This article discusses data governance trending topics in 2025.
This is the second article in a series where I share my impressions and key insights gathered during the #DGIQ and #EDW2025 conference, as well as the top trending topics. This series offers a general summary and does not focus on any specific presentation from the conference.
In this article, I will focus on four key topics in data governance discussed at the conference:
- Data Governance as a Driver of Business Value and Performance
- Practical Techniques for Implementing Data Governance Successfully
- Defining and Enabling Data Governance Roles
- Practical Use Cases of Data Governance in Action
Before diving into the review, I would like to align the definition of data governance with the readers.
The Definition of Data Governance
I have discussed various approaches to defining data governance in several publications. Those interested in the topic can consult my most recent article, Governance vs. Management: Clarifying the Divide. For this article, I will use the definition from DAMA-DMBOK2:
It is essential to agree on the following DG foundational principles.
The core tasks of governance include:
- Exercising authority: providing a vision on the role of data management and enforcing decisions, rules, and standards regarding data management
- Exercising control: monitoring, evaluating, and guiding data-related activities to ensure compliance with defined policies, standards, and objectives
- Exercising shared decision-making: collaboratively making data-related decisions by involving multiple stakeholders across business units, IT, and governance bodies
These tasks can be performed by:
- Establishing the data management governance framework, including the operating model and organizational structure (governing bodies and roles)
- Enforcing the development and implementation of data management policies, standards, and procedures
- Monitoring and coordinating data management activities across the organization
- Overseeing the implementation of IT tools that support data management capabilities
- Facilitating collaboration among business, IT, and governance stakeholders
- Supporting continuous improvement through issue resolution and policy updates
Let’s move to the first trending topic.
Data Governance as a Driver of Business Value and Performance
The conference discussions clearly emphasized that data governance, when executed as a strategic capability, significantly enhances business performance. Rather than being viewed as a control mechanism or a compliance task, data governance is increasingly positioned as an operational enabler that supports cost efficiency, process optimization, agility, and digital transformation.
Enabling Operational Excellence
One of the key insights was the alignment between data governance and operational excellence. Organizations that embed governance into their business processes can drive continuous improvement, ensure compliance, and mitigate risks. Governance provides the foundation for defining, monitoring, and optimizing data quality across the lifecycle. When coupled with structured methodologies, such as Six Sigma or Lean, it supports performance measurement, root cause analysis, and sustained improvements in business outcomes. This includes higher process reliability, more accurate reporting, and tangible cost savings.
Reducing Costs and Complexity
The importance of data traceability and transparency, achieved through data lineage, one of the key deliverables of metadata management, emerged as a recurring theme. The governance capability must support metadata management in leveraging advanced cataloging tools to automate lineage, classify data assets, and bridge the gap between technical systems and business users. Metadata management capabilities reduce redundancy, improve data discovery, and lower storage and compute costs.
Supporting Strategic and Agile Decision-Making
Equally important is the cultural and organizational aspect of data governance. Business impact is achieved when governance is aligned with stakeholder needs, embedded into workflows, and supported by collaborative decision-making. Building a shared understanding of goals, aligning terminology, and prioritizing based on business value are key factors in achieving success. A governance approach that evolves iteratively—starting small, delivering quick wins, and scaling based on impact—tends to gain stronger buy-in and produce more sustainable results.
Improving AI Readiness and Reliability
High-quality, well-governed data is essential for AI systems to deliver value. Organizations emphasized the importance of governance practices, including standardized metadata, data classification, and quality rules, to ensure that AI models utilize complete, trusted, and relevant data. Without this foundation, AI outputs risk being inaccurate or biased. As new AI tools and models emerge rapidly, data governance provides the structure needed to evaluate, monitor, and adapt these technologies safely and effectively.
Now, let’s discuss the next trending topic.
Practical Techniques for Implementing Data Governance Successfully
While data governance frameworks provide strategic direction, success ultimately depends on the practical techniques employed to activate and sustain them. A central message from the conference was that data governance must be tailored to each organization’s readiness, maturity level, and business context, supported by simple, flexible, and actionable practices.
Start Small, Think Big
Organizations often overestimate the initial resources needed for data governance. Several effective programs have started with limited budgets, basic tools (e.g., Excel, SharePoint), and small teams. The key is to focus on critical data elements, define minimal viable processes (such as business glossaries or quality rules), and build upon early wins. This approach demonstrates value quickly and builds momentum.
Align to Business Priorities and Culture
Techniques that succeed are grounded in business relevance. Governance should begin by identifying key use cases, pain points, and strategic priorities. These insights guide prioritization and enable data governance to be framed not as control, but as an enabler. Organizational buy-in is strengthened by embedding governance into existing workflows, being transparent about expected outcomes, and recognizing stakeholder contributions.
Embed Governance Through Structure and Simplicity
To be effective, governance requires more than just policy; it needs a structured approach. This includes defining data management and governance and aligning them with their corresponding responsibilities. Organizations can establish federated or hybrid models that strike a balance between consistency and flexibility. Sustained change is supported through clear documentation, issue resolution workflows, and repeatable practices that become part of the organizational routine.
Use Change Management as a Core Enabler
Governance efforts often fail not due to poor design, but because of weak adoption. Successful organizations integrate change management from the outset. This involves assessing readiness, addressing resistance, building a network of champions, and reinforcing the “why” behind data governance. Effective communication, training, and celebrating small successes facilitate the transition from an initiative to an ongoing practice.
Prioritize Process Over Tools
Rather than investing in complex software too early, effective programs focus on building lightweight, transparent processes that can be easily scaled. As governance matures, technology can then support and automate what is already working.
Let’s move to the next topic.
Defining and Enabling Data Governance Roles
A recurring insight from the conference was that successful data governance depends not only on frameworks and tools, but on clearly defined and empowered roles. When responsibilities are fragmented or ambiguous, governance efforts stall. But when roles are embedded in the organization with proper support, they become the engine that sustains governance.
Clarifying Roles and Responsibilities
The core governance roles—data stewards, data owners and users, and custodians—must be clearly defined and understood.
The role of the data steward is especially pivotal. Whether full-time or part-time, stewards act as the bridge between business users and technical teams. Their tasks encompass all data management capabilities, including, but not limited to, maintaining glossaries, resolving data quality issues, supporting classification, and facilitating the sharing of internal data. Success depends on giving stewards a structure to operate within: standardized templates, clear guidelines, and workflows supported by lightweight governance tools.
Data owners are accountable for defining the strategic value and quality of the data, managing metadata and business meaning, and organizing the data lifecycle management.
Data users are the role that is very often overlooked by data management professionals. They are accountable for using data according to the intended use specified by the owners and for providing data and data quality requirements.
Data stewards would play the role of either a data owner or user, depending on their location, and also participate in data chains.
The role of a custodian is often referred to as that of an IT professional. It is also an overlooked fact that the words “steward” and “custodian” are synonymous. Therefore, creating titles of roles must be done with caution. Without clarity, organizations face inconsistent data definitions, weak accountability, and ineffective issue resolution.
Supporting Roles with Structure and Tools
Effective governance roles cannot operate in a vacuum. They require a structured environment that includes training, documentation, and automation. Many organizations have introduced onboarding processes, knowledge portals, office hours, and shared playbooks to support their teams. These support systems enable role holders to perform consistently and reduce reliance on informal knowledge or ad hoc problem-solving.
Technology plays a supporting role by streamlining repetitive tasks and improving visibility. Common tools include catalog platforms, metadata management systems, workflow automation, and collaboration platforms like SharePoint or Confluence. However, the primary focus remains on people and process, not tooling.
Sustaining Engagement Through Culture and Community
Sustaining governance roles requires cultural integration. Organizations utilize community-building techniques, such as stewardship forums, open Q&A sessions, and recognition events, to foster and maintain engagement. Empowering stewards with flexibility, empathy, and clear value statements has proven more effective than enforcing rigid compliance.
Ultimately, governance roles should not be static job titles—they must be living functions embedded in the organization’s operations and culture. With clear expectations, supportive infrastructure, and ongoing engagement, these roles evolve from check-the-box responsibilities into catalysts for trust, collaboration, and better decision-making.
Now, it is time for the last trending topic.
Practical Use Cases of Data Governance in Action
Real-world examples shared at the conference demonstrated how data governance, when applied thoughtfully, can solve concrete business challenges and deliver measurable value.
Organizations are increasingly operationalizing governance through structured processes that manage data access, sharing, and usage. Formalizing request workflows, automating approvals, and clarifying responsibilities reduce confusion, improve turnaround time, and ensure data is used appropriately. These practices build transparency, accountability, and efficiency across departments.
Data glossaries and catalogs are also becoming central components of governance. When implemented with clear structure, such as templates, standard terms, and steward guidance, they help standardize language, improve data discoverability, and support quality management. As these assets mature, they are integrated into other processes, including solution development and data product delivery.
Beyond tools and processes, many organizations emphasize the human element of governance. Adoption grows when stakeholders are actively involved, communications are tailored to different working styles, and governance is positioned as an enabler rather than a control function. Engagement strategies that prioritize empathy, co-creation, and relevance help transform governance from a compliance obligation into a trusted business capability.
Conclusion and Recommendations
The insights shared during the conference clearly show that effective data governance is both a strategic capability and a practical discipline. To realize its full potential, organizations should consider the following key actions:
- Position governance as a business enabler by aligning it with strategic goals, operational improvement, and measurable outcomes.
- Start small and scale iteratively, focusing on quick wins that demonstrate value and build momentum for broader adoption.
- Define and support governance roles with clear responsibilities, structured onboarding, and ongoing training to ensure consistency and accountability.
- Embed governance in daily operations through simple, transparent processes and tools that integrate with existing workflows.
- Prioritize engagement and change management, ensuring that governance is co-created with stakeholders and adapted to the organization’s culture.
- Treat governance as a shared responsibility, combining leadership, collaboration, and continuous improvement to sustain long-term success.
By following these principles, data governance can evolve from a policy-driven function into a trusted, business-critical capability that supports agility, quality, and innovation.
The following article of this series will discuss trending topics related to integration of AI and Data Governance.
