Real-World Data Governance Webinar: Using Data Governance to Achieve Data Quality

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Copyright © 2018 Robert S. Seiner – KIK Consulting & Educational Services / TDAN.com
Non-Invasive Data Governance™ is a trademark of Robert S. Seiner & KIK Consulting
#RWDG @RSeiner
Real World Data Governance
Using Data Governance
to Achieve Data Quality
Monthly Webinar Series Hosted by DATAVERSITY
Robert S. Seiner – KIK Consulting / TDAN.com
April 19, 2018 – 11:00 a.m. PT / 2:00 p.m. ET

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#RWDG @RSeiner
Real-World Data Governance
Introduction
• Real-World Data Governance – Monthly Webinar Series
May 17, 2018 – Using Data Governance to Protect Sensitive Data
June 21, 2018 – Using Data Governance to Improve Data Understanding
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Home of Non-Invasive Data Governance™

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#RWDG @RSeiner
• In this webinar we will discuss:
– Defining Data Governance in Terms of Data Quality
– Delivering Roles Appropriate for Improving Data Quality
– Selecting Appropriate Data Quality Processes to Govern
– Using Working Groups to Focus on Data Quality Projects
– Measuring Quality to Demonstrate Governance Performance
Using Data Governance to Achieve Data Quality
Agenda for Today’s Webinar

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• Data Governance
– Data Governance is the Execution and Enforcement of Authority
Over the Management of Data and Data-Related Resources.
• Data Stewardship
– Data Stewardship is the Formalization of Accountability
Over the Management of Data and Data-Related Resources.
Defining Data Governance in Terms of Data Quality

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• Non-Invasive Data Governance™
– The practice of:
• applying formal accountability & behavior
• through non-invasive roles & responsibilities
• to existing and / or new processes
• to assure that the definition, production & usage of data
• assures regulatory compliance, security, privacy, protection & quality.
– Non-Invasive describes how governance is applied to assure
non-threatening management of valuable data assets.

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• Non-Invasive Data Governance™
– The practice of:
• applying formal accountability & behavior
• through non-invasive roles & responsibilities
• to existing and / or new processes
• to assure that the definition, production & usage of data
• assures regulatory compliance, security, privacy, protection & quality.
– Non-Invasive describes how governance is applied to assure
non-threatening management of valuable data assets.
• The goal is to be:
 Transparent.
 Supportive.
 Collaborative.

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• Client Definition of Data Governance
– Formalization of accountability & repeatable behavior for managing data as
an asset as it progresses throughout the data lifecycle to support …
– Data Quality initiatives associated with 1 of 2
• Master Data Management efforts
• Analytics efforts
• Information Asset Management
• To respond to an Audit
• Data Sharing & Extraction
• Risk Management, …

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• Client Definition of Data Governance
– Formalization of accountability & repeatable behavior for managing data as an
asset as it progresses throughout the data lifecycle to support …
– Data Quality initiatives associated with 2 of 2
• Security & Protection of Sensitive Data
• Regulatory Reporting & Compliance
• Business Intelligence / Data Warehousing / Data Lakes
• Data Integration
• Enterprise Transformation
• ERP and Package Implementation, …

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• Define Data Governance in terms of something that is SMART
– S – Specific
– M – Measurable
– A – Achievable
– R – Results-driven
– T – Time-bound
• Give your definition some teeth.

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Delivering Roles Appropriate for Improving Data Quality
• Why are data governance roles important in improving data quality?
1. Someone needs to have formal accountability to record data quality issues.
2. Someone needs to have formal accountability to receive / process data quality issues.
3. Someone needs to have formal accountability to prioritize data quality issues.
4. Someone needs to have accountability to decide which issues will receive resources.

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• Why are data governance roles so important in improving data quality?
1. Someone needs to have formal accountability to record data quality issues.
• Data Stewards
2. Someone needs to have formal accountability to receive / process data quality issues.
• Data Governance Office
3. Someone needs to have formal accountability to prioritize data quality issues.
• Enterprise Data Stewards / Data Domain Stewards
4. Someone needs to have accountability to decide which issues will receive resources.
• Data Governance Council

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• Core Component of NIDG DG Framework – Roles (Authority)
– The first foundational component of a successful Data Governance program is the
definition of roles and responsibilities.
– The manner in which roles are defined and how they operate are predictors of the
effort required to govern the data.
– Roles are represented through an Operating Model of Roles & Responsibilities.
– The pyramid diagram is represented in the first column of the Framework.
– The Operating Model includes a detailed description of formalized responsibilities,
escalation and decision paths, how roles are formally engaged in processes, and
communications that are shared with each level.

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NON-INVASIVE DATA GOVERNANCE™ FRAMEWORK
Endorse
Processes
Resolution
Processes
EXECUTIVE
STRATEGIC
TACTICAL
OPERATIONAL
SUPPORT
Domain
Processes
Daily
Processes
Enforce
Processes
Metric
Approval
Act
Metric
Acceptance
Domain
Metrics
Daily
Measurements
Collect
Report
Metrics
Policy
Directive
Best Practice
Guidelines
Standards
Requirements
Workflow
Glossary
Dictionary
Metadata
KIK Artifacts
(Common Data
Activity Matrices)
Support
Sponsor
Understand
Meetings
Status
Subject
Project
Orientation
On Boarding
Ongoing
Plan
Develop
Deliver
Steering
Committee
(Delegate)
Council
(Decision)
Domain
Stewards
Owners
(Subject)
Data
Stewards
(Daily)
DG Lead
Work Groups
Partners
(Function)
Copyright © 2017 – Robert S. Seiner and KIK Consulting & Educational Services, LLC
LEVEL
EXECUTIVE – SENIOR LEADERSHIP TEAM
STRATEGIC – BUSINESS & TECHNOLOGY
TACTICAL – SUBJECT MATTER EXPERTISE
OPERATIONAL – DAILY JOB FUNCTION
SUPPORT – FUNCTIONAL MANAGEMENT
LEVELS
STRATEGIC – BUSINESS & TECHNOLOGY MGMT
COMPONENTS
ROLES (AUTHORITY) – FORMAL RESPONSIBILITY
PROCESSES – APPLICATION and ENFORCEMENT
COMMUNICATIONS – EDUC, TRN, AWARENESS
METRICS – MEASUREMENTS and KPIs
TOOLS – ARTIFACTS PURCHASED & DEVELOPED
Levels
Components
ROLES(AUTHORITY)
COMMUNICATIONS
TOOLS
METRICS
PROCESSES

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#RWDG @RSeiner
NON-INVASIVE DATA GOVERNANCE™ FRAMEWORK
Endorse
Processes
Resolution
Processes
EXECUTIVE
STRATEGIC
TACTICAL
OPERATIONAL
SUPPORT
Domain
Processes
Daily
Processes
Enforce
Processes
Metric
Approval
Act
Metric
Acceptance
Domain
Metrics
Daily
Measurements
Collect
Report
Metrics
Policy
Directive
Best Practice
Guidelines
Standards
Requirements
Workflow
Glossary
Dictionary
Metadata
KIK Artifacts
(Common Data
Activity Matrices)
Support
Sponsor
Understand
Meetings
Status
Subject
Project
Orientation
On Boarding
Ongoing
Plan
Develop
Deliver
Steering
Committee
(Delegate)
Council
(Decision)
Domain
Stewards
Owners
(Subject)
Data
Stewards
(Daily)
DG Lead
Work Groups
Partners
(Function)
Copyright © 2017 – Robert S. Seiner and KIK Consulting & Educational Services, LLC
LEVEL
STRATEGIC – BUSINESS & TECHNOLOGY
LEVELS
STRATEGIC – BUSINESS & TECHNOLOGY MGMT
COMPONENTS
ROLES (AUTHORITY) – FORMAL RESPONSIBILITY
PROCESSES – APPLICATION and ENFORCEMENT
COMMUNICATIONS – EDUC, TRN, AWARENESS
METRICS – MEASUREMENTS and KPIs
TOOLS – ARTIFACTS PURCHASED & DEVELOPED
Levels
Components
ROLES(AUTHORITY)
COMMUNICATIONS
TOOLS
METRICS
PROCESSES
DATA GOVERNANCE

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• Typical Set of Data Governance Roles
– Data Governance Office – Supportive
– Data Governance Steering Committee – Executive
– Data Governance Council – Strategic
– Enterprise Data Stewards – Tactical
– Data Governance Working Groups – Tactical / Operational
– Data Stewards – Operational
– Data Governance Partners – Supportive
• Legal
• Project Management
• Communications
• Supply Chain, Marketing, …

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• Data Quality Processes to Govern
– Data Quality Issue Submission and Resolution
– Data Quality Issue Follow-up and Reporting
– Data Quality Issue Prioritization and Costing
– Data Quality Certification
– Metadata Management
Selecting Appropriate Data Quality Processes to Govern

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• Data Quality Issue Submission and Resolution
– Formal and repeatable process around submitting issue
• Data and work flow diagram
• RACI matrix
• Detailed description
– Formal training for Data Stewards
– Cadence of communication / awareness with Data Stewards
– Formal reporting of Data Quality issue efforts

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• Data Quality Issue Follow-up
– Immediate email thanking Data Stewards for submitting issue
– Responsive follow up with Data Steward to clarify understanding
– Email upon prioritization of Data Quality Issue
– Email upon Data Governance Council decision about prioritization
– Email about application about involvement
– Email about resources assigned

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• Data Quality Issue Reporting
– Issues opened / collected
– Issues closed / reason determination
– Issues reviewed
– Issues prioritized
– Issues with resources assigned

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• Data Quality Issue Prioritization
– Critical – Resources have been / will be assigned to solve this issue
– Medium – Considering the assignment of resources to solve this issue
– Low – Worth keeping on the list but assignment of resources not considered

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• Data Quality Issue Costing
– What is the cost associated with
solving the Data Quality Issue?
– What is the cost associated with
not solving the Data Quality Issue?

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• Data Quality Certification
Answer these questions …
– Is the data Accessible?
– Is the data Accurate ?
– Is the data Complete?
– Does the data Conform?
– Is the data Consistent?
– Is the data Timely?
– Is the data Unique?
– Is the data Valid?

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• Metadata Management …
– Metadata is data about data.
– Metadata is data about the definition, production and use of data.
– Metadata includes stewardship data.
• Somebody has to have the responsibility
– for collecting metadata.
– validating the metadata.
– for making metadata available.

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• Client examples of data quality activities:
– Data requirements definition – validation of business data objectives
and data development criteria
– Data standard definition – definition of rules for data production and collection
– Data quality assessment – evaluation of data compared to standards/definition of data
– Data production – assurance that data is produced to meet data requirements
– Data usage – assurance that data is used the way it is meant to be used, protected,
reported properly
– Data profiling – understanding of content, quality and rules for a specific
set of data under management
– Data cleansing – assurance that data is complete and accurate

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• When do data quality problems occur?
– When Data Stewards recognize problems with the data they use as part of
their everyday job.
– When data is being requested, merged and integrated for business purposes.
– When data is being created as part of a new or evolving process.
– When data is acquired or brought into your agency from an outside source.
Using Working Groups to Focus on Data Quality Projects

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• Recognize the Need for Working Group Efforts to Solve a Business Problem
– Data is not consistent from one information system or agency to another.
– Data is missing, incomplete or incorrect.
– Data changes are permitted by end users for data that should be locked
down and change management controlled.
– Known and documented data issues.
– Data is not linked across systems properly.
– Data should be eliminated.
– Data hierarchies are not synchronized.
– Institutional knowledge is required but not documented.

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• Steps to Convene a Working Group to Develop / Deliver / Improve Data Quality
– Recognize and communicate who should be involved
– Specify what will be ask of working group members
– Estimate and share with people how much time should be expected
– Plan for how to achieve results in improving data quality

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• Steps to Gain Approval of the Working Group to Focus on Data Quality Effort
– Identify who will need to approve the effort
– Formalize (cross) departmental involvement in the Data Quality Effort
– Gain approval of all represented departments

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• Steps to Get a Working Group Started with the Data Quality Effort
– Determine who will guide the effort
– Set agenda for meeting(s)
– Schedule people’s time for meeting(s)
– Arrange meeting logistics – place, web meeting(s)
– Distribute invitations and track responses

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• Methods Directed at Delivering Data Quality
– Improve Data Definitions and Standards
– Improve Data Production and Collection
– Improve Data Usage and Understanding of Business Data Rules
– Improve Data Quality through Data Documentation and Metadata

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• Improve Data Definition and Standards
– Efforts to build, approve, maintain and share Business Glossaries of terms
– Efforts to build, approve, maintain and share Data Dictionaries of CDE
– Efforts to define standards for CDE and data that is shared
• Results of Improved Data Definition and Standards
– Improved data production
– Improved data usage
– Improved data share-ability

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• Improve Data Production and Collection
– Efforts to assure that data entry meets standards
– Efforts to assure that data acquired from external sources meets standards
– Efforts to assure that information systems focus on the quality of data
• Results of Improved Data Production and Collection
– Improved data quality
– Improved data usage
– Improved data share-ability

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• Improve Data Usage and Understanding of Business Data Rules
– Efforts to assure that data entry meets standards
– Efforts to assure that data acquired from external sources meets standards
– Efforts to assure that information systems focus on the quality of data
• Results of Improved Data Usage and Understanding of Business Data Rules
– Improved data compliance
– Improved data protection
– Improved data understanding leading to better business decisions

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• Improve Data Quality through Data Documentation and Metadata
– Efforts to assure that glossaries, dictionaries and catalogs are complete
– Efforts to assure that data definitions meet business requirements
– Efforts to assure that information resources are well documented
• Results of Improved Data Quality through Data Documentation and Metadata
– Improved data definition, production and usage
– Improved capabilities that data require cross-functional data
– Improved understanding and knowledge of information resources

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• Organizations focus on specific dimensions of data quality –
adjectives of the aspect of data quality that is being addressed and measured.
• What is quality data?
– Quality data is data that is trusted and fit for intended uses in operations,
decision making and planning.
• Data quality processes control the usage of data for an application or a business
process.
• Data quality improves when people focus on improving the definition, production
and use of the data.
Measuring Quality to Demonstrate Governance Performance

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• Consider using the following dimensions to describe these aspects of data quality
that can be assessed, addressed and measured: 1 of 2
– Accessibility – ability to access required data
– Accuracy – criteria related to similarity of original intent
– Completeness – availability of required pieces of data
– Conformity – alignment of data with required standards
– Consistency – ability of data to follow specified patterns and uniformity rules

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• Consider using the following dimensions to describe these aspects of data quality
that can be assessed, addressed and measured: 2 of 2
– Integrity – accuracy of the data’s relationships to other data
– Timeliness – currency of content
– Uniqueness – redundancy of records or pieces of data
– Validity – measure of data’s being fit for purpose

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#RWDG @RSeiner
• In this webinar we discussed:
– Defining Data Governance in Terms of Data Quality
– Delivering Roles Appropriate for Improving Data Quality
– Selecting Appropriate Data Quality Processes to Govern
– Using Working Groups to Focus on Data Quality Projects
– Measuring Quality to Demonstrate Governance Performance
Today’s Webinar

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• Questions and Answers
Summary

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• Robert S. Seiner
KIK Consulting & Educational Services – KIKconsulting.com
The Data Administration Newsletter – TDAN.com
Post Office Box 112571, Upper St. Clair, Pennsylvania 15241
412.220.9643,
rseiner@tdan.com
@RSeiner @TDAN_com
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