Joint presentation with I&T's covering the proliferation of data available to insurance companies today and a high level view of searching for value and leveraging the relevant and useful buried in all of the trivia.

1. The Proliferation of Data: Finding
Meaning Amidst the Noise

2. 2
Big Data = Transactions + Interactions + Observations
The Proliferation of Data
Transactional– Informational – Behavioral - Environmental

3. So this is “Big”?
The Proliferation of Data
• Data availability and accumulation is accelerating in form and content
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Ø Variety: not just text and fax
q Images, blogs, telemetrics , social media, affiliation cards, etc.
q Integration of all interactions into “Warehouses”
Ø Volume: there is a lot of it
• More sources, more data, and the sources are like waterfalls vs trickles
• Increased collection of internal transaction and contact information
Ø Velocity: rate of change is accelerating
q More sources, more data, more churning
q Real-time sources like telemetrics and social media
• Two attributes of “Big Data” describe usefulness
Ø Veracity: When does “good enough” become “garbage”
Ø Value: Finally, the real question – what data is adding meaningful value

4. 4
Big Data Solutions Landscape Crowded and Diverse
The Proliferation of Data

5. “Exaponential” Growth in Data
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On the Internet, every 60 Seconds adds…
Ø 168 million+ emails sent
Ø 1.2 million+ Facebook posts
Ø 690,000+ Google searches
Ø 370,000+ minutes of calls on Skype
Ø 98,000 tweets on Twitter
Ø 20,000 new posts on Tumbler
Ø 13,000 IPhone apps downloaded
Source: go-globe.com
Ø 13,000+ hours Pandora music streamed
Ø 6,600+ pictures uploaded on Flickr
Ø 1,200+ new Craigslist ads
Ø 600+ videos are uploaded on YouTube
Ø 100+ questions asked on Answers.com
Ø 100 accounts created on LinkedIn
Ø 30 new domains are registered
One Exabyte =
10 to the 18th
ONE THOUSAND PETABYTES
ONE MILLION TERABYTES
ONE BILLION GIGABYTES
The Proliferation of Data

6. 6
The Proliferation of Data
Growth in Data is Personal…Private…Social
Privacy and security needs
intensified by personal nature
of data
Cyberinsurance huge market
Data “ownership” untested
• Data Theft / Loss
• Legacy Complications
• Collection Complexity
• Interpretation Issues
• Error Exposures
• Costs (Collect/Scrub)

7. Social Media Landscape Crowded…Huge Data Source
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The Proliferation of Data

8. Social Media a Double-Edged Sword
Key Issues
• Understand if/when/how your customers & potential customers are using it
• Messaging and themes should be integrated across media platforms
• Customers and noncustomers are empowered to say what they want, it is a free-for-all
• It takes qualified and dedicated staff and time to be effective
• Senior executive sponsorship and full support is critical
Risks to be managed
• Regulatory considerations – what you don’t know can hurt you
• Uncontrolled risks – agents and employees, at work and at home, impact company
• Power shift to Consumer – their voice is being heard whether or not you are listening
Operational Considerations
• Exposure assessment: where are all the uses, agent practices, reputational damage
• Competitive scan: what are the Best Practices, how are competitors positioned
• Strategy development: which, where, when, and how much; staffing & process changes
• Process integration: marketing, underwriting, claims, customer service
• Implementation planning: steps, staffing, governance, metrics
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Finding Meaning Amidst the Noise

9. Telemetrics (like UBI) Another Huge Source of Data
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§ UBI opened door
§ Health Information rapid source of growth
§ New Markets like Wearables
§ Product Differentiation
§ Pricing Accuracy
§ Makes Sense, Controllable
§ Minimize Hard to Understand Factors (credit)
Inactive
23%
Exploring
17%
Implemented
60%
UBI sales expected to grow from $50M in
2011 to approximately $2.6B by 2015.
The Proliferation of Data

10. UBI is “Big Deal” – Implications?
Key Issues
• Too many devices, not enough standards – which to pick and why? Vendor viability?
• More and more data – how is it scrubbed and integrated into existing data? Supplemented?
• Large and complex investment, works across silo’s and requires extensive collaboration
• It takes qualified and dedicated staff and time to be effective
• Legacy systems and redundant dirty data remain an issue
Risks to be managed
• Regulatory considerations? Mandated restrictions that create complexity, data risks
• What about privacy? Discoverability for nonrelated trials, consumer opinions over time
§ Diminished risk pooling? As use grows, disparity between risk classes will grow
§ Competitiveness? If more poor drivers, will non-UBI be able to pool risks to larger market
Operational Considerations
§ System selection process: vendor evaluation and assessment require discipline
§ Enterprise wide project management: PMO style oversight and coordination
§ Introduction impacts processes in operations: redesign and optimization efforts to integrate
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The Proliferation of Data

11. Add Demographic Shifts
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Finding Meaning Amidst the Noise
Five Generations of Consumers, Large New Wave Coming
(% of Total Population)
Increasing Ethnic Diversity
The Digital Generations
- powerful consumers
High Tech Low Tech
• Pricing Differentiation
• Total Value
• Risk Identification
• Service Insights
• Immediate Response
• Retention / Loyalty
• Market Sentiment
• Fraud Detection

12. Finding Meaning Amidst the Noise
Understand Changes in Service Expectations and Strategies
Increasing Customer Expectations
Company’s Areas of Focus
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• Immediacy / 24x7 access
• Transparency
• Personal service relationships
• Language differences
• Benchmarking performance within and outside the industry
Expanding Accessibility 100%
Accelerating Service Delivery 96%
Increasing Hours and Days of Service Availability 82%
Creating Different Levels of Customer Service 63%
Aligning Operations with Customer Markets 63%

13. “Big Data” + Analytics = Customer Awareness
Getting to Information from Big Data Requires:
• A “Value” filter has got to start being applied against volume and variety
• “Veracity”, or quality, is going to have to improve: TMI + GIGO = FAIL
• Data is meaningless without people who understand what it means (same with analytics)
• This is going to be an area filled with lessons learned, probably more so than any other
The Personal Nature of the Data Brings New Risks
• Regulatory issues – collection methods can become illegal overnight
• Privacy issues and exposure risk are all dynamic unknowns
• Sources can change their technologies, accessibility, fees, structure
• Externally gained date from vendor controlled domains create dependencies
• GIGO + TMI = FAIL
Operational Considerations – Key Factors in Getting Value from Big Data
• Value analysis of what data contributes to the business
• Governance and process design and monitoring
• Quality control practices and measures
• Typical implementation in an extremely high risk area
• Bringing ability to ensure business understanding is incorporated into “Big Data” projects
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Finding Meaning Amidst the Noise

14. Leads to Evolving Service Delivery Model
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One Size Fits All
§ Same service for all segments
§ Over invest in some, under invest in
others
§ One model to manage across lines
and customers
§ Differentiation at company level
based on brand or channel
§ Easier to match capabilities, one
target to work with
Finding Meaning Amidst the Noise
Individualized Service
§ Segmented customer needs and
economic value
§ Investment in service aligned with
need/value tradeoff
§ Service differentiation varies by
segment based on value
§ More models to manage, more
challenging
§ Delivery is people based, harder to
replicate

15. Technology Initiatives Continue to Face Challenges
- 5 of top 6 challenges are people issues (availability, expertise)
- The other one is budget, the place where people needs solved
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Finding Meaning Amidst the Noise

16. Finding Meaning Amidst the Noise
Operationalizing New Data and Tools Requires Solid Foundation
1. Best Practice Driven Transaction Processing Systems
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‒ Modular replacement if necessary; stepwise, wrap, LOB, or Bang
‒ Integrated and adaptable business rules, distributable (n-tier)
‒ Data adaptability, integrity/quality and accessibility
2. Multi-faceted Analytics
Ø Straddle pricing + marketing + underwriting + servicing + claims + external data
Ø Integrated across functions
Ø From workflow to predictive modeling, key to loss management
3. Empowered Accessibility
Ø Customer and agents apps for self-service, data capture, personalization
Ø Enterprise-wide standardized views and definitions

17. Execution Excellence Based on Three Guiding Principles
1. Have an executive sponsored roadmap that clearly outlines.
§ What resources will be needed for how long,
§ Where, when, and how will analytics enhance process and awareness,
§ Which tools will be used, and
§ How will success be measured.
2. Use data that is comprehensive, accurate, and current.
§ Not necessarily 100%, some have used only 70%
§ Must be representative.
3. Staff with talented and engaged people.
§ Completely understand business problem and are proficient with data.
§ Strength depends on team not individual – business and tool experts
§ Inquisitive and constantly challenging assumptions and perceived “givens”
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Finding Meaning Amidst the Noise

18. Steve Callahan
Practice Director
Steve_Callahan@renolan.com
Chad Hersh
Senior Vice President
chersh@renolan.com
512.491.7560