Chapter 3: Data Governance

3 Data Governance
• Data Governance is the core function of the data
management framework.
• Interact and influences each of the surrounding
ten data management functions.
• In this Chapter, will Define the data Governance
function and its concepts and Activities involved
.

3.1 Introduction
• Data Governance Defined:
• The exercise of Authority and Control ( Planning,
Monitoring, and Enforcement) over the
management of data assets.
• Guiding all other data management functions How to
performed.
• Considers as High-Level, executive data Stewardship.

The Context Diagram for Data Governance

3.2 Concepts and Activities
• Effective data management depends on an effective partnership between
DM stewards and DM professionals, especially in data governance.
• Shared decision is the Hallmark “The sign” of data governance,
• Effective data management requires working across organizational and
system boundaries and supporting integrated view of data.
• Some decisions are primarily business decisions made with input and
guidance from IT.
• Other are primarily technical decisions made with input and guidance data
stewards at all levels.
• In This Sections:
3.2.1. Data Governance,
3.2.2. Data Stewardship
3.2.3. Data Governance and Stewardship Organizations
3.2.4. Data Management Services Organizations
3.2.5. Data Management Executive
3.2.6. The Data Governance Office
• All will be described.

Figure 3.2 Data Governance Decision Spectrum

3.2.1 Data Governance
• Data Governance is Focused exclusively on the management of data
assets.
• Is the heart of managing data assets.
• Another imagination of The position of data Governance within other data
management functions as book describe “The roof”
• Therefore, Data Governance considers Top Layer and the first initiation of
the processes for data management between Data Stewardship and Data
Management Services.

Figure 3.3 Data Governance, Stewardship, and Services

3.2.2 Data Stewardship
• Start with Data Stewardship Accountability and Responsibilities in Overall Data
management functions and Deeply in Data Governance.
• Categorized Data Stewards into:
• Executive data Stewards: Senior managers who serve on data Governance
council.
• Coordinating data Stewards “important in large organizations”: leads and
represent teams of business data stewards in discussions across teams and
with executive data stewards. Coordinating data stewards.
• Business data Stewards: specialized in dedicated space or field to define
and control data.
• Data Governance is the making high-level data stewardship decisions, by
executive and coordinating data stewards.
• Then, The sections mentioned the other responsibilities with Data Stewardship
with others DM functions

Data Stewardship responsibilities in Data management beyond data Governance
DM Function Responsibilities
Data Architecture management Review, validate, approve and refine data architecture
Data Development Define data requirement and specifications into logical/physical data model
Data Operations Recovery, retention, performance, identify, acquire, and control externally sourced data
Data Security Security, privacy and confidentiality requirement, identify and resolve data security, assist in audits
Reference and Master Control the creation, update, and retirement of code values and other reference data, define master data requirements
Data Warehousing and BI Provide business intelligence requirements and management Metrics, identify / help BI issues
Document and Content Define enterprise taxonomies – resolve management issues
Meta-data Create – maintain /access and integration needs / use Business meta-data (names, meaning, business rules) to make
effective data stewardship and governance decisions
Data Quality Define data quality requirements and business rules, test, edits and validations, assist in Analysis, certification, auditing ,
promote data awareness

3.2.3 Data Governance and Stewardship Organizations
• The Scope and boundaries of Data Governance Program is:
• Data Strategy and policies: Defining, Communicating, monitoring
• Data Standards and Architecture: Reviewing, Approving, monitoring
• Regulatory Compliance: Communicating, monitoring, enforcing
• Issue Management: Identifying, defining, escalating, resolving
• Data management Projects: Sponsoring, overseeing
• Data Asset Valuation: Estimating, approving, monitoring
• Communication: Promoting, building awareness and appreciation
• In the next sections we will describe each Activities (scope) in detail.

• Thereafter, mentioned of Data Governance scope, The section provide the
mechanisms and methods “anarchy, dictatorship” and decisions making to
implements these boundaries.
• Three principles can be drawn from the representative government analogy:
• Responsibility for legal functions (policies and standards), judicial
functions (issue management) and executive functions (administration,
services, and compliance)
• Data Governance typically operates at both enterprise and local levels. In
large organizations, data governance may also be required at levels in
between, depending on the size of the enterprise.
• Separation of duties between data Stewardship (Legislative and Judicial)
and Data Management services (Executive) provides a degree of checks
and balances for the management of data.

• Three cross-functional data stewardship and governance organizations have
legislative and judicial responsibilities:
• The data Governance Council has enterprise-wide authority over data
management. Executive data stewards sitting on the council are senior
managers representing both departmental and enterprise perspectives.
• The data stewardship program steering committees support the data
governance council drafting policies and standards for review and approval by
the data governance council regarding specific initiatives and overseeing
these sponsored initiatives.
• Data Stewardship teams are focused groups of business data stewards
collaborating on data stewardship activities within a defined subject area. The
teams should define with subject matter “experts” (SMEs) Data specifications
“name, definitions…etc. ”, data quality requirements and business rules and
what must remain locally unique.

• Finally, The section present the most shine Rules, Duties, and characteristics of
Data Governance Stewards and professionals:
• Defined data strategy, policies, standards, procedures, and metrics.
• Defined enterprise data model and their business rules “data names,
definitions”
• The issues adjudicated by data governance include data security, data access,
data quality, regulatory compliance, policy and standards conformance issues,
name and definition conflicts, and data governance procedural issues.
• Activities such as “technical activities, for instance, requirement gathering,
data modeling performing”
• respect time and coordinate data governance activity-scheduling meeting,
tracking issues, follow-up on decisions
• Data Governance depends on the size of organization of divisional or
departmental with avoiding complexity.

3.2.4 Data Management Services Organizations
• Known also DMS “Data Management Services”: Data management professionals
within the IT department. And there may : Centralized DMS organization /
Multiple decentralized groups
• Some enterprise have both local DMS organizations as will as centralized
organization. Centralized DMS known as “Data Management Center of Excellence
(COE)”
• DMS include DM Professionals include Data Architects, Analysts, Modelers,
Quality Analysts , Database administrators, Data Security administrator, meta-
data administrator, data model administrators, data warehouse architects, data
integration architects, BI analysts
• The data management professionals across all organizations constitute a DM
professional community. And together with data stewards. They unite in DM
community of Interest (COI)

3.2.5 The Data Management Executive
• Should report directly to the CIO
• Responsible for coordinating data management, data stewardship and data
governance
• DMS and their staff should report to the data management executive, directly or
indirectly.
• Responsible for DM staffing, skills development, contractor management,
budgeting and resource allocation, management metrics, data steward
recruitment, collaboration across business and IT organizations. Cultural changes
• Work closely with peers' leaders of application development, infrastructure/
operations and other IT functions.
• Responsible for implementing the decisions of DM Governance, working close
with the chief data Stewards, by maintaining the data strategy and overseeing
DM Projects.

3.2.6 The Data Governance Office “DMO”
• Sometimes, DM members “executives, Architects.” may not find the time to
perform their activities, therefore, DMO or Data Management Office is should be
existed.
• DMO is a staff organization of data stewardship facilitators who support the
activities and decision making of business data stewards at all levels.
• DMO purpose is to provide full-time support for part-time business data
stewardship responsibilities.
• DMO Roles is support the data Governance Council, Data Stewardship
Committees, and Data Stewardship teams.
• Data Governance office may report to the data management executive, or it may
report outside of IT entirely.

The Diagram in Figure 3.4 depicts data organizations and their
relationships

3.3 Data Governance Activities
• The Activities comprising the data governance function are explained below.
Each of the activities is important for fully implementing the data governance
function within an organization.
• 3.3.1 Data Strategy
• 3.3.2 Data Policies
• 3.3.3 Data Architecture
• 3.3.4 Data Standards and Procedures
• 3.3.5 Regulatory Compliance
• 3.3.6 Issue Management
• 3.3.7 Data Management Projects
• 3.3.8 Data Management Services
• 3.3.9 Data Asset Valuation
• 3.3.10 Communication and Promotion
• 3.3.11 Related Governance Frameworks

3.3.1 Data Strategy
• A strategy is a set of choices and decisions that together chart a high-level course
of action to achieve high-level goals. Same as the moves of the game of chess.
• Data Strategy is a plan for maintaining and improving data quality, integrity,
security, and access. May also, include business plans to use information to
competitive advantages and support enterprise goals.
• Most come from full understanding of the data needs inherent in the business
strategies.
• Data architecture is a part of Data Strategy
• Owned and maintained by the data Governance council
• Should Address all data management functions relevant to the organization. For
instance, The data strategy should include “Meta-data management strategy”,
Figure 2.1

3.3.1 Data Strategy “Components”
• The components of data Strategy might include:
• A compelling vision for data management
• A summary business case for data management, with selected examples
• Guiding principles, values, and management perspectives.
• The mission and long-term directional goals of data management
• Management measures of data management success.
• Short-term (12-24 months) SMART
(specific/measurable/actionable/realistic/time-bound) data management
program objectives.
• Descriptions of data management roles and organizations, along with a
summary of their responsibilities and decision rights.
• Descriptions of data management program components and initiatives.
• An outline of the data management implementation roadmap ( projects and
action items)
• Scope boundaries and decisions to postpone investments and table certain
issues.

3.3.1 Data Strategy “deliverables”
• Data Strategy is often packaged into three separate deliverables:
1. Data management Program Charter: overall vision, business case, goals,
guiding principles, measures of success, critical success factors, recognized
risks
2. Data management Scope Statement: Goals and objectives for some
planning horizon, usually 3 years, and the roles, organizations, and
individual leaders accountable for achieving these objectives.
3. Data Management Implementation roadmap: identifying specific
programs, projects, task assignments and delivery milestones.
• These Deliverables are often published as part of a Data management Program
intranet website or platform.

3.3.2 Data Policies
• Data Policies are “the Rules” that governing the creation, acquisition, integrity,
security, quality, and use of data and information.
• Data policies Describe “what to do/ what not to do”, while Data standards “ how
to do”. Should be stated briefly and directly.
• Draft by DM professionals, review and refine from Data stewards and final
review, revision, and adoption by Data Governance Council.
• Data Policies must be effectively communicated, monitored, enforced and
periodically re-evaluated.

3.3.2 Data Policies ”Topics Covered ”
• Data Policies may cover topics such as:
• Data modeling and other data development activities within the SDLC.
• Development and use of data architecture
• Data quality expectations, roles, and responsibilities ( including meta-data
quality).
• Data security, including confidentiality classification policies, intellectual
property policies, personal data privacy policies, general data access and
usage policies, and data access by external parties.
• Database recovery and data retention.
• Access and use of externally sourced data.
• Sharing data internally and externally
• Data warehousing and BI policies
• Unstructured data policies (electronic files and physical records)

3.3.3 Data Architecture
• Data Architecture is framework of How DM professionals implement and apply
data strategy, policies and other activities. including data models and other
aspects.
• Data Governance Council sponsors and approves the enterprise data model and
other related aspects of data architecture.
• The enterprise data model should ultimately be reviewed, approved, and
formally adopted by the data governance council. And should with key business
strategies, processes, organizations, and systems.
• Data Architecture includes the Data technology architecture, the Data
integration architecture, the Data warehousing and BI architecture, and Meta-
data architecture. Also, include Information content management architecture
and Enterprise taxonomies. The council may delegate this responsibility to the
data Architecture Steering Committee.

3.3.4 Data Standards and Procedures
• DM standards “Guidelines” Include naming standards, requirement specification
standards, data modeling standards, database design standards, architecture
standards, and procedural standards for each DM function.
• Vary widely and draft by DM professionals and should reviewed, approved, and
adopted by Data Governance council. “In large enterprise authority delegated to
Data standards Steering Committee”
• Must be effectively communicated, monitored, enforced, and periodically re-
evaluated.
• DM procedures are documented methods, techniques, and steps followed to
accomplish a specific task or activity.
• Vary widely and draft by DM professionals and reviewed by Data Standards
Steering Committee.

3.3.4 Data Standards and Procedures
• Data Standards and procedural guidelines may include:
• Data modeling and architecture standards, including data naming
conventions, definition standards, standard domains, and standard
abbreviations.
• Standards business and technical meta-data to be captured, maintained, and
integrated.
• Data model management guidelines and procedures.
• Meta-data integration and usage procedures.
• Standards for database recovery and business continuity, database
performance, data retention, and external data acquisition.
• Data security standards and procedures.
• Reference data management control procedures.
• BI standards and procedures.
• Enterprise Content management standards and procedures, including use of
enterprise taxonomies, support for legal discovery and document and email
retention, electronic signature, report formatting standards, and report
distribution approaches.

3.3.5 Regulatory compliance “Regulations”
• Every enterprise is impacted by governmental and industry regulations.
• Many of these regulations dictate how data and information is to be managed.
• Part of data governance function is to monitor and ensure regulatory compliance.
• Mentioned as example of several regulation organization such as “HIPPA, Basel II
New Accord..etc.”
• Data Governance organizations work with other business and technical leadership
to find the best answers to a specific questions that identified the important and
useful of these regulations. Such as “how relevant is a regulation? Why is it
important for us? …etc. ”

3.3.6 Issue Management
• Data governance is the vehicle for identifying, managing and resolving several
different types of data related issues including:
• Data quality issues
• Data naming and definition conflicts
• Business rule conflicts and clarifications.
• Data security, privacy, and confidentiality issues.
• Regulatory non-compliance issues
• Non-Conformance issues ( policies, standards, architecture, and procedures)
• Conflicting policies, standards, architecture, and procedures.
• Conflicting stakeholder interests in data and information
• Organizational and cultural change management issues.
• Issues regarding data governance procedures and decision rights
• Negotiation and review of data sharing agreements.

Figure 3.5 Data issue Escalation path
• Issues can be resolved locally or escalated to the higher management

3.3.6 Issue Management
• Data governance requires control mechanisms and procedures for:
• Identifying, capturing, logging and updating issues.
• Tracking the status of issues.
• Documenting stakeholder viewpoints and resolution alternatives.
• Objective, neutral discussions where all viewpoints are heard.
• Escalating issues to higher levels of authority
• Determining, documenting, and communicating issue resolutions.
• Issue management consider of judicial branch responsibilities which should be
not underestimate and appreciate its value.

3.3.7 Data Management Projects
• Considers as initiatives used to improve overall data management function or
focus on a particular data management such as:
• Data Architecture Management
• Data Warehousing and BI Management
• Reference and Master Data Management
• Meta-data Management
• Data Quality Management
• The council helps define the business case for DM projects. And may engage
different department such as “PMO” and IT since the project IT tendency.

3.3.7 Data Management Projects
• DM projects and Program could be apart of ERP or CRM overall projects as long
achieve the following:
• Information quality is essential to the success of these projects
• A key project objective is to integrated information across the enterprise.
• DM provides these projects with:
• Data architecture
• Approaches to managing data quality and master DM
• Strategies, tools, structures, and support to enable business intelligence.
• proven approach to partnering with business leaders in governing enterprise
integration.

3.3.8 Data Management Services
• Data professionals provide many different services for the enterprise.
• DM services organizations may formalize the definition and delivery of these
service.
• These services range from high level governance coordination, enterprise
architectural definition and coordination, information requirements analysis,
data modeling facilitation, and data quality analysis to traditional database
design, implementation, and production support services.
• A collaboration between IT management and Data Governance council to
estimate the needs of these services and justification of staffing and funding to
provide these services.

3.3.9 Data Asset Valuation
• Organizations use many different approaches to estimate the value of their data
assets.
• These approaches is a group of estimations rates through identify the direct and
indirect business benefits of using data, identify the cost of lost, identify the
impacts of not have the current amount and the quality level of data through
several questions such as “what percentage change to revenue would occur?
What percentage change to costs would occur? And what risk exposures might
occur, what would be the potential financial impact?”
• Another estimate what competitors might pay for these assets

3.3.10 communication and Promotion
• Refers to communicate, educate, and promote the importance and value of data
and information assets.
• Rising awareness of the important of data value in all stakeholders of the
enterprise. Should everybody know about “Strategies, policies, plans…etc. ….of
the DM”
• Several approaches to communicating these key messages:
• Intranet website for a data management program
• Posting announcements electrical/hardcopy via email, social media platform
• Department meetings.
• Etc.

DM intranet website Characteristics :
• DM intranet website is a particularly effective vehicle for communicating:
• Executive messages regarding significant data management issues.
• The data management strategy and program charter, including vision,
benefits, goals and principles.
• The data management implementation roadmap.
• Data policies and data standards.
• Descriptions of data stewardship roles and responsibilities
• Procedures for issue identification and escalation.
• Documents and presentations describing key concepts, available for
download.
• Data governance organization descriptions, members, and contact
information
• Data Management Services organization rosters and contact information
• Individual profiles on data stewards and data management professionals
• Program news announcements.
• Descriptions and links to related online resources.
• Entry points to request services or capture issues.

3.3.11 Related Governance Frameworks
• No standard or commonly used frameworks for data governance, several
framework do exist for related governance topics, including:
• Corporate Governance (COSO ERM)
• IT Governance (COBIT)
• Enterprise Architecture (Zachman Framework, TOGAF)
• System Development Lifecycle (Rational Unified Process, for example)
• System Development Process Improvement (SEI CMMI)
• Project Management (PRINCE II, PMI PMBOK)
• IT Service Management (ITIL, ISO 2000)

3.4.1 Guiding Principles
• The Implementation of data governance into an organization follows 11
principles:
• Data management is a shared responsibility between business data stewards
(trustees) and data management professionals (expert custodians).
• Data Stewards have responsibilities in all 10 data management functions.
• Every data governance / data stewardship program is unique, taking into
account the unique characteristics of the organization and its culture.
• The best data stewards are found, not made. Whenever possible, appoint the
people already interested and involved.
• Shared decision making is the hallmark of data governance.
• Data governance councils, and data stewardship committees and teams
perform “ legislative” and “judicial” responsibilities, while data management
services organization perform “executive branch” responsibilities ( administer,
coordinate, serve, protect)
• Data governance occurs at both the enterprise and local levels and often at
levels in between.
• The is no substitute for visionary and active IT leadership in data
management. The Data management executive is the CIO’s right hand for
managing data and information.
• Some form of centralized organization of DM professionals is essential to
enterprise-wide data integration.

3.4.3 Organizational and Cultural Issues
• Important Questions when planning to implement Data Governance function.
• Q1: Why is every governance program unique ?
• A1: because organization is unique in structure, culture, and circumstances.
Data governance program should address the needs and sharing common
characteristics and principles with other programs and departments.
• Data Governance should have unique challenges, values, beliefs, expectations,
attitudes, rituals and symbols. And should hand the change occurred with
organization and change it into opportunities.

• Q2: should data stewardship be a part-time or full-time responsibility ?
• Data stewardship is a role, not a job. Therefore, data stewards recommend to
be part-time. Instead, need to be involved with the business to maintain
business knowledge, peer respect, and credibility as subject matter experts
and practical leaders.
• Q3: Can full-time IT/business liaisons be data stewards?
• Yes, and their roles vary widely across organizations. However, true business
leaders should also participate as data stewards, unless the scope and focus is
technical. Problems occur when liaisons represent the business or IT
exclusively, excluding either of their internal customers. Stewardship and
governance are mechanisms for liaisons to be more effective by bringing all
parties to the table.

• Q4: what qualifications and skills are required of data steward role
candidates?
• Business and Data knowledge ( most important)
• Soft skills required such as:
• Respected subject area expertise- information, processes, and rules.
• Organizational/ cultural knowledge and industry perspective.
• Strong verbal and written communication skills.
• Clarity and precision in thinking and communication
• Teamwork, diplomacy, and negotiation skills.
• Adoptability, objectivity, creativity, practicality, and openness to change.
• Ability to balance local and functional needs with enterprise needs.

• Q5: How are individual data stewards and data governance organizations
empowered? How do stewards earn respect?
• Strong sponsorship and support, and the ability to lead, others will follow.
• In conflict case, stay objective. Understand, appreciate both points of view
• Be optimistic and cheerful
• Impress people with facts and reasoning.
• Earn Trust not just respect by demonstrating sincere interest in others and by
being open with information.

Summary of The chapter:
• In this chapter, The first DM function has been discovered from single details and
demonstrated from the fundamentals into the advance concepts used in this
area.
• First, Data Governance defined as “The exercise of Authority and Control (
Planning, Monitoring, and Enforcement) over the management of data assets.”
and mentioned the characteristics, attributes, and the position with this function
among the others.
• Then, Context diagram has described all components used from input, tools,
activities, and the output or results gained from this function.

Summary of The chapter:
• Thereafter, The concepts and activities in this functions described in detail that
present all the case of applying Data Governance. Firstly, start from the
stewardship, organizations, executives, services, and the faculty “DMO”, then
provide a diagram facilitate the previous and their relationships in Data
Governance function. Figure 3.4
• Then, each of Data Governance Activities used in this function has been
described in detail. Start from ended with Communications and awareness.
• Finally, a guiding principles, processes in Data governance and a group of
Questions for Data Stewards and their mission in this function.

Chapter 3: Data Governance

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