Data Governance Roles Explained: Data Owner, Steward & Custodian

Last updated in February 2024.

Data governance roles are positions within an organisation that ensure data is managed and used effectively. Just like companies use corporate governance to run the business and make the most of their assets, these roles are critical for managing data as an asset and maximising its value.

In a nutshell, data governance depends on people. These people need clear roles, tasks, accountability, and communication. Without that, bad data will flow up and down. And this makes teams lose trust in data and robs decision-makers of the opportunity to gain a competitive edge.

So, by properly understanding different data governance roles and responsibilities, organisations can reap the rewards of an effective governance programme. One that supports the business strategy and creates real, measurable benefits from data.

Key takeaways:

1. Data Owners must be senior leaders, helping put data improvement on the SLT agenda.

2. Data Stewards are referees that ensure data is managed and performs according to rules.

3. Data Custodians are on the front line, implementing and maintaining data based on pre-agreed rules.

4. You can’t force roles on people. Instead, motivate them by demonstrating the benefits.

5. There is no copy-and-paste solution to data governance. Other roles exist and the approach you take must reflect your unique business goals and context.

Data governance roles and responsibilities: what’s the difference?

data governance senior leadership team structure diagram

The above data governance model is one we have used for a client.

It highlights why data governance is so important. Good data governance helps you manage data across the organisation, from top-level executives down to junior data analysts, helping everyone understand and use data correctly to bring the business strategy to life.

Senior roles provide direction on how data should be managed to support the business strategy and how to maximise value from data assets. Meanwhile, junior roles implement this guidance and feedback to leadership on any changes that are needed to ensure data creates value. Without the bottom, you have no action and feedback, and without the top, you have no control.

However, it’s important to recognise every organisation is different. The above model might not be right for you. What’s important is having a good grasp of the different data governance roles. This will help you identify what the right approach is for your organisation, so you can implement a data governance approach that creates tangible results.

Below, we describe the most common roles you’ll find in day-to-day data management: the Data Owner, the Data Custodian, and the Data Steward. You can also learn about the Chief Data Office and the role of the Chief Data Officer.

The Data Owner

What is a Data Owner?

Data Owners are senior leaders and executives in charge of managing data and information relating to their specific business area. For example, the Head of Marketing may also be the owner of customer data, as this is critical to marketing operations.

We cannot stress the importance of having senior leaders own the data in their areas.

It ensures that data is on their agenda and that they have data oversight. It also helps them understand how the business strategy impacts the data strategy, including how the data they own is used and managed across the organisation, and that they allocate the right amount of capital to data improvement efforts.

But when data owners are too junior, data remains under-invested in, as these people will often not be decision-makers or control budgets.

Having leaders own their data ensures accountability is distributed across the organisation, instead of falling on the shoulders of a single CIO or CDO. And while it’s true that data responsibilities can be a lot for already busy senior leaders to handle, giving this role to junior data owners or anyone else is not a viable option, since it means the data agenda is one more step away from senior leadership.

This prevents them from understanding the importance of good data to strategic objectives and positions data as a cost to reduce, rather than a valuable asset to exploit.

Questions for a Data Owner

  • Do you have the data you need to make effective decisions that fulfil your business purpose?
  • Do you know how your critical data is used, and what “good” use of it looks like?
  • Are you aware who is responsible for the maintenance of important data in your team, and who your Data Stewards and Custodians are?
  • For each data project, are there regular governance checkpoints, including steering groups, planning, and allocation groups, to ensure data projects are on-track and generating adequate returns?

Data Owner Responsibilities

  • Data asset management strategy, including allocation of capital and resources to maintain or improve their data
  • Appropriately governing, exploiting, and protecting their data asset
  • Reviewing use cases and evaluating the condition of the data to effectively execute these use cases
  • Reviewing data asset specific standards, policies, and processes
  • Updating processes according to regulatory requirements
  • Allocating and supporting data stewards and custodians for the data asset

The Data Steward

What is a Data Steward?

A Data Steward manages data based on the defined standards and specifications set in agreement with the Data Owner.

Their main responsibility is to monitor the condition assessment of the dataset to make sure data meets the needs of business users, and the standards set by senior leaders.

They are like referees: they make sure everybody is following the agreed rules, and help clarify and improve those rules based on what they see in action. 

Questions for a Data Steward

  • How are you making sure the data is fit for purpose?
  • If the data is not fit for purpose, how can you tell and what can you do to fix it?
  • Are the use cases regularly reviewed to ensure the data is fit for their business purpose?
  • When a new use case for a dataset emerges, do you have a clear process to clarify and record what “good” use looks like?

Data Steward Responsibilities

  • Collecting and collating use cases for the dataset
  • Developing, maintaining, and updating dataset-specific standards, policies, and procedures to appropriately govern the dataset, including its metadata
  • Data ontology (maintaining and updating the dataset inventory)
  • Measurements and methods of dataset condition
  • Ensuring data is available for its agreed use cases
  • Managing new requirements of the dataset based on existing standards, policies, and procedures

The Data Custodian

What is a Data Custodian?

On the “front line” of data governance, Data Custodians implement and maintain the business and technical rules to manage a dataset, set by the relevant Data Steward. They’re accountable for ensuring the safe custody, transport, and storage of data.

Questions for a Data Custodian

  • Do you encounter friction when providing data to other departments in the business?
  • If so, is there a clear route to working with business departments to reduce that friction?
  • Are there clear, nominated individuals who are responsible for preparing data for analytical or operational use, such as a data engineer?
  • How do you engage with the business to ensure their vision for data is technically feasible and can be implemented?

Data Custodian Responsibilities

  • Collecting and collating use cases for the dataset
  • Developing, maintaining, and updating dataset-specific standards, policies, and procedures to appropriately govern the dataset, including its metadata
  • Data ontology (maintaining and updating the dataset inventory)
  • Measurements and methods of dataset condition
  • Ensuring data is available for its agreed use cases
  • Managing new requirements of the dataset based on existing standards, policies, and procedures

Additional governance roles

In addition to the above roles, several other positions may benefit an organisation’s data governance. These include:

CDO Team: The CDO and their team members support other data governance roles by providing data services such as strategy, governance, architecture, science, and security. They should not be responsible for owning and fixing all data themselves, but instead support the business in improving their data and defining what good management looks like for the organisation.

Data Analyst: Uses the dataset to fulfil a business purpose and collects, analyses, and interprets datasets to identify patterns and trends.

Data Use Case Owner: Responsible for the outcome of a business process that uses a dataset.

Architect: Manages the organisation’s data and technology architecture, and ensures data has an appropriate business context and common structure.

Nevertheless, not every business will benefit from incorporating every role. There is no (successful) copy-and-paste approach to data governance, so data management should always be bespoke to every organisation.

For example, healthcare and financial services companies might need to heavily focus on the protection and security of people’s personal data, and can create specific roles to do so. In other industries, this approach is much less relevant.

How to make data governance roles work

Unfortunately, data roles are often forced upon team members or not explained properly. Since nobody wants role creep or a position they don’t understand, this creates friction and harms collaboration. This is why it’s important to properly upskill people in their roles, and motivate people to work together.

We have found that this is where data valuation can play a really important role. Accurate data valuation shows people how their data is used to create value across the business. This helps to identify other departments or business functions they can collaborate with, and prevents people from acting in silos.

Putting a monetary value on data also motivates people to take on a new role, as it’s easier to understand how the data contributes to the health of the organisation. Once people understand how data governance benefits them and the wider business, they will be more eager to collaborate and take on extra responsibilities.

Ultimately, this also leads to better data governance: a significant 87% of data leaders say assigning a monetary value to data would enable better data management in their organisation.

Anmut understands data is your most valuable resource 

Data governance is a critical component of data management, but it involves more than just roles, responsibilities, and rules. When done well, it supports the entire business, bringing the business strategy to life with the right, high-performing data for success.

At Anmut, we help organisations become industry leaders through the data they collect, hold, and maintain. To discover how to improve the performance of your data, contact us now.

Data governance roles and responsibilities : FAQs

Collaboration between different data governance roles

Question: How does the collaboration between Data Owners, Data Stewards, and Data Custodians practically unfold within large-scale projects, especially when handling cross-departmental data initiatives?
Answer: Collaboration between Data Owners, Stewards, and Custodians typically involves a structured framework where roles are clearly defined but flexible enough to adapt to project specifics. This often requires regular communication and defined processes for decision-making and conflict resolution.

Dealing with the challenges that arise between data governance roles

Question: Are there specific examples of challenges or conflicts that have arisen between these roles in real-world scenarios, and how were they resolved?
Answer: Real-world challenges between these roles can include disagreements over data access levels or interpretation of data governance policies, which are usually resolved through predefined escalation paths and mediation by higher governance bodies.

Measuring the effectiveness of a data governance framework

Question: How do organizations measure the effectiveness of their data governance framework, particularly the impact of these roles on overall data quality and business outcomes?
Answer: The effectiveness of a data governance framework is often measured through key performance indicators (KPIs) related to data quality, compliance with data standards, and the impact on business outcomes, such as improved decision-making and operational efficiency.