As data use cases and data diversity are constantly increasing, managing data effectively is the top priority for data-driven organizations. While there are many tools to handle data management, you need to embrace the right processes, like Data Stewardship. It ensures that you use data in compliance with business rules and requirements. 

In this article, we will cover the concept of data stewardship in detail and give you a practical guide to apply in your business operations. 

Why is Data Stewardship Important?

Data stewardship is a process that ensures data assets in an organization are accessible, useful, trusted, and secure. It is a collection of practices involved in every aspect of the data lifecycle, including creating, preparing, storing, sharing, using, archiving, and deleting data. Data stewardship encompasses: 

  • Deep understanding of the kind of data organizations possess.
  • Responsibility for efficient data storage. 
  • Ensuring data accountability, usability, accessibility, safety, and reliability of data. 
  • Preserving the clarity and precision of data lineage. 
  • Enforcing guidelines and standards for data usage. 
  • Assisting the organization to harness the potential of their data to get a competitive edge. 
  • Data advocacy for reliable data. 

Overall, data stewardship aims to promote data quality and integrity under the rules and requirements of an organization.

What is a Data Steward: Roles and Responsibilities?

A data steward is a professional responsible for the data stewardship process. Many organizations have individual roles for this position, but in some cases, data professionals perform this task in addition to their main tasks. The individual role of data steward requires a deep knowledge of the technical aspects of data management and business-oriented skills, including programming, storage concepts, data custodianship, project management, and more. They balance data science, engineering, and communication skills to collaborate across different teams in an organization and advocate ways for efficient data usage. 

A data steward is responsible for almost everything related to data. Some of the responsibilities are mentioned below: 

  • Data Lineage: Data lineage is the process of tracking data flow over time and knowing where data originated, how it has changed, and where it is stored in a data pipeline. Taking an audit trail of each data point’s origin, transformations, user interactions, and migrations along the system is a crucial responsibility for a data steward. 
  • Data Monitoring: Data stewards require an effective monitoring strategy to avoid potential misuse and fraud across the storage system. The strategy should effectively monitor every input and output activity related to data in an organization. This ensures all users access data in compliance with internal and external policies. 
  • Data Workflows: Data stewards work with data scientists and engineers to streamline data distribution with stakeholders or non-technical professionals. This includes setting up data streams to produce reports and visualizations and giving non-engineers access to tools to perform ad hoc analysis and discover new insights. 
  • Data Advocacy: Data stewards also serve as data advocates. They advocate the value of data as a vital organizational asset and promote its use in supporting business operations and strategic decision-making. Additionally, to create a data-driven culture, data stewards collaborate with various stakeholders to raise awareness about the importance of data quality by taking full data ownership.

Data Stewardship VS Data Governance

Many times, the data stewardship and governance definitions are used interchangeably. However, they both differ broadly. Let’s discover the difference between both data management concepts in detail: 

Data GovernanceData Stewardship
ScopeData governance is a broad concept that refers to a set of procedures, policies, roles, and rules that govern data in an organization. It is a multi-layered framework with specific focus areas, such as data access management and quality improvement.Data stewardship is a part of the data governance framework. While data governance is concerned with defining policies and rules, data stewardship is the implementation of the rules with procedures for data usage.
RoleStakeholders and council members of an organization typically oversee the data governance program. Data stewards are often assigned to specific business units and liaise between IT and business. They ensure that data governance policies are correctly implemented and followed in their business unit. 
Long-term PlanningThe major objective of data governance includes long-term data management plans, matching data practices with organizational goals, and ensuring data is well maintained over time.Data stewardship is more focused on short-term data management goals. The only concern of data steward is efficiently implementing policies and standards set by data governance at the time.
Data Stewardship VS Data Governance
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Data Stewardship Frameworks

A combination of different strategies and tactics of data stewardship form a framework. And it can operate within different frameworks, each having different techniques and use cases. Here are some of the well-known frameworks of data stewardship for responsible data handling: 

Ready, Set, Go 

Developed by a renowned finance company, Freddie Mac, this framework can leverage data for a competitive edge and increase business value. Here’s how it works: 

  • Ready: This stage involves laying the foundation for establishing the program. During this stage, the team of data professionals consults with stakeholders, the internal audit department, co-workers, and other professionals to identify the pain points in data management. Mostly, it involved listening intently and identifying the problems that needed to be addressed. 
  • Set: During this phase, the company forms different working groups to create data standards, including roles and duties for data stewardship. Every group takes care of a specific data module and has a supervisor. In times of disagreement, the team’s supervisor had the last say based on the facts provided. 
  • Go: This phase involves taking action on the required data management tasks. The C-suite employees and VPs come on board in this stage, and the team follows a top-down support approach. Lastly, Go also includes continuous planning every few years to evaluate, reconsider, and revise strategy throughout the system. 

Use Case

Following a strategic approach like the Ready, Set, Go framework is crucial for a highly regulated industry like Fintech. It allows you to closely examine every data management stage, enhancing decision-making to handle sensitive data.

In a fintech organization, data requires careful planning to prepare it for analytics and comply with industry regulations like GDPR. The ready stage of this framework allows you to build a solid foundation on unique goals. It could be either preserving the privacy of your customers while collaborating within the organizations or implementing a robust fraud detection model with data modeling. 

The Set stage enables you to leverage the qualities of different employees by assigning them to work on data modules of their expertise. This could involve making teams based on knowledge of banking systems, cybersecurity, financial regulations, or analytics of a professional. 

Lastly, the Go stage ensures you execute the plan by complying with different regulatory laws and security measures. This involves executing a data stewardship plan by complying with Know Your Customer (KYC) requirements, Anti-Money Laundering Laws (AML), cybersecurity standards, and other laws.

Pros and Cons of Ready, Set, Go

The Ready, Set, Go framework has a lot of advantages for organizations. This includes a strategic approach for in-depth examination, promoting collaboration by involving different levels of professionals, and futuristic planning for continuous growth. However, it also has some challenges, such as being resource-intensive and time-consuming. 

The Culture Campaign

This framework, initiated by Mary Levins and Cassie Elder, emphasizes observance rather than a strategic approach to data stewardship. It puts the culture of business at the center, supporting its aims without compromising security. 

Levins and Elder suggest gaining an initial understanding of organizational culture type by conducting staff surveys and observing staff behavior. This allows you to get a sense of the data culture of an organization. 

According to the framework, you can categorize culture into two types: Collaborative and Competent. The collaborative culture promotes harmony and team synergies, and competent one stresses the best talent to take pride in their work. 

If you have a high-collaborative organizational culture, data stewardship must be designed to promote more competency. However, in a competent culture, a data stewardship framework should be designed for better communication and engagement with people. 

Overall, the idea of this framework is to balance collaboration and competency in organizational culture to make the most out of data stewardship. 

Use Case

Instead of focusing on the technical side of data stewardship, this framework focuses on team management and culture building. This allows you to eliminate the tools and technologies that don’t fit into your company culture and be more productive in the data stewardship process. 

The framework’s primary use case lies in paying attention to the team’s behavior and achieving cultural balance in any organization. This balance ensures that data stewardship initiatives are successful and sensitive to cultural differences.

A unique facet of the culture campaign is that you don’t actually have to change the entire data stewardship process to harness its full potential. You can even use this with the Ready, Set, Go framework mentioned above. However, you have to observe the unique culture and align the data stewardship strategies accordingly. 

Pros and Cons of Culture Campaign

The Culture Campaign offers a range of benefits for data-driven organizations. This includes cultural assessment to understand the culture of your organization and cultural classification to identify predominant cultural traits. However, it also comes with some challenges, such as oversimplification of the diverse nature of organizational culture and lack of step-by-step guidance. 

Data Stewardship Best Practices

Here are the best practices you can use to perform data stewardship efficiently: 

Clearly Defining Data Steward’s Role

Usually, organizations have part-time data stewards who frequently work on their other official duties. This can lead to inefficiency in data stewardship. For an organization’s data governance procedures to be successful, data stewards must be full data team members. If they must remain informal members, constantly keeping them informed about data management practices is ideal. 

Data Classification

Data comes from many sources, and different sources have different formats, standards, and quality. Therefore, categorizing the data becomes crucial. Data classification involves categorizing data based on usage, importance, and sensitivity. Organizations can proficiently monitor and safeguard their data resources through methodical data categorization. Once the data is classified, you can streamline the tasks like data encryption, security protocols, and access controls.

Maintaining Detailed Documentation

Effective data stewardship depends on the documentation of tasks. Detailed documentation ensures that required professionals can understand the meaning and structure of data to use it for analysis and decision-making. Therefore, all data stewardship decisions, including business rules and data elements, must be documented in detail and in an easy-to-understand manner. Many tools, such as business glossaries, central issue logs, and metadata repositories, help to record data stewardship. 

Data Privacy Compliance

Data stewards are responsible for compliance activity as their role involves actively monitoring and implementing the required data privacy and access controls. Staying updated with the organization’s and government’s latest data protection regulations will allow you to comply with relevant laws. This promotes transparency and trust in users. Some popular international rules include the California Consumer Privacy Act, the General Data Protection Regulation, and the Health Insurance Portability and Accountability. 


Data stewardship is a crucial part of data management that ensures you use data assets correctly. This concept makes sure that data remains valuable, trustworthy, and secure. However, it can be tricky for data professionals, and that’s what data stewards are for.

They are dedicated professionals committed to growth and innovation, ensuring data is utilized responsibly in its lifecycle. By following the detailed guide mentioned above, including frameworks and best practices, you can also harness the full potential of data stewardship.

But as the volume of data increases, you need a permanent solution for tackling data silos and making data-driven decisions through data analytics. Here’s where Hevo Data, our automated data pipeline platform can help you.

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Jalaj Jain
Freelance Technical Content Writer, Hevo Data

Jalaj has extensive experience in freelance writing within the data industry and is passionate about simplifying the complexities of data integration and data analysis through informative content for those delving deeper into these subjects.

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