Data & Information Quality in a Rapidly Changing World CAS Annual Meeting Chicago November 2007

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Data & Information Quality Data & Information Quality in a Rapidly Changing in a Rapidly Changing

WorldWorld

CAS Annual MeetingCAS Annual MeetingChicago November 2007Chicago November 2007

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AgendaAgenda

IntroductionIntroduction Data & Information Quality, and Data & Information Quality, and

Transparency Transparency The Shifting Focus of Insurance The Shifting Focus of Insurance

Information and the Impact on Data & Information and the Impact on Data & Information QualityInformation Quality

Data Quality EvolutionData Quality Evolution– CAS Data Management Educational CAS Data Management Educational

Materials Working Party (WP5)Materials Working Party (WP5)

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Introduction Introduction

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PanelistsPanelists

Pete Marotta AIDM, FIDM - Insurance Pete Marotta AIDM, FIDM - Insurance Services OfficeServices Office

Gary Knoble AIDM, FIDM – US Asia BFSGary Knoble AIDM, FIDM – US Asia BFS Tom Nowak CPCU, CIDM, FIDM - AIG Tom Nowak CPCU, CIDM, FIDM - AIG Tracy Spadola CPCU, CIDM, AAM, FIDM – Tracy Spadola CPCU, CIDM, AAM, FIDM –

Teradata Corp.Teradata Corp.

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Data IssuesData Issues

““(Insurance professionals) … need solutions that will (Insurance professionals) … need solutions that will help them gain insight into risk, cause of loss, and help them gain insight into risk, cause of loss, and resulting claims. They need to model ways to resulting claims. They need to model ways to predict such eventualities … Success in these predict such eventualities … Success in these pursuits will increase profitability and much pursuits will increase profitability and much depends on the quality of the data. So, … why depends on the quality of the data. So, … why isn’t everyone who is dedicated to growing profits isn’t everyone who is dedicated to growing profits equally concerned about data?”equally concerned about data?”

Sharon Schwartzman, Editor-in-Chief, Techdecisions, “Culture Sharon Schwartzman, Editor-in-Chief, Techdecisions, “Culture Shock?” an editorial in the August 2007 edition of Shock?” an editorial in the August 2007 edition of

TechdecisionsTechdecisions

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Data IssuesData Issues Data drives the insurance process and is also the by-product Data drives the insurance process and is also the by-product

of the process of the process Many “users” including:Many “users” including:

– Insurers – individual and in aggregateInsurers – individual and in aggregate– RegulatorsRegulators– ConsumersConsumers

Growing number of external (public and private) data Growing number of external (public and private) data sourcessources

Increasing granularity of data Increasing granularity of data Technology allowing for improved access to data and Technology allowing for improved access to data and

analytical tools that use these dataanalytical tools that use these data Increasing use of data , technology and analytics to gain a Increasing use of data , technology and analytics to gain a

competitive advantagecompetitive advantage Line among financial, statistical and public data is blurringLine among financial, statistical and public data is blurring Most important data quality problems aren’t obvious, but Most important data quality problems aren’t obvious, but

many can be identified through difficulties in achieving many can be identified through difficulties in achieving important business objectives.important business objectives.

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Data IssuesData Issues

Expanding needs for data including:Expanding needs for data including:– Risk selectionRisk selection– Rate regulationRate regulation– Rate and price analysis Rate and price analysis – SolvencySolvency– Marketing Marketing – Product developmentProduct development– Market conduct monitoringMarket conduct monitoring– Fraud detection and preventionFraud detection and prevention– Loss controlLoss control

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Data IssuesData Issues

GIRO working party on data quality survey:GIRO working party on data quality survey:– On average any consultant, company or On average any consultant, company or

reinsurance actuary:reinsurance actuary:– spends spends 26%26% of their time on data of their time on data

quality issuesquality issues– 32%32% of projects affected by data of projects affected by data

quality problemsquality problems

General Insurance Research Organization General Insurance Research Organization (GIRO) is a part of the UK and Irish actuarial(GIRO) is a part of the UK and Irish actuarialorganization - the Institute of Actuariesorganization - the Institute of Actuaries

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Data & Information Data & Information

Quality and Quality and TransparencyTransparency

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Why Data Quality?Why Data Quality?

““The only people who need not worry The only people who need not worry about data quality are those who about data quality are those who neither create nor use data. No one neither create nor use data. No one participating in any modern participating in any modern economy can make that claim.”economy can make that claim.”

Data Quality: The Field Guide, Thomas C. Redman, Ph.D.Data Quality: The Field Guide, Thomas C. Redman, Ph.D.

Digital Press, 2001Digital Press, 2001

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Data Quality: Key CharacteristicsData Quality: Key Characteristics

Data Quality can be defined as the process for Data Quality can be defined as the process for ensuring that ensuring that data are fit for the use intendeddata are fit for the use intended by by measuring and improving its key characteristics.measuring and improving its key characteristics.– AccuracyAccuracy– ValidityValidity– Timeliness and Other Timing CriteriaTimeliness and Other Timing Criteria– Completeness or EntiretyCompleteness or Entirety– PrecisionPrecision– ReasonabilityReasonability– Absence of RedundancyAbsence of Redundancy– Accessibility, Availability and CohesivenessAccessibility, Availability and Cohesiveness– PrivacyPrivacy

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Data Transparency: Key CharacteristicsData Transparency: Key Characteristics

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 Clear, standardized, comparable

informationinformation Facilitates assessment of the health of Facilitates assessment of the health of

the systems using the datathe systems using the data Promotes better controlsPromotes better controls Improves operational and financial Improves operational and financial

performanceperformance Documents data elements, data element Documents data elements, data element

transformations and processestransformations and processes

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Data Quality vs Information QualityData Quality vs Information Quality

Information quality takes into account Information quality takes into account not just data quality not just data quality but processing quality but processing quality (and reporting quality)(and reporting quality)

DATA + ANALYSIS = RESULTS

T. Dasu and T. Johnson, Exploratory Data Mining and Data Cleaning,

Wiley, 2003

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Information Quality ComponentsInformation Quality Components

Information Definition Quality: definitions, specifications Information Definition Quality: definitions, specifications and business rulesand business rules

Information Content Quality: “raw materials” of information Information Content Quality: “raw materials” of information – key characteristics are completeness, validity, accuracy, – key characteristics are completeness, validity, accuracy, precisionprecision

Information Presentation Quality: the “finished product” – Information Presentation Quality: the “finished product” – key DQ characteristics are accessibility, timeliness, key DQ characteristics are accessibility, timeliness, presentation intuitiveness and freedom from biaspresentation intuitiveness and freedom from bias

““IQ Characteristics: Information Definition Quality” by Larry P. IQ Characteristics: Information Definition Quality” by Larry P. English, April 2006 Vol. 2, Issue 2, The Information and Data English, April 2006 Vol. 2, Issue 2, The Information and Data Quality NewsletterQuality Newsletter

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What Is Data Quality?What Is Data Quality?

““In the end, the customer determines In the end, the customer determines quality.”quality.”

Improving Data Warehouse and Business Information Quality,Improving Data Warehouse and Business Information Quality,

Larry P. English, Wiley Computer Publishing, 1999Larry P. English, Wiley Computer Publishing, 1999

So, another way of defining Data So, another way of defining Data Quality is Quality is meeting customer’s meeting customer’s

expectationsexpectations..

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Understanding Customer ExpectationsUnderstanding Customer Expectations

Complicating factorsComplicating factors– Customers do not know what they wantCustomers do not know what they want– Customers have a varied range of needsCustomers have a varied range of needs– Some customer needs conflictSome customer needs conflict– Customer needs change all the timeCustomer needs change all the time– Customers change over timeCustomers change over time

This is further complicated when the focus is on This is further complicated when the focus is on data, as in many cases the data underlying a data, as in many cases the data underlying a service or product goes unrecognized by the service or product goes unrecognized by the customercustomer

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Understanding Customer Expectations Understanding Customer Expectations

Identify customer needsIdentify customer needs– Identify the most important customersIdentify the most important customers– Identify which products and services are most Identify which products and services are most

important to themimportant to them– Learn how data and information support these Learn how data and information support these

products and servicesproducts and services– Determine customer expectations regarding these Determine customer expectations regarding these

products and services and the necessary data products and services and the necessary data quality levels needed to meet these expectationsquality levels needed to meet these expectations

– Assess the data quality levels of these products and Assess the data quality levels of these products and servicesservices

– Identify any data quality gaps Identify any data quality gaps – Identify and prioritize improvementsIdentify and prioritize improvements

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Expectations: An Actuarial PerspectiveExpectations: An Actuarial Perspective

Actuarial Standard Of Practice #23: Data Actuarial Standard Of Practice #23: Data QualityQuality– “… “… suitable for the intended purpose of an suitable for the intended purpose of an

analysis and relevant to the system or process analysis and relevant to the system or process being analyzed” being analyzed”

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

and comprehensivenessand comprehensiveness Making appropriate disclosuresMaking appropriate disclosures

– Does not recommend that actuaries audit dataDoes not recommend that actuaries audit data– Considerations in Selection of DataConsiderations in Selection of Data– Definition of DataDefinition of Data

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Expectations: A Financial PerspectiveExpectations: A Financial Perspective

Accountability, Quality and Transparency Accountability, Quality and Transparency RegulationsRegulations– 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 regulations similar to SOX addressing financial EU regulations similar to SOX addressing financial

reporting and public disclosurereporting and public disclosure

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

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

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Expectations: Impact of “SOX” on P & C Expectations: Impact of “SOX” on P & C InsurersInsurers

Processes and controlsProcesses and controls– Data control and reconciliationData control and reconciliation– Systems testingSystems testing– Testing and assessmentTesting and assessment

Data Quality and Data Transparency are keyData Quality and Data Transparency are key DocumentationDocumentation Strategic PlanningStrategic Planning ComplianceCompliance

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The Shifting Focus of The Shifting Focus of Insurance Information and the Insurance Information and the Impact on Data & Information Impact on Data & Information

QualityQuality

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Regulation: Changing EnvironmentRegulation: Changing Environment From Annual Statement to Market Conduct Annual From Annual Statement to Market Conduct Annual

Statements to NAIC Databases 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 to financial, From financial data used to monitor solvency to financial, statistical data and analytics used to monitor enterprise statistical data and analytics used to monitor enterprise risk risk

From US driven privacy regulations to internationally From US driven privacy regulations to internationally driven privacy regulations driven privacy regulations

Solvency and financial reporting: NAIC (National Solvency and financial reporting: NAIC (National Association of Insurance Commissioners) to NAIC and Association of Insurance Commissioners) to NAIC and IAIS (International Association of Insurance Supervisors) IAIS (International Association of Insurance Supervisors) and IASB (International Accounting Standards Board)and IASB (International Accounting Standards Board)

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Regulation: Impact on Data and Data Regulation: Impact on Data and Data Management - Within a CompanyManagement - Within a Company

The need for data transparency: The need for data transparency: – Documenting data elements, data element Documenting data elements, data element

transformations and processes transformations and processes – Supporting internal controlsSupporting internal controls– Promoting clear, standardized, comparable informationPromoting clear, standardized, comparable information

Increased emphasis on:Increased emphasis on:– Protecting the privacy and confidentiality of the Protecting the privacy and confidentiality of the

enterprise dataenterprise data– Compliance with rating and reporting laws and Compliance with rating and reporting laws and

regulationsregulations– Communication with regulatorsCommunication with regulators– Solvency and the measurement of solvencySolvency and the measurement of solvency

Recognition of the regulatory issues associated with re-Recognition of the regulatory issues associated with re-purposing data – purposing data – financial/statistical/operational/demographic/etc.financial/statistical/operational/demographic/etc.

Awareness of regulations beyond the insurance space Awareness of regulations beyond the insurance space and beyond the US borders.and beyond the US borders.

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Regulation: Impact on Data and Data Regulation: Impact on Data and Data Management - Within the IndustryManagement - Within the Industry

Promoting the interoperability of data and databases at Promoting the interoperability of data and databases at the local, state and national levelsthe local, state and national levels

The need for clear, standardized, comparable The need for clear, standardized, comparable informationinformation

The need for well defined data reporting requirements The need for well defined data reporting requirements and data dictionaries, promoting consistency across lines and data dictionaries, promoting consistency across lines of business and time of business and time

Awareness of regulations beyond the insurance space Awareness of regulations beyond the insurance space and beyond the US bordersand beyond the US borders

Recognition of the regulatory issues associated with re-Recognition of the regulatory issues associated with re-purposing data – purposing data – financial/statistical/operational/demographic/etcfinancial/statistical/operational/demographic/etc

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Regulation: Impact on D & IQRegulation: Impact on D & IQ Changing Changing

ExpectationsExpectations– Data transparency: Data transparency:

documentation, documentation, controls, etc.controls, etc.

– Privacy, confidentiality, Privacy, confidentiality, compliance, solvency compliance, solvency

– Regulatory issues Regulatory issues associated with re-associated with re-purposing datapurposing data

– Regulations beyond the Regulations beyond the insurance space and insurance space and beyond the US bordersbeyond the US borders

– Interoperability of data Interoperability of data and databasesand databases

D & IQ Impact D & IQ Impact – MetadataMetadata– Data modelsData models– Data and process flowsData and process flows– Mapping documentationMapping documentation– Data standardsData standards– Data transformation Data transformation

and generationand generation– Detailed specificationsDetailed specifications– Identification of data Identification of data

sources: internal and sources: internal and external external

– Regulatory monitoringRegulatory monitoring– AuditingAuditing– Knowledge Knowledge

Management Management – VersioningVersioning

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Data Analysis: Changing EnvironmentData Analysis: Changing Environment From traditional underwriting and pricing - using From traditional underwriting and pricing - using

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

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

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

From traditional actuarial pricing methodologies to use From traditional actuarial pricing methodologies to use of models – notably catastrophe modelsof models – notably catastrophe models

Use of non-insurance specific data used for pricing and Use of non-insurance specific data used for pricing and underwriting - credit scores, insured occupation, underwriting - credit scores, insured occupation, household data, etc.household data, etc.

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Data Analysis: Impact on Data and Data Data Analysis: Impact on Data and Data ManagementManagement

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

New, different and more granular dataNew, different and more granular data From data available on a periodic basis to data available From data available on a periodic basis to data available

real-timereal-time From statistical plans and edit packages to data From statistical plans and edit packages to data

dictionaries, schema and implementation guides dictionaries, schema and implementation guides From structured data to structured and unstructured From structured data to structured and unstructured

datadata From static geographic data points – ZIP Code, Territory, From static geographic data points – ZIP Code, Territory,

etc. – to dynamic Geographic Information Systems (GIS) etc. – to dynamic Geographic Information Systems (GIS) and real-time data continuumsand real-time data continuums

From internal data sources to internal, industry and From internal data sources to internal, industry and third-party data sourcesthird-party data sources

Increased use of analytical toolsIncreased use of analytical tools

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Data Analysis: Impact on Data and Data Analysis: Impact on Data and Data ManagementData Management

Reducing the cost and time associated with data Reducing the cost and time associated with data collection, storage, and dispersal, making data available collection, storage, and dispersal, making data available more quicklymore quickly

Promoting the interoperability of data and databases, Promoting the interoperability of data and databases, allowing for better data integration thereby giving the allowing for better data integration thereby giving the users more options for how data can be usedusers more options for how data can be used

Managing data content and definition across the Managing data content and definition across the organization which promotes consistency across organization which promotes consistency across business units and across time – internally and externallybusiness units and across time – internally and externally

Ensuring the quality of the enterprise data, enterprise Ensuring the quality of the enterprise data, enterprise communication among the various data sourcescommunication among the various data sources

Recognizing the issues associated with re-purposing Recognizing the issues associated with re-purposing data – contractual, regulatory, technological, data, data data – contractual, regulatory, technological, data, data quality, etc.quality, etc.

Mapping of data across disparate sources – documenting Mapping of data across disparate sources – documenting data gaps and significant differencesdata gaps and significant differences

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Data Analysis: Impact on D & IQData Analysis: Impact on D & IQ Changing Changing

ExpectationsExpectations– Increased emphasis on Increased emphasis on

transparency, transparency, completeness, accuracycompleteness, accuracy

– Integration of data from Integration of data from multiple sourcesmultiple sources

– Increased use of third Increased use of third party dataparty data

– Re-use of dataRe-use of data– More granular and More granular and

different data, including different data, including real-time data real-time data continuumscontinuums

– Data prep for use in Data prep for use in analytical tools and analytical tools and modelsmodels

D & IQ ImpactD & IQ Impact– MetadataMetadata– Master Data Master Data

Management (MDM)Management (MDM)– Mapping documentationMapping documentation– Data standardsData standards– Data transformation Data transformation

and generationand generation– Internal and external Internal and external

data sourcesdata sources– Data miningData mining– Text miningText mining– Unstructured dataUnstructured data– Need for more scientific Need for more scientific

measuresmeasures

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Technology: Changing EnvironmentTechnology: Changing Environment

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

From technology as a business enabler to technology as From technology as a business enabler to technology as a business drivera business driver

From mainframes to LANS and high powered PCsFrom mainframes to LANS and high powered PCs From data collection to ETL (Extract Transform, Load)From data collection to ETL (Extract Transform, Load) Data and access using new technologies, for example - Data and access using new technologies, for example -

– HandheldsHandhelds– VoIPVoIP– Smart PhonesSmart Phones

GPSs, Black Boxes, RFIDs, weather data, etc.GPSs, Black Boxes, RFIDs, weather data, etc.

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Technology: Impact on Data and Technology: Impact on Data and Data ManagementData Management

Managing data over many moving/continuous Managing data over many moving/continuous data points v. data points fixed in timedata points v. data points fixed in time

Measuring the quality of these new types of Measuring the quality of these new types of data  data 

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

How to use and store new types of data. The How to use and store new types of data. The need for data “trigger points”need for data “trigger points”

Protecting data from inappropriate use Protecting data from inappropriate use New methods for protecting the privacy and New methods for protecting the privacy and

confidentiality of dataconfidentiality of data

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Technology: Impact on Data and Technology: Impact on Data and Data ManagementData Management

Balancing the need for more granular data Balancing the need for more granular data with the cost and time associated with data with the cost and time associated with data collection, storage, and dispersalcollection, storage, and dispersal

Managing both structured data and Managing both structured data and unstructured dataunstructured data

From static geographic data points – ZIP From static geographic data points – ZIP Code, Territory, etc. – to dynamic Geographic Code, Territory, etc. – to dynamic Geographic Information Systems (GIS) and real-time data Information Systems (GIS) and real-time data continuumscontinuums

Recognizing the technological implications of Recognizing the technological implications of repurposing datarepurposing data

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Technology: Impact on D & IQTechnology: Impact on D & IQ Changing Changing

ExpectationsExpectations– Use and storage of new Use and storage of new

types of datatypes of data– Protecting data from Protecting data from

inappropriate useinappropriate use– Improving access to Improving access to

data and analytical data and analytical tools tools

– Interoperability of data Interoperability of data and databasesand databases

– New data exchange New data exchange tools and mechanisms tools and mechanisms (XML, for example)(XML, for example)

– Technology Technology convergence – across convergence – across industry, across countryindustry, across country

– OutsourcingOutsourcing

D & IQ ImpactD & IQ Impact– MetadataMetadata– Data modelsData models– Data and process flowsData and process flows– Mapping documentationMapping documentation– Data standardsData standards– Data transformation Data transformation

and generationand generation– Detailed specificationsDetailed specifications– Data security controlsData security controls– Data security auditsData security audits– VersioningVersioning

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Globalization: Changing EnvironmentGlobalization: Changing Environment

Outsourcing IT, data management, business Outsourcing IT, data management, business functions and the need to educate foreign functions and the need to educate foreign staff about US issues staff about US issues

Expanding business beyond US borders and Expanding business beyond US borders and the need to educate US staff about foreign the need to educate US staff about foreign issuesissues

Cultural differencesCultural differences

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Globalization: Impact on Data and Globalization: Impact on Data and Data ManagementData Management

Expanding the data quality focus to recognize Expanding the data quality focus to recognize cultural differences cultural differences

The need for procedural manuals, edit The need for procedural manuals, edit packages, data dictionaries, schema and packages, data dictionaries, schema and implementation guides to recognize implementation guides to recognize differences in terminologies and definitions differences in terminologies and definitions

The need for cross-border transparencyThe need for cross-border transparency Increased emphasis on compliance with Increased emphasis on compliance with

international rating, reporting laws and international rating, reporting laws and solvency regulations solvency regulations

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Globalization: Impact on Data and Globalization: Impact on Data and Data ManagementData Management

Recognizing the cost and time associated with Recognizing the cost and time associated with international data collection, storage, and dispersal international data collection, storage, and dispersal

Recognizing differences in technologies across bordersRecognizing differences in technologies across borders Promoting the interoperability of data and databases, Promoting the interoperability of data and databases,

allowing for better data integration thereby giving the allowing for better data integration thereby giving the users more options for how data can be usedusers more options for how data can be used

Increased emphasis on industry data and data exchange Increased emphasis on industry data and data exchange standardsstandards

Managing data content and definition across borders Managing data content and definition across borders

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Globalization: Impact on D & IQGlobalization: Impact on D & IQ Changing ExpectationsChanging Expectations

– Data transparency: Data transparency: documentation, controls, documentation, controls, etc.etc.

– Privacy, confidentiality, Privacy, confidentiality, compliance, solvency compliance, solvency

– Interoperability of data Interoperability of data and databasesand databases

– Outsourcing to other Outsourcing to other countriescountries

– Expanding business Expanding business beyond national bordersbeyond national borders

– Recognize cultural Recognize cultural differencesdifferences

– Terminology differencesTerminology differences– Language differencesLanguage differences– Time zone differencesTime zone differences

D & IQ ImpactD & IQ Impact– MetadataMetadata– International naming International naming

conventionsconventions– Data modelsData models– Data and process flowsData and process flows– Mapping documentationMapping documentation– Data standardsData standards– Data transformation Data transformation

and generationand generation– Detailed specificationsDetailed specifications– Identification of data Identification of data

sources: internal and sources: internal and external external

– Regulatory monitoringRegulatory monitoring– Need for more scientific Need for more scientific

measuresmeasures

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Data Quality EvolutionData Quality Evolution

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The Data Quality EvolutionThe Data Quality Evolution

Information Quality StewardshipInformation Quality Stewardship Strategic Data Planning and Data Strategic Data Planning and Data

GovernanceGovernance Controls – internal and from 3Controls – internal and from 3rdrd party party

data sourcesdata sources Measures - internal and from 3Measures - internal and from 3rdrd party party

data sources – especially timeliness, data sources – especially timeliness, completeness and redundancycompleteness and redundancy

Master Data Management Master Data Management Chief Data Officer v. Data Quality Officer Chief Data Officer v. Data Quality Officer

v. Chief Information Officerv. Chief Information Officer

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The Data Quality EvolutionThe Data Quality Evolution

Identify data sourcesIdentify data sources Metadata – internal and from 3Metadata – internal and from 3rdrd party data sources party data sources MappingMapping Data confidentiality and security - Data confidentiality and security -

– Encryption – data in transit and data at rest – Encryption – data in transit and data at rest – security v. cost.security v. cost.

– Fobs, etc.Fobs, etc. Data re-use v. DQ levels Data re-use v. DQ levels Changing use of data v. DQ levelsChanging use of data v. DQ levels Data standards – business, industry, cross-industry, Data standards – business, industry, cross-industry,

cross-border, technologycross-border, technology

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The Data Quality Evolution: Master The Data Quality Evolution: Master Data ManagementData Management

Master data management (MDM) is the set of processes Master data management (MDM) is the set of processes to create and maintain a single view of reference data to create and maintain a single view of reference data that is shared across systems. It is used to classify and that is shared across systems. It is used to classify and define transactional data through the use of a define transactional data through the use of a centralized integration manager. centralized integration manager. – It leverages policies and procedures for access, update It leverages policies and procedures for access, update

and overall management of this central resource and its and overall management of this central resource and its coordination with other participating systems across the coordination with other participating systems across the enterprise. enterprise.

– Areas such as customer data integration (CDI), which Areas such as customer data integration (CDI), which involves management of customer reference data and involves management of customer reference data and product information management (PIM), which includes product information management (PIM), which includes management of product and supplier reference data, are management of product and supplier reference data, are domain-specific subsets of MDM.domain-specific subsets of MDM.

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The Data Quality Evolution: Chief The Data Quality Evolution: Chief Data OfficerData Officer

““The Body Has a Heart and Soul, Roles and The Body Has a Heart and Soul, Roles and Responsibilities of the Chief Data Officer” by Thomas C. Responsibilities of the Chief Data Officer” by Thomas C. Redman, Information and Data Quality Newsletter Redman, Information and Data Quality Newsletter January 2007, IAIDQ:January 2007, IAIDQ:– Applies corporate data strategy and data polices Applies corporate data strategy and data polices

defined by corporate data stewards/data councildefined by corporate data stewards/data council– Leads the data quality programLeads the data quality program– Leads the application of corporate data strategy and Leads the application of corporate data strategy and

data polices to data suppliersdata polices to data suppliers– Owns and houses the metadata processOwns and houses the metadata process

Need for a Chief DQ Officer?Need for a Chief DQ Officer?

4343

CAS Data Management CAS Data Management Educational Materials Educational Materials Working Party (WP5)Working Party (WP5)

4444

Actuarial IQActuarial IQ

Introduction to Data Quality and Data Introduction to Data Quality and Data Management being written by the CAS Data Management being written by the CAS Data Management Educational Materials Working Management Educational Materials Working PartyParty

Directed at actuarial analysts as much as Directed at actuarial analysts as much as actuarial data managers: actuarial data managers: – what every actuary should know about data what every actuary should know about data

quality and data managementquality and data management

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Why Actuaries?Why Actuaries?

Both: Both: – Information consumers and Information consumers and

information providersinformation providers Both: Both:

– Have knowledge of data and high Have knowledge of data and high stakes in qualitystakes in quality

Both: Both: – Skillful and influentialSkillful and influential

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Actuarial IQ Key IdeasActuarial IQ Key Ideas

Information Quality concerns not only bad Information Quality concerns not only bad data: data: – information which is poorly processed or information which is poorly processed or

poorly presented is also of low qualitypoorly presented is also of low quality Cleansing data helps: Cleansing data helps:

– but is just a band aidbut is just a band aid There are plenty of things actuarial analysts There are plenty of things actuarial analysts

can do to improve their IQcan do to improve their IQ There are even more things actuarial data There are even more things actuarial data

managers can domanagers can do Actuaries are uniquely positioned to become Actuaries are uniquely positioned to become

information quality advocatesinformation quality advocates

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Working Party PublicationsWorking Party Publications

Book reviews of data management and data Book reviews of data management and data quality texts in the Actuarial Review quality texts in the Actuarial Review starting with the August 2006 editionstarting with the August 2006 edition

These reviews are combined and compared These reviews are combined and compared in “Survey of Data Management and Data in “Survey of Data Management and Data Quality Texts,” CAS Forum, Winter 2007, Quality Texts,” CAS Forum, Winter 2007, www.casact.orgwww.casact.org

Upcoming paper: “Actuarial IQ Upcoming paper: “Actuarial IQ (Information Quality)” to be published in (Information Quality)” to be published in the Winter 2008 edition of the CAS Forumthe Winter 2008 edition of the CAS Forum

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Questions and Questions and CommentaryCommentary