Let’s talk about challenges in reshaping data governance.
This first article of the series examines the most significant challenges organizations encounter when implementing or reshaping governance frameworks. The analysis of these challenges has identified actionable steps to address them.
The Results of Various Polls
First, let me present the key findings and highlight the most striking conclusions.
Implementation
As shown in Figure 1, most organizations acknowledge the importance of data governance, with 60% actively working on establishing a framework—36% in the development phase and 24% in implementation. However, despite this recognition, only 16% have a fully operational framework, underscoring the challenges of moving from planning to execution.
Figure 1: The Status of Governance Frameworks in Organizations Worldwide.
This gap suggests that while organizations understand the necessity of data governance, they often encounter significant obstacles, including resource constraints, a lack of clear strategies, and difficulties embedding governance into daily operations. Additionally, 16% of organizations have no framework at all, while 8% rely on ad hoc approaches, further highlighting the need for structured, sustainable governance strategies.
Most Organizations Recognize the Need for Change in Their Data Governance Framework
As shown in Figure 2, most organizations recognize that their current data governance framework is either inadequate or nonexistent. 48% need to adjust their existing framework, while 44% must build one from scratch. This reflects a widespread understanding that improvements are necessary—whether through refining existing structures or developing entirely new governance models.
However, only 4% believe no changes are needed, indicating that mature, fully effective data governance frameworks remain rare. Additionally, another 4% are uncertain about their organization’s stance, suggesting a potential lack of clarity or awareness regarding governance initiatives.
Figure 2. The Organizations’ Needs Regarding Data Governance Frameworks.
Organizations Face Persistent and Evolving Challenges in Implementing Data Governance Capabilities
One of the most striking trends, as shown in Figure 3, is the increasing difficulty in securing C-suite and business support, which is projected to become the most significant barrier by 2025. This suggests that while organizations recognize the importance of data governance, leadership buy-in remains a critical missing piece for successful implementation.
Figure 3: Key Challenges in Implementing a Data Governance Capability.
Governance challenges are rarely isolated; they are often interconnected and multifaceted. The high percentage of respondents selecting “All of the above” indicates that organizations struggle with governance holistically, rather than with individual components, underscoring the need for comprehensive and integrated approaches.
Multiple Industry Frameworks Exist, but They Fall Short in Guiding Practical Implementation
As shown in Figure 4, the adoption of self-developed frameworks has increased significantly in recent years, reflecting a growing preference for tailored solutions.
Discussions with data management professionals worldwide reveal that even organizations that claim to follow an industry framework often adapt it to fit their specific needs. These adaptations typically involve modifying capabilities, adjusting scope, or addressing framework limitations. For example, many organizations that cite DAMA-DMBOK2 as their primary reference still make substantial adjustments to align it with their operational realities.
Figure 4: Trends in the Use of Industry and Self-Developed Guidelines.
Key Challenges Associated with Existing Frameworks
- Lack of Alignment Across Data Management Capabilities
Various industry guidelines present divergent perspectives on data management and its core components. Even when frameworks align on terminology, their underlying deliverables often differ. Those interested in this topic can refer to my series of articles comparing DAMA-DMBOK2 and DCAM®.
These inconsistencies also extend to IT vendor solutions, as organizations frequently rely on technology tools to drive and enable governance frameworks. However, the lack of a unified viewpoint complicates both framework implementation and tool integration, making it harder to establish a streamlined approach.
- Industry Guidelines Overlook Logical Dependencies Between Capabilities
Understanding interdependencies between data management capabilities is crucial for defining the scope of an initiative and developing an integrated implementation plan. The DAMA-DMBOK2 Wheel has unintentionally reinforced the misconception that data management capabilities can be implemented independently.
In reality, this misconception has contributed to the failure of many data management initiatives, particularly data quality programs. To improve data quality, organizations must first establish key artifacts from data governance, data modeling, data and application architecture, and metadata management.
- Lack of Practical Implementation Guidelines
The absence of a clear mapping of dependencies between data management capabilities leads to a significant gap in implementation guidance. Organizations struggle to build a feasible, integrated implementation plan without a clear understanding of how different capabilities—and especially their artifacts—are interlinked.
Governance Framework Architecture Is Becoming More Complex Due to the Need to Harmonize Data and AI Practices
As organizations increasingly adopt AI practices, they face a key challenge: AI initiatives require a strong data management foundation. The quality of AI outputs is directly dependent on data quality, as AI systems are inherently complex products involving data, metadata, and IT infrastructure.
There is no one-size-fits-all approach to managing AI and data governance. Some organizations separate data and AI management functions, while others choose to integrate or, at minimum, harmonize them. Regardless of the approach, embedding AI practices into business and data operations inevitably adds complexity to governance frameworks.
Analyzing these trends leads to actionable recommendations for organizations looking to establish a new governance framework or enhance an existing one.
Actionable Steps for Establishing or Adjusting a Governance Framework
From Recognition to Execution
Organizations must move beyond simply acknowledging the importance of data governance and focus on effective implementation. This requires developing a phased roadmap, securing executive sponsorship, and embedding governance into daily operations to transition from planning to execution.
Strengthening or Rebuilding the Governance Framework
Organizations should begin with a gap analysis to assess their current governance state. Engaging key stakeholders ensures alignment with business needs, while establishing clear policies and processes provides the necessary structure, accountability, and long-term sustainability.
Securing Leadership Buy-in
Organizations must demonstrate the business value of data governance to gain executive support. This can be achieved by:
- Linking governance initiatives to strategic priorities,
- Showcasing quick wins, and
- Defining measurable performance indicators that resonate with leadership.
Adapting Frameworks to Fit Organizational Needs
Since industry frameworks often lack practical implementation guidance, organizations should customize their governance models while leveraging best practices. This ensures governance frameworks are not only theoretically sound but also actionable and aligned with business realities.
Defining a Feasible Scope Before Implementing an IT Solution
Before selecting and implementing a data governance solution, organizations must:
- Analyze business needs
- Determine a feasible scope aligned with priorities and available resources
- Ensure alignment with required data management capabilities
Governance implementation must follow a process-based approach, considering the logical dependencies between data management capabilities. Organizations risk misalignment, inefficiencies, and implementation failures without this structured approach.
Aligning Data Management Components
To create a cohesive and effective governance structure, organizations should establish a standardized approach by clarifying the dependencies between governance, IT, and business functions. This alignment ensures a well-integrated governance framework.
Defining a Structured Implementation Plan
To prevent ad hoc governance efforts, organizations should:
- Develop step-by-step implementation guidelines
- Conduct regular governance maturity assessments
- Apply best practices to ensure a structured, scalable, and sustainable approach
Managing the Growing Complexity of Governance Architecture Due to AI Integration
Organizations adopting AI-driven initiatives must establish a strong data management foundation. Since AI relies on high-quality data and integrates multiple disciplines (data, metadata, and IT), organizations should determine whether to separate, integrate, or harmonize their data and AI management functions. Regardless of the approach, embedding AI governance into business and data operations is essential for consistency, compliance, and avoiding fragmentation.
This article has explored why organizations may need to adjust their existing governance frameworks and outlined what should be done. However, in practice, the most critical question is how to implement these changes effectively. This topic will be the focus of the next article.
