This document contains summaries of case studies demonstrating how various organizations have successfully implemented data governance programs. One case study describes how a construction firm used a data governance assessment to benchmark their maturity and prioritize initiatives. Another case study highlights how end-user training was critical to adoption at an enterprise organization. A third case study examines which tools and frameworks, such as a data catalog, were important starting points for a financial organization's data governance efforts. The last case study outlines how a federal agency developed a long-term roadmap for their data governance program after an initial 12 week accelerator to demonstrate value from a data catalog solution.

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

4. Case Study:
Benchmarking Data
Governance Maturity

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

8. Case Study:
End User Training

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.

19. Case Study:
Long-Term Roadmap

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