1 cas ratemaking seminar march 2006: data-3: the actuary and data standards data-1: the actuary and...

11

CAS Ratemaking Seminar CAS Ratemaking Seminar

March 2006: March 2006:

Data-3: The Actuary and Data Data-3: The Actuary and Data StandardsStandards

Data-1: The Actuary and The Data Data-1: The Actuary and The Data ManagerManager

22

The Actuary and Data The Actuary and Data StandardsStandards

Yesterday, Today and Yesterday, Today and TomorrowTomorrow


March 2006March 2006

33

AgendaAgenda Strategic Data PlanningStrategic Data Planning Timelines Timelines The Shifting Focus of Insurance InformationThe Shifting Focus of Insurance Information How Do We Get There?How Do We Get There?

– Enterprise Data StrategiesEnterprise Data Strategies– Standards Standards – Standards and Data Management Best PracticesStandards and Data Management Best Practices

10 Guidelines of Data Management10 Guidelines of Data Management Questions and CommentaryQuestions and Commentary

44

PanelistsPanelists

Art Cadorine, ACAS, ISOArt Cadorine, ACAS, ISO Gary Knoble, AIDMGary Knoble, AIDM Pete Marotta, AIDM, ISOPete Marotta, AIDM, ISO

55

Strategic Data Strategic Data PlanningPlanning

66

Data - A Corporate AssetData - A Corporate Asset

Data, like all corporate assets, requires Data, like all corporate assets, requires managing to ensure the maximum benefit managing to ensure the maximum benefit is achieved by the organization.is achieved by the organization.

Well-managed, high-quality data aids good Well-managed, high-quality data aids good corporate governance by providing corporate governance by providing management with a cohesive and management with a cohesive and objective view of an organization’s activity objective view of an organization’s activity and promotes data transparency.and promotes data transparency.

Poorly-managed data can result in faulty Poorly-managed data can result in faulty business decisions.business decisions.

77

Data and Strategic PlanningData and Strategic Planning

Data supports corporate decision-making: Data supports corporate decision-making: In providing a cohesive and objective view In providing a cohesive and objective view

of corporate activities.of corporate activities. In viewing the external landscape.In viewing the external landscape. In predicting the future.In predicting the future. In developing the corporate strategic plan.In developing the corporate strategic plan. In identifying process improvements and In identifying process improvements and

other efficiencies.other efficiencies. In measuring results.In measuring results.

88

PWC StudyPWC Study““Data is the currency of the new Data is the currency of the new economy.”economy.”

““Companies that manage their data as Companies that manage their data as a strategic resource and invest in its a strategic resource and invest in its quality are already pulling ahead in quality are already pulling ahead in terms of reputation and profitability terms of reputation and profitability from those that fail to do so.” from those that fail to do so.”

Global Data Management Survey 2001, Global Data Management Survey 2001, PriceWaterhouseCoopersPriceWaterhouseCoopers

99

Enterprise Data Strategy: A Enterprise Data Strategy: A DefinitionDefinition

A plan that establishes a long-term direction for A plan that establishes a long-term direction for effectively using data resources in support of, and effectively using data resources in support of, and indivisible from, an organization's goals and indivisible from, an organization's goals and objectives.objectives.

An Enterprise data strategy requires both business An Enterprise data strategy requires both business and technology input to:and technology input to:– Facilitate IT planning.Facilitate IT planning.– Support the overall business plan.Support the overall business plan.– Promote and maintain clearly and consistently Promote and maintain clearly and consistently

defined data across the corporation.defined data across the corporation.

1010

Components of an Enterprise Components of an Enterprise Data StrategyData Strategy

Organizational level:Organizational level: Data StewardshipData Stewardship

– Senior level oversight of corporate data.Senior level oversight of corporate data.– From an enterprise-wide perspective.From an enterprise-wide perspective.

Data Architecture – What to Run, Where to Run, Data Architecture – What to Run, Where to Run, How to Run – Software and Hardware:How to Run – Software and Hardware:– Ownership: Customer and DataOwnership: Customer and Data– Data LocationData Location– Software v. ServiceSoftware v. Service– Product DefinitionProduct Definition

Data and Process ModelsData and Process Models

1111

Components of an Enterprise Components of an Enterprise Data StrategyData Strategy

Data level : Data level : Data Element ManagementData Element Management

– Data Definition and AttributesData Definition and Attributes– Code Value and Data Set ManagementCode Value and Data Set Management– Data Mapping ManagementData Mapping Management

Data QualityData Quality Data StandardsData Standards

– Business and Efficiency DrivenBusiness and Efficiency Driven– Internal and ExternalInternal and External

Data Privacy and SecurityData Privacy and Security– Compliance with Privacy Polices and Compliance with Privacy Polices and

RegulationsRegulations– Data from Reputable SourcesData from Reputable Sources– Data Security Data Security

1212

Strategic Data PlanningStrategic Data Planning

Strategic Data Planning is primarily a Strategic Data Planning is primarily a business, not an IT function.business, not an IT function.

IT critical to any enterprise data IT critical to any enterprise data strategy.strategy.

1313

Enterprise Data Strategy and IT: Enterprise Data Strategy and IT: Architecture Supports Business StrategyArchitecture Supports Business Strategy

Business Strategy

IT Architecture

Infr

astr

uct

ure

Ap

pli

cati

on

Dat

a

A set of guiding principles that define why and what we do

A set of guiding principles that define how we do what we do

1414

Results of a Successful Results of a Successful Enterprise Data StrategyEnterprise Data Strategy

Provide a process and a set of tools to Provide a process and a set of tools to facilitate Business and IT planning and facilitate Business and IT planning and decision-makingdecision-making

Maintain a common and consistent view Maintain a common and consistent view of data that is shared company wide of data that is shared company wide

Facilitate alignment and traceability of Facilitate alignment and traceability of significant IT investments to their significant IT investments to their respective business driversrespective business drivers

1515

Business Results of Enterprise Business Results of Enterprise DataData

Ease of doing businessEase of doing business Speed to marketSpeed to market Facilitate R&DFacilitate R&D Customer ServiceCustomer Service ComplianceCompliance

1616

TimelinesTimelines

1717

The PastThe Past

Regulators/BusinessRegulators/Business

(underwriters, actuaries, etc.)(underwriters, actuaries, etc.)

Coverage FormsCoverage Forms

(changes in forms and coverages)(changes in forms and coverages)

Data StandardsData Standards

1818

TodayToday

TechnologyTechnology FinancialFinancial 33rdrd Parties Parties(Internet, XML, (SOX, GLB, HIPAA, etc.) (Credit, DMV,(Internet, XML, (SOX, GLB, HIPAA, etc.) (Credit, DMV,Black Boxes, RFIDs)Black Boxes, RFIDs) etc.) etc.)


1919

TomorrowTomorrow

Business Needs: Business, regulatory, technology, etc.Business Needs: Business, regulatory, technology, etc.(Profitability, Loss Control, Consumer Protection, Solvency, (Profitability, Loss Control, Consumer Protection, Solvency,

Privacy, Confidentiality, etc.)Privacy, Confidentiality, etc.)

Data NeedsData Needs


2020

The Shifting Focus of The Shifting Focus of Insurance Information Insurance Information

2121

Regulation Regulation From Annual Statement to Market Conduct From Annual Statement to Market Conduct

Annual Statements to NAIC Databases Annual Statements to NAIC Databases – Financial Data Repository (FDR) Financial Data Repository (FDR) – National Insurance Producer Registry (NIPR) National Insurance Producer Registry (NIPR) – Fingerprint Repository Fingerprint Repository – On-Line Fraud Reporting System (OFRS) On-Line Fraud Reporting System (OFRS) – Uninsured Motorist Identification DatabaseUninsured Motorist Identification Database

From financial data used to monitor solvency From financial data used to monitor solvency to financial, statistical data and analytics used to financial, statistical data and analytics used to monitor solvency to monitor solvency

From US driven regulations to EU and From US driven regulations to EU and internationally driven regulations internationally driven regulations

2222

PricingPricing From traditional underwriting and pricing - using From traditional underwriting and pricing - using

traditional data sources (risk data, industry traditional data sources (risk data, industry statistics) to predictive modeling and analytics - statistics) to predictive modeling and analytics - using non-traditional data sources using non-traditional data sources (demographics, GIS, 3rd party data, non-(demographics, GIS, 3rd party data, non-insurance data, non-verifiable data sources, etc.) insurance data, non-verifiable data sources, etc.)

From a stable risk control and claims environment From a stable risk control and claims environment to a dynamic environment of new hazards - mold, to a dynamic environment of new hazards - mold, terrorism, computer viruses, cyber terrorism, etc. terrorism, computer viruses, cyber terrorism, etc.

From risk-specific risk management to enterprise From risk-specific risk management to enterprise risk management risk management

2323

DataData From a data quality focus on validity, From a data quality focus on validity,

timeliness and accuracy to a data quality timeliness and accuracy to a data quality focus on transparency, completeness and focus on transparency, completeness and accuracy accuracy

From data available on a periodic basis to From data available on a periodic basis to data available real-time data available real-time

From statistical plans and edit packages to From statistical plans and edit packages to data dictionaries, schema and data dictionaries, schema and implementation guides implementation guides

From sharing data for the common good to From sharing data for the common good to protecting data for the common good protecting data for the common good

2424

TechnologyTechnology

From centralized highly controlled From centralized highly controlled technologies to ASPs, the, Internet, technologies to ASPs, the, Internet, XML, LANs, PCs, etc. XML, LANs, PCs, etc.

From IT as an business enabler to IT From IT as an business enabler to IT as a business driveras a business driver

From mainframes to LANS and high From mainframes to LANS and high powered PCs powered PCs

2525

How Do We Get There?How Do We Get There?

2626

How do we get there?How do we get there?

Enterprise Data StrategiesEnterprise Data Strategies– Assemble the right teamAssemble the right team– Business Needs – internal and external, Business Needs – internal and external,

current and futurecurrent and future– Technology – current and futureTechnology – current and future– New ProductsNew Products

New ProcessesNew Processes StandardsStandards Best PracticesBest Practices

2727

Data Users, Data Definers Data Users, Data Definers & Data Enablers& Data Enablers

Business Units (Underwriters)Business Units (Underwriters) Information TechnologyInformation Technology Finance and AccountingFinance and Accounting ActuariesActuaries ClaimsClaims Government AffairsGovernment Affairs Sales and MarketingSales and Marketing ResearchResearch Data ManagementData Management Data Element ManagementData Element Management

2828

New Processes: The Goal – Single EntryNew Processes: The Goal – Single Entry

A B

D

C

A – Form/Msg from Producer (agent/broker) to Carrier

Producer either waits for download, or does data entry to process binder, ID cards, certs.

Producer/ agent/ Broker

Carrier

ReinsurerServic

e Provid

er

Solution Provider/Vendor

“enabler”

B – Carrier processes data, syncronizes with agency data base through download

C – Messages from Carrier to Service Providers (CLUE, MVR)

D – Data may continue along the process to be used by Reinsurers, etc.

Real Time data entry

Download

Re-use of data

2929

Straight Through Processing Straight Through Processing (STP)(STP)

The use of common, industry standard The use of common, industry standard data elements, throughout all data elements, throughout all interactions of all parties, in all interactions of all parties, in all insurance transactions or processes. insurance transactions or processes.

STP allows data to flow effortlessly STP allows data to flow effortlessly through the industry without through the industry without redefinition, mappings or translations.redefinition, mappings or translations.

3030

STP VisionSTP Vision

Provides a common set of definitionsProvides a common set of definitions– Data definitionsData definitions– Not of every transaction or messageNot of every transaction or message

Allows consistent industry solutionsAllows consistent industry solutions– Vendor provided software solutionsVendor provided software solutions– Internally developed applicationsInternally developed applications

Facilitates exchange of informationFacilitates exchange of information Eliminates mappings and translationsEliminates mappings and translations Minimizes friction Minimizes friction

3131

STP ValueSTP Value Improves data quality, utilityImproves data quality, utility

– better benchmarkingbetter benchmarking Lessens data translations, eliminates Lessens data translations, eliminates

return transactions for clarificationreturn transactions for clarification Reduces friction in insurance Reduces friction in insurance

processesprocesses Allows companies to differentiate on Allows companies to differentiate on

value addedvalue added Facilitates “plug and play” solutionsFacilitates “plug and play” solutions

3232

STP BenefitsSTP Benefits

Improved Customer RelationshipImproved Customer Relationship– Less Time ProcessingLess Time Processing

Ease of Doing Business Ease of Doing Business Retention and GrowthRetention and Growth ProfitabilityProfitability

3333

StandardsStandards

3434

What are Standards?What are Standards?

Definition: Standard (n.) Definition: Standard (n.) “Anything “Anything recognized as correct by common recognized as correct by common consent, by approved custom, or by consent, by approved custom, or by those most competent to decide; a those most competent to decide; a model; a criterion.”model; a criterion.”

-- Webster’s New Universal Dictionary-- Webster’s New Universal Dictionary

3535

Types of StandardsTypes of Standards

Business ModelsBusiness Models– Identify All the Major Processes and Identify All the Major Processes and

RelationshipsRelationships Common Insurance TerminologyCommon Insurance Terminology Coverage and FormsCoverage and Forms Process StandardsProcess Standards

– Application Forms, Report of Injury or Application Forms, Report of Injury or Claim, Licensing, etc.Claim, Licensing, etc.

3636

Types of Standards (Continued)Types of Standards (Continued)

OtherOther– Solvency StandardsSolvency Standards– Financial Information Exchange Financial Information Exchange

StandardsStandards– Market Conduct Information StandardsMarket Conduct Information Standards– Ratemaking StandardsRatemaking Standards– Operating Data StandardsOperating Data Standards– Data Exchange StandardsData Exchange Standards– Data Quality StandardsData Quality Standards

3737

ACORD StandardsACORD Standards

Doing Things Once Has Many BenefitsDoing Things Once Has Many Benefits Data namesData names Data definitionsData definitions Paper or electronic operational forms Paper or electronic operational forms Machine readable formatsMachine readable formats Business Process ModelsBusiness Process Models Code list definitionsCode list definitions Data transmission standardsData transmission standards

3838

Data Collection Organization Data Collection Organization StandardsStandards

Policy Forms and CoveragesPolicy Forms and Coverages Rate Making StandardsRate Making Standards Data Reporting StandardsData Reporting Standards Data Quality StandardsData Quality Standards Data Element DefinitionsData Element Definitions Code List DefinitionsCode List Definitions

3939

Business ProcessBusiness Process

A business process is a collection of A business process is a collection of related structural activities that related structural activities that produce something of value to the produce something of value to the organization, its stake holders or its organization, its stake holders or its customers. customers.

It is, for example, the process through It is, for example, the process through which an organization realizes its which an organization realizes its services to its customers.services to its customers.

4040

Business RulesBusiness Rules

Business rules describe the Business rules describe the operations, definitions and operations, definitions and constraints that apply to an constraints that apply to an organization in achieving its goals. organization in achieving its goals.

For example a business rule might For example a business rule might state that state that no credit check is to be no credit check is to be performed on return customersperformed on return customers..

4141

Need for Industry CollaborationNeed for Industry Collaboration

Claims Management Applications

Auditing

RegulatoryCompliance

Payment transactions

Premium transactions

Broker/Insurer

Ins/Reinsurer

Claims

Submission

Reinsurer

Insurance Agency

Agent/Producer

Service Providers

RegulatoryAuthorities

Insurance Carriers

4242

Benefits of Industry Data StandardsBenefits of Industry Data Standards

Agent/Producer

InsuranceCarriers

RegulatoryAuthorities

ServiceProviders

InsuranceAgency

Reinsurer

Claims Management Applications

Regulatory Compliance

Payment transactions

Premium transactions

Broker/Insurer

Ins/Reinsurer

Claims

Submission

STANDARDS&

IMPLEMENTATIONAuditing

4343

StandardsStandards and Data and Data Management Best Management Best

PracticesPractices

4444

10 Guidelines of Data 10 Guidelines of Data ManagementManagement

1.1. Data must be fit for the intended business use. Data must be fit for the intended business use.

2.2. Data should be obtained from the authoritative Data should be obtained from the authoritative and appropriate source.and appropriate source.

4545


3.3. Data should be input only once and edited, Data should be input only once and edited, validated, and corrected at the point of entry. validated, and corrected at the point of entry.

4.4. Data should be captured and stored as Data should be captured and stored as informational values, not codes.informational values, not codes.

4646


5.5. Data should have a different steward responsible for Data should have a different steward responsible for defining the data, identifying and enforcing the business defining the data, identifying and enforcing the business rules, reconciling the data to the benchmark source, rules, reconciling the data to the benchmark source, assuring completeness, and managing data quality.assuring completeness, and managing data quality.

6.6. Common data elements must have a single documented Common data elements must have a single documented definition and be supported by documented business definition and be supported by documented business rules.rules.

4747


7.7. Metadata must be readily available to all Metadata must be readily available to all authorized users of the dataauthorized users of the data

8.8. Industry standards must be consulted Industry standards must be consulted and reviewed before a new data element and reviewed before a new data element is createdis created

4848


9.9. Data must be readily available to all Data must be readily available to all appropriate users and protected against appropriate users and protected against inappropriate access and useinappropriate access and use

10.10. Data users will use agreed upon common Data users will use agreed upon common tools and platforms throughout the enterprisetools and platforms throughout the enterprise

4949

Questions and Questions and CommentaryCommentary

5050

The Actuary and The The Actuary and The Data ManagerData Manager

Custodians of Enterprise Data Custodians of Enterprise Data AssetsAssets


March 2006March 2006

5151

AgendaAgenda

Data Management Best PracticesData Management Best Practices 10 Guidelines of Data Management10 Guidelines of Data Management TimelinesTimelines The Shifting Focus of Insurance InformationThe Shifting Focus of Insurance Information Information Quality and AssuranceInformation Quality and Assurance

– Data QualityData Quality– Data TransparencyData Transparency– ASOP #23ASOP #23

Regulatory Requirements and the Role of Data Regulatory Requirements and the Role of Data IDMA Data Management Value PropositionsIDMA Data Management Value Propositions Questions and CommentaryQuestions and Commentary Organizations That Can HelpOrganizations That Can Help

5252

PanelistsPanelists

Art Cadorine, ACAS, ISOArt Cadorine, ACAS, ISO Bruce Tollefson, MN WC Rating Bruce Tollefson, MN WC Rating

BureauBureau Christine Siekierski, WI Comp. Rating Christine Siekierski, WI Comp. Rating

BureauBureau Pete Marotta, AIDM, ISOPete Marotta, AIDM, ISO

5353

Data Management Data Management Best PracticesBest Practices

5454

Data Management Best PracticesData Management Best Practices Data Stewardship – establish a Data Stewardship – establish a

corporate data steward corporate data steward Data and Data Quality Standards – Data and Data Quality Standards –

foster the development and adoption foster the development and adoption of data and data quality standardsof data and data quality standards

Organizational Issues – structure Organizational Issues – structure organization to promote good data organization to promote good data management and data qualitymanagement and data quality

5555

Data Management Best PracticesData Management Best Practices Operations and Processes – Operations and Processes –

establish processes to maximize establish processes to maximize data quality and utilitydata quality and utility

Data Element Development and Data Element Development and Specification – design and maintain Specification – design and maintain data, systems and reporting data, systems and reporting mechanisms in a manner that mechanisms in a manner that promotes good data management promotes good data management and data qualityand data quality

5656


5757


1.1. Data must be fit for the intended business use. Data must be fit for the intended business use.

2.2. Data should be obtained from the authoritative Data should be obtained from the authoritative and appropriate source.and appropriate source.

5858


3.3. Data should be input only once and edited, Data should be input only once and edited, validated, and corrected at the point of entry. validated, and corrected at the point of entry.

4.4. Data should be captured and stored as Data should be captured and stored as informational values, not codes.informational values, not codes.

5959


5.5. Data should have a different steward responsible for Data should have a different steward responsible for defining the data, identifying and enforcing the business defining the data, identifying and enforcing the business rules, reconciling the data to the benchmark source, rules, reconciling the data to the benchmark source, assuring completeness, and managing data quality.assuring completeness, and managing data quality.

6.6. Common data elements must have a single documented Common data elements must have a single documented definition and be supported by documented business definition and be supported by documented business rules.rules.

6060


7.7. Metadata must be readily available to all Metadata must be readily available to all authorized users of the dataauthorized users of the data

8.8. Industry standards must be consulted Industry standards must be consulted and reviewed before a new data element and reviewed before a new data element is createdis created

6161


9.9. Data must be readily available to all Data must be readily available to all appropriate users and protected against appropriate users and protected against inappropriate access and useinappropriate access and use

10.10. Data users will use agreed upon common Data users will use agreed upon common tools and platforms throughout the enterprisetools and platforms throughout the enterprise

6262

TimelinesTimelines

6363

The PastThe Past

Regulators/BusinessRegulators/Business

(underwriters, actuaries, etc.)(underwriters, actuaries, etc.)

Coverage FormsCoverage Forms

(changes in forms and coverages)(changes in forms and coverages)


6464

TodayToday

TechnologyTechnology FinancialFinancial 33rdrd Parties Parties(Internet, XML, (SOX, GLB, HIPAA, etc.) (Credit, DMV,(Internet, XML, (SOX, GLB, HIPAA, etc.) (Credit, DMV,Black Boxes, RFIDs)Black Boxes, RFIDs) etc.) etc.)


6565

TomorrowTomorrow

Business Needs: Business, regulatory, technology, etc.Business Needs: Business, regulatory, technology, etc.(Profitability, Loss Control, Consumer Protection, Solvency, (Profitability, Loss Control, Consumer Protection, Solvency,

Privacy, Confidentiality, etc.)Privacy, Confidentiality, etc.)

Data NeedsData Needs


6666

The Shifting Focus of The Shifting Focus of Insurance Information Insurance Information

6767

Regulation Regulation From Annual Statement to Market Conduct From Annual Statement to Market Conduct

Annual Statements to NAIC Databases Annual Statements to NAIC Databases – Financial Data Repository (FDR) Financial Data Repository (FDR) – National Insurance Producer Registry (NIPR) National Insurance Producer Registry (NIPR) – Fingerprint Repository Fingerprint Repository – On-Line Fraud Reporting System (OFRS) On-Line Fraud Reporting System (OFRS) – Uninsured Motorist Identification DatabaseUninsured Motorist Identification Database

From financial data used to monitor solvency From financial data used to monitor solvency to financial, statistical data and analytics used to financial, statistical data and analytics used to monitor solvency to monitor solvency

From US driven regulations to EU and From US driven regulations to EU and internationally driven regulations internationally driven regulations

6868

PricingPricing From traditional underwriting and pricing - using From traditional underwriting and pricing - using

traditional data sources (risk data, industry traditional data sources (risk data, industry statistics) to predictive modeling and analytics - statistics) to predictive modeling and analytics - using non-traditional data sources using non-traditional data sources (demographics, GIS, 3rd party data, non-(demographics, GIS, 3rd party data, non-insurance data, non-verifiable data sources, etc.) insurance data, non-verifiable data sources, etc.)

From a stable risk control and claims environment From a stable risk control and claims environment to a dynamic environment of new hazards - mold, to a dynamic environment of new hazards - mold, terrorism, computer viruses, cyber terrorism, etc. terrorism, computer viruses, cyber terrorism, etc.

From risk-specific risk management to enterprise From risk-specific risk management to enterprise risk management risk management

6969

DataData From a data quality focus on validity, From a data quality focus on validity,

timeliness and accuracy to a data quality timeliness and accuracy to a data quality focus on transparency, completeness and focus on transparency, completeness and accuracy accuracy

From data available on a periodic basis to From data available on a periodic basis to data available real-time data available real-time

From statistical plans and edit packages to From statistical plans and edit packages to data dictionaries, schema and data dictionaries, schema and implementation guides implementation guides

From sharing data for the common good to From sharing data for the common good to protecting data for the common good protecting data for the common good

7070

TechnologyTechnology

From centralized highly controlled From centralized highly controlled technologies to ASPs, the, Internet, technologies to ASPs, the, Internet, XML, LANs, PCs, etc. XML, LANs, PCs, etc.

From IT as an business enabler to IT From IT as an business enabler to IT as a business driveras a business driver

From mainframes to LANS and high From mainframes to LANS and high powered PCs powered PCs

7171

Information Quality Information Quality

and Assuranceand Assurance

7272

Data QualityData Quality

Data Quality is defined as the Data Quality is defined as the process for ensuring that data are process for ensuring that data are

fit for the use intended by fit for the use intended by measuring and improving itsmeasuring and improving its

key characteristics.key characteristics.

7373

Managing Data & Data Quality: Managing Data & Data Quality: Guiding PrinciplesGuiding Principles

Data is a corporate assetData is a corporate asset Data should be fit for the use Data should be fit for the use

intendedintended Data should flow from underlying Data should flow from underlying

business processesbusiness processes Data quality should be managed as Data quality should be managed as

close to the source as possibleclose to the source as possible Best Practices are ever evolvingBest Practices are ever evolving

7474

Data Quality: Key CharacteristicsData Quality: Key Characteristics

Fit for its intended useFit for its intended use AccuracyAccuracy ValidityValidity Timeliness and Other Timing CriteriaTimeliness and Other Timing Criteria Completeness or EntiretyCompleteness or Entirety ReasonabilityReasonability Absence of RedundancyAbsence of Redundancy Accessibility, Availability and Accessibility, Availability and

CohesivenessCohesiveness PrivacyPrivacy

7575

Data Transparency: Key Data Transparency: Key CharacteristicsCharacteristics

Data defined and documentedData defined and documented Utility across time and sourceUtility across time and source Supports internal controls.Supports internal controls. Clear, standardized, comparable informationClear, standardized, comparable information Facilitates assessment of the health of the Facilitates assessment of the health of the

systems using the datasystems using the data Promotes better controlsPromotes better controls Improves operational and financial performanceImproves operational and financial performance Documents data elements, data element Documents data elements, data element

transformations and processestransformations and processes

7676

ASOP #23: Data QualityASOP #23: Data Quality

Purpose is to give guidance in:Purpose is to give guidance in:– Selecting dataSelecting data– Reviewing data for appropriateness, Reviewing data for appropriateness,

reasonableness, and reasonableness, and comprehensivenesscomprehensiveness

– Making appropriate disclosuresMaking appropriate disclosures Does not recommend that actuaries Does not recommend that actuaries

audit dataaudit data

7777

ASAP #23: Data QualityASAP #23: Data QualityConsiderations in Selection of DataConsiderations in Selection of Data

Appropriateness for intended Appropriateness for intended purposepurpose

Reasonableness, Reasonableness, comprehensiveness, and consistencycomprehensiveness, and consistency

Limitations of or modifications to Limitations of or modifications to datadata

Cost and feasibility of alternativesCost and feasibility of alternatives Sampling methodsSampling methods

7878

ASOP #23: Data QualityASOP #23: Data QualityDefinition of DataDefinition of Data

Numerical, census, or class Numerical, census, or class informationinformation

Not actuarial assumptionsNot actuarial assumptions Not computer softwareNot computer software Definition of comprehensiveDefinition of comprehensive Definition of appropriateDefinition of appropriate

7979

ASAP #23: Data QualityASAP #23: Data QualityOther ConsiderationsOther Considerations

Imperfect DataImperfect Data Reliance on OthersReliance on Others Documentation/DisclosureDocumentation/Disclosure

8080

Regulatory Regulatory Requirements and the Requirements and the

Role of DataRole of Data

8181

Why Regulation?Why Regulation?

It’s all about consumer protectionIt’s all about consumer protection– SolvencySolvency

Ensuring that companies are financially Ensuring that companies are financially sound and able to pay claimssound and able to pay claims

– Market ConductMarket ConductPoint of sale and servicePoint of sale and serviceEnsuring that the agent is licensed and Ensuring that the agent is licensed and

appointed, the customer understands the appointed, the customer understands the coverage, claims are handled effectively (i.e. coverage, claims are handled effectively (i.e. injured workers are paid on a timely basis)injured workers are paid on a timely basis)

– Rate AdequacyRate Adequacy

8282

The Impact of Standards on the The Impact of Standards on the US Regulatory LandscapeUS Regulatory Landscape

US Office of Management & Budget Circular US Office of Management & Budget Circular A-119A-119– ““[Government] agencies should recognize [Government] agencies should recognize

the positive contribution of standards the positive contribution of standards development and related activities. When development and related activities. When properly conducted, standards properly conducted, standards development can increase productivity development can increase productivity and efficiency in Government and and efficiency in Government and industry, expand opportunities for industry, expand opportunities for international trade, conserve resources...”international trade, conserve resources...”

8383

The Impact of Standards on the The Impact of Standards on the US Regulatory LandscapeUS Regulatory Landscape

Government should utilize standards Government should utilize standards built by the industry and built by the industry and implemented within company implemented within company operationsoperations– Cuts expensesCuts expenses– Ensures STP and qualityEnsures STP and quality

8484

Insurance Company

Data Collection

Data Storage

Data Sharing

Rating Bureaus

Stat Agencies

Residual Market Plans

DOIs

WC Commissions

DMVs

DOTs

SEC

Treasury

Homeland Security

HHS

Industry, State and Federal Industry, State and Federal RequirementsRequirements

Industry State

Federal

8585

Regulatory Issues & DataRegulatory Issues & Data Reporting RequirementsReporting Requirements

– FinancialFinancial– DMVDMV– Workers CompensationWorkers Compensation– StatisticalStatistical

Market ConductMarket Conduct OperationsOperations

– Electronic ApplicationsElectronic ApplicationsUETAUETAeSIGNeSIGNPrivacy (HIPAA, GLB)Privacy (HIPAA, GLB)

8686

Current Successes in Standardizing Current Successes in Standardizing Data for Regulatory PurposesData for Regulatory Purposes

Workers Compensation InsuranceWorkers Compensation Insurance– Boards and bureaus (statistical reporting)Boards and bureaus (statistical reporting)– State WC Commissions (proof of coverage and State WC Commissions (proof of coverage and

monitoring claims)monitoring claims) Producer licensing and appointmentsProducer licensing and appointments

– Producer to carrier information needsProducer to carrier information needs– State issues such as National Producer NumberState issues such as National Producer Number

State application compliance and filingsState application compliance and filings– Interstate CompactInterstate Compact

8787

Accountability, Quality,Accountability, Quality, Transparency Regulations Transparency Regulations

Sarbanes Oxley Sarbanes Oxley – US law ensuring accuracy of financial data with US law ensuring accuracy of financial data with

accountability of company executivesaccountability of company executives Solvency IISolvency II

– EU proposal similar to SOX addressing financial EU proposal similar to SOX addressing financial reporting and public disclosurereporting and public disclosure

Reinsurance TransparencyReinsurance Transparency– International Association of Insurance International Association of Insurance

Supervisors working group to explore solvency Supervisors working group to explore solvency of reinsurers worldwide. Differences in data of reinsurers worldwide. Differences in data definitions are presenting a challengedefinitions are presenting a challenge

8888

““SOX” and the Data ManagerSOX” and the Data Manager

The importance and visibility of Data The importance and visibility of Data Management among senior executives and Management among senior executives and regulators has increased.regulators has increased.

The importance of Data as an important The importance of Data as an important corporate resources has increased.corporate resources has increased.

The contribution of Data Management to The contribution of Data Management to proper data and process control is more proper data and process control is more widely recognized.widely recognized.

The demand for data quality has The demand for data quality has increased.increased.

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IDMA Data Management IDMA Data Management Value PropositionsValue Propositions

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Data Management ValueData Management Value

Product Development and Revenue Product Development and Revenue Generation: Maintains data management Generation: Maintains data management processes and tools that promote speed-to-processes and tools that promote speed-to-market of new products and servicesmarket of new products and services

Enhances customer acquisition, retention, Enhances customer acquisition, retention, service and satisfaction through good service and satisfaction through good quality customer dataquality customer data

Maintains the data management processes Maintains the data management processes and tools that support the pricing of and tools that support the pricing of insurance productsinsurance products

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Data Management ValueData Management Value Provides an enterprise Provides an enterprise

communication channel for new communication channel for new products, services, programs and products, services, programs and technologies that allows all facets of technologies that allows all facets of the organization to evaluate the the organization to evaluate the impact of these changesimpact of these changes

Specifies data needed to support new Specifies data needed to support new products and ensures that these data products and ensures that these data are assessable in a timely mannerare assessable in a timely manner

9292

Data Management ValueData Management Value Efficiency and UtilityEfficiency and Utility

– Reduces the cost of data collection, storage, and Reduces the cost of data collection, storage, and dispersaldispersal

– Manages data content and definition across the Manages data content and definition across the organizationorganization

– Advocates industry and enterprise data standards Advocates industry and enterprise data standards which insure consistent definitions and values for which insure consistent definitions and values for enterprise data elementsenterprise data elements

– Ensures accurate booking of premium and loss Ensures accurate booking of premium and loss transactionstransactions

– Ensures the quality of the enterprise dataEnsures the quality of the enterprise data– Promotes the interoperability of data and Promotes the interoperability of data and

databasesdatabases

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Data Management ValueData Management Value Strategic PlanningStrategic Planning

– Participates in the development of an Participates in the development of an enterprise data vision and strategyenterprise data vision and strategy

– Monitors external activities and reporting on Monitors external activities and reporting on potential impact on enterprisepotential impact on enterprise

ComplianceCompliance– Protects the privacy and confidentiality of the Protects the privacy and confidentiality of the

enterprise dataenterprise data– Ensures compliance with data reporting laws Ensures compliance with data reporting laws

and regulations,and regulations,– Represents the organization to regulators, Represents the organization to regulators,

workers’ compensation administrators, workers’ compensation administrators, advisory organizations, research advisory organizations, research organizations, standards organizations and organizations, standards organizations and other industry groupsother industry groups

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Questions and Questions and CommentaryCommentary

1 cas ratemaking seminar march 2006: data-3: the actuary and data standards data-1: the actuary and...

Documents

data standards data

data architecture

managed data

data data location software

regulations data

data resources

data transparency

data manager slide