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Data Stewardship: Definition, Responsibilities, and Governance

People   |   Paul Warburg   |   Dec 15, 2023

Data stewardship is the technical or business role that implements data governance for an organization, lines of business, or business units. Data governance refers to high-level policies protecting data against loss, corruption, theft, or misuse. As a functional role in data management and governance, a Data Steward ensures that the policies, processes, guidelines, and standards that render data trustworthy are put into practice.

As an advocate for the proper use of and attention to data, a Data Steward plays a key role in building up an organization’s institutional confidence in its data. Without that trust, organizations lack confidence in their strategic business decisions. Or worse yet, they make business decisions that steer them in the wrong direction—financially, organizationally, or legally.

Data Stewards are often seen as guardians of data quality, ensuring the data that drives key business decisions can be trusted to be complete, accurate, and valid. They help create a sense of security and trust in the data.

Data Stewards are also seen as go-to experts on data; they’re the people who know what data an organization has, where it’s located, where it comes from and where it’s going, who uses it, and how it’s used.

What Are the Responsibilities of a Data Steward?

A Data Steward defines data processes, creates processes or procedures, maintains the quality of an organization’s data, optimizes workflows, monitors data usage, assists teams, and ensures data compliance and security. A Data Steward is responsible for a far-reaching and essential scope of work.

Managing and Maintaining Data Quality

Data Stewards maintain a collection of practices that confirm and ensure an organization’s data is accessible and usable and can be trusted to be complete, accurate, and valid. Some areas they are responsible for include:

  1. Profiling and assessing data
  2. Helping to find and fix data errors, anomalies, and inconsistencies
  3. Validating data to ensure profiled data is clean, accurate, and of high quality

Managing Data Lineage

Data Stewards safeguard the transparency and accuracy of data lineage. Data lineage is the lifecycle of a piece of data: where it originates, what its business context is, what happens to it, what is done to it, and where it moves over time. When data is in use — for example, in analytics or AI/ML — Data Stewards can help trace data errors or problems back to their root causes.

Supporting the User Community

Data Stewards champion the user community as part of an organization’s data operations. This cross-functional team of data suppliers, data preparers, and data consumers works together to create a data supply chain to build analytics for lines of business. A Data Steward is a critical link in this overall data supply chain, straddling the work of the data team and the user community’s needs.

Creating and Enforcing Data Governance Policies and Procedures

Data Stewards create, carry out, monitor, and enforce data usage rules, regulations, and other policies set forth by data governance initiatives. They ensure every aspect of the data lifecycle follows and upholds an organization’s data governance principles.

Data Stewards in the Organization

Data Stewards typically sit within an organization’s IT or data departments, though their exact placement can vary depending on the organization’s structure and needs; they may also be found in departments that rely heavily on data, like finance, marketing, or operations.

In larger organizations, Data Stewards may be part of a centralized data governance team, working alongside data analysts, data scientists, and IT professionals.

Data Stewardship After AI

The evolution of AI is likely to impact data stewardship significantly. AI’s ability to process and analyze large volumes of data at unprecedented speeds will require reevaluating current data stewardship practices.


The advent of generative AI has made it possible for many routine tasks, like data profiling, classification, tagging, cleansing, and integration, to be automated, removing a significant amount of manual work from the Data Steward’s plate. With AI handling tactical tasks, we will likely see data stewards transition to focusing on more strategic activities, like overseeing AI’s work and making critical decisions.

New Types of Data

Natural language processing and computer vision allow new unstructured data asset types (like images, videos, and photographs) to be analyzed and interpreted. New policies for how to input, store, and decipher these data types will need to be created and maintained.

Data Ethics and Privacy

One of the most significant shifts for the Data Steward since the rapid acceleration of AI is their ownership over ethical considerations, data privacy, and governance in an AI-augmented environment. AI processes vast amounts of data, which makes ethical considerations and data privacy even more critical. In fact, when surveyed, 47% of data leaders who are not yet using generative AI listed “data privacy” as the main reason for why. The complexity of how AI accesses and generates data will necessitate close collaboration between Data Stewards, data scientists, and IT teams. Data Stewards may shift their focus to addressing potential biases in data and algorithms and mitigating privacy risks with the time they have freed up through automation.

How Does Alteryx Help Data Stewards?

Alteryx Analytic Cloud Platform helps Data Stewards quickly access data, audit data reliability, track data lineage, and ensure high-quality, trustworthy, and consumable data at any scale. This intelligent, collaborative, self-service data engineering cloud platform helps Data Stewards:

Connect to data from any source. Alteryx makes it fast and easy for Data Stewards to connect to data from any source through a self-service architecture that offers universal data connectivity. This enables Data Stewards to ensure line-of-business stakeholders have data access to complete, high-quality data to feed analytics.

Ensure data quality. Alteryx’s Analytics Cloud Platform’s Adaptive Data Quality rules help Data Stewards interpret and understand the reliability of data and provide intelligent suggestions to correct anomalies as they arise. With Designer Cloud, Data Stewards can quickly profile datasets and observe changes in data, allowing for easy data quality monitoring and fast identification of issues like data drift that could break data pipelines.

Collaborate with users across the organization on data projects. Alteryx Analytics Cloud Platform’s collaborative data engineering platform allows users of varying technical skill levels to collaborate on their data projects. This collaborative approach allows Data Stewards to easily communicate and enforce data usage policies, processes, and procedures in active data projects across their organizations.

What’s Next

Read: Why No One Trusts Your Data – And How to Fix It

Watch: The Power of Trusted Data in Modern Analytics