CAS Ratemaking Seminar March 2006: Data-3: The Actuary and Data Standards
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Transcript of CAS Ratemaking Seminar March 2006: Data-3: The Actuary and Data Standards
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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
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The Actuary and Data The Actuary and Data StandardsStandards
Yesterday, Today and Yesterday, Today and TomorrowTomorrow
CAS Ratemaking Seminar CAS Ratemaking Seminar March 2006March 2006
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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
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PanelistsPanelists
Art Cadorine, ACAS, ISOArt Cadorine, ACAS, ISO Gary Knoble, AIDMGary Knoble, AIDM Pete Marotta, AIDM, ISOPete Marotta, AIDM, ISO
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Strategic Data Strategic Data PlanningPlanning
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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.
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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.
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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 a Companies that manage their data as a strategic resource and invest in its strategic resource and invest in its quality are already pulling ahead in quality are already pulling ahead in terms of reputation and profitability from terms of reputation and profitability from those that fail to do so.” those that fail to do so.”
Global Data Management Survey 2001, Global Data Management Survey 2001, PriceWaterhouseCoopersPriceWaterhouseCoopers
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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.
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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
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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
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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.
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Enterprise Data Strategy and IT: Enterprise Data Strategy and IT: Architecture Supports Business StrategyArchitecture Supports Business Strategy
Business Strategy
IT Architecture
Infr
astr
uctu
re
App
licat
ion
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
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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
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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
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TimelinesTimelines
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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
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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.)
Data StandardsData Standards
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TomorrowTomorrowBusiness 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
Data StandardsData Standards
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The Shifting Focus of The Shifting Focus of Insurance Information Insurance Information
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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
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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
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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
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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
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How Do We Get There?How Do We Get There?
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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
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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
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New Processes: The Goal – Single EntryNew Processes: The Goal – Single Entry
A B
D
C
A – Form/Msg from Producer (agent/broker) to CarrierProducer either waits for download, or does data entry to process binder, ID cards, certs.
Producer/ agent/ Broker
Carrier
Reinsurer Service
Provider
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
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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.
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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
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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
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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
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StandardsStandards
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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
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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.
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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
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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
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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
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Business ProcessBusiness ProcessA business process is a collection of A business process is a collection of related structural activities that produce related structural activities that produce something of value to the organization, something of value to the organization, its stake holders or its customers. its stake holders or its 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.
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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..
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Need for Industry CollaborationNeed for Industry Collaboration
Claims Management Applications
Auditing
RegulatoryCompliance
Payment transactions
Premium transactions
Broker/InsurerIns/Reinsurer
Claims
Submission
Reinsurer
Insurance Agency
Agent/Producer
Service Providers
RegulatoryAuthorities
Insurance Carriers
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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/InsurerIns/Reinsurer
Claims
Submission
STANDARDS&
IMPLEMENTATIONAuditing
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StandardsStandards and Data and Data Management Best Management Best
PracticesPractices
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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.
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10 Guidelines of Data 10 Guidelines of Data ManagementManagement
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.
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10 Guidelines of Data 10 Guidelines of Data ManagementManagement
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.
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10 Guidelines of Data 10 Guidelines of Data ManagementManagement
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
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10 Guidelines of Data 10 Guidelines of Data ManagementManagement
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
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Questions and Questions and CommentaryCommentary
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The Actuary and The The Actuary and The Data ManagerData Manager
Custodians of Enterprise Data Custodians of Enterprise Data AssetsAssets
CAS Ratemaking Seminar CAS Ratemaking Seminar March 2006March 2006
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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
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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
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Data Management Data Management Best PracticesBest Practices
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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
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Data Management Best PracticesData Management Best Practices Operations and Processes – establish Operations and Processes – establish
processes to maximize data quality processes to maximize data quality and utilityand 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
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10 Guidelines of Data 10 Guidelines of Data ManagementManagement
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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.
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10 Guidelines of Data 10 Guidelines of Data ManagementManagement
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.
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10 Guidelines of Data 10 Guidelines of Data ManagementManagement
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.
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10 Guidelines of Data 10 Guidelines of Data ManagementManagement
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
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10 Guidelines of Data 10 Guidelines of Data ManagementManagement
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
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TimelinesTimelines
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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
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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.)
Data StandardsData Standards
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TomorrowTomorrowBusiness 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
Data StandardsData Standards
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The Shifting Focus of The Shifting Focus of Insurance Information Insurance Information
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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
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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
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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
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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
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Information Quality Information Quality
and Assuranceand Assurance
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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.
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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 intendedData should be fit for the use intended 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
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Data Quality: Key CharacteristicsData Quality: Key CharacteristicsFit 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
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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
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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
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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
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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
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ASAP #23: Data QualityASAP #23: Data QualityOther ConsiderationsOther Considerations
Imperfect DataImperfect Data Reliance on OthersReliance on Others Documentation/DisclosureDocumentation/Disclosure
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Regulatory Regulatory Requirements and the Requirements and the
Role of DataRole of Data
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Why Regulation?Why Regulation? It’s all about consumer protectionIt’s all about consumer protection
– SolvencySolvencyEnsuring that companies are financially Ensuring that companies are financially
sound and able to pay claimssound and able to pay claims– Market ConductMarket Conduct
Point 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
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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...”
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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
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Insurance Company
Data CollectionData StorageData Sharing
Rating BureausStat Agencies
Residual Market Plans
DOIsWC Commissions
DMVsDOTs
SECTreasury
Homeland SecurityHHS
Industry, State and Federal Industry, State and Federal RequirementsRequirements
Industry State
Federal
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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)
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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
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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
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““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 increased.The demand for data quality has 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 quality service and satisfaction through good quality customer datacustomer data
Maintains the data management processes Maintains the data management processes and tools that support the pricing of insurance and tools that support the pricing of insurance productsproducts
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Data Management ValueData Management Value Provides an enterprise communication Provides an enterprise communication
channel for new products, services, channel for new products, services, programs and technologies that allows programs and technologies that allows all facets of the organization to all facets of the organization to evaluate the impact of these changesevaluate the impact 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
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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