1

SAS Global Forum 2009Marty Ellingsworth (iiA)

The views expressed by the presenter does not necessarily represent the views, positions, or opinions of ISO.

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OverviewOverview

• Analytic Environment• About ISO• Analytics Framework

– Ecosystem– Innovation process– Data opportunities– Sample Problem

• What’s next – Good to Great

• Analytic Environment• About ISO• Analytics Framework

– Ecosystem– Innovation process– Data opportunities– Sample Problem

• What’s next – Good to Great

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The Market Electronic connectivity is expected Touch point knowledge is anticipated Personalized service is assumed Ease of doing business is desired Low tolerance for not learning

Business Environment

Why things are becoming so data driven.

Each Company Define, attract, retain, and grow “good” customers Match offering to customer Improve ‘customer facing processes’ Reduce expenses while building skills

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Sales and Distribution

Producer SegmentationMarket PlanningRevenue ForecastingCross sell and Up sellRetention and Profitability

Underwriting

Risk Selection and PricingPortfolio ManagementPremium AdequacyBilling and Collections Management

Claims

Payment AccuracyClaim Collaboration > Fraud Detection > Subrogation > Risk Transfer > 3rd Party Deductible > Reinsurance Recoverable

General Organizational OverviewAn information business focused on risk taking.Make. Sell. Serve.

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Analytic Value Effort Framework

Reporting = “Having the data”Timeliness and accuracyReports and TablesSurfacing data with agility

Descriptive Analyses = “Seeing the data”Scorecards / MeasurementsProfiles and ExceptionsSegmentation

Analytic Modeling = “Knowing the data”Understand TrendsEvaluate Business PracticesChoice Models and “What ifs”

Predictive Analytics = “Acting on the data”Informed decision-makingActionable Information Engines

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Better

AnalyticsBetterData

Better DecisionSupport

ISO’s StrategyISO’s Strategy

BestCustomerDecisions

property/casualty insurancemortgage lendinghealthcaregovernment, and human resources. 

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ISO Family Of CompaniesISO Family Of CompaniesISO Family Of CompaniesISO Family Of Companies

DomusSystems

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Strategic Space (2008+)Strategic Space (2008+)

DATA

LOSS PREDICTION

RISK SELECTION & PRICING

FRAUD DETECTION & PREVENTION

LOSS QUANTIFICATION

COMPLIANCE & REPORTING

HazardsHazards LossesLosses

AssetsAssets

RiskRisk

An

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World-Class StaffWorld-Class Staff

• Actuarial science• Data management• Mathematics• Statistical modeling and

predictive analytics• Operations Research• Economics• Chemical, environmental,

electrical, and other engineering disciplines

• Actuarial science• Data management• Mathematics• Statistical modeling and

predictive analytics• Operations Research• Economics• Chemical, environmental,

electrical, and other engineering disciplines

• Healthcare• Soil mechanics• Geology • Remote sensing• Meteorology• Atmospheric and climate

science• Oceanography• Applied physics• Many other disciplines

• Healthcare• Soil mechanics• Geology • Remote sensing• Meteorology• Atmospheric and climate

science• Oceanography• Applied physics• Many other disciplines

We have more than 400 individuals with advanced degrees, certifications, and professional designations in such fields as:

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ISO Family Of CompaniesISO Family Of CompaniesISO Family Of CompaniesISO Family Of Companies

DomusSystems

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Emerging Value in the EnterpriseEmerging Value in the Enterprise

• What way can we create value together?• What are we already doing?• What’s working / not working?• Some ideas on next steps

• What way can we create value together?• What are we already doing?• What’s working / not working?• Some ideas on next steps

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The iiA Role

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Critical Success FactorsCritical Success Factors

• Technical Expertise– in Statistical Modeling, Data Mining, and Data

Management

• Intimate Market Awareness• Strong Coordination

– with other company units– Underwriting, Loss Control, Claims, Sales/Agents

• Senior Executive Commitment and Support• Access to Data• Project selection and execution

• Technical Expertise– in Statistical Modeling, Data Mining, and Data

Management

• Intimate Market Awareness• Strong Coordination

– with other company units– Underwriting, Loss Control, Claims, Sales/Agents

• Senior Executive Commitment and Support• Access to Data• Project selection and execution

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Golden Rule of AnalysisGolden Rule of Analysis

Your product is not computers, application software systems, user interfaces or database connections

Your product is reliable information that helps answer compelling business questions.

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Predictive Modeling Projects you should do

Loss Control

Fraud PreventionProperty InspectionsAssess Work sitesRe-underwriting

Cost Avoidance

Automate Manual WorkAppetite QualificationUnderwriting GuidesRedundant ProcessesVendor SourcingSpend Analysis

Cash-flow Opportunity

SubrogationCredit to LossThird Party DeductiblePremium Audit (Comm) Account Identification Audit OrderingInsured to Value (PI)

Better DecisionMaking

Risk Selection Renewal (Attrition) New (Acquisition)Cross-sell & Up-sellPortfolio ManagementBroker/Agent ProfilesMedical ManagementLitigation ManagementLarge Loss ReservingImproved Collaboration

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Roles in the analytic processRoles in the analytic process

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Predictive Modeling Staff Portfolio Challenge Predictive Modeling Staff Portfolio Challenge

• Limited Resources– People – need to train– Recruiting/retaining

• Limited Time– Decision on whether and/or how to audit

• Limited Funds – Need to show value of audit process ROI

• More work than people

• Limited Resources– People – need to train– Recruiting/retaining

• Limited Time– Decision on whether and/or how to audit

• Limited Funds – Need to show value of audit process ROI

• More work than people

Predictive Model Development Group Identified Concerns

Predictive Model Development Group Identified Concerns

• Pressures – Time, turnaround, goal attainment

• Identify "best bang for buck" • Measure of Project’s value/success • Market getting softer (turning)

– More price competition– Less U/W accuracy– More “oops” moments reveal themselves

• Pressures – Time, turnaround, goal attainment

• Identify "best bang for buck" • Measure of Project’s value/success • Market getting softer (turning)

– More price competition– Less U/W accuracy– More “oops” moments reveal themselves

Key need is to efficiently allocate scarce resources to optimize your efforts across the Insurance Value Chain

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innovation

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7 SOURCES OF INNOVATION IMPULSES (Drucker)7 SOURCES OF INNOVATION IMPULSES (Drucker)

INTERNAL 1. unexpected event2. contradiction3. change of work process4. change in the structure of industry or market

EXTERNAL– Demographic changes– Changes in the world view– New knowledge

INTERNAL 1. unexpected event2. contradiction3. change of work process4. change in the structure of industry or market

EXTERNAL– Demographic changes– Changes in the world view– New knowledge

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# 7. New knowledge # 7. New knowledge

• Based on convergence or synergy of various kinds of knowledge, their success requires, high rate of risk – Thorough analysis of all factors. identify the “missing

elements” of the chain and possibilities of their supplementing or substitution;

– Focus on winning the strategic position at the market. the second chance usually does not come;

– Entrepreneurial management style. Quality is not what is technically perfect but what adds the product its value for the end user

• Based on convergence or synergy of various kinds of knowledge, their success requires, high rate of risk – Thorough analysis of all factors. identify the “missing

elements” of the chain and possibilities of their supplementing or substitution;

– Focus on winning the strategic position at the market. the second chance usually does not come;

– Entrepreneurial management style. Quality is not what is technically perfect but what adds the product its value for the end user

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What’s in ‘analysis’?What’s in ‘analysis’?

•Information Theory

•Applied Statistics

•Machine Learning

•Algorithms

•Database Management

ANALYTICS

•High Performance Computers

•Visualization

•New Techniques •More/Better Data•FEEDBACK

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Why text works – academic origins…

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Improve the Quality of KnowledgeImprove the Quality of Knowledge

Transform Knowledge Up the Value Taxonomy

Capability

Expertise

Knowledge

Information

Data

Sensory

Transform Knowledge Up the Value Taxonomy

Capability

Expertise

Knowledge

Information

Data

Sensory

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Types of CapabilitiesTypes of Capabilities

ActuarialStatistical analysisVisualizationGeospatialText miningNew DataBetter Data

ActuarialStatistical analysisVisualizationGeospatialText miningNew DataBetter Data

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The Role of SynergyThe Role of Synergy

• Synergy means that the whole is more than the sum of the parts.

• Synergy leads to:1. Increased customer and shareholder value2. Strategic focus in the management process3. Efficient operating costs4. Savvy investment through collaboration5. Serendipitous Opportunities

• Synergy means that the whole is more than the sum of the parts.

• Synergy leads to:1. Increased customer and shareholder value2. Strategic focus in the management process3. Efficient operating costs4. Savvy investment through collaboration5. Serendipitous Opportunities

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Expect the UnexpectedExpect the Unexpected

Results:

– Trend Following

– Need Spotting

– Market Research

– Solution Search

– Serendipity

Source: Expect the Unexpected, The Economist Technology Quarterly, September 2003

Success to Failure Rates

1:3

2:1

4:1

7:1

13:1

Serendipity => Taking advantage of unplanned opportunity

Creating Successful Innovations

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Structured dataSemi-structured dataUnstructured dataTextPictographicGraphicsMultimediaVoiceVideoGeospatialMulti-SpectralClimatologicAtmospheric

Types of Data and the Data Opportunity

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What to learn from Structured DataSignificant pre-processing of raw data is neededto create useful informational features.

Repeatable Patterns Trends, Seasons, Cycle Propensities, Likelihood Causation and Interaction Ratios between Dollars and Distances Stakeholder Behavior Unlikely Occurrences Proximity of stakeholders Ownership interests of stakeholders

Data Fusion and Learning is the key to successful Data Mining

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Deriving Data = PowerDepending on the target variable, there are many factors that may be relevant for modeling.

• Totals: Household Income• Trends: Rate of Medical Bill Increases• Ratios: Claims/Premium, Target/Median • Friction: Level of inconvenience, ratio of rental to damage• Sequences: Lawyer-Doctor, Auto-Life Policy• Circumstances: Minimal Impact Severe Trauma• Temporal: Loss shortly after adding collision• Spatial: Distance to Service, proximity of stakeholders• Logged: Progress Notes, Diaries,

• Who did it, When, “Why”

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Deriving Data = Power (Cont’d)Depending on the target variable, there are many factors that may be relevant for modeling.

• Behavioral: Deviation from past usage, spike buying • Experience Profiles: Vendor, Doctor, Premium Audit• Channel: How applied, How reported, Service Chain• Legal Jurisdiction: Venue Disposition, Rules • Demographics: Working, Weekly wage, lost income• Firmographics: Industry Class Code Vs Injuries Claimed• Inflation: Wage, Medical, Goods, Auto, COLA • Gov’t Statistics: Crime Rate, Employment, Traffic• Other Stats: Rents, Occupancy, Zoning, Mgd Care

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Extraction Engines Extraction Engines

Identify and type language featuresExamples:

People namesCompany namesGeographic location namesDatesMonetary amountPhone numbersOthers… (domain specific)

Identify and type language featuresExamples:

People namesCompany namesGeographic location namesDatesMonetary amountPhone numbersOthers… (domain specific)

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Building Chronologies can be very usefulProcess flow and cash flow are traceable.

Date ofInjury

Date of 1stTreatment

Date of First Report of Injury:

Employer Insurer

Date Accepted or Denied

Date ofReturn to Work

Date of MMIor P & S

Date of 1stPayment

Date Claim Closed

Date Claim Re-Open

Date Claim Re-Closed

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Roll up and roll down the data for the proper level of analysis. Roll up and roll down the data for the proper level of analysis.

Claim System

Claim File $x,xxx.xx

Medical Payments Medical Bill Review Systems

Bill Record

Payments

Reserves

Indemnity Payments

Expense Payments Bill Line Item Detail

Reduction ReasonsCharged versus Paid

• Bill Review Rule• Fee Schedule • U&C Repricing• PPO Discount

• Other Savings

Bill Review Rule Reasons

Bill Review Vendor

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Accident: 170824130 - Employee Injured In Fall From Second-Floor Decking

Inspection Open Date SIC Establishment Name

127366367 07/29/1996 1521 xxxxxxxxxxxxxxxxxxxxxxxx

Employee #1 was atop of the second floor decking of a newly constructed home, connecting frame work for a wall. He fell 18 ft 6 in., sustaining injuries that required hospitalization. Employee #1 was not tied off, nor were any other means of fall protection in use. He had not been trained in working from an elevated work surface, the company did not have a written safety program, and regular inspections were not performed. Keywords: decking, fall, tie-off, untrained, work rules, fall

protection, construction

Inspection Age Sex Degree Nature Occupation

1 127366367

29 M Hospitalized injuries

Cut/Laceration

Carpenters

Source: U.S. Department of Labor Occupational Safety & Health Administration

Accident Report Detail Accident Investigation Summaries (OSHA-170 form) which result from OSHA accident inspections.

See for yourself ---The importance and relevance of text

not tied off, nor were any other means of fall protection in use.

He had not been trained in working from elevated work surface

the company did not have a written safety program, and

regular inspections were not performed.

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GeoSpatial layersGeoSpatial layers

– TeleAtlas Dynamap 2000 Files (includes a Roadbase, Landmarks, Water bodies, etc.)

– Zip Code Boundaries– State/County/Municipal Boundaries– Census boundaries: Track > Block Group > Block– Aerial Imagery – DigitalGlobe/GlobeXplorer– All LOCATION GIS Layers– FireLine and historical wildfire burn perimeters– ISO statistical data and related analytics (ZIP-level)– CAP Index Crime Information – USGS Topography– US Census Demographics– Government promulgated natural catastrophe and historical

weather layers– Coastlines– US Labor Statistics– Custom datasets (e.g., customer portfolios/individual risks) – County Tax Assessor data, for 75M homes– Flood Information Mapping – Current weather conditions/current wildfire activity feeds

Location Analyst taps into ISO GIS Repository:

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What can help?What can help?

Integration of data with other fraudsBridging to new data sourcesSmarter transformation of dataText Mining – expose informationGIS Platform – geospatial elementsGraph mining – highlight social networksGrid computing – diagonal scaling *

Integration of data with other fraudsBridging to new data sourcesSmarter transformation of dataText Mining – expose informationGIS Platform – geospatial elementsGraph mining – highlight social networksGrid computing – diagonal scaling *

*diagonal scaling = you can scale up and out at the same time

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P&C Personal Lines Situation

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Market Demand - OpportunityMarket Demand - Opportunity

• Top carriers control large markets– E.g., Personal Auto – Top 25 carriers hold over 80% of

market (over $120B of a total market >$160B)– Strong motivation to –

• “Protect” market share• Grow against stiff odds

• Predictive analytics has gained senior leadership attention as a mechanism to – – Execute risk-based pricing and segmentation– Create competitive/strategic differentiation– Generate operational efficiencies

• Top carriers control large markets– E.g., Personal Auto – Top 25 carriers hold over 80% of

market (over $120B of a total market >$160B)– Strong motivation to –

• “Protect” market share• Grow against stiff odds

• Predictive analytics has gained senior leadership attention as a mechanism to – – Execute risk-based pricing and segmentation– Create competitive/strategic differentiation– Generate operational efficiencies

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Number of Companies writing Personal Auto Insurance in the US

Indication of Increased Competition

1/3 of companies gone in 12 years

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50%

60%

70%

80%

90%

100%

1995 2000 2005 2007

Mar

ket

Sh

are

Consolidation of Auto Insurance Markets

Top 10

Top 25

Top 50

Top CarrierGroup

Below 50 now has only 9% for remaining280 groups

Below 50 now has only 9% for remaining280 groups

Indication of Increased Competition

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How Analytics Fuel CompetitionHow Analytics Fuel Competition

$600 $800 $1000

$600 $800 $1000

$600 $800 $1000

My Book of Business(Actual Cost per Policy)

My Rate(Average)

If your competitor has advanced analytics, your book and your profitability are vulnerable

Total Revenue

$1000 $1000

$900 $1800

$800 $2400

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Predictive Analytics for theCommunity Environment

Predictive Analytics for theCommunity Environment

The Environment is the Exposure

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In Depth for Auto Weather ComponentIn Depth for Auto Weather Component

Coverage

Frequency Severity

Traffic Generators

Experience and Trend

Traffic DensityWeatherTraffic

Composition

Neural NetWeather

RBF

Neural Net Weather

MLP

Weather Precipitation

Scale

Clusters & Other

Summaries

Weather Temperature

Scale

Weather SummaryVariables

35 Years ofWeather Data

Environmental Model Loss Cost

by Coverage

Frequency×

Severity

Causes of Loss Frequency

Sub Model

Data Summary Variable

Raw Data

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Combining Environmental VariablesCombining Environmental Variablesat a Particular Garage Addressat a Particular Garage AddressCombining Environmental VariablesCombining Environmental Variablesat a Particular Garage Addressat a Particular Garage Address

• Individually, the geographic variables Individually, the geographic variables have a predictable effect on accident rate have a predictable effect on accident rate and severity.and severity.

• Variables for a particular location could Variables for a particular location could have a combination of positive and have a combination of positive and negative effects.negative effects.

• Individually, the geographic variables Individually, the geographic variables have a predictable effect on accident rate have a predictable effect on accident rate and severity.and severity.

• Variables for a particular location could Variables for a particular location could have a combination of positive and have a combination of positive and negative effects.negative effects.

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Techniques Employed in Variable ReductionTechniques Employed in Variable Reduction

• EDA (Exploratory Data Analysis) – univariate analysis, transformations, known relationships

• Statistical Techniques – greedy selection, machine learning techniques

• Sampling – cross validation, bootstrap• Sub models/data reduction – neural

nets, splines, principal component analysis, variable clustering

• Spatial Smoothing – At various distances and/or with parameters related to auto insurance loss patterns

• EDA (Exploratory Data Analysis) – univariate analysis, transformations, known relationships

• Statistical Techniques – greedy selection, machine learning techniques

• Sampling – cross validation, bootstrap• Sub models/data reduction – neural

nets, splines, principal component analysis, variable clustering

• Spatial Smoothing – At various distances and/or with parameters related to auto insurance loss patterns

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• Weather:– Measures of snowfall,

rainfall, temperature• Traffic Density and Driving

Patterns:– Commute patterns– Public transportation

usage• Traffic Composition:

– Size of vehicles– Age and cost of vehicles

• Weather:– Measures of snowfall,

rainfall, temperature• Traffic Density and Driving

Patterns:– Commute patterns– Public transportation

usage• Traffic Composition:

– Size of vehicles– Age and cost of vehicles

• Traffic Generators:– Transportation hubs– Shopping centers– Hospitals/medical centers– Entertainment districts

• Experience and trend:– ISO loss cost– State frequency and

severity trends from ISO lost cost analysis

• Traffic Generators:– Transportation hubs– Shopping centers– Hospitals/medical centers– Entertainment districts

• Experience and trend:– ISO loss cost– State frequency and

severity trends from ISO lost cost analysis

Breakthroughs in Personal Auto AnalyticsFactors Affecting Auto Loss Experience

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State

Territory

Vehicle Age& Symbol

Limits &Deductibles

SpecialAdjustments

Environmental Risk Module:

Weather, Street, Businesses, Traffic Density, Driving Patterns

etc

Vehicle Risk Module:Weight, Engine Size, etc.

Class Refined Points Module

No Change

No Change

Policy Risk ModuleInteractions of all indicators

State

VIN

Address

Personal Identifiers

Address, Drivers,Vehicles

Rating PlanISO Risk Analyzer

Input

Credit Module (optional)

ISO Risk Analyzer ®

Personal Auto FrameworkISO Risk Analyzer ®

Personal Auto Framework

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What has the impact been?What has the impact been?

• Major innovations in an historically static rate plan

• Increased competition• Profitable growth for adopters of

advanced analytics• Hunger for the next innovation

• Major innovations in an historically static rate plan

• Increased competition• Profitable growth for adopters of

advanced analytics• Hunger for the next innovation

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Good to Great

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What was Not WorkingWhat was Not Working

• Infrastructure impacting work productivity• Constant appetite for more “computing” capacity • Limited ability to process large datasets• Need to build core capabilities –

– Data access– Leveraging multiple modeling methodologies– Geo-spatial analysis– Managing and maintaining multiple versions of models – Text analytics (e.g. cause of loss and entity extraction)– Identity resolution– ISO Search and Retrieve information

• Remote team collaboration is cumbersome• Critical KSA’s sometimes ‘outside’

• Infrastructure impacting work productivity• Constant appetite for more “computing” capacity • Limited ability to process large datasets• Need to build core capabilities –

– Data access– Leveraging multiple modeling methodologies– Geo-spatial analysis– Managing and maintaining multiple versions of models – Text analytics (e.g. cause of loss and entity extraction)– Identity resolution– ISO Search and Retrieve information

• Remote team collaboration is cumbersome• Critical KSA’s sometimes ‘outside’

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Next Generation iiA SystemsNext Generation iiA Systems

Analytics Platform– Hardware

• Exploring a single large analytics server or a grid solution that ties together many commodity processors

• either solution will be a true client/server analytics– Software – SAS Enterprise Miner

• Industry standard predictive analytics software suite• Will increase analyst productivity as well as the quality of the final

models and documentation• Analytics Data Store

– Goal: Professional management of the data used by iiA for model development and production model scoring

– Characteristics• Professional• Scalable• Well-documented

Analytics Platform– Hardware

• Exploring a single large analytics server or a grid solution that ties together many commodity processors

• either solution will be a true client/server analytics– Software – SAS Enterprise Miner

• Industry standard predictive analytics software suite• Will increase analyst productivity as well as the quality of the final

models and documentation• Analytics Data Store

– Goal: Professional management of the data used by iiA for model development and production model scoring

– Characteristics• Professional• Scalable• Well-documented

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Highlights of the Proposed SolutionHighlights of the Proposed Solution

• SAS GRID computing infrastructure– Allows “diagonal” scalability

• Add higher-capacity machines to grid to support future growth• Protects and increases “life-span” of investment in hardware

– Holy grail of scalable, adaptive, on-demand computing• SAS EnterpriseMiner

– Full-function, grid-enabled data mining platform• Extensive suite of data processing and modeling methodologies

– One of two top Analytics products in the market– Industry-tested stability and reliability – wide usage

• SAS JMP Visual BI– Powerful visualization and visual data exploration software

• SAS Model Manager– Seamless management of models – assessing new models,

archiving old models, and deploying/using current models in production

• SAS GRID computing infrastructure– Allows “diagonal” scalability

• Add higher-capacity machines to grid to support future growth• Protects and increases “life-span” of investment in hardware

– Holy grail of scalable, adaptive, on-demand computing• SAS EnterpriseMiner

– Full-function, grid-enabled data mining platform• Extensive suite of data processing and modeling methodologies

– One of two top Analytics products in the market– Industry-tested stability and reliability – wide usage

• SAS JMP Visual BI– Powerful visualization and visual data exploration software

• SAS Model Manager– Seamless management of models – assessing new models,

archiving old models, and deploying/using current models in production

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Highlights of the Proposed SolutionHighlights of the Proposed Solution

• Benefits of choosing SAS– ISO is a long-standing SAS customer (since 1982)

• Can leverage loyalty discounts• Known vendor with proven value to ISO• Additional discounts obtained in other SAS licenses (e.g.,

Mainframe) – SAS is the most common platform in the industry

• Easier to find candidates with SAS/Eminer knowledge and experience

– SAS offers comprehensive training (compared to other competitors)• Easier to keep staff on the cutting-edge of new modeling

methodologies and business applications

• Benefits of choosing SAS– ISO is a long-standing SAS customer (since 1982)

• Can leverage loyalty discounts• Known vendor with proven value to ISO• Additional discounts obtained in other SAS licenses (e.g.,

Mainframe) – SAS is the most common platform in the industry

• Easier to find candidates with SAS/Eminer knowledge and experience

– SAS offers comprehensive training (compared to other competitors)• Easier to keep staff on the cutting-edge of new modeling

methodologies and business applications

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Grid Processing Improves Speed & CapacityGrid Processing Improves Speed & Capacity

Increasing Job Size

Increasing Number of Users & Jobs

Optimize the Efficiency and Utilization of Computing Resources

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SAS Enterprise Miner – Parallelized Workload Balancing

Parallel Processing Reduces Time to Results

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Key Benefits of Infrastructure InvestmentKey Benefits of Infrastructure Investment

• Stable, high-availability platform• Increased bandwidth for simultaneous users• One platform offering multiple tools/methods• Build models quicker and fail faster for better models• Visualization capabilities will significantly reduce data

exploration timelines• Model assessment and comparison capabilities built-in

– no separate coding necessary• Significant risk mitigation in model maintenance and

archiving• Data warehousing capability will shorten the cycle on

re-use of data in other initiatives

• Stable, high-availability platform• Increased bandwidth for simultaneous users• One platform offering multiple tools/methods• Build models quicker and fail faster for better models• Visualization capabilities will significantly reduce data

exploration timelines• Model assessment and comparison capabilities built-in

– no separate coding necessary• Significant risk mitigation in model maintenance and

archiving• Data warehousing capability will shorten the cycle on

re-use of data in other initiatives

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SummarySummary

• Centralized, shared environment• Dynamic resource allocation to meet peak demand• Policies and prioritization for use of resources• Run large more complex analysis• De-couple applications from infrastructure• Ease maintenance of computing infrastructure• Improve price/performance with commodity hardware• Scale out cost effectively as needs grow

• Centralized, shared environment• Dynamic resource allocation to meet peak demand• Policies and prioritization for use of resources• Run large more complex analysis• De-couple applications from infrastructure• Ease maintenance of computing infrastructure• Improve price/performance with commodity hardware• Scale out cost effectively as needs grow

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Why are we really here…

Why will we be back here next year…

CONCLUSION

Why things are becoming so data driven.

More data-savvy Executives

Ever improving analytic solutionsIndustry, Third party, and Government DataStructured, Unstructured, and Location DataFaster, Cheaper, Better – Processors,

Storage, & Tools

Growing Skill Sets of Staff and Vendors

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Marty Ellingsworthmellingsworth@iso.com

Marty Ellingsworthmellingsworth@iso.com

The views expressed by the presenter does not necessarily represent the views, positions, or opinions of ISO.