Your data governance program was launched with executive support, but six months later, progress has stalled. The reason? Your newly appointed Data Stewards, who are also full-time business analysts or product managers, treat their stewardship duties as a low-priority, “side-of-desk” task.
This is the most common failure mode for new governance programs. This article moves beyond simple definitions to provide a blueprint for formally empowering and resourcing your governance team, turning these critical roles from symbolic titles into effective agents of change.
Key Takeaways
| Common business problem | Data governance role-based solution |
| Inconsistent reports and conflicting metrics are presented in leadership meetings, eroding trust and paralyzing decision-making. | Empowered Data Stewards are made responsible for defining, certifying, and managing official business terms and metrics for their domain, creating a single, trusted source of truth. |
| The endless “IT vs. Business” tug-of-war over who is responsible for bad data creates organizational friction and stalls progress. | A formal framework assigns Data Owners (business leaders) with ultimate accountability for data assets, while Data Custodians (IT) are responsible for the technical environment, clarifying roles for all parties. |
| A new data governance program fails to gain momentum because assigned stewards treat it as a low-priority, “side-of-desk” task. | The Data Steward role is formally recognized with dedicated time, clear performance metrics, and strong executive sponsorship from the Data Owner and CDO to ensure it is prioritized. |
How governance teams work: a simple model for collaboration
Before diving into individual titles, it’s crucial to understand how the roles fit together. A successful program isn’t just a list of people; it’s a collaborative system designed to translate business goals into practice.
The most effective way to visualize this is through a simple three-layer model that clarifies the function of each part of the team.
This model provides a clear structure for your team’s collaboration, and it’s a key part of building a comprehensive data governance framework.
- The strategic layer: this is the “why” of data governance. It consists of senior leadership who provide the vision, sponsorship, and final approval for enterprise-wide data policies.
- The tactical layer: this is the “what.” It’s where business context meets policy, managed by subject-matter experts who are responsible for specific data domains like customer or product data.
- The operational layer: this is the “how.” It represents the frontline where technical rules are implemented and data is secured, managed, and used on a daily basis by IT professionals and data consumers.
Assembling your data governance team: key roles and responsibilities
With a collaborative model in place, the next step is to define the players. A successful data governance team is composed of several distinct roles, each with a specific mission.
This section will be a deep dive into these core players, explaining their mission and responsibilities with simple, real-world examples to make the concepts tangible.
The Chief Data Officer: the strategic leader of data governance
The chief data officer, or CDO, is the executive sponsor and visionary for the entire data program. This role sits at the strategic layer, responsible for ensuring that all data activities align with overarching business goals and for championing the program from the top down.
Their primary responsibilities include securing the budget, providing executive sponsorship, and leading the data governance council.
The CDO’s primary job is to ensure the data program aligns with overarching business goals. This alignment is the core of a robust data governance strategy, which we explore in-depth in our dedicated guide.
The Data Governance Council: the cross-functional steering committee
The Data Governance Council is a committee of senior leaders from across the business, including heads of marketing, sales, finance, and IT.
This group acts as the primary decision-making body for data governance.
Their key responsibilities include:
- approving enterprise-wide data policies,
- resolving high-level disputes between departments,
- and ensuring the program has the resources it needs to succeed.
A key responsibility of the council is to review and approve enterprise-wide data policies. For a deep dive into creating these essential documents, read our guide on crafting an effective data governance policy.
The Data Owner: the executive with ultimate responsibility for data
A Data Owner is a senior business leader who has the ultimate authority and accountability for a specific data domain, like customer or financial data.
This is a critical distinction: while others manage the data day-to-day, the owner is ultimately responsible for it.
They approve data definitions, sign off on access policies, and empower the Data Steward to execute the governance strategy within their domain.
For example, the VP of Sales is the Data Owner for all sales data, accountable for its quality and security.
The Data Steward: the hands-on expert responsible for data quality
The Data Steward is arguably the most pivotal role in the entire program. They are subject-matter experts from a business unit who are responsible for the day-to-day management of a data domain.
Their responsibilities are tactical and hands-on:
- they define business terms in the data catalog,
- monitor and fix data quality issues,
- and implement the access rules set by the data owner.
A Data Steward’s work is critical in highly regulated industries.
For example, when we worked with a leading Swiss bank, their data stewards were essential in defining and cataloging sensitive customer data elements within Collibra, ensuring both compliance and data usability.
The Data Custodian: the technical guardian of data management
The Data Custodian is the technical counterpart to the business-focused Data Steward. Typically operating within the IT department, the Custodian is responsible for the technical systems where data is stored and processed.
They manage the databases, implement security controls, perform backups, and handle the technical side of access control based on the rules defined by Owners and Stewards.
A key task for a Custodian is ensuring data lineage – the path data takes from source to report – is clear.
At Murdio, we’ve executed complex projects like building custom technical lineage from Snowflake and SAP into Collibra, giving Custodians the visibility they need to guarantee data integrity.
Implementing these technical controls is vital for complying with regulations like GDPR.
Implementing a data governance program
Designing a data governance team on paper is the first step, but success lies in practical implementation.
A one-size-fits-all approach is destined to fail; the most effective strategies are tailored to an organization’s specific context and maturity.
This section provides actionable advice for bringing your governance team to life.
Defining data governance roles and responsibilities for your organization
The structure of your data governance team should be based on your company’s size and data maturity, not a rigid template.
In a startup, for example, roles are often informal and shared out of necessity. The Head of Product might act as the de-facto Data Owner and Steward for product analytics data, while a Lead Engineer serves as the Data Custodian.
The focus is on agility and mitigating critical risks without creating bureaucracy.
Conversely, a large enterprise requires a formal, dedicated structure to manage complexity and scale. For a large enterprise, a formal structure is non-negotiable.
When we helped an international retail chain implement Collibra, defining clear roles for their large, distributed team was the first step to success.
Overcoming common hurdles for effective data governance
One of the most common challenges in implementing data governance is the long-standing debate over whether the business or IT owns the data.
This often leads to territorial conflicts and stalls progress. The solution is to establish a clear principle: the business has the responsibility for data quality and definitions, while IT acts as the Custodian of the technology that stores and protects it.
Accelerating your journey with the right tools and expertise
Acknowledging and building a data governance program is a significant undertaking, often constrained by limited resources and a shortage of specialized skills.
While powerful platforms like Collibra provide the engine to operationalize the roles and responsibilities discussed here, successful implementation requires deep expertise. This is where a specialist partner becomes invaluable.
At Murdio, we specialize in implementing Collibra solutions, helping organizations translate governance theory into practical reality.
Our track record with clients ranging from international retailers to DACH-based enterprises demonstrates our ability to empower your new governance teams to drive value from day one. If you’re ready to move from planning to action, learn how Murdio can accelerate your Collibra implementation.
The evolution of data governance roles for data analytics and AI
The world is changing with the rapid rise of artificial intelligence, and data governance roles must evolve with it. The manual, reactive governance models of the past are insufficient for the speed and scale of the AI era.
How AI is augmenting the role in data and enhancing data quality
AI is not replacing data governance roles but making them more powerful and strategic. It achieves this by automating the tedious, manual tasks that previously consumed a significant amount of time, freeing human experts to focus on higher-value work.
AI-driven tools can automatically scan for sensitive information, classify data, monitor for quality issues, and map complex data lineage or semantic layer, tasks that were once laborious bottlenecks.
For example, a Data Steward might have previously spent weeks manually checking for duplicate customer records. Today, an AI can scan millions of records in minutes and present the Steward with a list of probable duplicates to approve, turning a week of work into an hour of strategic review.
The rise of new governance teams: meet the AI Steward
As AI becomes more integrated into business operations, a new class of governance roles is emerging to manage the unique risks and requirements of AI systems themselves. The most prominent of these is the AI Steward or AI Governance Lead. This person is responsible not just for the data that feeds AI models, but for the governance of the models themselves.
Key responsibilities for this role include ensuring the quality and fairness of training data to mitigate algorithmic bias, monitoring deployed models for performance drift, and ensuring model decisions are transparent and explainable for regulatory compliance.
Why strong data governance is essential for effective data management
The journey through the landscape of data governance roles reveals a clear and compelling truth: building a data-driven organization is not about buying technology, but about empowering people.
Strong data governance is not a static, bureaucratic project to be completed, but a dynamic business capability that underpins the entire enterprise.
It begins with moving beyond a simple list of titles to create a collaborative system where strategy, tactics, and operations are seamlessly connected. Success depends on clearly defining your key roles and responsibilities, from the strategic vision of the CDO to the hands-on expertise of the Data Steward.
While the prospect of implementing a full-scale program can seem daunting, the path forward begins with a single, manageable step.
You don’t need to hire a full team tomorrow. Start by identifying your most critical data asset and appointing your first data steward. Empower them to make a difference, and you will have taken the first and most important step toward building a truly data-driven organization.
Your first 90-day best practices data governance action plan
Use this checklist to translate the principles in this article into immediate, tangible actions for your organization.
- [ ] Honestly evaluate whether your organization operates like a startup (informal, agile), a scaling company (formalizing processes), or an enterprise (complex, established). This will determine the right size and scope for your initial team.
- [ ] Identify a senior business leader to champion the program. Frame the initiative not as a cost, but as a strategic enabler that improves decision-making, reduces risk, and accelerates key business goals.
- [ ] Identify a subject-matter expert within a critical business domain (e.g., a senior sales analyst for customer data). Formally recognize this role, even if it’s part-time initially, and allocate dedicated time for their stewardship duties.
- [ ] Partner your new Data Steward with their corresponding Data Owner (e.g., the VP of Sales) for strategic authority and a Data Custodian (e.g., the lead database administrator) for technical implementation. This establishes the core collaborative trio.
- [ ] Do not try to govern all data at once. Task your new domain team with a specific, high-impact goal, such as improving the quality of a single critical report or defining the top five metrics for their domain.
- [ ] Assemble a small, informal group of stakeholders from different departments to act as a precursor to a formal Data Governance Council. Use this forum to resolve initial cross-domain issues and build consensus.
- [ ] Assess whether your current tools (e.g., data catalog, quality monitoring) can support the roles you’ve defined. Identify gaps that a platform like Collibra could fill to automate tasks and empower your team.
- [ ] Track the progress of your initial goal and broadcast its success. Demonstrating tangible value – like reducing the time to create a report or increasing trust in a key metric – is the best way to build momentum and secure resources for expansion.
