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Case Studies: Applications of Data
Governance in the Enterprise
Lulit Tesfaye and Thomas Mitrevski
Data Governance and Information Quality 2023
⬢ 15+ years of experience leading diverse
information and data management
initiatives, specializing in technologies and
integrations
⬢ Most recently focused on employing
advanced Enterprise AI and semantic
capabilities for optimizing enterprise data
and information assets
Lulit Tesfaye
VP OF KNOWLEDGE AND DATA SERVICES, ENTERPRISE KNOWLEDGE
Thomas Mitrevski
SENIOR CONSULTANT, ENTERPRISE KNOWLEDGE
⬢ 8+ years of experience in
product/project management.
Specifically supporting data
management strategy, data catalog
implementations, and data
governance strategy efforts
⬢ Conducted complex data catalog and
knowledge graph implementations for
clients in a wide variety of commercial
and government industries
ENTERPRISE KNOWLEDGE
ENTERPRISE KNOWLEDGE
Outline
Introductions Enterprise Case Studies Expected Outcomes
What You Will Learn
⬢ How Leading Organizations are Benchmarking Their Data Governance Maturity
⬢ Why End-User Training was Imperative in Seeing Scaled Governance Program Adoption
⬢ Which Tools and Frameworks were Critical in Getting Started with Data Governance
⬢ How Organizations Achieved Success with Data Governance in Under 12 Weeks
⬢ What Successful Data Governance Implementation Roadmaps Really Look Like
Case Study:
Benchmarking Data
Governance Maturity
Case Study Overview & Scope
Project Background
A construction management firm was seeking:
● An assessment of their data governance needs and advisory support for selecting a tool to address
them.
● To clearly define and prioritize their data management and governance use cases.
Data Governance
Solutions Architecture
Recommendation
Tool Evaluation
Matrix & Vendor
Recommendation
Data Governance
Needs Assessment &
Use Case Definition
Data Governance Findings
● Identify gaps in
standardized processes
for data governance
● Document and
communicate processes
across the organization,
and define roles and
responsibilities
● Focus on standardizing
data governance
processes
● Create an organization
where individuals are
eager to engage in
potential data
management initiatives
● Encourage staff to upskill
and pursue data literacy
training to help fill data
governance gaps in their
daily operations
● Identify underutilized
customer and project
data
● Correlate previously
unrelated data to
generate new insights
● Ability to gain insights
from diverse types of data
(structured/
semi-structured/
unstructured)
● Identify potential tools for
better unifying and
governing their data
● Unify data across source
systems for an
enterprise-level view
regardless of physical
location of data
● Build more automated
integrations for business
opportunities, projects,
and skills
● Create the support and
engagement to pursue
initiatives for overarching
data governance and
management needs
● Prioritize a data-centric
culture that enables
discovery and connection
of data
We worked with this organization to categorize key takeaways into five data governance themes.
PEOPLE PROCESS CONTENT TECHNOLOGY CULTURE
Inventory and Surface
Existing Data Using a
Data Catalog
Priorities to Address Data Governance Challenges
This is a selection of recommendations that enabled this organization to begin addressing their key data governance needs
and challenges. These recommendations lay the foundation for longer term data governance strategy and initiatives.
Manage and Track Access
Requirements
Elevate Relationships
Between Disparate
Datasets
Manage and Leverage
Unstructured Data
Enhance Metadata
Identify and Upskill Data
Stewards
Improve Data
Lineage
Establish Governance
Organization
Case Study:
End User Training
End User Training
PEOPLE PROCESS CONTENT TECHNOLOGY CULTURE
● Training focused on
how to establish
data governance
actions around
Usage, Access, and
Sharing at an
enterprise
organization.
● Training focused
around identifying the
correct structure for
creating a data
stewardship hierarchy.
● Training providing
introductory guidance
on the concepts of
data governance and
data stewardship.
● Training providing an
introduction to
business stakeholders
on the concept of a
data catalog and the
value it will bring to
their divisions.
● Training focused on
how to communicate
the value of data
stewardship to various
business stakeholders
within an
organization.
We constructed a comprehensive training plan to upskill the organization in five key areas.
Outcomes were directly tied to needs identified from the data governance assessment.
Guides data stewardship evolutions by
providing insight into business and
strategic objectives.
Offers a technical perspective around
suggested stewardship changes and
leads changes in systems.
Guides meetings and draws on
stewardship best practices to inform
decisions. Manages requests.
Strategic Leads:
Member of the data
stewardship council.
System Administrators:
Member of the data
stewardship council.
Data Stewards:
Do not have voting
rights, but inform the
stewardship council
during decision-making
processes.
Stewardship Lead:
Member of the data
stewardship council.
Stewardship Council:
Decision-making body of the
data stewardship team.
Each recommended role builds
on the preceding one in terms of
responsibility level and decision
power.
Data Stewardship Hierarchy
Provides strategic direction and
collective decision-making.
Offers program area or operational area
perspective on stewardship needs.
ENTERPRISE KNOWLEDGE
Access, Usage, and Sharing
Aggregate
User(s) can create a dataset that is
made up of but not linked to
existing data assets.
Branch
User(s) can create a dataset that is
made up of and linked to existing
data assets.
See
User(s) can see the data asset in a
list of results, but cannot view it.
View
User(s) can read the contents of a
data asset.
Edit
User(s) can add to, modify, or
remove pieces of an existing data
asset.
Draft
User(s) can add a new data
asset.
Manage/Own
User(s) can archive or delete a data
asset.
Comment
User(s) can view a data asset and
attach non-edit notes.
Govern
User(s) can determine the standards
and structure of a data system.
CONNECT
CREATE
MANAGE
ENHANCE
FIND
Case Study:
Tools and Frameworks
Case Study Overview & Scope
Project Background
● A multi-national financial organization was facing difficulties in unifying governance, discovery, and
search across multiple metadata storage platforms within their global enterprise.
● In order to rectify their existing data quality and governance issues with a standardized metadata
platform, this organization identified a data catalog as a foundational solution to start addressing
these challenges and consulted with us to lead the implementation.
Maintain compliance
tracking through
external ontologies
A new metadata
platform that
enabled more
advanced use cases
Increased data
maturity and
development
8,000+
Business
glossary objects
5,500+
resources
Why is a Data Catalog Foundational?
A data catalog serves as a data governance tool that allows us to collect,
aggregate, and present logical and physical metadata to end users.
A modern data catalog….
• Contextualizes and enriches information with meaning of data based on
business or data domains.
• Establishes relationships across disparate data sources and across business
and technical concepts.
• Unifies unstructured and structured data to connect data of all formats.
• Makes data and information easily searchable and discoverable.
ENTERPRISE KNOWLEDGE
Data Catalog Business Value
COST
SAVINGS &
INCREASED
REVENUE
● Tag data using terms from a customized business
glossary
● Increase the accuracy and range of search
Provide
Structure
● Target access to data to specific audiences
● Enable faster access to the right data and the people
who manage it
Improve
Findability
● Implement a user-centric and scalable data inventory
● Help users organize, find, and discover data
Improve
Discoverability
● Find and connect existing data for reuse
● Minimize duplication of existing data and dashboards
● Standardize data schemas across sources
Reuse Content
● Integrate multiple disparate sources of data
● Connect both structured and unstructured datasets
Integrate
Sources
ENTERPRISE KNOWLEDGE
How a Data Catalog Fits into Data Governance Tools
Application
Data
Fabric
Layer
Sources
Integration/
Processing
Layer
Presentation
Layer
Extract, Transform, Load (ETL)
Pipelines
APIs
Search
Research &
Analytics
Recommendations /
Chatbot
Admin /
Governance
Context
and
Metadata
Metadata Service Taxonomy / Ontology
Management
Master Data
Management
Data Lake / Data
Warehouse
Client
Connectivity Data
Livestream Data
Knowledge
Graph
Data Catalog Content Storage
APIs
A data fabric
enables data
federation and
virtualization of
semantic labels
or rules (e.g.
taxonomies/
business
glossaries or
ontologies) to
capture and
connect data
based on
business or
domain meaning
and value.
ENTERPRISE KNOWLEDGE
Case Study:
12 Week Accelerator
Data Catalog Implementation Components
Use Cases and Business
Capabilities Backlog
02 Prioritization, design, and validation of use cases and
capabilities for the implementation backlog.
Platform Architecture and
Configuration
01
Installation and integration of solution with source systems,
definition and enforcement of security management, and
finalization of access controls.
Data Organization,
Enrichment, and Connectivity
04
Metadata modeling, metadata glossary design, automated
enrichment, and connection of data concepts based on
relationships (datasets, documents, applications, etc.).
User Onboarding and
Enablement
03
Phased user provisioning and enablement through
foundational tasks teams need to learn to be successful on
the data catalog platform.
Governance and Analytics
06
Agile approach to metadata stewardship and governance
with configured governance workflows in data catalog to
enable collaboration and provide end-to-end visibility
throughout the analytics lifecycle.
Training and Adoption
05
Module-based introductory and advanced training with
hands-on practice labs customized to fit business and
technical personas and derive successful program adoption.
Case Study:
Long-Term Roadmap
Case Study Overview & Scope
Project Background
● A federal agency required a comprehensive data catalog solution to serve as a central facilitator for
their overarching data strategy and governance program and as a source to search, discover, and
gain insights into enterprise-wide data assets.
● This included the integration with a variety of systems and applications both internally and
externally.
Establish metadata
quality standards and
operating policies in an
Agile Data Governance
Playbook.
Ingest and actively
govern glossary
terms.
Define and embed
governance roles and
responsibilities across
multiple divisions.
ENTERPRISE KNOWLEDGE
Long-Term Roadmap
The goal of this roadmap is to
enable an organization to
iteratively achieve a state
where metadata is
standardized, governance is
embedded, and collaboration
on data is consistent.
Onboarding and
Foundational
Configuration
Expand to
Prioritized Use
Cases
Governance and
Analytics
Playbook
Enhance and
Optimize
Development of
Advanced Use
Cases
In less than 20 weeks, validate and demonstrate
to your organization how a data catalog can
provide value before making a long-term
investment.
Demonstrate Value Early
Findings from the initial use cases will inform a
repeatable approach and long-term roadmap to scale
the data catalog in line with organizational objectives.
Align on Data Catalog
Strategy for Scale
Encourage Adoption
Implementing a data catalog quickly will create
interest and ownership by allowing users to see
tangibly how their data challenges will be addressed.
Gain Valuable
Governance Insights
The process of implementing a catalog will reveal
valuable insights into your own governance processes,
shining a light on what processes and procedures are
effective, and which ones need to be improved.
Key Takeaways

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DGIQ - Case Studies_ Applications of Data Governance in the Enterprise (Final).pdf

  • 1. Case Studies: Applications of Data Governance in the Enterprise Lulit Tesfaye and Thomas Mitrevski Data Governance and Information Quality 2023
  • 2. ⬢ 15+ years of experience leading diverse information and data management initiatives, specializing in technologies and integrations ⬢ Most recently focused on employing advanced Enterprise AI and semantic capabilities for optimizing enterprise data and information assets Lulit Tesfaye VP OF KNOWLEDGE AND DATA SERVICES, ENTERPRISE KNOWLEDGE Thomas Mitrevski SENIOR CONSULTANT, ENTERPRISE KNOWLEDGE ⬢ 8+ years of experience in product/project management. Specifically supporting data management strategy, data catalog implementations, and data governance strategy efforts ⬢ Conducted complex data catalog and knowledge graph implementations for clients in a wide variety of commercial and government industries ENTERPRISE KNOWLEDGE
  • 3. ENTERPRISE KNOWLEDGE Outline Introductions Enterprise Case Studies Expected Outcomes What You Will Learn ⬢ How Leading Organizations are Benchmarking Their Data Governance Maturity ⬢ Why End-User Training was Imperative in Seeing Scaled Governance Program Adoption ⬢ Which Tools and Frameworks were Critical in Getting Started with Data Governance ⬢ How Organizations Achieved Success with Data Governance in Under 12 Weeks ⬢ What Successful Data Governance Implementation Roadmaps Really Look Like
  • 5. Case Study Overview & Scope Project Background A construction management firm was seeking: ● An assessment of their data governance needs and advisory support for selecting a tool to address them. ● To clearly define and prioritize their data management and governance use cases. Data Governance Solutions Architecture Recommendation Tool Evaluation Matrix & Vendor Recommendation Data Governance Needs Assessment & Use Case Definition
  • 6. Data Governance Findings ● Identify gaps in standardized processes for data governance ● Document and communicate processes across the organization, and define roles and responsibilities ● Focus on standardizing data governance processes ● Create an organization where individuals are eager to engage in potential data management initiatives ● Encourage staff to upskill and pursue data literacy training to help fill data governance gaps in their daily operations ● Identify underutilized customer and project data ● Correlate previously unrelated data to generate new insights ● Ability to gain insights from diverse types of data (structured/ semi-structured/ unstructured) ● Identify potential tools for better unifying and governing their data ● Unify data across source systems for an enterprise-level view regardless of physical location of data ● Build more automated integrations for business opportunities, projects, and skills ● Create the support and engagement to pursue initiatives for overarching data governance and management needs ● Prioritize a data-centric culture that enables discovery and connection of data We worked with this organization to categorize key takeaways into five data governance themes. PEOPLE PROCESS CONTENT TECHNOLOGY CULTURE
  • 7. Inventory and Surface Existing Data Using a Data Catalog Priorities to Address Data Governance Challenges This is a selection of recommendations that enabled this organization to begin addressing their key data governance needs and challenges. These recommendations lay the foundation for longer term data governance strategy and initiatives. Manage and Track Access Requirements Elevate Relationships Between Disparate Datasets Manage and Leverage Unstructured Data Enhance Metadata Identify and Upskill Data Stewards Improve Data Lineage Establish Governance Organization
  • 9. End User Training PEOPLE PROCESS CONTENT TECHNOLOGY CULTURE ● Training focused on how to establish data governance actions around Usage, Access, and Sharing at an enterprise organization. ● Training focused around identifying the correct structure for creating a data stewardship hierarchy. ● Training providing introductory guidance on the concepts of data governance and data stewardship. ● Training providing an introduction to business stakeholders on the concept of a data catalog and the value it will bring to their divisions. ● Training focused on how to communicate the value of data stewardship to various business stakeholders within an organization. We constructed a comprehensive training plan to upskill the organization in five key areas. Outcomes were directly tied to needs identified from the data governance assessment.
  • 10. Guides data stewardship evolutions by providing insight into business and strategic objectives. Offers a technical perspective around suggested stewardship changes and leads changes in systems. Guides meetings and draws on stewardship best practices to inform decisions. Manages requests. Strategic Leads: Member of the data stewardship council. System Administrators: Member of the data stewardship council. Data Stewards: Do not have voting rights, but inform the stewardship council during decision-making processes. Stewardship Lead: Member of the data stewardship council. Stewardship Council: Decision-making body of the data stewardship team. Each recommended role builds on the preceding one in terms of responsibility level and decision power. Data Stewardship Hierarchy Provides strategic direction and collective decision-making. Offers program area or operational area perspective on stewardship needs.
  • 11. ENTERPRISE KNOWLEDGE Access, Usage, and Sharing Aggregate User(s) can create a dataset that is made up of but not linked to existing data assets. Branch User(s) can create a dataset that is made up of and linked to existing data assets. See User(s) can see the data asset in a list of results, but cannot view it. View User(s) can read the contents of a data asset. Edit User(s) can add to, modify, or remove pieces of an existing data asset. Draft User(s) can add a new data asset. Manage/Own User(s) can archive or delete a data asset. Comment User(s) can view a data asset and attach non-edit notes. Govern User(s) can determine the standards and structure of a data system. CONNECT CREATE MANAGE ENHANCE FIND
  • 12. Case Study: Tools and Frameworks
  • 13. Case Study Overview & Scope Project Background ● A multi-national financial organization was facing difficulties in unifying governance, discovery, and search across multiple metadata storage platforms within their global enterprise. ● In order to rectify their existing data quality and governance issues with a standardized metadata platform, this organization identified a data catalog as a foundational solution to start addressing these challenges and consulted with us to lead the implementation. Maintain compliance tracking through external ontologies A new metadata platform that enabled more advanced use cases Increased data maturity and development 8,000+ Business glossary objects 5,500+ resources
  • 14. Why is a Data Catalog Foundational? A data catalog serves as a data governance tool that allows us to collect, aggregate, and present logical and physical metadata to end users. A modern data catalog…. • Contextualizes and enriches information with meaning of data based on business or data domains. • Establishes relationships across disparate data sources and across business and technical concepts. • Unifies unstructured and structured data to connect data of all formats. • Makes data and information easily searchable and discoverable. ENTERPRISE KNOWLEDGE
  • 15. Data Catalog Business Value COST SAVINGS & INCREASED REVENUE ● Tag data using terms from a customized business glossary ● Increase the accuracy and range of search Provide Structure ● Target access to data to specific audiences ● Enable faster access to the right data and the people who manage it Improve Findability ● Implement a user-centric and scalable data inventory ● Help users organize, find, and discover data Improve Discoverability ● Find and connect existing data for reuse ● Minimize duplication of existing data and dashboards ● Standardize data schemas across sources Reuse Content ● Integrate multiple disparate sources of data ● Connect both structured and unstructured datasets Integrate Sources ENTERPRISE KNOWLEDGE
  • 16. How a Data Catalog Fits into Data Governance Tools Application Data Fabric Layer Sources Integration/ Processing Layer Presentation Layer Extract, Transform, Load (ETL) Pipelines APIs Search Research & Analytics Recommendations / Chatbot Admin / Governance Context and Metadata Metadata Service Taxonomy / Ontology Management Master Data Management Data Lake / Data Warehouse Client Connectivity Data Livestream Data Knowledge Graph Data Catalog Content Storage APIs A data fabric enables data federation and virtualization of semantic labels or rules (e.g. taxonomies/ business glossaries or ontologies) to capture and connect data based on business or domain meaning and value. ENTERPRISE KNOWLEDGE
  • 17. Case Study: 12 Week Accelerator
  • 18. Data Catalog Implementation Components Use Cases and Business Capabilities Backlog 02 Prioritization, design, and validation of use cases and capabilities for the implementation backlog. Platform Architecture and Configuration 01 Installation and integration of solution with source systems, definition and enforcement of security management, and finalization of access controls. Data Organization, Enrichment, and Connectivity 04 Metadata modeling, metadata glossary design, automated enrichment, and connection of data concepts based on relationships (datasets, documents, applications, etc.). User Onboarding and Enablement 03 Phased user provisioning and enablement through foundational tasks teams need to learn to be successful on the data catalog platform. Governance and Analytics 06 Agile approach to metadata stewardship and governance with configured governance workflows in data catalog to enable collaboration and provide end-to-end visibility throughout the analytics lifecycle. Training and Adoption 05 Module-based introductory and advanced training with hands-on practice labs customized to fit business and technical personas and derive successful program adoption.
  • 20. Case Study Overview & Scope Project Background ● A federal agency required a comprehensive data catalog solution to serve as a central facilitator for their overarching data strategy and governance program and as a source to search, discover, and gain insights into enterprise-wide data assets. ● This included the integration with a variety of systems and applications both internally and externally. Establish metadata quality standards and operating policies in an Agile Data Governance Playbook. Ingest and actively govern glossary terms. Define and embed governance roles and responsibilities across multiple divisions.
  • 21. ENTERPRISE KNOWLEDGE Long-Term Roadmap The goal of this roadmap is to enable an organization to iteratively achieve a state where metadata is standardized, governance is embedded, and collaboration on data is consistent. Onboarding and Foundational Configuration Expand to Prioritized Use Cases Governance and Analytics Playbook Enhance and Optimize Development of Advanced Use Cases
  • 22. In less than 20 weeks, validate and demonstrate to your organization how a data catalog can provide value before making a long-term investment. Demonstrate Value Early Findings from the initial use cases will inform a repeatable approach and long-term roadmap to scale the data catalog in line with organizational objectives. Align on Data Catalog Strategy for Scale Encourage Adoption Implementing a data catalog quickly will create interest and ownership by allowing users to see tangibly how their data challenges will be addressed. Gain Valuable Governance Insights The process of implementing a catalog will reveal valuable insights into your own governance processes, shining a light on what processes and procedures are effective, and which ones need to be improved. Key Takeaways