EY presentation at the Chief Data Officer Insurance 2016

Fueling Growth & Innovation Through Data Governance Kevin Koenig - Global Insurance Data & Analytics Leader

EY FSO - Advisory Services September, 2016 1

EY - Enabling the Intelligent Enterprise - Fueling Growth & Innovation Through Data Governance © 2016

1 TDWI estimate based on cost-savings cited by survey respondents and others who have cleaned up name and address data, combined with Dunn & Bradstreet counts of U.S. businesses by number of employees. The Data Warehousing Institute 2 15 Important Big Data Facts for IT Professionals, Tek Systems “The Next Frontier” February 4, 2014

We see a very consistent, strong and relevant correlation between data quality and business performance.

Any CEO, regardless of the type of company, is responsible for the well-being of the organization they lead. Simply put, nothing is more important to that health than the data it uses to operate the business and

leverage for strategic position. Everyone has a respective level of accountability, and a constant need to be able to trust data so every level of the organization can operate as expected.

$611b

It costs the economy an extra

annually due to poor quality

data1

50%Of IT leaders,

slightly less than

have no idea who owns data2

10-25The cost of poor

data may be

percent of total revenues1


A case in point for executive accountability and why the governance of data is so critical to corporate financial health.

Consider the following real-life example: An insurance company receives 2 million claims per month with 377 data elements per claim. Even at an error rate of .001, the claims data contains more than 754,000 errors per month and more than 9.04 million errors per year! If the insurance company determines that 10 percent of the data elements are critical to its business decisions and processes, the firm still must fix almost 1 million errors each year that could damage its ability to do business.

What is the insurance company’s exposure to these errors? Let’s say the firm estimates its risk at $10 per error. This covers staff time required to fix the error downstream after a customer discovers it, the subsequent loss of customer trust and loyalty, and erroneous payouts (both high and low.) Even at $10 per error (an extremely conservative estimate) the company’s risk exposure to poor quality claims data is $10 million a year! And this doesn’t include the firm’s exposure to poor quality data in its financial, sales, human resources, decision support, and other applications.


Today, most insurers either struggle with legacy environments or fail to see a realistic path to enable the full potential of data.

Capability assets: data excellence, tools and technologies, methods, talent, roles and organizational designBehavioral assets: Mental models, behavioral norms, learning orientation, holistic thinking, and collaboration

The Integrated Enterprise: analytical knowledge leveraged for cross-functional gain and return

The Evolved Enterprise: data leveraged as business intelligence to optimize business performance

The Optimized Enterprise:data leveraged to create new operational efficiencies in functional areas

The Unknown Customer:

the beginnings of rudimentary sales data collection

Organizations must seek a way to evolve - ensuring clarity of the past, optimizing and protecting what’s been built and being insightful enough to predict and grow.

Known Customers & Markets:the beginnings of customer and market data collection

The Predictive Enterprise: insightful intelligence leveraged across businesses and channels to inform and create value-driven opportunities and return


35%

Leaders seek more value in the integration and governance of data, while laggards optimize status quo.

Organizational data priorities:

1. Improving confidence in the data 2. Protecting the data 3. Reducing cost of data management 4. Ensuring freshness at point-of-use 5. Connecting disparate data sources

Shared top priorities

Sorted by difference

Leaders Laggards

2016 EY Global Insurance Sensor Data Survey

20%

35%24%

39%29%

33%24%

Integrating external data sources

Adding granularity to the data collected today

Adhering to governance policies

Enabling more business users

30%23%

34%29%

33%28%

Adding new types of information not collected today

Defining data consistently throughout the organization

Reducing complexity of the data environment


Quantitative analytics & analytics

The maturity of Insurers and the “challenge” of mastering data governance supports all other data related services.

Actuarial & regulatory

Cost

Profit & growth

“How good is my Solvency II data?” “Who’s accountable for my data?”

“Does the existing governance structure meet regulatory requirements?”

“Is my data safe in the new architecture?”

“What customers or market segments do I want to focus on or exit?”

“How should I engage customers”

“How accessible is my data?” “What is the best source to get a true

understanding of exposure?"Efficiency

Key questions Data management capabilities

"What tools are available so my quants can focus on analysis not sourcing?”

"How can I reduce my overhead costs related to reporting"

Key business drivers

Defensive

Offensive

Business intelligence &

reporting

Data Architecture

Data Quality

Data Gov.


Leading insurers see significant opportunities to leverage data - taking immediate action to empower and govern the enterprise.

Leaders believe new data sources drive improvements in how they:

Market & sell

75% Laggards 28%

Differentiate value

71%Laggards 27%

Engage customers

71%Laggards 27%

Set strategy

69%Laggards 23%

Work with others

69%Laggards 22%

Model, manage risk

70%Laggards 24%

“Leaders see 3x more opportunity than laggards”

2016 EY Global Insurance Sensor Data Survey


▶Technical foundation for driving excellence

▶Adherence to and support of policies and standards

▶Ability to access and exploit data as appropriate

▶Basic understanding of fundamental management practices

▶ Broad understanding across the organization

▶ Individual modeling skills ▶ Standardized tools to support

individual needs ▶ Scalable and flexible infrastructure to

support emerging needs

▶ Capacity and acumen to explore and discovery

▶Willingness and ability to reach across areas

▶ Constant drive to test and learn

▶ Leadership driven by evidence ▶Analytics is viewed as a strategic

asset ▶Relentless focus on testing

hypotheses ▶ Strategy and operations driven by

data insights

Data governance is a critical component necessary in empowering business capabilities across four key stages of activity.

Continuous Improvement Loop

Data transformation

rules, algorithms

Insights ImprovementsRelevance

Transactional, Operational, Financial, Customer, Product …

Architecture Exploration Decisioning As Usual

Business intelligence &

reporting

Improved performance & risk

management

Structured & Unstructured

Trusted & Ad-hoc Supplemental Data

Test, New & Unknown Data


Industry change is creating a burning platform for the responsible governance of data, driving the frontier of growth and profitability.

The Empowered Customer: ▶ Increasingly self directed

▶ Higher expectations for speed and ease of use

▶ Demand for more personalized service and recognition of loyalty

Changing Business Models: ▶ Comparison shopping sites becoming more prevalent

▶ Lower growth for traditional products

▶ Challenging distribution channel adjustments

▶ Pay-as-you-live

Increased Risk & Regulatory: ▶ Solvency Capital Requirements and reporting

▶ Systemically Important Financial Institutions

▶ NAIC Risk Management and Own Risk and Solvency Assessment Model Act Implementation

Increasing & New Competition: ▶ Traditional retailers entering the insurance market

▶ New, innovative and agile digital entrants disrupting the market and usurping the engagement with customers

▶ Data aggregators looking at ways to leverage and monetize knowledge

9


Old Model: Legacy processes and analytical models supporting underwriting and the transference of risk are proving to be archaic and insufficient in meeting the demands of a new era.

New Model: Non-traditional players have emerged in the market creating a new ecosystem of valuable data which can be leveraged analytically to create vast new opportunities and a better way to facilitate the management of risk empowered by interconnected devices rich with data.

I want toknow why my rates

don’t reflect my drivinghabits!

How can Ibetter manage my health and

insuranceplan?

How did thefire start and could I have prevented

it?

There must bea more complete

coverage that meets my needs

and isn’t socostly.

We Are Connected

Getting closer to the policyholder is a top priority, and everyone is in a race to get better connected – empowered by data and analytics.


▶Claimant details ▶Accident details ▶Travel history ▶Third party details ▶Customer service scores ▶Mobile phone / GPS

▶Social Media (tweets, postings, reviews) ▶Customer Initiated Feedback (audios,

emails, video, txt..) ▶Web clickstreams ▶Focus Groups

The modern insurer is actively looking for ways in which to manage more complex, multi-dimensional data environments.

Claimant details Accident details

Travel history Third party details

Customer service scores Mobile phone / GPS

Customer Generated Company Generated

Structured

Unstructured

Social Media (tweets, postings, reviews) Customer Initiated Feedback (audios,

emails, video, txt..) Web clickstreams

Focus Groups

▶ Transactions ▶ Sales records ▶ Financials ▶ Customer profiles ▶ Scores (churn, cross sell, segment) ▶ Agents data

▶ Call centre scripts ▶ Market Research ▶ Surveys ▶ Employee Social Networks ▶ Benchmarking

EY - Engineering The Right DnA - Fueling Growth & Innovation Through Data Governance © 2016

The industry must adapt to a sea of technology trends that if adopted has the ability to drive significant optimization and growth.

Big data: ▶ Smart insurers are accessing vast amounts of new data to

target the best customers

▶ Differentiating insurers are looking at layering new data on top of traditional views

▶ The rest are “racing to the bottom” to maintain the same policy volume

Social network: ▶ Need to better manage social influence (positive &

negative)

▶ Consumers expect transparency and engagement

▶ Possibility to leverage social data to improve product manufacturing, pricing, risk

Digital connectivity: ▶ Growth of channel and device options determining

engagement models impacting the need to predict

▶ Rapidly multiplying new sources of customer-related data being leveraged by competitors

▶ Consumers connecting platforms to do their own analytics, making more informed decisions

Cloud computing: ▶ Lower service costs for analytical infrastructure

▶ Improved agility of IT systems

▶ Support for quick business expansion

▶ Analytics as a service


New ways of governing data are emerging, focusing on everything from cloud, robotics and factory enabled to accelerate impact.

Metadata Repository

Systematic Metadata

Business Glossary

Data Profiling

Data Quality

Executive Dashboards

Policy and Standards

Create a scalable, repeatable set of work streams for managing large segments of data management in an industrialized, standardized manner, thereby making case for increasing maturity.

Insurers are enhancing data governance capabilities by building “toolkits” for data management.

► Product ► Underwriting ► Finance ► Technology ► Operations ► Regulatory ► Actuarial ► Distribution

Prioritization process/filter to determine order of inputs to the factory

▶ Data lineage ▶ Business Glossary ▶ Controls

▶ Quality Assurance ▶ Testing

▶ Data lineage ▶ Quality Dashboards ▶ Authorized Sources

▶ Business Definitions ▶ Business Rules ▶ Control Catalog

▶ Report ▶ Process ▶ Domain

Data Consumers On-boarding

Maintenance Tools

Outputs:Processing:Inputs:


The road to accelerated business value is paved by sound principles in data governance which feed the value chain.

2

4

3

Marketing Analytics focused

process maps libraryAdvanced analytics applications library:► Media mix modeling► Customer journey► Multi-variant A/B testing► Target segments► Sentiment analysis

1

Underwriting & Pricing

Analytics focused

process maps libraryAdvanced analytics applications library:► Desirability score► Policyholder behavior► Fraud detection► Segment elasticity► Network analytics

Distribution Analytics focused process maps libraryAdvanced analytics applications library:► Producer segmentation► Recommendation engine► Lead optimization

► Advisor targeting► Attribution modeling

Customer Service

Analytics focused

process maps libraryAdvanced analytics applications library:► Customer lifetime value► Customer segmentation► Call center optimization

► Needs analysis► Voice to text analytics

A library of process maps accelerates the diagnostic process and identifies opportunities to introduce structured, data driven decision making

A library of advanced analytics applications can then be vetted for impact, alignment and “implementability” supported by trusted data

Cutting edge applications can also be explored when organizations are primed for accelerated learning

Fueling Growth & Innovation Through Data Governance Kevin Koenig - Global Insurance Data & Analytics Leader

EY FSO - Advisory Services September, 2016 15

EY presentation at the Chief Data Officer Insurance 2016

Data & Analytics

Transcript of EY presentation at the Chief Data Officer Insurance 2016