Information Assurance

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Information Assurance

• The Coordinated Approach
• To Improving
• Enterprise Data Quality

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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

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Agenda

• What is Information Assurance?
• Nationwide Activities and Results
• Your Benefits

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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

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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?

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Team Interaction Model
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Information Assurance Process Flow
Start
Is element within DQ compliance limits?
End
Yes
Data Analysis Project Initiation
Data Steward Appoints Data Quality Analysis Team
No
Document participants, roles, responsibilities,
time commitments
DQ Team identifies remediation options
recommendations
Document what can we do, how much will it cost,
what benefit will we see
DQ Team identifies key elements acceptable DQ
compliance levels
Document which elements, how good is good
enough, why, what metrics to use
Data Steward DGC select appropriate remediation
action(s)
Document selected option, reasons for
selection, how it will be implemented
DQ Team Data Architect(s) perform Data Quality
Analysis
Document who, what, why, how, when, and results
for each pass thru data
Remediation actions successfully implemented
Document complete new project documentation
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Stepping up to Business Value
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Organizational Structure
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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

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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

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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

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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

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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

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Our Deliverables to Date

• 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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New Metrics Development
Guarantees Unique Names Definitions Improves
Data Quality Insures Accuracy
Reliability Promotes Reusability Provides for a
"Single Version of the Truth"
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Error Management
Insures Common Error Reporting and
Management Improves Error Tracking Issue
Resolution Operations Provides Common Issue
Escalation Practices Release in 2004
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Data Quality Analysis
Release in 2004
Business Must Apply ROI Discipline
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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

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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

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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

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Your Benefits

• Improving Business Processes and Decision Making
• Leveraging Organizational Structure,
  Communication, and Cooperation
• Coordinating Technological Operations to Reduce
  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

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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

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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

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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

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The Authors

• Ann Moore, Officer, Strategic Projects
• With 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 Architect
• With 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
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