The Coordinated Approach
To Improving
Enterprise Data Quality

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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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•
•

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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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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?
Select Value (or accept default):
Status Unknown {default}
Open Account
Closed Account
Account Locked
No Value
A
C
D
E
A
B

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Information
Assurance
Team
Systems

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Start
Data Analysis
Project Initiation
Data Steward
Appoints Data
Quality Analysis Team
DQ Team identifies key elements & acceptable
DQ compliance levels
DQ Team & Data
Architect(s) perform
Data Quality Analysis
Document
– participants, roles, responsibilities, time commitments
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
Is element within
DQ compliance limits?
Yes
End
No
DQ Team identifies remediation options
& recommendations
Document
– what can we do, how much will it cost, what benefit will we see
Data Steward & DGC select appropriate remediation action(s)
Document
– selected option, reasons for selection, how it will be implemented
Remediation actions successfully implemented
Document
– complete new project documentation

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Business
Advantage
Wisdom +
Information +
Shared
Knowledge
+
Agreed
Meaning
Data
Governance
Accurate
Application
BI,EIS, &
Data Mining
Data +
Input
Processes
OLTP &
Data Capture
Business
Context
Metadata
Repository
Communication Complexity
Timely
Use
Decisions &
Implementation

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Senior
Executive
Information
Technology
Data Quality
Administration
Data Quality Committee
Finance
Data Governance
Chairperson
Chief Operating
Officer
Business Units Corporate Audit
Business
Information
Architect
Internal Audits
Business Unit
"A"
Business Unit
"B"
Business Unit
"C"
Business Unit
"D"
BTC BTC Data Steward Data Steward Data Steward Data Steward
Data Stewardship Team
Data Quality Analyst Data Quality Analyst

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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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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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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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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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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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Data Governance Certification Process
New Metrics Development Process
Error Management Process (2004)
Data Quality Analysis Process (2004)

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Guarantees Unique Names
& Definitions
Improves Data Quality
Insures Accuracy &
Reliability
Promotes Reusability
Provides for a "Single
Version of the Truth"
Business
Approval
Yes
User
Acceptance
Testing
Yes
End
Start
New Metric
Proposed
This is a sub-process that may occur multiple times during a given project lifecycle. The section from "Start" to
"DGG Approval" occurs during the inception phase of the project.
Does
Metric Exist
Yes
Use Existing
Metric
Gather
Requirements
Name Metric
DGG
Approval
Yes
Identify Data
Elements
What you need to measure
What units you will use
What results constitute success
Name must be unique
Full name cannot contain any abbreviations or acronyms
Short name may contain abbreviations and/or acronyms
Name must be descriptive and in non-technical terms
Possible Determinations:
WOW : Good metric, well-defined, reusable.
Now : Good metric, but limited to current project only.
No!
: Metric does not meet acceptance requirements
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?
Build & Test
Prototype
Submit DGG
Approval &
Test Results

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Insures Common Error
Reporting and
Management
Improves Error Tracking
& Issue Resolution
Operations
Provides Common Issue
Escalation Practices
Release in 2004
Start
New Error
Detected
Immediate e-mail
Notifications
Is Metadata Repository
Available
Yes
Error Logged to Metadata
Repository
Root Cause
Analysis
RCA activities logged into
Repository
RCA activities presented to
DGG
Appropriate Data Custodian(s)
Data Goverenance Chairperson
Impacted System Owners
Project Lead, where applicable
Date & Time error detected
Date & Time error occurred
Person identifying & documenting error
All affected systems, data sets, applications, reports, & processes
Any corrective actions taken
Data Steward is Facilitator
Corrective action?
Yes
Corrective
Action(s) taken
Problem
Resolved?
No
No
Mitigating action?
Yes
Mitigating
Action(s) taken
No
Escalation
Process
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 lead are unable to resolve errors and or root cause analysis issues, the Data Governance Group will be notified and will provide for escalation to insure satisfactory resolution.
End

Release in 2004
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Business Must
Apply ROI
Discipline

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

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• 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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• 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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• 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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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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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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