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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
7
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
8
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
9
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
10
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
11
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)
15
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"
17
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
19
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
20
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
21
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
22
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
23
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
24
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
25
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
26
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
27
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
28
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