Data Governance is the core function of the Data Management Framework shown in Figures 1.3. and 1.4. It interacts with and influences each of the surrounding ten data management functions. Chapter 3 defines the data governance function and explains the concepts and activities involved in data governance.
3.1 Introduction
Data governance is the exercise of authority and control (planning, monitoring, and enforcement) over the management of data assets. The data governance function guides how all other data management functions are performed. Data governance is high-level, executive data stewardship.
The context diagram for the data governance function is shown in Figure 3.1.
Figure 3.1 Data Governance Context Diagram
3.2 Concepts and Activities
Chapters 1 and 2 state that data management is a shared responsibility between business data stewards, representing stakeholders across the organization, and data professionals, who work on their behalf. Business data stewards are the trustees of enterprise data assets; data management professionals are the expert custodians of these assets. Effective data management depends on an effective partnership between business data stewards and data management professionals, especially in data governance.
Shared decision making is the hallmark of data governance, as shown in Figure 3.2. Effective data management requires working across organizational and system boundaries. Data Governance enables shared responsibility for selected decisions, crossing these boundaries and supporting an integrated view of data. Some decisions are primarily business decisions made with input and guidance from IT, others are primarily technical decisions made with input and guidance from business data stewards at all levels.
Figure 3.2 Data Governance Decision Spectrum
3.2.1 Data Governance
Data governance is accomplished most effectively as an on-going program and a continual improvement process.
Every effective data governance program is unique, taking into account distinctive organizational and cultural issues, and the immediate data management challenges and opportunities. Data governance is a relatively new term, and many organizations continue to pioneer new approaches. Nevertheless, effective data governance programs share many common characteristics, based on basic concepts and principles.
Data governance is not the same thing as IT governance. IT governance makes decisions about IT investments, the IT application portfolio, and the IT project portfolio. IT governance aligns the IT strategies and investments with enterprise goals and strategies. CobiT (Control Objectives for Information and related Technology) provides standards for IT governance, but only a small portion of the CobiT framework addresses managing information. Some critical issues, such as Sarbanes-Oxley compliance, span the concerns of corporate governance, IT governance, and data governance. Data governance is focused exclusively on the management of data assets.
Data governance is at the heart of managing data assets. In the circular depiction of the ten data management functions introduced in Chapter One, data governance is shown in the center.
Another way of depicting the controlling position of data governance is as “the management roof” over other data management functions, as shown in Figure 3.3.
Figure 3.3 Data Governance, Stewardship, and Services
3.2.2 Data Stewardship
Data stewardship is the formal accountability for business responsibilities ensuring effective control and use of data assets. Some of these responsibilities are data governance responsibilities, but there are also significant data stewardship responsibilities within each of the other major data management functions.
A data steward is a business leader and / or recognized subject matter expert designated as accountable for these responsibilities. As in other endeavors, a good steward carefully protects, manages, and leverages the resources for which he / she is entrusted.
The best data stewards are found, not made. Many of these activities are performed by business professionals even before a formal data stewardship program is implemented. To that extent, data stewardship responsibilities are not new and additional responsibilities for these people. Whenever possible, appoint the people already interested and involved. Their appointment to a data stewardship role is a recognition and confirmation of the work they are already performing. Appointing data stewards formalizes their accountability.
Data stewards manage data assets on behalf of others and in the best interests of the organization. Data stewards are appointed to represent the data interests of all stakeholders, including but not limited to, the interests of their own functional departments and divisions. Data stewards must take an enterprise perspective to ensure the quality and effective use of enterprise data.
Organizations often differentiate between executive, coordinating, and business data stewards:
Data governance is high-level, executive data stewardship. In other words, data governance is the making of high-level data stewardship decisions, primarily by executive and coordinating data stewards.
Data stewardship responsibilities exist in data management functions beyond data governance:
3.2.3 Data Governance and Stewardship Organizations
Data governance guides each of the other data management functions. Every data governance program has a slightly different scope, but that scope may include:
Data governance is essentially “the government of data” within the enterprise. Like other governments, there are many different models of data governance – anarchy, dictatorship, and everything in between. Some decisions can be made without risk by individual managers. But the need for shared decision making and risk control drives most organizations to a representative form of data governance, so that all stakeholders and constituencies can be heard.
Data management professionals have responsibility for administering data policies, standards, and procedures, for managing and implementing data architecture, for protecting data assets and stakeholder interests, and for providing data management services.
In particular, three principles can be drawn from the representative government analogy:
Typically, three cross-functional data stewardship and governance organizations have legislative and judicial responsibilities:
The rules defined by data governance organizations include the overall data strategy, data policies, data standards, data management procedures, data management metrics, the business data names, business definitions and business rules found in the enterprise data model, additional data requirement specifications, and data quality business rules.
The issues adjudicated by data governance organizations include data security issues, data access issues, data quality issues, regulatory compliance issues, policy and standards conformance issues, name and definition conflicts, and data governance procedural issues.
Data management professionals perform executive branch responsibilities much like governmental departments and agencies. They administer, monitor and enforce data policies, standards, and procedures. They coordinate, maintain, and implement data architecture. Data management professionals gather and review requirements, facilitate data modeling to serve stakeholder interests, and enable data delivery by implementing databases and applications. They acquire and protect data assets, monitor data quality, and audit data quality and security.
In addition to their other professional duties, some data management professionals provide staff support for data governance organizations. Business data stewards are business professionals and managers with part-time stewardship responsibilities. Data management professionals must respect their time and coordinate data governance activity—scheduling meetings, planning and publishing agendas, providing documents for review prior to each meeting, facilitating the meetings, tracking issues, following up on decisions, and publishing meeting minutes. Data architects facilitate each data stewardship team. The Data Management Executive and / or the enterprise data architect may staff Data Stewardship Program Steering Committees. The Data Management Executive and the Chief Information Officer (CIO) guide the Data Governance Council, often with assistance from a Data Governance Office (see 3.2.6 below).
At the same time, each organization should be chaired by a business representative. Coordinating data stewards chair their data stewardship teams. An executive data steward from the Data Governance Council should chair each Data Stewardship Coordinating Committee. A Chief Data Steward, selected from among the executive data stewards, chairs the Data Governance Council.
Large organizations may have divisional or departmental data governance councils working under the auspices of the Enterprise Data Governance Council. Smaller organizations should try to avoid such complexity.
3.2.4 Data Management Services Organizations
Data management professionals within the IT department report to one or more Data Management Services (DMS) organizations. In many enterprises, there may be a centralized DMS organization, while in others there are multiple decentralized groups. Some enterprises have both local DMS organizations as well as a centralized organization. A centralized DMS organization is sometimes known as a Data Management Center of Excellence (COE).
Data management professionals within DMS organizations may include data architects, data analysts, data modelers, data quality analysts, database administrators, data security administrators, meta-data administrators, data model administrators, data warehouse architects, data integration architects, and business intelligence analysts. These organizations may also include data integration developers and analytics / report developers, although often they remain in the Application Development organization with other developers. Decentralized organizations may include only a few of these roles. The data management professionals across all organizations constitute a data management professional community, and together with data stewards, they unite in a Data Management Community of Interest (COI).
3.2.5 The Data Management Executive
There is no substitute for the leadership of a CIO and a dedicated Data Management Executive, guiding the data management function and promoting the data management program. Visionary and active leadership is a critical success factor for effective data management.
The Data Management Executive leads the data management function, serving as the CIO’s right hand for information. The Data Management Executive should report directly to the CIO, responsible for coordinating data management, data stewardship, and data governance. Given the broad scope of the CIO’s responsibilities, the CIO needs one person accountable for managing data and information assets.
Data Management Services organizations and their staff should report to the Data Management Executive, directly or indirectly. The Data Management Executive is responsible for data management professional staffing, skills development, contractor management, budgeting and resource allocation, management metrics, data steward recruitment, collaboration across business and IT organizations, and management of the organizational and cultural changes required to support data management. The Data Management Executive works closely with peer leaders of Application Development, Infrastructure / Operations and other IT functions.
The Data Management Executive is responsible for implementing the decisions of the Data Governance Council. He or she serves as the operational coordinator for the Data Governance Council, working in close partnership with the Chief Data Steward, by maintaining the data strategy and overseeing data management projects.
3.2.6 The Data Governance Office
In larger enterprises, The Data Governance Office is a staff organization of data stewardship facilitators who support the activities and decision making of business data stewards at all levels. The purpose of the Data Governance Office is to provide full-time support for part-time business data stewardship responsibilities.
Much as a congressional committee is supported by staff professionals, the data stewardship facilitators perform the legwork required to obtain the information that enables business data stewards to make informed and effective decisions. In larger enterprises, the addition of staff responsibilities to data management responsibilities may be overwhelming. The Data Management Executive, data architects, and data quality analysts may not be able to find the necessary time to effectively coordinate the communicating, information gathering, and decision making required for data governance and stewardship. When this happens, organizations should consider creating a Data Governance Office.
It is critical that full-time data stewardship facilitators do not assume responsibility for data stewardship. Their role is to support the Data Governance Council, Data Stewardship Committees, and Data Stewardship Teams. The 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 these 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.
Figure 3.4 Data Management Organizations–Governance, Stewardship, Services
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. In the game of chess, a strategy is a sequenced set of moves to win by checkmate or to survive by stalemate. A strategic plan is a high-level course of action to achieve high-level goals.
Typically, a data strategy is a data management program strategy–a plan for maintaining and improving data quality, integrity, security, and access. However, a data strategy may also include business plans to use information to competitive advantage and support enterprise goals. Data strategy must come from an understanding of the data needs inherent in the business strategies. These data needs drive the data strategy.
Data strategy is not the same thing as data architecture. The decision to define data architecture may be part of a strategy, and the decisions to implement components of data architecture are strategic decisions. The strategy may influence the architecture, which, in turn, supports the strategy, guiding other decisions.
In many organizations, the data strategy is owned and maintained by the Data Governance Council, with guidance from the Chief Information Officer and the Data Management Executive. In other organizations, these executives may retain ownership and control of the data strategy; however, sharing ownership builds a data management partnership with the business. Often, the Data Management Executive will draft an initial data strategy even before a Data Governance Council is formed, in order to gain senior management commitment for establishing data stewardship and governance.
The components of a data strategy might include:
The data strategy is often packaged into three separate deliverables, including:
These deliverables are often published as part of a Data Management Program intranet website.
The data strategy should address all data management functions relevant to the organization. For instance, the data strategy should include the meta-data management strategy. See Figure 2.1 for the complete list of data management functions.
3.3.2 Data Policies
Data policies are short statements of management intent and fundamental rules governing the creation, acquisition, integrity, security, quality, and use of data and information. Data policies are more fundamental, global, and business critical than detailed data standards. Data policies vary widely across organizations. Data policies describe “what” to do and what not to do, while standards and procedures describe “how” to do something. There should be relatively few data policies, and they should be stated briefly and directly.
Data policies are typically drafted by data management professionals. Next, data stewards and management review and refine the policies. The Data Governance Council conducts the final review, revision, and adoption of the data policies. The Data Governance Council may delegate this authority to the Data Stewardship Committee or the Data Management Services Organization.
Data policies must be effectively communicated, monitored, enforced, and periodically re-evaluated. Data policies may cover topics such as:
3.3.3 Data Architecture
The Data Governance Council sponsors and approves the enterprise data model and other related aspects of data architecture. The Data Governance Council may appoint an Enterprise Data Architecture Steering Committee to oversee the program and its iterative projects. The enterprise data model should be developed and maintained jointly by data architects and data stewards working together in data stewardship teams oriented by subject area, and coordinated by the enterprise data architect.
As data stewardship teams propose changes and develop extensions to the enterprise data model, the Data Architecture Steering Committee oversees the project and reviews changes. The enterprise data model should ultimately be reviewed, approved, and formally adopted by the Data Governance Council. Executive data stewards on the Council should pay particular attention to the alignment of the enterprise data model with key business strategies, processes, organizations, and systems.
Similarly, the general approach, business case, and less technical aspects of related data architecture should also be reviewed, approved, and adopted by the Data Governance Council. This includes the data technology architecture, the data integration architecture, the data warehousing and business intelligence architecture, and the meta-data architecture. It may 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
Data standards and guidelines include naming standards, requirement specification standards, data modeling standards, database design standards, architecture standards, and procedural standards for each data management function. Standards and guidelines vary widely within and across organizations. Data standards are usually drafted by data management professionals. Data standards should be reviewed, approved and adopted by the Data Governance Council, unless this authority is delegated to a Data Standards Steering Committee. Data standards and guidelines must be effectively communicated, monitored, enforced, and periodically re-evaluated.
Data management procedures are the documented methods, techniques, and steps followed to accomplish a specific activity or task. Like policies and standards, procedures vary widely across organizations. Procedural documentation is usually drafted by data management professionals, and may be reviewed by a Data Standards Steering Committee.
Data standards and procedural guidelines may include:
3.3.5 Regulatory Compliance
Every enterprise is impacted by governmental and industry regulations. Many of these regulations dictate how data and information is to be managed. Generally, compliance with these regulations is not optional. Part of the data governance function is to monitor and ensure regulatory compliance. In fact, regulatory compliance is often the initial reason for implementing data governance. Data governance guides the implementation of adequate controls to ensure, document, and monitor compliance with data-related regulations.
For companies publicly traded in the United States, the Sarbanes-Oxley Act of 2002 established stringent financial reporting and auditing requirements. It was designed to make executives more responsible and accountable for oversight of their companies. There are several other regulations with significant implications on how information assets are managed. For example:
Data governance organizations work with other business and technical leadership to find the best answers to the following regulatory compliance questions:
3.3.6 Issue Management
Data governance is the vehicle for identifying, managing, and resolving several different types of data related issues, including:
Most issues can be resolved locally in Data Stewardship Teams. Issues requiring communication and / or escalation must be logged. Issues may be escalated to the Data Stewardship Committee, or higher to the Data Governance Council, as shown in Figure 3.5. Issues that cannot be resolved by the Data Governance Council should be escalated to corporate management and / or governance.
Figure 3.5 Data Issue Escalation Path
Data governance requires control mechanisms and procedures for:
Do not underestimate the importance and value of data issue management; and the need for these control mechanisms and procedures should not be underestimated, either. The judicial branch, which has responsibility for issue management, is an equal third partner with the legislative branch, which has responsibility for defining policies, standards, and the enterprise data architecture, and with the executive branch, which has responsibility for protecting and serving administrative responsibilities.
3.3.7 Data Management Projects
Data management initiatives usually provide enterprise-wide benefits requiring cross-functional sponsorship from the Data Governance Council. Some of these projects and programs are designed to implement or improve the overall data management function. Other projects and programs focus on one particular data management function, such as:
Significant organizational change is often required to implement more effective data management. Implementing a data strategy usually requires making some organizational and cultural changes to support that strategy. A data management roadmap sets out a course of action for initiating and / or improving data management functions. The roadmap typically consists of an assessment of current functions, definition of a target environment and target objectives, and a transition plan outlining the steps required to reach these targets, including an approach to organizational change management.
Every data management project should follow the project management standards of the organization. At a minimum, every project should begin with a clearly defined and documented project charter, outlining the mission, objectives, scope, resources, and delivery expectations of the sponsors, which in these cases, is the Data Governance Council. The Council helps define the business case for data management projects and oversees project status and progress. The Council coordinates its efforts with a Project Management Office (PMO), where one exists. Data management projects may be considered part of the overall IT project portfolio.
The Data Governance Council may also coordinate data management efforts with the sponsors of related projects, particularly large programs with enterprise-wide scope. These include enterprise resource planning (ERP) and customer relationship management (CRM) projects, or in the public sector, citizen relationship management projects. Such large programs benefit from formal data management, because:
Data management provides these projects with:
3.3.8 Data Management Services
As the expert custodians and curators for data and information assets, data professionals provide many different services for the enterprise. Data Management Services organizations may formalize the definition and delivery of these service, in order to be more focused on meeting enterprise needs. 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.
By offering the full range of data management activities as services, IT management can involve the Data Governance Council in the estimation of enterprise needs for these services and the justification of staffing and funding to provide these services. As sponsors of these on-going services, the Data Governance Council can oversee their effectiveness from a business perspective, vouch for data valuation assumptions, and confirm assessments of data value and data management business value contribution.
3.3.9 Data Asset Valuation
Data and information are truly assets because they have business value, tangible or intangible. Today’s accounting practices consider data and information as intangible assets, much like software, documentation, expert knowledge, trade secrets, and other intellectual property. Goodwill is the accounting term for the additional amount of money a company is worth beyond the value of its tangible assets and any specifically referenced other intangible assets.
Organizations use many different approaches to estimate the value of their data assets. One way is to identify the direct and indirect business benefits derived from use of the data. Another way is to identify the cost of its loss, identifying the impacts of not having the current amount and quality level of data:
Seen in this light, the impacts are often estimated to be quite large, but because there are so many other contributing factors, of which the loss of any might result in similar negative impacts, these impacts are understood to be somewhat disproportional. Typically, business leaders negotiate and agree on a conservative percentage of the total potential impact, which might be considered as the contribution to revenue (for instance) made by data assets in relative proportion to other contributing resources and factors.
Another way to determine data asset value is to estimate what competitors might pay for these assets, if offered exclusive of any other assets. Making these estimates and earning their acceptance requires a significant and on-going dialog with accountants and financial executives. These conversations are typically new and somewhat foreign to most IT managers.
Sometimes business stewards find it easier to estimate the value of business losses due to inadequate information. Information gaps–the difference between what information is needed and whatever trustworthy information is currently available–represent business liabilities. Closing and preventing these gaps represent opportunities for data management programs to provide some estimate of business value.
3.3.10 Communication and Promotion
Data stewards at all levels and data management professionals must continually communicate, educate, and promote the importance and value of data and information assets and the business contribution of data management functions. Raising stakeholder awareness and appreciation of data management issues and benefits is an on-going responsibility of everyone in the data management community.
All data producers and information consumers must understand data policies and their organization’s commitment to data quality, data security, data protection, data delivery, and data support. All stakeholders should be aware of data stewardship and governance programs, organizations, roles, and responsibilities. All stakeholders should also be aware of organizational investments in data management projects, and the objectives and expectations for these projects. All stakeholders must understand whatever responsibilities they have to conform to data standards and comply with external regulations.
Every individual data management role and organization is responsible for communicating these key messages. However, organizations should specifically assign responsibility for communication planning to one or two individuals.
Organizations typically use several approaches to communicating these key messages. These approaches include:
A data management intranet website is a particularly effective vehicle for communicating:
3.3.11 Related Governance Frameworks
At the time of this writing, there are no standard or commonly used frameworks for data governance, although some proprietary frameworks have been developed by a few consulting firms. Several frameworks do exist for related governance topics, including:
3.4 Summary
The guiding principles for implementing data governance into an organization, a summary table of the roles for each data governance activity, and organizational and cultural issues that may arise during implementation of a data governance function are summarized below.
3.4.1 Guiding Principles
The implementation of data governance into an organization follows eleven guiding principles:
3.4.2 Process Summary
The process summary for the data governance function is shown in Table 3.1. The deliverables, responsible roles, approving roles, and contributing roles are shown for each activity in the data governance function. The Table is also shown in Appendix A9.
Activities |
Deliverables |
Responsible Roles |
Approving Roles |
Contributing Roles |
|
1.1.1 Understand Strategic Enterprise Data Needs (P) |
Strategic Enterprise Data Needs |
DM Executive |
Data Governance Council, CIO |
Data Stewards, Data management professionals |
|
1.1.2 Develop and Maintain the Data Strategy (P) |
Data Strategy – Vision, Mission, Bus. Case, Goals, Objectives, Principles, Components, Metrics, Implementation Roadmap |
DM Executive |
Data Governance Council, CIO |
Data Stewards, Data management professionals |
|
1.1.3 Establish Data Management Professional Roles and Organizations (P) |
Data Management Services organizations and staff |
CIO |
Data Governance Council |
DM Executive |
|
1.1.4 Establish Data Governance and Stewardship Organizations (P) |
Data Governance Council, Data Stewardship Committee, Data Stewardship Teams |
DM Executive, CIO, Data Governance Council |
Senior Mgmt |
Data Stewards, Data management professionals |
|
1.1.5 Identify and Appoint Data Stewards (P) |
Business Data Stewards, Coordinating Data Stewards, Executive Data Stewards |
DM Executive, Executive Data Stewards |
Data Governance Council |
Coordinating Data Stewards, Data management professionals |
|
1.1.6 Develop, Review and Approve Data Policies, Standards, and Procedures (P) |
Data Policies, Data Standards, Data Management Procedures |
DM Executive |
Data Governance Council, CIO |
Data Stewardship Committee, Data Stewardship Teams, Data management professionals |
|
1.1.7 Review and Approve Data Architecture (P) |
Adopted Enterprise Data Model, Related Data Architecture |
Data Governance Council |
Data Governance Council, CIO |
Enterprise Data Architect, Data Stewardship Committee, Data Stewards, Data Architects, DM Executive |
|
1.1.8 Plan and Sponsor Data Management Projects and Services (P) |
Data Management Projects, Data Management Services |
Data Governance Council |
Data Governance Council, CIO, IT Steering Committee |
DM Executive, Data management professionals, Data Stewards |
|
1.1.9 Estimate Data Asset Value and Associated Costs (P) |
Data Asset Value Estimates, Data Mgmt. Cost Estimates |
Data Stewards |
Data Governance Council |
DM Executive, Data management professionals |
|
1.2.1 Supervise Data Professional Organizations and Staff (C)
|
Data Management Services organization(s) and staff |
DM Executive(s) |
CIO |
Data management professionals |
|
1.2.2 Coordinate Data Governance Activities (C) |
Data Governance Organization Schedules, Meetings, Agendas, Documents, Minutes |
DM Executive, Enterprise Data Architect, Data Architects |
Data Governance Council, Data Stewardship Committee, Data Stewardship Teams, CIO |
Data management professionals |
|
1.2.3 Manage and Resolve Data Related Issues (C) |
Issue Log, Issue Resolutions |
Data Stewardship Teams, Data Stewardship Committee, Data Governance Council |
Data Stewardship Teams, Data Stewardship Committee, Data Governance Council |
DM Executive, Data management professionals |
|
1.2.4 Monitor and Ensure Regulatory Compliance (C) |
Compliance Reporting, Non-compliance Issues |
Data management professionals |
Data Governance Council |
DM Executive, CIO |
|
1.2.5 Communicate, Monitor and Enforce Conformance with Data Policies, Standards, Procedures, and Architecture (C) |
Policy / Standards / Arch / Procedure Communication, Non-conformance Issues |
Data management professionals, Data Stewards |
Data Governance Council, Data Stewardship Committee |
DM Executive |
|
1.2.6 Oversee Data Management Projects and Services (C) |
DM Executive |
Data Governance Council |
Data management professionals |
||
1.2.7 Communicate and Promote the Value of Data and Data Management (C) |
Data Management Website, Data Management Newsletter, Understanding and Recognition |
DM Executive, Data management professionals, Data Stewards, CIO |
Data Governance Council |
Data Stewards |
Table 3.1 Data Governance Process Summary Table
3.4.3 Organizational and Cultural Issues
Questions may arise when an organization is planning to implement the data governance function. A few of the common questions are listed below with a general answer.
Q1: Why is every governance program unique?
A1: Each organization is unique in structure, culture, and circumstances. Each data governance program should be unique to address the needs of the organization, while at the same time sharing some common characteristics and basic principles. Each data governance program has different sponsoring individuals, business drivers, scope boundaries, regional and departmental organizations, approaches to business and IT liaison, relationships with other governance programs and major projects, collaboration and teamwork challenges, organizational heritage, shared values and beliefs, common expectations and attitudes, and unique meaning to organizational rites, rituals, and symbols. As the organization changes, the challenges posed for data governance also change. Good data governance programs address these challenges and take advantage of the opportunities they present.
Q2: Should data stewardship be a part-time or full-time responsibility?
A2: Experts generally recommend data stewards be given part-time responsibility for data stewardship. Data stewardship is a role, not a job. Data stewards 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?
A3: 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?
A4: First and foremost, business knowledge and understanding of the data is required. People can be taught data management concepts and techniques, such as how to read a data model. Soft skills are also very important in data stewardship, including:
Q5: How are individual data stewards and data governance organizations empowered? How do stewards earn respect?
A5: Maintaining the importance of data governance and data stewardship to the organization can be shown in several ways:
3.5 Recommended Reading
The references listed below provide additional reading that supports the material presented in Chapter 3. These recommended readings are also included in the Bibliography at the end of the Guide.
3.5.1 Websites
The Data Administration Newsletter (TDAN)–http://www.TDAN.com
DM Review Magazine–www.dmreview.com. Note: www.dmreview.com is now www.information-management.com.
EIM Insight, published by The Enterprise Information Management Institute–
SearchDataManagement.com white paper library–
http://go.techtarget.com/r/3762877/5626178
3.5.2 Prominent Books
There are very few books specifically devoted to data governance. Perhaps the most pertinent book published to date is:
Thomas, Gwen. Alpha Males and Data Disasters: The Case for Data Governance. Brass Cannon Press, 2006. ISBN-10: 0-978-6579-0-X. 221 pages.
3.5.3 Regulatory and Compliance Books
Compliance is an important data governance issue. The following book is particularly focused on regulatory compliance:
Bloem, Jaap, Menno van Doorn, and Piyush Mittal. Making IT Governance Work in a Sarbanes-Oxley World. John Wiley & Sons, 2005. ISBN 0-471-74359-3. 304 pages.
3.5.4 General Books
The books and other materials listed below describe IT governance in general, which as noted above, is not at all the same thing as data governance. Nevertheless, they are closely related concepts, and these publications can be helpful:
Benson, Robert J., Tom Bugnitz, and Bill Walton. From Business Strategy to IT Action: Right Decisions for a Better Bottom Line. John Wiley & Sons, 2004. ISBN 0-471-49191-8. 309 pages.
IT Governance Institute. Control Objectives for Information and related Technology (CobiT©). www.isaca.org/cobit
Lutchen, Mark. Managing IT as a Business: A Survival Guide for CEOs. John Wiley & Sons, 2003. ISBN 0-471-47104-6. 256 pages.
Maizlish, Bryan and Robert Handler. IT Portfolio Management Step-By-Step: Unlocking the Business Value of Technology. John Wiley & Sons, 2005. ISBN 0-471-64984-8. 400 pages.
Van Grembergen, Wim and Steven Dehaes. Enterprise Governance of Information Technology: Achieving Strategic Alignment and Value. Springer, 2009. ISBN 0-387-84881-5, 360 pages.
Van Grembergen, Wim and Steven Dehaes. Implementing Information Technology Governance: Models, Practices and Cases. IGI Publishing, 2007. ISBN 1-599-04924-3, 255 pages.
Van Grembergen, Wim and Steven Dehaes. Strategies for Information Technology Governance. IGI Publishing, 2003. ISBN 1-591-40284-0. 406 pages.
Weill, Peter and Jeanne Ross. IT Governance: How Top Performers Manage IT Decision Rights for Superior Results. Harvard Business School Press, 2004. ISBN 1-291-39253-5. 288 pages.