needs of

multiple operating units

• Define the required capabilities

and operating model changes

that would be needed in order to

support the broad strategy, with

an initial emphasis on technical

capabilities in data and

technology

Outcomes:

• Gained consensus over 25+

business capabilities and 30+

technology enablers, as well as

5 personnel and process

initiatives

• Client is current working through

the roadmap

Business Capability

Gaps and Expected

Outcomes

28

Current State - 12 month development cycle

Modeler submits IT

request to capture

quote submissions – IT

project required to

develop data extract

from multiple data

sources

60%-70% of modeling

effort focused on data

preparation

Modeler lacks tools to

illustrate insights- build

custom Excel reports as

a workaround

No standard method to

integrate model. IT has

to build a custom

service to run in real-

time on @Work

application

Art of the Possible with New Architecture - 3 month development cycle

Modeler can rapidly

search and browse

quote submissions data

sets available in the

Data Lake.

Integrated team of

modelers + field

analysts communicate

insights more intuitively

through collaborative

visualization tools.

Model originally

developed in open

source languages can

be directly deployed

through analytic app

store

Data discovery tools

identify relationships

across data sets,

reducing the time to

design integration rules

Data Lake,

Data Catalog

Predictive Modeling,

Data Discovery Tools

Data Visualization,

Collaboration ToolsReal Time Analytics,

Analytic App Store

Large Insurer Data/Analytics Journey (page 2 of 3)

Designed Current State and Art of the Possible For Business Capabilities (e.g. Predict Quote Submissions Propensity To Bind)

29

Enablement

Schedule

deliverable

Q2 2016 Q3 2016 Q1 2017 Q2 2017 Q4 2017 Q1 2018Q4 2016 Q3 2017

Data Lake

1.0

Analytics &

Discovery

Enablement

Self Service

BI

Streaming

Ingestion

Initiate

Cloud

Migration

Real Time

Analytics

Automation

via Analytic

Apps

Operating

in the

Cloud

Data co-located

and controlled

in a single

environment

Accelerate data

extraction,

preparation,

and modeling

Empower users

with easy-to-

use data

visualization

tools

Capture data

sources in flight

for faster data

availability

Faster

deployment

cycles / cloud

based

performance &

reliability

Embed

analytics in

business

processes and

on data in

motion

Reduced cycle

time and more

efficient

processes for

model creation

Platform

Elasticity with

Performance

Key

Release

Theme

New

Features

Make all

Data

Available

Apply

Analytics to

All Data

Collaborate

to Accelerate

BI Delivery

Right-time

Data

Availability

Flexible

Deployment

Options

Operational

Decisioning

Create

Analytic

Apps On

Demand

Scale to

Business

Needs

Value

To

End Users

Large Insurer Data/Analytics Journey (page 3 of 3)

Delivered End-To-End Journey

30

Agenda

WHY PARTNER WITH ACCENTURE

31

Accenture: Expertise and Alliances

56 Offices and operations

from which Accenture

serves clients

23+Accenture Innovation Centers including

5 Advanced Analytics Centers

• Athens• Bangalore • Barcelona• Beijing• Buenos Aires• Chengdu• Chicago• Dublin

• Gurgaon• Johannesburg• Kolkata• Madrid• Melbourne• Milan• Mumbai• Murray Hill

• San Jose• Shanghai• Singapore• Sophia Antipolis• Tokyo• Toronto• Warsaw

475+Patents and

patents pending

for data and

analytics-related

content

100+Technology and

research alliances

with market & industry

leaders

20+Years of

advanced

analytics

experience

Deep Expertise Global Reach Strategic Alliances

12,000+Architecture

Practitioners

900+Enterprise

Architects & TOGAF

Practitioners

3,000+Big Data trained

professionals

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The Analytics Connected Experience

The Insight Driven

Enterprise

StrategyEnabling companies to act on insights and make

decisions by bringing to life the analytics journey

to ROI from vision to value

TeamEmbedding a multidisciplinary team with

differentiated talents and expertise allowing clients to

unlock new sources of value

Data

Platform

Integrated platform of data technologies

enabling speed and rapid insight generation

Insight

Visualization

Scaled, repeatable processes focused on the “What”

and the “Why”

Immersive

Environment

Cutting-edge immersive and collaborative

environment, enabling data-driven decision making,

exploratory analytics, and interactive relationship

development

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Why Accenture?Proven set of methodologies, frameworks and templates

Analytics Mobilization checklist & Communications

Off-the shelf analytics workplan

Stakeholder Engagement Plan

Workshop playbooks & Deliverable Storyboards

Analytic Vision & Strategies

Analytics Issues Tree

Functional Analytics Capabilities Library

Utilities Analytics Use Case Library

Analytics Use Case Prioritization

Analytics Value Proposition

Analytics Portfolio analysis

ANALYTICS INVESTMENT PORTFOLIO DIAGNOSTIC

The investment portfolio structure chart can be replicated based on

the newly recommended portfolio, to highlight the shift to more value

driving investments

A few charts can be created to provide an overview of the current portfolio (e.g. by initiative count or by total investment $)

Below are some sample charts to be leveraged and customized to analyze the current and recommended portfolio of analytic initiatives. It is meant to be a source of inspiration more than a prescriptive way to approach

the analysis

The waterfall chart i l lustrates how the portfolio would evolve based on restructuring recommendations (initiatives can be put on hold, changed in

approach, continued, industrialized or new initiatives can be recommended). The chart needs to be customized in the Data tab

Next

Generation Architecture

15%

Bus iness

Value Driving

Investments54%

Technology

Enablement

31%

Investment Portfolio Structure

19%

20%

39%

27%

18%

27%

24%

27%

0%

0%

Total Spend

# of Initiatives

Investment Spend per Function

Marketing Sales/Customer Supply Chain Enterprise Human Capital Other (specify)

2

5

4

2

Qualitative study (research)

Basic (data pulls, reporting)

Intermediate (dashboards,visualization)

Advanced (predictive modeling,experimentation, optimization)

Initiative Count vs. Level of Analytics Complexity

6

6

0

5

People

Process

Data

Technology

Initiative Count vs. Primary Enabling Components

Data Management

38%

Distribution & Logistics

12%

Predictive Maintenance

9%

Pricing Optimization

11%

Retail Execution8%

Revenue Management

16%

Technology Upgrades

6%

Investment Spend ($) per Capability

Portfolio Restructuring Recommendation

Data Management Distribution & Logistics Predictive Maintenance Pricing Optimization

Retail Execution Revenue Management Technology Upgrades

Analytics Functional Capabilities Diagnostic

Analytics Foundational Capabilities Diagnostic

Analytics Operating Model

Organizational Fit, Construct & Roles

Analytics Integrated Roadmap

Analytics use case project charters

Analytics Change Tracker

Off Track

Unsustainable

On Track

Pushi

ng the

limits

Cruising

Achieved

with loss

of heartOn the

Run

Battling

it out

Yes, but…

In the

Dark

Bumpy

ride

Sleepy in

Success

Building

Momentu

m

Business

as Usual Good,

but not

yet

great

Wash

ed up

Rocky

ground

Burning

platform

Case

for

Actio

nDownward

Spiral

Struggling

under pressure

Flatlining

Just get

on with it…

High

Performance

Improving Performance

Achieving Objectives

Low Perf. Norm High Perf.

Turbulence

• Risk and Roadblocks

High Low

• Changes Taking Place

Resources Low High

• Training & Capability

• Sy stems & Processes

Aligned Direction

• Vision and Direction

• Communication

Change Leadership

• Management Commitment

• Team Leadership

Work Roles

• Inv olv ement

• Accountability

Emotional Energy

• Passion and Driv e

• Disturbance

Low Perf. Norm High Perf.

The Analytics Change Management Toolkit

LEADERSHIP

ALIGNMENT PLAN

STAKEHOLDER

ANALYSIS

CHANGE IMPACT ASSESSMENT &

STAKEHOLDER ENGAGEMENT PLAN

ROLE-BASED TRAINING

& RAISING ANALYTICS IQ

CHANGE NETWORK

SUSTAINMENT PLAN

CHANGE MEASUREMENT PLAN

VALUE SCORECARD

C-LEVEL COACHING & COMMUNICATION PLAN

Act AdjustAlign Adopt Adapt

CHANGE TRACKING

Surveys and analysis for leaders and teams to monitor if change programs are on track and to take required corrective actions

every 6 months

GAMIFICATION

We bring unparalleled analytics strategy related assets and accelerators that allow

us to efficiently work through the issues and opportunities with Grange.

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We work with leading insurers around the globe, delivering high

impact through data-driven business solutions

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