Data Governance vs Data Stewardship: 5 Key Differences

Updated November 02nd, 2023
header image

Share this article

Data governance is a set of procedures and guidelines that detail how data is to be properly managed, accessed, and used. On the other hand, data stewardship is about day-to-day management and oversight of data assets to ensure data quality, consistency, and accuracy.

Data stewards perform a crucial role within the data governance structure by carrying out the data governance plan on a day-to-day basis. They work closely with data owners and other stakeholders.

Both data governance and data stewardship are two key concepts in the realm of data management. While often used interchangeably, they have distinct roles and responsibilities.

Modern data problems require modern solutions - Try Atlan, the data catalog of choice for forward-looking data teams! 👉 Book your demo today

In this article, we will uncover:

  1. The differences between data governance vs data stewardship
  2. The risks that comes up when these crucial aspects are neglected
  3. The importance of a comprehensive approach that combines both data governance and data stewardship practices.

Ready? Let’s dive in!

Table of contents

  1. What is the difference between data governance and data stewardship?
  2. Is data stewardship part of data governance?
  3. Data governance and data stewardship in action: Real-world scenarios compared
  4. Risks of neglecting data governance and data stewardship in organizations
  5. What is the difference between data governance and data stewardship: A tabular view
  6. Going beyond the basics: Exploring key aspects of data governance and data stewardship
  7. Rounding it all up together
  8. Data governance vs data stewardship: Related reads

What is the difference between data governance and data stewardship?

Data governance and data stewardship are two critical elements of managing data, but they represent different aspects of the overall process.

Let us try to understand them one by one:

What is data governance?


This refers to an overarching strategy for managing and maintaining the quality, availability, usability, and security of data. This usually involves a set of procedures and a plan to execute those procedures that are in line with the company’s overall strategy.

Data governance might involve determining who has access to data, who is responsible for data accuracy, and how data is stored and protected.

What is data stewardship?


Data stewardship involves tasks like ensuring data quality, handling data-related requests and permissions. Furthermore, it also includes enforcing data policies and managing data issues. The data steward would work closely with various stakeholders.

These stakeholders include data users, data custodians (who handle the technical aspects of data storage and maintenance), and the data governance committee.

So in essence, data governance is the strategy and set of policies, while data stewardship is the execution of those policies. Both are critical to ensuring that your company’s data is reliable, secure, and used effectively.

Is data stewardship part of data governance?

Yes. Data governance is the overall framework and processes that govern data management, while data stewardship is a specific role within data governance responsible for the day-to-day management and oversight of data assets. Data governance sets the strategic direction and policies, while data stewards execute and implement them to ensure data quality, consistency, and compliance.

Let’s dive into the distinction between data governance vs data stewardship:

Data governance


  • Definition: Data governance refers to the overall management of the availability, usability, integrity, and security of data used in an enterprise. It’s a set of processes, policies, standards, and metrics that ensure the effective and efficient use of information in enabling an organization to achieve its goals.

  • Scope: It typically encompasses a set of principles and practices that ensure high quality throughout the lifecycle of your data. Data governance initiatives typically include establishing policies, standards, roles, responsibilities, and performance metrics.

  • Role: The data governance program usually involves a council or board-level oversight that involves leaders from different parts of the business.

Data stewardship


  • Definition: Data stewardship refers to the operational tasks and responsibilities needed to manage, curate, and maintain data assets to ensure they are compliant with governance policies and procedures.

  • Scope: It’s the actual execution and enforcement of the policies set by data governance. Data stewards are often responsible for data quality, metadata management, data access, and the actual hands-on task of ensuring data is trustworthy and used correctly.

  • Role: Data stewards are often assigned to specific business units or domains and act as the bridge between IT and business. They ensure that the data governance policies are correctly applied and adhered to in their respective areas.

In essence, while data governance provides the overarching framework, guidelines, and policies for how data should be handled, data stewardship deals with the tactical execution of those policies.

To use an analogy, think of data governance as the laws a country might establish about road safety. In contrast, data stewardship would be the traffic cops ensuring those laws are enforced and the drivers (or data users) are adhering to them.

How data governance and data stewardship fit together: Real-world scenarios compared

Let’s dive a bit deeper into data governance and data stewardship with a broader lens and discuss some use cases. This will help you understand it better from a practical perspective.

How does data governance work?


Data governance involves the development and enforcement of rules, procedures, and protocols regarding data management in an organization. This usually requires input from various levels within the organization and may even involve external stakeholders.

Here’s a use case to illustrate:

  • Healthcare industry

In a healthcare organization, sensitive patient data needs to be handled according to legal and ethical standards (for example: HIPAA in the U.S.).

Data governance in this context might involve creating policies that dictate who can access patient data, and under what circumstances. Further, it can also check how this data must be stored and transmitted to maintain patient privacy.

Furthermore, it could involve creating processes to de-identify data for research purposes while still preserving its utility. These rules would be created by a data governance committee, including representatives from medical, IT, legal, and administrative departments.

How does data stewardship work?


Data stewardship is more about the day-to-day management and application of these policies. Data stewards ensure that data governance policies are followed and that data is correctly handled.

Here’s a use case for context:

  • Finance/Banking industry

In a bank, a data steward might work to ensure that customer data is accurate and up-to-date. This is critical for risk management, loan processing, and other banking operations.

They would also ensure that only authorized individuals have access to certain types of data to protect customer privacy and comply with banking regulations.

For instance, they might manage permissions so that a loan officer can access a client’s financial data, but not their unrelated personal information. They would work closely with the IT department to address any data-related issues.

Further, they would keep the data governance committee informed about data quality, usage, and security.

Comparing the real-world scenarios


Data governance is strategic, defining the “what” and “why”. It sets the principles and procedures regarding how data is to be managed.

Data stewardship, on the other hand, is more operational and defines the “how”. It is about executing and managing the procedures set by the governance strategy on a daily basis. Both are necessary to ensure effective data management. Without governance, there would be no rules or direction.

This may lead to potential misuse of data, poor data quality, and potential violations of privacy or legal requirements. Without stewardship, the rules set by the governance would not be implemented or enforced, leading to the same problems.

So, while they are distinct concepts, data governance and data stewardship work hand-in-hand. They are both essential components of a comprehensive data management strategy.

Risks of neglecting data governance and data stewardship in organizations

Indeed, not having both data governance and data stewardship can lead to several risks. This can ultimately harms an organization’s effectiveness, reputation, and compliance with laws and regulations.

Let us learn about the potential risks of both of them one by one:

What are the risks of lacking data governance?


  • Poor data quality

Without a data governance policy, there may be no defined procedures or standards for data collection, maintenance, and usage. This can lead to inconsistent, inaccurate, or outdated data, which can in turn lead to poor decision-making.

  • Non-compliance with regulations

Many industries are subject to data-related regulations (GDPR in the EU, CCPA in California, HIPAA in healthcare, etc.). Without proper data governance, an organization may fail to comply with these regulations. This can lead to severe penalties and legal repercussions.

  • Security vulnerabilities

Data governance includes setting policies for data security. Without this, an organization may be more susceptible to data breaches or misuse of data. This is leading to reputational damage and potential legal consequences.

  • Inefficiencies

Without proper governance, data may be siloed or duplicated across the organization. This can lead to inefficiencies in data usage and increased costs.

What are the risks of lacking data stewardship ?


  • Poor implementation of governance policies

Even with a robust data governance strategy in place, without data stewards, there may be a lack of consistent execution of these policies. This can lead to the same issues as having no governance at all, including poor data quality, non-compliance, security issues, and inefficiencies.

  • Ineffective data usage

Data stewards often serve as a bridge between data creators (like IT) and data users (like business analysts). Without this role, there can be a disconnect, leading to ineffective use of data.

  • Lack of accountability

Data stewards are often tasked with specific responsibilities like ensuring data quality and managing access permissions. Without this role, it may be unclear who is responsible for these tasks, leading to potential oversights and errors.

  • Lack of knowledge and training

Part of the data steward’s role often involves training others in the organization on proper data usage. Without this, employees may not know how to use data effectively and responsibly.

In summary, a lack of data governance can lead to strategic risks, such as poor decision-making, non-compliance, and security issues.

While a lack of data stewardship can lead to operational risks, such as ineffective data usage, lack of accountability, and lack of training. Both are essential to manage and mitigate risks associated with data in an organization.

What is the difference between data governance and data stewardship: A tabular view

In the realm of data management, it is crucial to understand the distinctions between data governance and data security.

To provide a clear and concise comparison, this section presents a tabular view that highlights the key differences between data governance and data security.

By examining their respective components, objectives, and approaches, we can gain a deeper understanding of how these two disciplines contribute to the overall data management landscape.

 **Data governance****Data stewardship**
**Definition**A set of strategic policies and procedures that dictate how an organization's data is to be managed, including aspects like data quality, security, privacy, and compliance.1. The role or practice of executing the data governance policies on a day-to-day basis
2. Managing data quality, handling data-related issues, and
3. Working with various stakeholders to ensure compliance with the established rules.
**Focus**Strategic, defining "what" and "why". Establishes principles and policies for data management.Operational, defining "how". Executes and manages the procedures set by governance on a daily basis.
**Role**1. Developing data management strategies
2. Creating policies for data quality, security, privacy, and compliance, and
3. Defining roles and responsibilities around data management.		1. Implementing data management strategies
2. Ensuring data quality
3. Managing data access and permissions
4. Resolving data-related issues, and training users on data policies.
**Risks of absence**Poor data quality, non-compliance with regulations, increased security vulnerabilities, and organizational inefficiencies.Poor implementation of governance policies, ineffective data usage, lack of accountability, lack of knowledge and training.
**Example use case**A healthcare organization creates a data governance policy to comply with HIPAA, dictating how patient data is stored, transmitted, and who can access it.A data steward in a bank ensures customer data is accurate and up-to-date, manages who has access to this data, and works with IT to address any data-related issues.

Going beyond the basics: Exploring key aspects of data governance and data stewardship

Both data governance and data stewardship are key to the effective management of data within an organization, and both involve several core components and considerations.

While we saw an overall approach to both these concepts, let us now look at a few additional aspects to keep in mind:

What are the key aspects of data governance?


  • Organization-wide involvement

Effective data governance requires buy-in and collaboration from various levels. Further, it is also effective for various departments within an organization, from executives to IT to users of data.

  • Adherence to regulations

Data governance must take into account all relevant legal and regulatory requirements, which can vary by industry and location.

  • Data privacy and ethics

As data privacy becomes an increasingly important concern, governance policies should consider how to handle sensitive data and respect user privacy.

  • Continuous improvement

Data governance is not a one-time effort, but an ongoing process that needs to be adjusted as an organization evolves and as data technologies and regulations change.

What are the key aspects of data stewardship?

  • Defined roles and responsibilities

The specific responsibilities of data stewards can vary depending on the organization’s needs. But these should be clearly defined and communicated.

  • Technical and business knowledge

Data stewards often need to bridge the gap between the technical aspects of data management (such as databases and data architecture) and the business uses of data. As such, they often need a blend of technical and business skills.

  • Data quality management

One key role of data stewards is ensuring the quality of data. This involves cleaning data, managing metadata, and working with data users and creators to address data quality issues.

  • Communication and training

Data stewards often need to communicate data policies to users and may also be involved in training users on how to use and handle data responsibly.

In the broader context, data governance and data stewardship are part of a larger data management strategy.

It may also involve aspects like data architecture, data integration, and data analytics. By understanding and effectively managing these elements, organizations can unlock the full potential of their data.

Further, it would help in supporting better decision-making, innovation, and growth.

Rounding it all up together

Data governance sets the strategic policies for data management, while data stewardship executes those policies. Without them, organizations face risks like poor data quality, security vulnerabilities, and compliance issues.

They are two sides of the same coin - the governance sets the policies, and the stewardship ensures these are followed. In the context of data management, data governance, and data stewardship is part of a broader strategy. This includes data architecture, data integration, and data analytics.

By implementing and effectively managing data governance and data stewardship, organizations can ensure they are using their data responsibly, effectively, and in a way that drives value for the business.

Share this article

[Website env: production]