1. Data Governance
A Strategic Imperative
Ken Fogarty
Managing Consultant

2. 1 What is Data Governance?
2 Why addressing Data Governance is an imperative activity
Data Governance project concepts
Critical success factors to enable Data Governance
The 8 steps to Data Governance initiation
3
4
5
Discussion Topics

3. What is Data Governance?What is Data Governance?What is Data Governance?What is Data Governance?
A sound Data Governance strategy….
• Blends discovery, control and automation to help
business decision-makers determine who needs
access to business-critical data
• Helps determine whether data resides in structured
formats within applications and databases or in
unstructured formats within documents and
spreadsheets
• Helps companies meet ever-evolving business
demands without compromising security or
compliance requirements

4. A Historical PerspectiveA Historical PerspectiveA Historical PerspectiveA Historical Perspective
1980 - 19901960-1980 1990-2000 2000- present Future….
• Data was a
byproduct of
running the
business
• Data was not
treated as a
valuable,
shared asset,
so need for
governance
did not arise.
• Organizations
attempted
governing data
through
enterprise data
modeling.
• Governance
efforts did not
have
organizational
support and
authority to
enforce
compliance.
• The rigidity of
packaged
applications
further reduced
effectiveness.
• Awareness that the
value of data
extends beyond
transactions begins.
• Increasingly large
amounts of data
created in distant
parts of an
organization for a
different purpose.
• Large scale
repositories, like
data warehouses
are built
• ERP and ERP
consolidation — the
notion of having an
integrated set of
plumbing run the
business — is driven
by the same
philosophy.
• The need for data
consolidation
becomes apparent.
In reality, the
opposite has
occurred.
• Systems and data
repositories
proliferated; data
complexity and
volume continue to
explode.
• Business has grown
more sophisticated in
their use of data,
which drives new
demand that require
different ways to
combine,
manipulate, store,
and present
information.
• Successful implementations of policy-
centric data governance will produce
pervasive and long-lasting
improvements in business performance.
• The scope of these data governance
programs will cover all major areas of
competence: model, quality, security
and lifecycle.
• Clearly defined and enforced policies
will cover all high-value data assets, the
business processes that produce and
consume them, and systems that store
and manipulate them.
• More importantly, a strong culture that
values data will become firmly
entrenched in every aspect of doing
business. The organization for data
governance will become distinct and
institutionalized, viewed as critical to
business in a way no different than
other permanent business functions like
human resources and finance

5. A Historical PerspectiveA Historical PerspectiveA Historical PerspectiveA Historical Perspective
1980 - 19901960-1980 1990-2000 2000- present Future….
• Data was
byproduct of
running the
business
• Data was not
treated as a
valuable,
shared asset,
so need for
governance
did not arise.
• Organizations
attempted
governing data
through
enterprise data
modeling.
• Governance
efforts did not
have
organizational
support and
authority to
enforce
compliance.
• The rigidity of
packaged
applications
further reduced
effectiveness.
• Awareness that the
value of data
extends beyond
transactions begins.
• Increasingly large
amounts of data
created in distant
parts of an
organization for a
different purpose.
• Large scale
repositories,like data
warehouses are built
• ERP and ERP
consolidation — the
notion of having an
integrated set of
plumbing run the
business — is driven
by the same
philosophy.
• The need for data
consolidation
becomes apparent.
In reality, the
opposite has
occurred.
• Systems and data
repositories
proliferated; data
complexity and
volume continue to
explode.
• Business has grown
more sophisticated in
their use of data,
which drives new
demand that require
different ways to
combine,
manipulate, store,
and present
information.
• Successful implementations of policy-
centric data governance will produce
pervasive and long-lasting
improvements in business performance.
• The scope of these data governance
programs will cover all major areas of
competence: model, quality, security
and lifecycle.
• Clearly defined and enforced policies
will cover all high-value data assets, the
business processes that produce and
consume them, and systems that store
and manipulate them.
• More importantly, a strong culture that
values data will become firmly
entrenched in every aspect of doing
business. The organization for data
governance will become distinct and
institutionalized, viewed as critical to
business in a way no different than
other permanent business functions like
human resources and finance

6. A Historical PerspectiveA Historical PerspectiveA Historical PerspectiveA Historical Perspective
1980 - 19901960-1980 1990-2000 2000- present Future….
• Data was
byproduct of
running the
business
• Data was not
treated as a
valuable,
shared asset,
so need for
governance
did not arise.
• Organizations
attempted
governing data
through
enterprise data
modeling.
• Governance
efforts did not
have
organizational
support and
authority to
enforce
compliance.
• The rigidity of
packaged
applications
further reduced
effectiveness.
• Awareness that the
value of data
extends beyond
transactions begins.
• Increasingly large
amounts of data
created in distant
parts of an
organization for a
different purpose.
• Large scale
repositories, like
data warehouses
are built
• ERP and ERP
consolidation — the
notion of having an
integrated set of
plumbing run the
business — is driven
by the same
philosophy.
• The need for data
consolidation
becomes apparent.
In reality, the
opposite has
occurred.
• Systems and data
repositories
proliferated; data
complexity and
volume continue to
explode.
• Business has grown
more sophisticated in
their use of data,
which drives new
demand that require
different ways to
combine,
manipulate, store,
and present
information.
• Successful implementations of policy-
centric data governance will produce
pervasive and long-lasting
improvements in business performance.
• The scope of these data governance
programs will cover all major areas of
competence: model, quality, security
and lifecycle.
• Clearly defined and enforced policies
will cover all high-value data assets, the
business processes that produce and
consume them, and systems that store
and manipulate them.
• More importantly, a strong culture that
values data will become firmly
entrenched in every aspect of doing
business. The organization for data
governance will become distinct and
institutionalized, viewed as critical to
business in a way no different than
other permanent business functions like
human resources and finance

7. • Organizations
attempted
governing data
through
enterprise data
modeling.
• Governance
efforts did not
have
organizational
support and
authority to
enforce
compliance.
• The rigidity of
packaged
applications
further reduced
effectiveness.
A Historical PerspectiveA Historical PerspectiveA Historical PerspectiveA Historical Perspective
1980 - 19901960-1980 1990-2000 2000- present Future….
• Data was
byproduct of
running the
business
• Data was not
treated as a
valuable,
shared asset,
so need for
governance
did not arise.
• Awareness that the
value of data
extends beyond
transactions begins.
• Increasingly large
amounts of data
created in distant
parts of an
organization for a
different purpose.
• Large scale
repositories, like
data warehouses
are built
• ERP and ERP
consolidation — the
notion of having an
integrated set of
plumbing run the
business — is
driven by the same
philosophy.
• The need for data
consolidation
becomes apparent.
In reality, the
opposite has
occurred.
• Systems and data
repositories
proliferated; data
complexity and
volume continue to
explode.
• Business has grown
more sophisticated in
their use of data,
which drives new
demand that require
different ways to
combine,
manipulate, store,
and present
information.
• Successful implementations of policy-
centric data governance will produce
pervasive and long-lasting
improvements in business performance.
• The scope of these data governance
programs will cover all major areas of
competence: model, quality, security
and lifecycle.
• Clearly defined and enforced policies
will cover all high-value data assets, the
business processes that produce and
consume them, and systems that store
and manipulate them.
• More importantly, a strong culture that
values data will become firmly
entrenched in every aspect of doing
business. The organization for data
governance will become distinct and
institutionalized, viewed as critical to
business in a way no different than
other permanent business functions like
human resources and finance

8. • Organizations
attempted
governing data
through
enterprise data
modeling.
• Governance
efforts did not
have
organizational
support and
authority to
enforce
compliance.
• The rigidity of
packaged
applications
further reduced
effectiveness.
A Historical PerspectiveA Historical PerspectiveA Historical PerspectiveA Historical Perspective
1980 - 19901960-1980 1990-2000 2000- present Future….
• Data was
byproduct of
running the
business
• Data was not
treated as a
valuable,
shared asset,
so need for
governance
did not arise.
• Awareness that the
value of data
extends beyond
transactions begins.
• Increasingly large
amounts of data
created in distant
parts of an
organization for a
different purpose.
• Large scale
repositories, like
data warehouses
are built
• ERP and ERP
consolidation — the
notion of having an
integrated set of
plumbing run the
business — is
driven by the same
philosophy.
• The need for data
consolidation
becomes apparent.
In reality, the
opposite has
occurred.
• Systems and data
repositories
proliferated; data
complexity and
volume continue to
explode.
• Business has grown
more sophisticated in
their use of data,
which drives new
demand that require
different ways to
combine,
manipulate, store,
and present
information.
• Successful implementations of policy-
centric data governance will produce
pervasive and long-lasting
improvements in business performance.
• The scope of these data governance
programs will cover all major areas of
competence: model, quality, security
and lifecycle.
• Clearly defined and enforced policies
will cover all high-value data assets, the
business processes that produce and
consume them, and systems that store
and manipulate them.
• More importantly, a strong culture that
values data will become firmly
entrenched in every aspect of doing
business. The organization for data
governance will become distinct and
institutionalized, viewed as critical to
business in a way no different than
other permanent business functions like
human resources and finance

9. • Organizations
attempted
governing data
through
enterprise data
modeling.
• Governance
efforts did not
have
organizational
support and
authority to
enforce
compliance.
• The rigidity of
packaged
applications
further reduced
effectiveness.
A Historical PerspectiveA Historical PerspectiveA Historical PerspectiveA Historical Perspective
1980 - 19901960-1980 1990-2000 2000- present Future….
• Data was
byproduct of
running the
business
• Data was not
treated as a
valuable,
shared asset,
so need for
governance
did not arise.
• Awareness that the
value of data
extends beyond
transactions begins.
• Increasingly large
amounts of data
created in distant
parts of an
organization for a
different purpose.
• Large scale
repositories, like
data warehouses
are built
• ERP and ERP
consolidation — the
notion of having an
integrated set of
plumbing run the
business — is
driven by the same
philosophy.
• The need for data
consolidation
becomes apparent.
In reality, the
opposite has
occurred.
• Systems and data
repositories
proliferated; data
complexity and
volume continue to
explode.
• Business has grown
more sophisticated in
their use of data,
which drives new
demand that require
different ways to
combine,
manipulate, store,
and present
information.
• Successful implementations of policy-
centric data governance will produce
pervasive and long-lasting
improvements in business performance.
• Scope of data governance programs
will cover all major areas of
competence: model, quality, security
and lifecycle.
• Clearly defined and enforced policies
will cover all high-value data assets, the
business processes that produce and
consume them, and systems that store
and manipulate them.
• More importantly, a strong culture that
values data will become firmly
entrenched in every aspect of doing
business. The organization for data
governance will become distinct and
institutionalized, viewed as critical to
business in a way no different than
other permanent business functions
like human resources and finance.

10. Corporate / Organizational Leadership
IT ACCOUNTING CUSTOMER
SERVICE
SUPPLY
CHAIN
SALES BICC
What Is Data Governance?What Is Data Governance?What Is Data Governance?What Is Data Governance?

11. Corporate / Organizational Leadership
IT ACCOUNTING CUSTOMER
SERVICE
SUPPLY
CHAIN
SALES DATA
GOVERNANCE?
What Is Data Governance?What Is Data Governance?What Is Data Governance?What Is Data Governance?

12. Corporate / Organizational Leadership
IT ACCOUNTING CUSTOMER
SERVICE
SUPPLY
CHAIN
SALES DATA
GOVERNANCE
What Is Data Governance?What Is Data Governance?What Is Data Governance?What Is Data Governance?
Enterprise Data Governance Program

13. Why is Data Governance Imperative?Why is Data Governance Imperative?Why is Data Governance Imperative?Why is Data Governance Imperative?
• You cannot properly implement analytics without it
• Data Sharing needs require data organization
• Time and money will be lost due to inability to institute Data Reusability
• Data Governance simplifies storage and backup of data
• Data Governance tools provide data security

14. Why is Data Governance Imperative?Why is Data Governance Imperative?Why is Data Governance Imperative?Why is Data Governance Imperative?
• You cannot properly implement analytics without it
• Data Sharing needs require data organization
• Time and money will be lost due to inability to institute Data Reusability
• Data Governance simplifies storage and backup of data
• Data Governance tools provide data security
Who needs data related to this transaction?

15. Why is Data Governance Imperative?Why is Data Governance Imperative?Why is Data Governance Imperative?Why is Data Governance Imperative?
• You cannot properly implement analytics without it
• Data Sharing needs require data organization
• Time and money will be lost due to inability to institute Data Reusability
• It simplifies storage and backup of data
• Data Governance tools provide data security
Cumulation argument
• ... data that was generated once can be reused many times (contrary to tangible raw materials)
Aggregation argument
• ... collective efforts create volumes of data that can generate new data (only when the data is
used for new purposes or in new contexts)
Growth argument
• … data reuse allows going back in time (whereas generating new data can only start today or in
the future). The result is that data reuse also can produce growing data sets

16. Why is Data Governance Imperative?Why is Data Governance Imperative?Why is Data Governance Imperative?Why is Data Governance Imperative?
• You cannot properly implement analytics without it
• Data Sharing needs require data organization
• Time and money will be lost due to inability to institute Data Reusability
• Data Governance simplifies storage and backup of data
• Data Governance tools provide data security
What should
be backed
up?

17. Why is Data Governance Imperative?Why is Data Governance Imperative?Why is Data Governance Imperative?Why is Data Governance Imperative?
• You cannot properly implement analytics without it
• Data Sharing needs require data organization
• Time and money will be lost due to inability to institute Data Reusability
• Data Governance simplifies storage and backup of data
• Data Governance tools provide data security
• Error identification in data and security can quickly be
implemented by using a professional data quality tool.
• Data quality analysis and profiling, duplicate detection,
data standardization and cleansing, and data security
monitoring are critical to keep tabs on your data, identify
areas for cost savings, and ensure that integrity and
quality are upheld.

18. Data Governance ProjectData Governance ProjectData Governance ProjectData Governance Project---- The ValueThe ValueThe ValueThe Value
If it is true that:
• Successful organizations treat information as they treat their factories,
supply chains, vendors, and customers.
• … and, it it is true that:
• In the twenty-first century, no manager argues with standards for material
handling, depreciation rules, or customer privacy.
• Then, there is no debate over whether you should have standards or
controls; these are accepted business practices.

19. Data Governance ProjectData Governance ProjectData Governance ProjectData Governance Project---- The ValueThe ValueThe ValueThe Value
If it is true that:
• Successful organizations treat information as they treat their factories,
supply chains, vendors, and customers.
• … and, it it is true that:
• In the twenty-first century, no manager argues with standards for material
handling, depreciation rules, or customer privacy.
• Then, there is no debate over whether you should have standards or
controls; these are accepted business practices.
Yet it is easy to spread data all over an organization to the point that:
1. It is excessively expensive to manage.
2. You cannot find it, make sense of it, or agree on its meaning.

20. Data Governance ProjectData Governance ProjectData Governance ProjectData Governance Project---- The ScopeThe ScopeThe ScopeThe Scope
A large multinational company does not have to deploy
a global Data Governance program. The scope can be
a self-contained line of business.
Global company with an integrated international
supply chain:
• The scope is most likely global.
Large, international chemical company:
• Business model may contain material, agricultural,
and refining divisions.
• All would operate on a more or less self-contained
basis.
• You may have three Data Governance “programs”
that are each similar in makeup, but separately
accountable.

21. Business
Alignment
Business
Process
Policy Organization Business
Information
Data Technology
Documents and
files that express
business
direction,
performance, and
measurement.
Ensuring
business
alignment to
information
asset
management.
Events and
actions related to
data. Artifacts
from a process
modeling tool
would be
addressed here.
Review all
process
elements to
ensure proper
documentation
Artifacts that
document
desired or
required
behavior.
Governance of
documents
(legal, risk, and
practical
policies), which
are often in
conflict.
Who the
stakeholders/
decision makers are.
This is not an element
you would place in an
expensive tool. Larger
organizations may
require a database or
use of organizational
entities in enterprise
modeling tool.
Critical to ensuring
business
alignment. The
poor tracking of
requirements is a
common mistake.
A critical function
is to monitor the
development of
any Enterprise
Info mgt
requirements.
Knowing where
data is and what it
means. The term
metadata is
distorted by
vendors. This
element represents
all of the “data”
required to operate
the DG program.
It is critical to
track the
technology that
can use and
affect data. This
represents the
details about
technology
used to
manage
information
assets.
• Strategy
• Goal
• Objective
• Plan
• Information
levers
• Event
• Meeting
• Communication
• Training
• Process
• Workflow
• Lifecycle
Methodology
• Function
• Principles
• Policies
• Standards
• Controls
• Rule
• Regulation
• Level
• Role (RACI)
• Location
• Assignment
• Community
• Department
• Team
• Roster
• Stakeholder
• Type
• Steward
• Custodian
• Metric or
measurement
• Manuals
• Charters
• Presentations
• Project
deliverables
• E-mail
• Policy and
principals-
written versions
• Metrics
• Models
• Standards
• Dictionary
• Definitions
• Metadata
• Digital processes
• Blog/ Wiki
• Files
• Bureau of
Internal Revenue
• File Location
• Product
• Hardware
• Software
• User
Concepts and Service DescriptionsConcepts and Service DescriptionsConcepts and Service DescriptionsConcepts and Service Descriptions

22. Elements of a Data Governance Program: OrganizationElements of a Data Governance Program: OrganizationElements of a Data Governance Program: OrganizationElements of a Data Governance Program: Organization
There needs to be a formal statement of roles. The official designation of accountability and
responsibility are key factors to the survival of Data Governance. Most important to new Data
Governance programs is the concept of accountability for data.
1. Organization
2. Principals
3. Policies
4. Functions
5. Metrics
6. Technology
APPROVES
• Tie-breaker decision maker
• Approves information principles and policies
• Monitors information metric scorecard reports
Information
Executive
Information
Manager
Information
Custodian
DEFINES
• Understands Specific information uses and risks
• Decides who can, and how to, use information
• Ensures assets are properly managed
ENFORCES
• Initiates quality audits and ensures policy compliance
• Executes activities in line with policies & procedures
• Coordinates work and education across the business

23. Principles are generally adopted rules that guide conduct and application of data philosophy
1. Organization
2. Principles
3. Policies
4. Functions
5. Metrics
6. Technology
Elements of a Data Governance Program: PrinciplesElements of a Data Governance Program: PrinciplesElements of a Data Governance Program: PrinciplesElements of a Data Governance Program: Principles
• Crucial elements in data governance and can even justify the entire
program because with principles in place, there can be fewer meetings
• Will succeed where a batch of rules and policies will not
• They are foundational
“Data sets from external sources must be registered with the Data
Governance team before production use………. “

24. Policies are formally defined processes with strength of support.
1. Organization
2. Principles
3. Policies
4. Functions
5. Metrics
6. Technology
Elements of a Data Governance Program: PoliciesElements of a Data Governance Program: PoliciesElements of a Data Governance Program: PoliciesElements of a Data Governance Program: Policies
• Very common situation; you already have most of your Data
Governance policies floating around in the form of a disconnected IT,
data, or compliance policy (and commonly life goes on and the policies
are disregarded).
• The marriage of principle and policy prevents this in the Data
Governance program.
“…We’re going to get a data set from (external) each month for this
project. Maybe other teams could benefit?? Lets have a meeting about
it……”

25. Function describes the “what” has to happen.
1. Organization
2. Principles
3. Policies
4. Functions
5. Metrics
6. Technology
Elements of a Data Governance Program: FunctionsElements of a Data Governance Program: FunctionsElements of a Data Governance Program: FunctionsElements of a Data Governance Program: Functions
• These functions will appear to be embedded in the Data Governance
“department” but over time they need to evolve into day-to-day activities
within all areas.
• Functions will be custom to the organizations and goals, examples can
include:
• Execute processes to support data access
• Develop Customer/Vendor hierarchies
• Mediate and resolve conflicts pertaining to data
• Enforce data principles, policies and standards

26. Data Governance programs require a means to monitor effectiveness.
1. Organization
2. Principles
3. Policies
4. Functions
5. Metrics
6. Technology
Elements of a Data Governance Program: MetricsElements of a Data Governance Program: MetricsElements of a Data Governance Program: MetricsElements of a Data Governance Program: Metrics
• At the outset, the metrics will be hard to collect
• Eventually, the metrics will evolve from simple surveys and counts to
true monitoring of activity.
• Common metrics could include:
– IMM Index (Information Management Maturity)
– Data Governance Stewardship
– Data Quality
– Business Value

27. There is not a clear-cut category or market for pure Data Governance technology; most efforts
cover various technologies (regardless, you will need to assemble a toolbox of capabilities.)
1. Organization
2. Principles
3. Policies
4. Functions
5. Metrics
6. Technology
Elements of a Data Governance Program: TechnologyElements of a Data Governance Program: TechnologyElements of a Data Governance Program: TechnologyElements of a Data Governance Program: Technology
• Traditional places to store artifacts, like SharePoint and Excel, are useful,
but only if managed governed.
• Some features of Data Governance tools that can be considered are:
• Principle and policy administration
• Business rules and standards administration
• Organization management
• Work flow for issues and audits
• Data dictionary
• Enterprise search
• Document management

28. The 8 Steps to Data Governance InitiationThe 8 Steps to Data Governance InitiationThe 8 Steps to Data Governance InitiationThe 8 Steps to Data Governance Initiation
Scope and
Initiation
Assess
Vision
Align
and
Business
Value
Functional
Design
Governing
Framework
Design
Roadmap
Roll out
and
Sustain
1. Scope and Initiation
2. Assess
3. Vision
4. Align and Business Value
5. Functional Design
6. Governing Framework Design
7. Roadmap
8. Roll out and Sustain

29. Data Governance Program Steps: Scope and InitiationData Governance Program Steps: Scope and InitiationData Governance Program Steps: Scope and InitiationData Governance Program Steps: Scope and Initiation
Considerations
• Most Data Governance programs get started within an
information technology (IT) area.
• If a CIO is driving the management of data and making it
a powerful asset, verification that the scope of Data
Governance includes enterprise wide creation and
enforcement of broad-spectrum policies is critical.
• If an organization is highly regulated, then the compliance
area needs to be brought into the Data Governance effort.
1. Scope and Initiation
2. Assess
3. Vision
4. Align and Business Value
5. Functional Design
6. Governing Framework Design
7. Roadmap
8. Roll out and Sustain
A Data Governance program affects several segments of your organization.
You need an understanding of how “deep” the Data Governance program
will go.

30. Data Governance Program Steps: AssessData Governance Program Steps: AssessData Governance Program Steps: AssessData Governance Program Steps: Assess
• Unlike assessments done for data quality or enterprise
architecture, Data Governance assessments are focused on the
organization’s ability to govern and to be governed.
• We use the alliterative phrase capacity, culture, collaborate.
1. Scope and Initiation
2. Assess
3. Vision
4. Align and Business Value
5. Functional Design
6. Governing Framework Design
7. Roadmap
8. Roll out and Sustain
Once scope is understood and approved, the next step is to perform the required
assessments.

31. Data Governance Program Steps: AssessData Governance Program Steps: AssessData Governance Program Steps: AssessData Governance Program Steps: Assess
1. Scope and Initiation
2. Assess
3. Vision
4. Align and Business Value
5. Functional Design
6. Governing Framework Design
7. Roadmap
8. Roll out and Sustain

32. Data Governance Program Steps: VisionData Governance Program Steps: VisionData Governance Program Steps: VisionData Governance Program Steps: Vision
1. Scope and Initiation
2. Assess
3. Vision
4. Align and Business Value
5. Functional Design
6. Governing Framework Design
7. Roadmap
8. Roll out and Sustain
The “Vision” phase demonstrates to stakeholders and leadership the definition
and meaning of Data Governance to the organization.
• The goal is to achieve an understanding of what the data
governance program might look like and where the critical
touch points for Data Governance might appear.
• Until you show a “day-in-the-life” presentation, many
people do not comprehend what Data Governance means
to their position or work environment. In the context of
Data Governance, this phase is similar to a conceptual
prototype.
VISION STEPS:
1. Define Data Governance for your organization
2. Define preliminary Data Governance requirements
3. Develop representations of future Data Governance
4. Develop a one-page “day-in-the-life” slide; likely the most
significant output of this activity

33. Data Governance Program Steps: Align Business and ValueData Governance Program Steps: Align Business and ValueData Governance Program Steps: Align Business and ValueData Governance Program Steps: Align Business and Value
This phase develops the financial value statement and baseline for ongoing
measurement of the Data Governance deployment. A link will be developed between
Data Governance and improving the organization in a financially recognizable way.
1. Scope and Initiation
2. Assess
3. Vision
4. Align and Business Value
5. Functional Design
6. Governing Framework Design
7. Roadmap
8. Roll out and Sustain
Considerations:
Two aspects to this phase merit careful consideration:
1. Determine if there is an overall Enterprise Information
Management program, or sponsoring efforts like MDM or data
quality, then some of the efforts in this phase may have
already been done
2. It is good news if some or all of it was performed as part of
another effort. Even if there is an associated program, you
need understand how Data Governance will support the
business, even if it is indirectly through the data quality
or MDM efforts.

34. Data Governance Program Steps: Functional DesignData Governance Program Steps: Functional DesignData Governance Program Steps: Functional DesignData Governance Program Steps: Functional Design
1. The deployment team determines the core list of what
Data Governance will be accomplishing.
2. Identify/refine Information Management functions and
processes
3. Identify preliminary accountability and ownership model
(the essential lists of Data Governance and IM processes
are not at all useful until the Data Governance team
identifies who does what, and what the various levels of
responsibility are)
4. Present EIM Data Governance functional model to
business leadership. It is very important to educate
and present the new responsibilities and
accountabilities to management.
1. Scope and Initiation
2. Assess
3. Vision
4. Align and Business Value
5. Functional Design
6. Governing Framework Design
7. Roadmap
8. Roll out and Sustain
Determine core information principles This activity is arguably the most important in
the development and deployment of Data Governance.

35. Data Governance Program Steps: Governing Framework DesignData Governance Program Steps: Governing Framework DesignData Governance Program Steps: Governing Framework DesignData Governance Program Steps: Governing Framework Design
This step is kept separate from the functional design for three
reasons:
1. The team stayed focused on required processes and
workflow (in the functional design phase) without worrying
about people and personalities.
2. The actual organization that executes Data Governance will
be very different from one organization to another, even
within the same industry.
3. In our experience, the organization framework originally
proposed rarely resembles the Data Governance organization
two years later.
1. Scope and Initiation
2. Assess
3. Vision
4. Align and Business Value
5. Functional Design
6. Governing Framework Design
7. Roadmap
8. Roll out and Sustain
Once the functions are determined, the next step is to place the functional design for
Data Governance into an organization framework.

36. Governing Framework Activities:
• Design Data Governance organization framework. This series
of tasks determines where and what levels will execute,
manage, and be accountable for managing information assets.
• Organization charters are drafted so that stewards and
owners will have reference material for rolling out Data
Governance.
• Complete roles and responsibility identification. The Data
Governance team will place names with roles. There are
several potential obstacles to the timely completion of this
activity:
• Perceived political threats from some getting “power” over data.
• Human capital (or HR) concerns on changing job descriptions.
• Fear that adding additional responsibilities will damage current
productivity.
“Data stewardship is not a job. It
is the formalization of data
responsibilities that are likely in
place in an informal way.”
- David Plotkin
1. Scope and Initiation
2. Assess
3. Vision
4. Align and Business Value
5. Functional Design
6. Governing Framework Design
7. Roadmap
8. Roll out and Sustain
Data Governance Program Steps: Governing Framework DesignData Governance Program Steps: Governing Framework DesignData Governance Program Steps: Governing Framework DesignData Governance Program Steps: Governing Framework Design

37. Data Governance Program Steps:Data Governance Program Steps:Data Governance Program Steps:Data Governance Program Steps: RoadmapRoadmapRoadmapRoadmap
1. The team will define the events that take the organization from
a non-governed to a governed state for its data assets.
2. The requirements and groundwork are laid to sustain the Data
Governance program (i.e., detailed preparations to address the
changes required by the Data Governance program).
Activities
1. Integrate Data Governance with other efforts
2. Design Data Governance metrics and reporting requirements
3. Define the sustaining requirements
4. Design change management plan
5. Define Data Governance operational roll out
1. Scope and Initiation
2. Assess
3. Vision
4. Align and Business Value
5. Functional Design
6. Governing Framework Design
7. Roadmap
8. Roll out and Sustain
This phase is the step where Data Governance plans the details around the “go live”
events of Data Governance.

38. Data Governance Program Steps: Rollout and SustainData Governance Program Steps: Rollout and SustainData Governance Program Steps: Rollout and SustainData Governance Program Steps: Rollout and Sustain
• Until Data Governance is totally internalized there will be the
need to manage the transformation from non-governed data
assets to governed data assets.
• There will be reactive responses to open resistance (and there
will be proactive tactics to head off resistance)
• The main emphasis will be to ensure that there is on-going
visible support for Data Governance.
Considerations
Data Governance is not self-sustaining. First and foremost, this
must be accepted. The Data Governance program needs to adapt
without losing focus.
1. Scope and Initiation
2. Assess
3. Vision
4. Align and Business Value
5. Functional Design
6. Governing Framework Design
7. Roadmap
8. Roll out and Sustain
During this phase, the Data Governance team works to ensure the Data Governance
program remains effective and meets or exceeds expectations

39. Data Governance Program Steps: Rollout and SustainData Governance Program Steps: Rollout and SustainData Governance Program Steps: Rollout and SustainData Governance Program Steps: Rollout and Sustain
Activities:
1. Data Governance Operating Roll-out: The Data Governance
team, along with the appropriate project teams and Data
Governance forums start to “do governance.”
2. Execute the Data Governance change plan: All of the activity
defined to address sustaining Data Governance occurs here.
Communications, training, check points, data collection, etc.
Any specific tasks to deal with resistance can be placed here.
3. Confirm Operation and Effectiveness of Data Governance
Operations - The Data Governance framework needs to be
scrutinized for effectiveness. A separate forum or a central
Data Governance group will carry this out if one exists.
Principles, policies, and incentives need to be reviewed for
effectiveness
1. Scope and Initiation
2. Assess
3. Vision
4. Align and Business Value
5. Functional Design
6. Governing Framework Design
7. Roadmap
8. Roll out and Sustain

40. The 8 Steps to Data Governance InitiationThe 8 Steps to Data Governance InitiationThe 8 Steps to Data Governance InitiationThe 8 Steps to Data Governance Initiation
Scope and
Initiation
Assess
Vision
Align
and
Business
Value
Functional
Design
Governing
Framework
Design
Roadmap
Roll out
and
Sustain
CORE SUCCESS FACTORS
There are three core factors critical to
Data Governance success:
1. Data Governance requires culture
change management. By definition,
you are moving from an undesirable
state to a desired state. That means
changes are in order.
2. Data Governance “organization” is
not a stand-alone, brand-new
department. In most organizations
Data Governance will end up being a
virtual activity.
3. Data Governance, even if started as a
stand-alone concept, needs to be tied
to an initiative.

41. Questions?
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312-878-6600

42. Additional InformationAdditional InformationAdditional InformationAdditional Information
Don’t fall into the scope trap of identifying the scope of Data Governance with size or market dominance. You need to rationally
consider influencing factors we have presented, i.e., the business model, the assets to be managed, and what type of
federation is required. Let’s expand the global retailer example:
Business model - The business model is global, with heavy dependence on economy of scale across the supply chain. So our
scope will lean toward the entire organization - we will not be excluding any functions, like merchandising or warehouse.
Content being managed - Obviously there is a lot of content in a large organization, but consider the variety retail is, at its core,
pretty simple. You buy stuff from one place and sell it to someone else. The main content is anything used or descriptive of the
“stuff” and getting it sold. Be careful it isn’t just the items what about the people on the sales floor? What about the trucks and
trains to move items about? All are integral to the business. So from a scope standpoint we need to consider almost all of the
content within this type of enterprise. The key guidance to apply is the scope of data governance is a function of the assets
being managed (i.e., the content and information being governed).
Federation - We have stated the entire enterprise is in scope, and all content relevant to the business model is in scope. We
have not narrowed this down much, have we? When we examine the content (remember we are considering all of it), we see
that it stratifies into global, regional, and local. This is significant. If a region or locality can buy items to sell, what is the intensity
of Data Governance in those supply chains versus the global ones? We have to consider that local data may not be worth close
governance and may be okay with a more relaxed level of intensity.
All content is in scope, but due to size, geography, and markets, we need to consciously identify which specific content is
managed centrally, regionally, or locally. The organization would state that Data Governance scope is all content relevant to the
business model, but the intensity of Data Governance will vary based on a specifically defined set of federated layers.

43. Additional InformationAdditional InformationAdditional InformationAdditional Information
Farfel Emporiums. Farfel has one collaborative mechanism, FarfelNet, which was developed to support the merchandising
area. It is a website that allows access to merchandiser notes, proposals, supplier catalogs, and purchase orders for
merchandise.

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Data Federation
Federation Definition
“1. an encompassing political or societal entity formed by uniting smaller or more localized entities: as a : a federal government, b : a union of
organizations”
Scope factors that affect the federated layers and activities are:
Enterprise sized - Obviously, huge organizations will need to federate their Data Governance programs, and carefully choose the critical areas
where Data Governance adds the most value.
Brands - Organizations with strong brands may want to consider this in their Data Governance scoping exercise. One brand may need a more
centrally managed data portfolio than another.
Divisions - One division may be more highly regulated, therefore requiring a different intensity of Data Governance.
Countries - Various nations have different regulations and customs, therefore affecting how you can govern certain types of information.
IT portfolio condition - When a Data Governance effort is getting started, it is usually understood at some intuitive level, the nature and condition
of the existing information technology portfolio. An organization embarking on a massive overhaul of applications (usually via implementing
a large SAP or Oracle enterprise suite) will have definite and specific Data Governance federation requirements.
Culture and information maturity – Culture dictates the ability of an organization to use information and data is referred to as its information
management maturity (IMM). In combination, the specific IMM and culture of an organization will affect the scope and design of the Data
Governance program. For example, an organization that is rigid in its thinking and has a low level of maturity will require more centralized
control in its Data Governance program, as well as more significant change management issues.

45. Additional InformationAdditional InformationAdditional InformationAdditional Information
HELPFUL HINT
When you are around the vision or business case activities, you will undoubtedly encounter the first layer of resistance
to Data Governance. You will attempt to present to an executive level and three things will happen:
1. A lower level will be told to deal with it. The executives will be too busy.
2. Your sponsors or business representatives will get cold feet when it is time to educate in an upward direction and
dilute the message.
3. The executive level will humor you and sit through a presentation, ask some good questions, and then forget you
ever met.
Sadly, all three represent a lack of leadership and understanding. Our experience has shown that the highest levels of
resistance are usually put forth by the organizations most in need of business alignment! However, repeated education
and reinforcement of the message, accompanied by some good metrics will start to open doors. You may have to
revisit and repeat vision and business case activities over a period of years as you penetrate more areas of your
company.

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