Issue Management is the process for identifying, quantifying, prioritizing, and resolving data governance-related issues, including:

• Authority: Questions regarding decision rights and procedures

• Change management escalations: Issues arising from the change management process

• Compliance: Issues with meeting compliance requirements

• Conflicts: Conflicting policies, procedures, business rules, names, definitions, standards, architecture, data ownerships and conflicting stakeholder interests in data and information

• Conformance: Issue related to conformance to policies, standards, architecture, and procedures

• Contracts: Negotiation and review of data sharing agreements, buying and selling data, and cloud storage

• Data security and identity: Privacy and confidentiality issues, including breach investigations

• Data quality: Detection and resolution of data quality issues, including disasters or security breaches

Many issues can arise locally in Data Stewardship teams. Issues requiring communication and / or escalation must be logged, and may be escalated to the Data Stewardship teams, or higher to the DGC, as shown in this figure. A Data Governance scorecard can be used to identify trends related to issues, such as where within the organization they occur, what their root causes are, etc. Issues that cannot be resolved by the DGC should be escalated to corporate governance and / or management.

Data governance requires control mechanisms and procedures for:

• Identifying, capturing, logging, tracking and updating issues

• Assignment and tracking of action items

• Documenting stakeholder viewpoints and resolution alternatives

• Determining, documenting, and communicating issue resolutions

• Facilitating objective, neutral discussions where all viewpoints are heard

• Escalating issues to higher levels of authority

Data issue management is very important. It builds credibility for the DG team, has direct, positive effects on data consumers, and relieves the burden on production support teams. Solving issue management requires control mechanisms that demonstrate the work effort and impact of resolution.

While developing a basic definition of Data Governance (DG) is easy, creating an operating model that an organization will adopt can be difficult. Consider these areas when constructing an organization's operating model:

• Value of data to the organization: If an organization sells data, obviously DG has a huge business impact. Organizations that use data as a crucial commodity (e.g. Facebook, Amazon) will need an operating model that reflects the role of the data. For organizations where data is an operational lubricant, the form of DG will be less intense.

• Business model: Decentralized business vs. centralized, local vs. international, etc. are factors that influence how business occurs, and therefore, how the DG operating model is defined. Links with specific IT strategy, Data Architecture, and application integration functions should be reflected in the target operating framework design (per previous post on DMBoK Figure 16).

• Cultural factors: Such as acceptance of discipline and adaptability to change. Some organizations will resist the imposition of governance by policy and principle. Governance strategy will need to advocate for an operating model that fits the organizational culture, while still progressing change.

• Impact of regulation: Highly regulated organizations will have a different mindset and operating model of DG than those less regulated. There may be links to the Risk Management group or Legal as well.

Layers of data governance are often part of the solution. This means determining where accountability should reside for stewardship activities, who owns the data, etc. The operating model also defines the interaction between the governance organization and the people responsible for data management projects or initiatives, the engagement of change management activities to introduce this new program, and the model for issue management resolution pathways through governance. This figure shows an example of an operating framework. The example is illustrative. This kind of artifact must be customized to meet the needs of a specific organization.

Part of alignment includes developing organizational touchpoints for Data Governance work. The CDO Organizational Touch Points figure illustrates examples of touch points that support alignment and cohesiveness of an enterprise data governance and data management approach in areas outside the direct authority of the Chief Data Officer.

• Procurements and Contracts: The CDO works with a Vendor/Partner Management or Procurement to develop and enforce standard contract language vis-a-vis data management contracts. These could include Data-as-a-Service (DaaS) and cloud-related procurements, other outsourcing arrangements, third-party development efforts, or content acquisition / licensing deals, and possibly data-centric IT tools acquisitions and upgrades.
• Budget and Funding: If the CDO is not directly in control of all data acquisition-related budgets, then the office can be a focal point for preventing duplicate efforts and ensuring optimizations of acquired data assets.
• Regulatory Compliance: The CDO understands and works within required local, national, and international regulatory environments, and how these impact the organization and their data management activities. Ongoing monitoring is performed to identify and track new and potential impacts and requirements.

• SDLC / development framework: The data governance program identifies control points where enterprise policies, processes, and standards can be developed in the system or application development lifecycles.

The touch points that the CDO influences support the organization's cohesiveness in managing its data, therefore, increasing its nimbleness to use its data. In essence, this is a vision of how DG will be perceived by the organization.

September 2023 Newsletter.pdf

In a centralized model, one Data Governance organization oversees all activities in all subject areas. In a replicated model, the same DG operating model and standards are adopted by each business unit. In a federated model, one Data Governance organization coordinates with multiple Business Units to maintain consistent definitions and standards.

The most formal and mature data management operating model is a centralized one. Here everything is owned by the Data Management Organization. Those involved in governing and managing data report directly to a data management leader who is responsible for Governance, Stewardship, Metadata Management, Data Quality Management, Master and Reference Data Management, Data Architecture, Business Analysis, etc.

The core word in governance is govern. Data governance can be understood in terms of political governance. It includes legislative-like functions (defining policies, standards and the Enterprise Data Architecture), judicial-like functions (issue management and escalation), and executive functions (protecting and serving, administrative responsibilities). To better manage risk, most organizations adopt a representative form of data governance, so that all stakeholders can be heard.

Each organization should adopt a governance model that supports its business strategy and it likely to succeed within its own cultural context. Organizations should also be prepared to evolve that model to meet new challenges. Models differ with respect to their organizational structure, level of formality, and approach to decision-making. Some models are centrally organized, while others are distributed.

Data governance organizations may also have multiple layers to address concerns at different levels within an enterprise - local, divisional, and enterprise-wide. The work of governance is often divided among multiple committees, each with a purpose and level of oversight different from others.

This Figure represents a generic data governance model, with activities at different levels within the organization (vertical axis), as well as separation of governance responsibilities within organizational functions and between technical (IT) and business areas.

Just as an auditor controls financial processes but does not actually executive financial management, data governance ensures data is properly managed without directly executing data management. Data governance represents an inherent separation of duty between oversight and execution.

A data-centric organization values data as an asset and manages data through all phases of its lifecycle, including project development and ongoing operations. To become data-centric, and organization must change the way it translates strategy into action. Data is no longer treated as a by-product of process and applications. Ensuring data is of high quality is a goal of business processes. As organizations strive to make decisions based on insights gained from analytics, effective data management becomes a very high priority.

People tend to conflate data and information technology. To become data-centric, organizations need to think differently and recognize that managing data is different from managing IT. This shift is not easy. Existing culture, with its internal politics, ambiguity about ownership, budgetary competition, and legacy systems, can be a huge obstacle to establishing an enterprise vision of data governance and data management.

While each organization needs to evolve its own principles, those that seek to get more value from their data are likely to share the following:

• Data should be managed as a corporate asset

• Data management best practices should be incented across the organization

• Enterprise data strategy must be directly aligned with overall business strategy

• Data management processes should be continuously improved

Data Governance (DG) is defined as the exercise of authority and control (planning, monitoring, and enforcement) over the management of data assets. All organizations make decisions about data, regardless of whether they have a formal Data Governance function. Those that establish a formal Data Governance program exercise authority and control with greater intentionality (Seiner, 2014). Such organizations are better able to increase the value they get from their data assets.

The Data Governance function guides all other data management functions. The purpose of Data Governance is to ensure that data is managed properly, according to policies and best practices (Ladley, 2012). While the driver of data management overall is to ensure an organization gets value out of its data, Data Governance focuses on how decisions are made about data and how people and processes are expected to behave in relation to data. The scope and focus of a particular data governance program will depend upon organizational needs, but most programs include:

• Strategy: Defining, communicating, and driving execution of Data Strategy and Data Governance Strategy

• Policy: Setting and enforcing policies related to data and Metadata management, access, usage, security, and quality

• Standards and quality: Setting and enforcing Data Quality and Data Architecture standards

• Oversight: Providing hands-on observation, audit, and correction in key areas of quality, policy and data management (often referred to as stewardship)

• Compliance: Ensuring the organization can meet data-related regulatory compliance requirements

• Issue management: Identifying, defining, escalating, and resolving issues related to data security, data access, data quality, regulatory compliance, data ownership, policy, standards, terminology, or data governance procedures

• Data management projects: Sponsoring efforts to improve data management practices

• Data asset valuation: Setting standards and processes to consistently define the business value of data assets

To accomplish these goals, a Data Governance program will develop policies and procedures, cultivate data stewardship practices at multiple levels within the organization, and engage in organizational change management efforts that actively communicate to the organization the benefits of improved data governance and the behaviors necessary to successfully manage data as an asset.

For most organizations, adopting formal Data Governance requires the support of organizational change management, as well as sponsorship from a C-Level executive, such as Chief Risk Officer, Chief Financial Officer, or Chief Data Officer.

Are you ready to take your career to the next level? We're thrilled to announce the launch of our 12-week virtual Certified Data Management Professional (CDMP) Study Group, designed to help you succeed in the world of data management. Learn more about the CDMP Exam HERE.

Start Date: Week of September 4th
Meeting Time: Thursday evenings from 6:00 PM to 7:00 PM
Location: Virtual (Online Platform)

What you can expect:
• Engaging weekly study sessions to cover CDMP exam topics.
• Interactive discussions and Q&A sessions to enhance your understanding.
• A 1/2 day virtual exam prep deep dive the last week in November.
• "Pay if you Pass" (PIYP) virtual exam event on November 2nd.

Informational Sessions:
Dates: August (24th and 28th)
Where: Online (Email for meeting registration info)
Purpose: Learn about the study group and CDMP exam, get your questions answered.

How to Get Involved:

To learn more, register, or secure your spot for this enriching opportunity, simply send an email to: ProfessionalDevelopmentVP@damarmc.org. Hurry, spaces are limited!

Don't miss out on this chance to boost your career prospects and become a certified data management professional. We look forward to embarking on this exciting journey with you!

Don't forget to order your essential study resource, the #DMBOK (Data Management Body of Knowledge), to ensure you're fully prepared to excel in both your studies and the CDMP exam. Order through DAMA-RMC at a discounted rate HERE. 

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Stay tuned for more updates and detailed schedules.

Projects that use personal data should have a disciplined approach to the use of that data. They should account for:

• How they select their population for study (arrow 1)

• How data will be captured (arrow 2)

• What activities analytics will focus on (arrow 3)

• How the results will be made accessible (arrow 4)

Within each area of consideration, they should address potential ethical risks, with a particular focus on possible negative effects on customers or citizens.

A risk model can be used to determine whether to execute the project. It will also influence how to execute the project. For example, the data will be made anonymous, the private information removed from the file, the security on the files tightened or confirmed, and a review of the local and other applicable privacy law reviewed with legal. Dropping customers may not be permitted under law if the organization is a monopoly in a jurisdiction, and citizens have no other provider options such as energy or water.

Because data analytics projects are complex, people may not see the ethical challenges. Organizations need to actively identify potential risks. They also need to protect whistleblowers who do see risks and raise concerns. Automated monitoring is not sufficient protection from unethical activities. People - the analysts themselves - need to reflect on possible bias. Cultural norms and ethics in the workplace influence corporate behavior - learn and use the ethical risk model. DAMA International encourages data professionals to take a professional stand, and present the risk situation to business leaders who may not have recognized the implications of particular uses of data and these implications in their work.