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Data Governance
A Strategic Imperative
Ken Fogarty
Managing Consultant
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
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
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

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This document discusses the importance of data quality and data governance. It states that poor data quality can lead to wrong decisions, bad reputation, and wasted money. It then provides examples of different dimensions of data quality like accuracy, completeness, currency, and uniqueness. It also discusses methods and tools for ensuring data quality, such as validation, data merging, and minimizing human errors. Finally, it defines data governance as a set of policies and standards to maintain data quality and provides examples of data governance team missions and a sample data quality scorecard.

Data Governance Trends and Best Practices To Implement Today
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1) The document discusses best practices for data protection on Google Cloud, including setting data policies, governing access, classifying sensitive data, controlling access, encryption, secure collaboration, and incident response. 2) It provides examples of how to limit access to data and sensitive information, gain visibility into where sensitive data resides, encrypt data with customer-controlled keys, harden workloads, run workloads confidentially, collaborate securely with untrusted parties, and address cloud security incidents. 3) The key recommendations are to protect data at rest and in use through classification, access controls, encryption, confidential computing; securely share data through techniques like secure multi-party computation; and have an incident response plan to quickly address threats.

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Too often I hear the question “Can you help me with our Data Strategy?” Unfortunately, for most, this is the wrong request because it focuses on the least valuable component – the Data Strategy itself. A more useful request is this: “Can you help me apply data strategically?”Yes, at early maturity phases the process of developing strategic thinking about data is more important than the actual product! Trying to write a good (must less perfect) Data Strategy on the first attempt is generally not productive –particularly given the widespread acceptance of Mike Tyson’s truism: “Everybody has a plan until they get punched in the face.” Refocus on learning how to iteratively improve the way data is strategically applied. This will permit data-based strategy components to keep up with agile, evolving organizational strategies. This approach can also contribute to three primary organizational data goals. In this webinar, you will learn how improving your organization’s data, the way your people use data, and the way your people use data to achieve your organizational strategy will help in ways never imagined. Data are your sole non-depletable, non-degradable, durable strategic assets, and they are pervasively shared across every organizational area. Addressing existing challenges programmatically includes overcoming necessary but insufficient prerequisites and developing a disciplined, repeatable means of improving business objectives. This process (based on the theory of constraints) is where the strategic data work really occurs, as organizations identify prioritized areas where better assets, literacy, and support (Data Strategy components) can help an organization better achieve specific strategic objectives. Then the process becomes lather, rinse, and repeat. Several complementary concepts are also covered, including: - A cohesive argument for why Data Strategy is necessary for effective Data Governance - An overview of prerequisites for effective strategic use of Data Strategy, as well as common pitfalls - A repeatable process for identifying and removing data constraints - The importance of balancing business operation and innovation

datadata managementdataversity
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
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
• 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
• 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

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cdochief data officerdata strategy
• 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.
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CHAIN
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IT ACCOUNTING CUSTOMER
SERVICE
SUPPLY
CHAIN
SALES DATA
GOVERNANCE
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Data Governance is often referred to as the people, processes, and policies around data and information, and these aspects are critical to the success of any data governance implementation. But just as critical is the technical infrastructure that supports the diverse data environments that run the business. Data models can be the critical link between business definitions and rules and the technical data systems that support them. Without the valuable metadata these models provide, data governance often lacks the “teeth” to be applied in operational and reporting systems. Join Donna Burbank and her guest, Nigel Turner, as they discuss how data models & metadata-driven data governance can be applied in your organization in order to achieve improved data quality.

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Data Literacy is an increasing concern, as organizations look to become more data-driven. As the rise of the citizen data scientist and self-service data analytics becomes increasingly common, the need for business users to understand core Data Management fundamentals is more important than ever. At the same time, technical roles need a strong foundation in Data Architecture principles and best practices. Join this webinar to understand the key components of Data Literacy, and practical ways to implement a Data Literacy program in your organization.

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Why is Data Governance Imperative?Why is Data Governance Imperative?Why is Data Governance Imperative?Why is Data Governance Imperative?
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• 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
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?
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
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?

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• Time and money will be lost due to inability to institute Data Reusability
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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
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• Then, there is no debate over whether you should have standards or
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• 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.
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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.

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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
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Artifacts that
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Governance of
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practical
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are often in
conflict.
Who the
stakeholders/
decision makers are.
This is not an element
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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
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
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………. “
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……”

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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
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
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
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

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The document discusses challenges facing asset management firms including increased competition, new products and regulations, and more demanding investors. It argues that to address these challenges, firms need a holistic and agile operational environment with a unified information ecosystem and effective data management strategies. This includes applying best practices like data provenance, integration, and analytics to achieve a cohesive and trusted data environment across the organization.

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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.
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.
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
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

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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.
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.
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.
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

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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.
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
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
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.

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Questions?
www.analytics8.com
312-878-6600
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.
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.
Additional InformationAdditional InformationAdditional InformationAdditional Information
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.

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This document discusses the role of information systems in business and management. It covers how information systems have transformed organizations by enabling globalization, the rise of the information economy, changes to the business enterprise, and the emergence of the digital firm. The challenges of building and using information systems are also examined, including designing competitive systems, understanding global requirements, and ensuring user control and ethical use of systems. Information systems are defined and their functions explained, demonstrating how they support business processes and decision making.

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The document discusses data warehousing, data mining, and business intelligence. It defines each topic and explains their key processes and purposes. Data warehousing involves collecting, storing, and managing large amounts of data from different sources for analysis and decision making. Data mining analyzes large datasets to identify patterns and relationships for informed decisions. Business intelligence provides technologies and methods to analyze business data for insights, performance improvement, and informed decision making.

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.
Additional InformationAdditional InformationAdditional InformationAdditional Information
Additional InformationAdditional InformationAdditional InformationAdditional Information
Additional InformationAdditional InformationAdditional InformationAdditional Information

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flexibledata architecturelogical data warehouse
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This document discusses management information systems (MIS). It provides definitions of MIS from various authors that describe MIS as an integrated user-machine system that provides information to support decision-making. MIS aims to provide the right information to the right person at the right time. It discusses how MIS utilizes computers, software, databases and procedures to transform data into useful reports. MIS helps improve decision-making and organizational effectiveness.

Data Governance Strategies - With Great Power Comes Great Accountability
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Much like project team management and home improvement, data governance sounds a lot simpler than it actually is. In a nutshell, data governance is the process by which an organization delegates responsibility and exercises control over mission-critical data assets. In practice, though, data governance directs how all other data management functions are performed, meaning that much of your data management strategy’s capacity to function at all depends on your effectiveness in governing its implementation. Understanding these aspects of governance is necessary to eliminate the ambiguity that often surrounds effective data management and stewardship programs, since the goal of governance is to manage the data that supports organizational strategy. This webinar will: -Illustrate what data governance functions are required for effective data management, how they fit with other data management disciplines, and why data governance can be tricky for many organizations -Help you develop a detailed vocabulary and set of narratives to facilitate understanding of your business objectives and imperatives that demand governance -Provide direction for selling data governance to organizational management as a specifically motivated initiative

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Building a Data Governance Strategy

  • 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.
  • 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.
  • 44. Additional InformationAdditional InformationAdditional InformationAdditional Information 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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