What are the core differences between data governance and data stewardship?

Explore the core differences between data governance and data stewardship, including their respective roles and responsibilities in data management.
Last updated
May 2, 2024

What are the core differences between data governance and data stewardship?

Data governance is a strategic framework that outlines the objectives and principles for managing an organization's data, focusing on compliance, quality, and efficiency. It defines the "what" and "why" behind data management practices.

Data stewardship, on the other hand, is an operational role that involves the practical execution of the data governance plan. Stewards are tasked with the "how," ensuring daily adherence to the policies and procedures set forth by the governance framework.

  • Data governance sets the vision and strategic goals for data management, while data stewardship implements these goals on a day-to-day basis.
  • While data governance is concerned with establishing policies, data stewardship is about policy enforcement and maintenance of data quality.
  • Data stewards act as advocates for the data, working to maximize its value in alignment with governance strategies.
  • Effective data governance requires a strong stewardship component to translate policies into actionable practices.
  • Both roles are essential for ensuring that data serves the strategic objectives of the organization and complies with relevant regulations.

How do data governance and data stewardship complement each other?

Data governance and data stewardship are interdependent, with governance providing the framework and stewardship executing the framework's directives.

Together, they ensure that data is managed as a valuable asset, with governance focusing on the alignment of data management with business strategy and stewardship ensuring the practical application of these strategies.

  • Data governance provides the strategic direction, and data stewardship brings that direction to life through daily management activities.
  • Stewards are often responsible for communicating governance policies to the wider organization and ensuring their understanding and compliance.
  • Both functions are necessary to maintain the balance between data accessibility and security within an organization.

What responsibilities do data stewards have in a data governance framework?

Data stewards are responsible for the operational management of data within the governance framework. This includes maintaining data quality, managing data access, and ensuring data lifecycle management.

They act as the bridge between the data governance team and the end-users of the data, translating policies into practice and providing feedback to governance bodies on policy effectiveness.

  • Data stewards ensure that data governance policies are implemented correctly and consistently across the organization.
  • They play a key role in data quality initiatives, monitoring and improving the quality of data within their purview.
  • Stewards also manage data access, balancing the need for data security with the need for data availability for business operations.
  • What challenges arise in distinguishing between data governance and data stewardship?

    One of the main challenges is the overlap in responsibilities, which can lead to confusion about the distinct roles of governance and stewardship.

    Another challenge is ensuring that both governance and stewardship adapt to the evolving data landscape, including changes in technology, regulations, and business needs.

  • Clear communication and well-defined roles are essential to differentiate between the strategic and operational aspects of data management.
  • Organizations must invest in training and development to ensure that both governance bodies and stewards are equipped to handle their respective responsibilities.
  • It's important to establish metrics and KPIs to measure the effectiveness of both data governance and stewardship efforts.
  • Can data stewardship exist without data governance?

    While data stewardship can technically exist without a formal data governance framework, it is not recommended. Without governance, stewardship lacks strategic direction and may not align with organizational goals.

    Data stewardship without governance is akin to navigating without a map; it can lead to inconsistent practices and suboptimal data management.

  • Data stewardship is most effective when it operates within a well-defined governance framework.
  • Lack of governance can lead to data silos, inconsistent data quality, and potential non-compliance with regulations.
  • Implementing data governance provides the necessary structure for effective stewardship and overall data management.
  • How does Secoda facilitate better data governance and stewardship?

    Secoda offers AI-powered tools that streamline the search, cataloging, lineage, and documentation of data, which are essential components of both data governance and stewardship.

    By automating and simplifying these processes, Secoda helps data teams to efficiently manage data sprawl and scale their data infrastructure, supporting the objectives of governance and the tasks of stewardship.

  • Secoda enhances data observability and governance by providing a clear view of data assets and their lineage.
  • The platform reduces the time and effort required to document and catalog data, allowing stewards to focus on value-added activities.
  • With Secoda, data teams can overcome common integration hurdles, making it easier to enforce governance policies and stewardship practices.
  • What role does behavioral science play in data governance and stewardship?

    Behavioral science can inform the design of data governance frameworks and stewardship practices by understanding how people interact with data and are influenced by policies and procedures.

    Incorporating behavioral insights can lead to more effective adoption of data governance initiatives and compliance with stewardship responsibilities.

  • Understanding behavioral drivers can help in crafting governance policies that are more likely to be followed.
  • Behavioral science can aid in designing stewardship programs that encourage responsible data use and collaboration.
  • It can also help identify potential points of resistance and develop strategies to overcome them, ensuring smoother implementation of data management practices.
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