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Introduction to Data Governance
John Bao Vuu
Director of Data Management
www.enterpriseim.com
www.linkedin.com/in/johnvuu
Director of DM
Consultant | Advisor
John Vuu SPECIALTIES
✓ EIM Strategy & Solutions
✓ Data Governance / DQ
✓ Business Analytics
✓ Data Warehouse
INDUSTRIES
✓ Banking
✓ Insurance
✓ Ecommerce
✓ Healthcare
• 18 years experience in Data Management
• Founder of 2 technology companies
• Former Accenture BI Consultant
• DM Director at EIM Partners
• BA degree in Finance – Western WWU, Washington, USA
• BS degree Information Systems – WWU, Washington, USA
About the Speaker
• IM Foundational Disciplines
• Cross-functional Workflow Exchange
• Key Objectives of the Data Governance Framework
• Components of a Data Governance Framework
• Key Roles in Data Governance
• Data Governance Committee (DGC)
• 4 Data Governance Policy Areas
• 3 Challenges to Implementing Data Governance
• Data Governance Success Factors
Presentation Outline
IM Foundational Disciplines
DATA WAREHOUSE
✓Data integration
✓Enterprise data model
✓Common data dictionary
✓Data standardization
✓Data mapping / ETL
✓Applications support
BUSINESS INTELLIGENCE
✓Web portal BI
✓Decentralized reporting
✓Authorization & security
✓Applications development
✓Data marts
✓Advanced Data Analytics*
DATA GOVERNANCE
✓Policies & procedures
✓Stewardship / Ownership
✓Metadata management
✓Business glossary
✓DQ management
✓Data remediation / cleansing
✓MDM
Bank’s fragmented Information
Management architecture and
poor data quality can present
operational risks, strategic
uncertainty and the potential for
loss of revenue. Inconsistent and
improper handling of data across
departments can also contribute
to lower staff productivity,
workflow inefficiency and
additional cost of maintenance
and support for lack of robust
enterprise information and data
governance strategy.
Example: Cross-functional Workflow Exchange
Department A
provides
reports & data
to Department
B
Department B
checks
Department A
work; validates
accuracy of
data, makes or
requests
corrections
Department B
takes action
on approvals,
scoring, rating,
campaigns,
cust. offerings,
commissions,
etc.
Department B
deals with the
consequences
of any errors in
reporting and
from poor
data quality
ACTION
Poor data quality cost as much as 25%
of an organization’s revenue each year.
TDWI – The Data Warehouse Institute
Typical workflow exchange between departments:
✓ Reports become more accurate
✓ Greater confidence in decision-making – lower risks
✓ Increase productivity and efficiency across business functions
✓ More effective campaigns programs – cross & up selling
✓ Reduce operational costs – increase in revenue
Key Objectives of the Data Governance Framework
Data Governance framework baseline components:
1. Establish accountability by defining key roles and responsibilities within the DG framework
2. Define DG procedures and the methodology used to execute them
3. Define guidelines for data policies, data quality, data provisioning, metadata and reference data
4. Provide guidance for data management practices to maximize business value such as redundancy and
improving data consistency
5. Provide guidance for creating and maintaining standards and tools for managing corporate data
Components of a Data Governance Framework
Key Roles in Data Governance
Data Owner: person that has direct operational / business responsibility within a business unit for the
management of one or more types of data
Data Steward: person that assigns and delegates appropriate responsibility for the management of data to
respective individuals
Data Custodian: person that is responsible for the operation and management of systems and servers which
collect, manage, and provide data access
Data Governance Committee (DGC)
1. Oversees the execution of vision and objectives of
the Data Governance program
2. DGC is responsible for taking data architecture
decisions, data remediation, setting up and
enforcing data standards / policies and driving data
quality
3. DGC acts as the point of escalation and decision
making for Data Governance related policies and
procedures
4. DGC helps to define KDE (key data elements),
standards and metrics, conduct root cause analysis
of issues and propose solutions
Executives
Cross-functional
Team Members (Owners)
Cross-functional
Stewards / Custodians
Business Consumers
DG Council
DG Steering Committee
Data Stewards
End Users
Data Governance Organization Structure
4 Data Governance Policy Areas
DG policies are principles or rules that guide data-related
decisions.
Four primary DG policy areas:
1. Data Quality: establish how to define, measure and improve DQ
2. Data Provisioning: providing guidelines and best practices for
provisioning data in efficient and effective ways
3. Metadata: organizing and classifying data for reference and use
in the right business context
4. Reference Data: management of define values, i.e. KDE (key
data elements), data dictionary, glossary, business rules,
reference code values, etc.
Data
Quality
Completeness
Consistency
Conformity
Accuracy Integrity
Timeliness
6 Data Quality Dimensions
3 Challenges to Implementing Data Governance
Organization Fit – the strategy and approach are not clearly defined and do not take into
account resource involvement and their level of understanding
Ignored Efforts – lack of stakeholder buy-in and cross-functional collaboration leading to
independent workarounds by departments looking for quick solutions to their problems
Lack of Perceived Value – the value of data governance is often not as explicit as in other
projects. Internal bureaucracies and seemingly intrusive efforts of the DG program can
impede progress and muddy value
Data Governance Success Factors
Data governance is a discipline that evolves over time as part of the organization’s data-driven culture
Requirements for achieving an effective Data Governance program:
1. Enforced policies and standards
2. Development and execution of processes and procedures
3. Involvement of senior leadership
4. A clearly define DG framework structure (governing body)
Business Value
The goal of Data Governance is to provide appropriate
guidance for the organization to enable business effectiveness.
People
ProcessTechnology
BUSINESS : www.enterpriseim.com
LINKEDIN : www.linkedin.com/in/johnvuu
BLOG : www.johnvuu.com
MOBILE: +84 090.264.0230
Thank You!

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Introduction to Data Governance

  • 1. Introduction to Data Governance John Bao Vuu Director of Data Management www.enterpriseim.com www.linkedin.com/in/johnvuu
  • 2. Director of DM Consultant | Advisor John Vuu SPECIALTIES ✓ EIM Strategy & Solutions ✓ Data Governance / DQ ✓ Business Analytics ✓ Data Warehouse INDUSTRIES ✓ Banking ✓ Insurance ✓ Ecommerce ✓ Healthcare • 18 years experience in Data Management • Founder of 2 technology companies • Former Accenture BI Consultant • DM Director at EIM Partners • BA degree in Finance – Western WWU, Washington, USA • BS degree Information Systems – WWU, Washington, USA About the Speaker
  • 3. • IM Foundational Disciplines • Cross-functional Workflow Exchange • Key Objectives of the Data Governance Framework • Components of a Data Governance Framework • Key Roles in Data Governance • Data Governance Committee (DGC) • 4 Data Governance Policy Areas • 3 Challenges to Implementing Data Governance • Data Governance Success Factors Presentation Outline
  • 4. IM Foundational Disciplines DATA WAREHOUSE ✓Data integration ✓Enterprise data model ✓Common data dictionary ✓Data standardization ✓Data mapping / ETL ✓Applications support BUSINESS INTELLIGENCE ✓Web portal BI ✓Decentralized reporting ✓Authorization & security ✓Applications development ✓Data marts ✓Advanced Data Analytics* DATA GOVERNANCE ✓Policies & procedures ✓Stewardship / Ownership ✓Metadata management ✓Business glossary ✓DQ management ✓Data remediation / cleansing ✓MDM Bank’s fragmented Information Management architecture and poor data quality can present operational risks, strategic uncertainty and the potential for loss of revenue. Inconsistent and improper handling of data across departments can also contribute to lower staff productivity, workflow inefficiency and additional cost of maintenance and support for lack of robust enterprise information and data governance strategy.
  • 5. Example: Cross-functional Workflow Exchange Department A provides reports & data to Department B Department B checks Department A work; validates accuracy of data, makes or requests corrections Department B takes action on approvals, scoring, rating, campaigns, cust. offerings, commissions, etc. Department B deals with the consequences of any errors in reporting and from poor data quality ACTION Poor data quality cost as much as 25% of an organization’s revenue each year. TDWI – The Data Warehouse Institute Typical workflow exchange between departments: ✓ Reports become more accurate ✓ Greater confidence in decision-making – lower risks ✓ Increase productivity and efficiency across business functions ✓ More effective campaigns programs – cross & up selling ✓ Reduce operational costs – increase in revenue
  • 6. Key Objectives of the Data Governance Framework Data Governance framework baseline components: 1. Establish accountability by defining key roles and responsibilities within the DG framework 2. Define DG procedures and the methodology used to execute them 3. Define guidelines for data policies, data quality, data provisioning, metadata and reference data 4. Provide guidance for data management practices to maximize business value such as redundancy and improving data consistency 5. Provide guidance for creating and maintaining standards and tools for managing corporate data
  • 7. Components of a Data Governance Framework
  • 8. Key Roles in Data Governance Data Owner: person that has direct operational / business responsibility within a business unit for the management of one or more types of data Data Steward: person that assigns and delegates appropriate responsibility for the management of data to respective individuals Data Custodian: person that is responsible for the operation and management of systems and servers which collect, manage, and provide data access
  • 9. Data Governance Committee (DGC) 1. Oversees the execution of vision and objectives of the Data Governance program 2. DGC is responsible for taking data architecture decisions, data remediation, setting up and enforcing data standards / policies and driving data quality 3. DGC acts as the point of escalation and decision making for Data Governance related policies and procedures 4. DGC helps to define KDE (key data elements), standards and metrics, conduct root cause analysis of issues and propose solutions Executives Cross-functional Team Members (Owners) Cross-functional Stewards / Custodians Business Consumers DG Council DG Steering Committee Data Stewards End Users Data Governance Organization Structure
  • 10. 4 Data Governance Policy Areas DG policies are principles or rules that guide data-related decisions. Four primary DG policy areas: 1. Data Quality: establish how to define, measure and improve DQ 2. Data Provisioning: providing guidelines and best practices for provisioning data in efficient and effective ways 3. Metadata: organizing and classifying data for reference and use in the right business context 4. Reference Data: management of define values, i.e. KDE (key data elements), data dictionary, glossary, business rules, reference code values, etc. Data Quality Completeness Consistency Conformity Accuracy Integrity Timeliness 6 Data Quality Dimensions
  • 11. 3 Challenges to Implementing Data Governance Organization Fit – the strategy and approach are not clearly defined and do not take into account resource involvement and their level of understanding Ignored Efforts – lack of stakeholder buy-in and cross-functional collaboration leading to independent workarounds by departments looking for quick solutions to their problems Lack of Perceived Value – the value of data governance is often not as explicit as in other projects. Internal bureaucracies and seemingly intrusive efforts of the DG program can impede progress and muddy value
  • 12. Data Governance Success Factors Data governance is a discipline that evolves over time as part of the organization’s data-driven culture Requirements for achieving an effective Data Governance program: 1. Enforced policies and standards 2. Development and execution of processes and procedures 3. Involvement of senior leadership 4. A clearly define DG framework structure (governing body) Business Value The goal of Data Governance is to provide appropriate guidance for the organization to enable business effectiveness. People ProcessTechnology
  • 13. BUSINESS : www.enterpriseim.com LINKEDIN : www.linkedin.com/in/johnvuu BLOG : www.johnvuu.com MOBILE: +84 090.264.0230 Thank You!