Information Information AssuranceAssurance

The Coordinated ApproachThe Coordinated Approach

To ImprovingTo Improving

Enterprise Data QualityEnterprise Data Quality

Introduction

Information Assurance requires the coordinated efforts of multiple teams working on strategy, tactics, and projects

Information Assurance team members share responsibility, resources, and rewards

Information Assurance requires the coordinated efforts of multiple teams working on strategy, tactics, and projects

Information Assurance team members share responsibility, resources, and rewards

2

Agenda

• What is Information Assurance?

• Nationwide Activities and Results

• Your Benefits

• What is Information Assurance?

• Nationwide Activities and Results

• Your Benefits

3

What is Information Assurance?

A Method for Addressing Data Quality Issues and Improving Business Value Using:

– A Coordinated Team Interaction Model

– A Standard IA Process Flow Model

– A Focused Organizational Structure

– A Defined Set of Responsibilities

A Method for Addressing Data Quality Issues and Improving Business Value Using:

– A Coordinated Team Interaction Model

– A Standard IA Process Flow Model

– A Focused Organizational Structure

– A Defined Set of Responsibilities

4

Typical Data Quality Issues

Have you encountered:– Data management

processes that generate data inconsistent with your business operations?

– User interfaces that encourage data entry personnel to select a specific data value whether or not it is the correct value?

Have you encountered:– Data management

processes that generate data inconsistent with your business operations?

– User interfaces that encourage data entry personnel to select a specific data value whether or not it is the correct value?

AASelect Value (or accept default):

ENo Value

DAccount Locked

CClosed Account

BOpen Account

AStatus Unknown {default}

5

Team Interaction Model

6

Business

Business

SystemsSystems

Fina

nce

Fina

nce

Bus

ines

s

Bus

ines

s

Info

rmat

ion

Arc

hite

ct

Info

rmat

ion

Arc

hite

ct

Data Q

uality

Data Q

uality

Adm

inistration

Adm

inistration

Internal AuditsInternal Audits

Information nformation Assurance Assurance

TeamTeam

Information Assurance Process FlowStart

Data AnalysisProject Initiation

Data StewardAppoints Data

Quality Analysis Team

DQ Team identifies keyelements & acceptableDQ compliance levels

DQ Team & DataArchitect(s) perform

Data Quality Analysis

Iselement withinDQ compliance

limits?

DQ Team identifiesremediation options& recommendations

Data Steward & DGCselect appropriate

remediation action(s)

Remediation actionssuccessfully implemented

EndYes

NoDocument – participants, roles, responsibilities, time commitments

Document – participants, roles, responsibilities, time commitments

Document – which elements, how good is good enough, why, what metrics to use

Document – which elements, how good is good enough, why, what metrics to use

Document – who, what, why, how, when, and results for each pass thru data

Document – who, what, why, how, when, and results for each pass thru data

Document – what can we do, how much will it cost, what benefit will we see

Document – what can we do, how much will it cost, what benefit will we see

Document – selected option, reasons for selection, how it will be implemented

Document – selected option, reasons for selection, how it will be implemented

Document – complete new project documentation

Document – complete new project documentation

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Stepping up to Business Value

Data

Information

SharedKnowledge

Wisdom

BusinessAdvantage

+

+

+

+

Communication Complexity

Bus

ines

s V

alue

MetadataRepository

BusinessContext

DataGovernance

AgreedMeaning

BI,EIS, &Data Mining

AccurateApplication

Decisions &Implementation

TimelyUse

OLTP &Data Capture

InputProcesses

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Organizational StructureSenior

Executive

Business UnitsFinance

Data GovernanceChairperson

Data Quality Analyst

BTC BTC

Data Quality Analyst

Data Steward

Business Unit"A"

Business Unit"B"

Business Unit"C"

Data Steward Data StewardData Steward

Data Stewardship Team

Data QualityAdministration

Data Quality Committee

Internal Audits

Business Unit"D"

Corporate AuditInformationTechnology

Chief OperatingOfficer

BusinessInformation

Architect

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Information Assurance Responsibilities

Data Governance Committee– Guidance, Standards, Common Definitions, Metrics, Business Rules

Data Stewardship Team– Validation, Metadata Management, Business Usage, Data Quality

Analysis

Data Quality Committee– Prioritization, Funding Allocation, Data Quality Oversight, Senior

Escalation Point for Data Quality Issues

Information Assurance Team– Data Quality Analysis and Reporting, Data Quality Training

Data Governance Committee– Guidance, Standards, Common Definitions, Metrics, Business Rules

Data Stewardship Team– Validation, Metadata Management, Business Usage, Data Quality

Analysis

Data Quality Committee– Prioritization, Funding Allocation, Data Quality Oversight, Senior

Escalation Point for Data Quality Issues

Information Assurance Team– Data Quality Analysis and Reporting, Data Quality Training

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Nationwide Activities and Results

Why an Information Assurance Focus

Current Information Assurance State

The Problems We Addressed

Our Deliverables to Date

The Results of Our Efforts

Why an Information Assurance Focus

Current Information Assurance State

The Problems We Addressed

Our Deliverables to Date

The Results of Our Efforts

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Why an Information Assurance Focus

Information Assurance encourages a "Collaborative Assault" on data quality issues

Information Assurance enables a Speed to Market strategy in support of business operations

Information Assurance insures that Front-Line Decision Makers have access to reliable and timely information on which to base their decisions

Information Assurance encourages a "Collaborative Assault" on data quality issues

Information Assurance enables a Speed to Market strategy in support of business operations

Information Assurance insures that Front-Line Decision Makers have access to reliable and timely information on which to base their decisions

12

Current Information Assurance State

Data Governance Committee fully operational

Information Assurance Team being staffed

Data Stewards being identified for most areas

Data Quality Committee established, supported by Metadata Management team

Internal Audit approval of process models

Data Quality Administration providing detailed data quality analysis

Data Governance Committee fully operational

Information Assurance Team being staffed

Data Stewards being identified for most areas

Data Quality Committee established, supported by Metadata Management team

Internal Audit approval of process models

Data Quality Administration providing detailed data quality analysis

13

The Problems We Addressed

No standard review, approval, and certification process for new data warehouse projects

Inconsistent definition, testing, and approval for new metrics

Fragmented error management processes – no enforceable service level agreements

Project and team based data quality analysis processes provided unverifiable results

No standard review, approval, and certification process for new data warehouse projects

Inconsistent definition, testing, and approval for new metrics

Fragmented error management processes – no enforceable service level agreements

Project and team based data quality analysis processes provided unverifiable results

14

Our Deliverables to Date

Data Governance Certification Process

New Metrics Development Process

Error Management Process (2004)

Data Quality Analysis Process (2004)

Data Governance Certification Process

New Metrics Development Process

Error Management Process (2004)

Data Quality Analysis Process (2004)

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Data Governance Certification

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Possible Determinations:WOW: Good metric, well-defined, reusable.Now: Good metric, but limited to current project only.No!: Metric does not meet acceptance requirements

Start

New MetricProposed

DoesMetric Exist

GatherRequirements

Name Metric

Identify DataElements

Build & TestPrototype

BusinessApproval

UserAcceptance

Testing

Submit DGGApproval &

Test Results

End

Yes

Yes

Use ExistingMetric

Yes

What you need to measureWhat units you will useWhat results constitute success

Name must be uniqueFull name cannot contain any abbreviations or acronymsShort name may contain abbreviations and/or acronymsName must be descriptive and in non-technical terms

How will you source the data?How will you verify the accuracy of the data?How will you use each data element?How will you calculate your metric?How often will you make the calculation?

Do test results match stated requirements?

DGGApproval

Yes

This is a sub-process that may occurmultiple times during a given projectlifecycle. The section from "Start" to

"DGG Approval" occurs during theinception phase of the project.

New Metrics Development

Guarantees Unique Names & Definitions

Improves Data Quality

Insures Accuracy & Reliability

Promotes Reusability

Provides for a "Single Version of the Truth"

Guarantees Unique Names & Definitions

Improves Data Quality

Insures Accuracy & Reliability

Promotes Reusability

Provides for a "Single Version of the Truth"

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Start

New ErrorDetected

Immediatee-mail

Notifications

Appropriate Data Custodian(s)Data Goverenance ChairpersonImpacted System OwnersProject Lead, where applicable

Error Loggedto MetadataRepository

Is Meta-data Repository

Available

Date & Time error detectedDate & Time error occurredPerson identifying & documenting errorAll affected systems, data sets, applications, reports, & processesAny corrective actions taken

Root CauseAnalysis

RCA activitieslogged intoRepository

Correctiveaction?

Mitigatingaction?

RCA activitiespresented to

DGG

ProblemResolved?

EscalationProcess

CorrectiveAction(s) taken

MitigatingAction(s) taken

Yes

No

Yes Yes

No

No

End

Data Steward is Facilitator

Possible Actions Include: Correcting invalid/inaccurate data at source Deleting invalid data from target system Reloading corrected data to target system Recreating reports using corrected data Restating history to reflect values in corrected data

In cases where the data custodian and/or project leadare unable to resolve errors and or root causeanalysis issues, the Data Governance Group will benotified and will provide for escalation to insuresatisfactory resolution.

Error Management

Insures Common Error Reporting and Management

Improves Error Tracking & Issue Resolution Operations

Provides Common Issue Escalation Practices

Release in 2004

Insures Common Error Reporting and Management

Improves Error Tracking & Issue Resolution Operations

Provides Common Issue Escalation Practices

Release in 200418

Business MustBusiness MustApply ROI Apply ROI DisciplineDiscipline

Data Quality AnalysisRelease in 2004

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The Results of Our Efforts

Simplified, common review, approval, and certification for data warehouse projects

Consistent, enforceable process for new metrics development and approval

Common error management process, supported by realistic service level agreements

Centrally managed data quality analysis processes for raw data sets providing verifiable business value for effort

Simplified, common review, approval, and certification for data warehouse projects

Consistent, enforceable process for new metrics development and approval

Common error management process, supported by realistic service level agreements

Centrally managed data quality analysis processes for raw data sets providing verifiable business value for effort

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The Lessons We Learned

Each process checkpoint must add value

Process tasks must prevent bottlenecks in the design and development lifecycle

Get the right people into a room and don't leave until the issues have been identified and addressed

Each defined activity must be associated with an enforceable service level agreement

Each process checkpoint must add value

Process tasks must prevent bottlenecks in the design and development lifecycle

Get the right people into a room and don't leave until the issues have been identified and addressed

Each defined activity must be associated with an enforceable service level agreement

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Future Programs

Expanding Data Governance Committee structure and authority to include all business data sets

Initiating Data Stewardship program for all business units

Establishing Data Quality Committee as senior escalation point on data quality issues

Establishing Information Assurance team and program as shared business resources

Expanding Data Governance Committee structure and authority to include all business data sets

Initiating Data Stewardship program for all business units

Establishing Data Quality Committee as senior escalation point on data quality issues

Establishing Information Assurance team and program as shared business resources

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Your Benefits

Improving Business Processes and Decision Improving Business Processes and Decision MakingMaking

Leveraging Organizational Structure, Leveraging Organizational Structure, Communication, and CooperationCommunication, and Cooperation

Coordinating Technological Operations to Coordinating Technological Operations to Reduce RedundancyReduce Redundancy

Improving Business Processes and Decision Improving Business Processes and Decision MakingMaking

Leveraging Organizational Structure, Leveraging Organizational Structure, Communication, and CooperationCommunication, and Cooperation

Coordinating Technological Operations to Coordinating Technological Operations to Reduce RedundancyReduce Redundancy

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Improving Business Processes

• Improved data quality

• Increased information value

• Value based decisions driven by measurable ROI

• Emphasis on quality, not quantity, of work

• Improved metadata accuracy and increased content

• Improved data quality

• Increased information value

• Value based decisions driven by measurable ROI

• Emphasis on quality, not quantity, of work

• Improved metadata accuracy and increased content

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Leveraging Organizational Structure

• Shared effort among business, finance, and technology teams

• Team Interaction Model encourages idea exchange and joint development efforts

• New/improved processes emphasize organizational strengths

• Shared effort among business, finance, and technology teams

• Team Interaction Model encourages idea exchange and joint development efforts

• New/improved processes emphasize organizational strengths

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Coordinating Technological Operations• Enables use of common and standardized process

models

• Encourages development of and adherence to best practices

• Coordinates review and improvement of Data Quality concepts and processes

• Leverages staff resource strengths

• Minimizes risks due to resource rebalancing

• Enables use of common and standardized process models

• Encourages development of and adherence to best practices

• Coordinates review and improvement of Data Quality concepts and processes

• Leverages staff resource strengths

• Minimizes risks due to resource rebalancing

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Conclusion

The goal of Information Assurance is to provide business units with the highest quality data possible

The establishment of a business focused Information Assurance team is of utmost importance

Information Assurance activities must involve the coordinated effort of multiple teams relying on skilled specialists

Each Information Assurance activity must provide a verifiable net improvement in overall data quality

The goal of Information Assurance is to provide business units with the highest quality data possible

The establishment of a business focused Information Assurance team is of utmost importance

Information Assurance activities must involve the coordinated effort of multiple teams relying on skilled specialists

Each Information Assurance activity must provide a verifiable net improvement in overall data quality

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The Authors

Ann Moore, Officer, Strategic ProjectsWith a background in sales management, Claims, NI

Systems management, Internal Audits, and NI Data Governance, Ann brings both business and technical expertise to Information Assurance operations and processes

Ronald Borland, Data ArchitectWith three years in NIS data architecture and a background

in project management, data quality, metadata management, and application design and development, Ron is able to bring a strong cross discipline approach to Information Assurance operations and processes

Ann Moore, Officer, Strategic ProjectsWith a background in sales management, Claims, NI

Systems management, Internal Audits, and NI Data Governance, Ann brings both business and technical expertise to Information Assurance operations and processes

Ronald Borland, Data ArchitectWith three years in NIS data architecture and a background

in project management, data quality, metadata management, and application design and development, Ron is able to bring a strong cross discipline approach to Information Assurance operations and processes

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Information Assurance is a state of mind as much as a

technological process.

The goal is to provide the business with the highest

quality information possible

Information Assurance is a state of mind as much as a

technological process.

The goal is to provide the business with the highest

quality information possible

29