This article is the first in a four-part series exploring the transformative role of AI Agents and their potential to address persistent challenges in data governance. In this opening article, we introduce the concept of the Digital Data Steward (DDS), laying the foundation for how AI agents – within a coordinated agent framework – reshape and augment the role of a Data Steward.

This framework consists of various specialized AI agents – rangingfrom Data Strategy and Data Quality Agents to Metadata Management, Master Data, and Data Retention Agents – each supporting a critical aspect of data stewardship and contributing to the advancement of organizational data management maturity.

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Data has become the lifeblood of modern organizations, driving decisions, innovations, and competitive advantages. Yet, as data volumes grow exponentially, so do the challenges of managing it effectively.

Even with the importance of data across organizations, the level of maturity regarding data governance and management continues to lag in most companies. One pivotal role in establishing mature data governance and management practices is that of the data steward.

Data stewards occupy an important leadership role in the ecosystem of the Chief Data Office (CDO). They are essential to the successful execution of the CDO’s data strategy by assuming responsibility for managing one or more domains, where a “data domain” can be a particular subject area of data or business function in “stewardship” for the enterprise. They are the hands-on team that helps operationalize standards and policies, uphold data quality, and drive the data culture and ultimately driving the business value of their data domain.

Because their top priorities and responsibilities span technical, business, and change management skills, their role has traditionally been difficult to staff and sustain. Even when qualified individuals are appointed, data stewards face several barriers to effectiveness, such as constrained time and resources, the demand for specialized domain expertise, reliance on manual tasks, time-consuming organizational alignment, and cultural resistance (see Figure 1). As a result, their operational performance and value generation have often been lacking.

To address these key inhibitors, we – Maria Villar, Christine Legner, Mike Alvarez, and Elizabeth Hiatt – formed a working group of data management industry experts to investigate how AI agents can help accelerate and reshape the role of the data steward. Our goal is to unpack how AI technology can augment the current data steward roles and responsibilities that are so incredibly important to any data governance program.

In this four-part series, we seek to provide thought-provoking innovative guidance to Chief Data Officers (CDOs), data governance leaders, and business and technology leaders to help them identify areas of AI agent opportunities within their organizations.

A Digital Data Steward leveraging AI agents: A glimpse into the future of data management

Imagine AI agents that not only manage your data but also actively help define and execute your data strategy. Envision agents that listen to executive meetings, scan industry reports, and identify emerging trends — all while maintaining impeccable data quality and automating tedious governance tasks. This isn’t a distant future; it’s the promise of agentic AI.

At its core, agentic AI is centered around one or more agents that act independently, understand context and meaning, enable complex tasks, and apply reasoning to make decisions. Now, imagine being able to leverage this technology to build a cohesive set of data management capabilities centered around the various responsibilities of a data steward.

We envision the Digital Data Steward as a system of specialized AI agents, each designed to support a specific facet of data stewardship and address a comprehensive range of tasks, including — but not limited to — the following:

• Domain Data Strategy Agents assist in defining the data strategy, identifying critical data elements, and outlining the data capabilities and roadmap for a specific domain. By perusing external public financial data, internal audit findings, and regulatory exams, they build a data strategy targeted to solve domain data challenges, ultimately contributing to the larger enterprise data strategy.

• Data Quality Agents are the guardians of data quality for the domain. They ensure data is accurate, consistent, and reliable by establishing and enforcing data quality rules, conducting data audits, and resolving data quality issues. The data quality agents leverage AI to uncover data anomalies, implement controls, and track progress toward continuous data quality improvements.

  They also play a key role in operationalizing data governance frameworks, ensuring data is handled and used in compliance with established policies, standards, and regulatory requirements, including those related to data privacy and security.

• Metadata Management Agents assist in creating and consistently maintaining the data dictionaries and metadata repositories for their domain, providing valuable insights into data elements’ meaning, structure, and relationships. By leveraging AI, a Metadata Management Agent can analyze various unstructured data sources – such as documents, e-mails, and reports – to extract key terms and definitions.

• Master Data Agents manage the domain critical data element’s Create, Read, Update, and Delete (CRUD) processes. The domain master agent could identify inconsistent definitions for “customer” in domain metadata, unify them, or escalate for reconciliation, ensuring a unified source of truth.

• Data Retention Agents focus explicitly on managing organizational data lifecycles by determining what data to keep, for how long, and when and how it should be safely archived or destroyed. They ensure compliance with regulatory requirements and optimize data storage costs and efficiency.

The agents can operate independently or collaborate as a coordinated team. When working as a team, a Digital Data Seward is defined to assume the role of an Agent “Shepherd” orchestrating interactions among the agents and ensuring strategic goal alignment.

How to create the Digital Data Steward Agent Map for your organization

By creating a Digital Data Steward Agent Map (see Figure 2) that aligns with your organizational structure, you can accelerate your data management maturity and selectively deploy capabilities that provide the most strategic benefit for your organization.

Key questions to consider in creating your Digital Data Steward Agent Map:

• What is your organization’s data management maturity?

• Where do you have data management gaps? And where do you need data management acceleration?

• What are the tasks, actions, processes, and decisions that you can delegate to the agents? Where will the “human” need to be in the processes?

• How will you deploy the agents? At the enterprise layer, within a given BU domain, defined by Data Domain, or a combination?

• What is the multi-agent interaction model?

• How will the Digital Data Steward report progress?

It should go without saying — AI is only as good as the data it is trained on and the instructions it receives. While AI agents can efficiently detect anomalies in data, they require clear guidance on appropriate remediation actions and an understanding of complex dependencies to be truly effective. The fundamental work of defining standard operating procedures, establishing organizational context, and implementing governance guardrails remains essential human input.

You can collaborate with AI agents to develop these frameworks, but without structured instructions and contextual understanding, even the most sophisticated agent will struggle to serve your data management function effectively. The technology might be advanced, but successful implementation still depends on thoughtful human direction.

With many items to consider, taking the time to define the Digital Data Steward Agent Map will position your organization for success.

Conclusion

As data continues to grow in complexity and importance, organizations need smarter, more agile approaches to manage it. The Digital Data Steward, powered by AI agents, represents the next evolution in data management — combining the best of human expertise with the power of artificial intelligence. For business leaders looking to stay ahead in the data-driven economy, now is the time to explore how AI can transform not just your data but also accelerate the data management maturity of your entire organization.

In Part 2 of this series, we will unpack how AI technology can augment the current data steward roles and responsibilities related to data strategy, focusing specifically on how Data Strategy Agents can support the development of a domain-level data strategy.

About the Authors

Maria C. Villar brings over 30 years of experience as a transformational technology executive, having served as Chief Data Officer in both the technology and financial sectors. Currently, she is Co-founder and Managing Partner of Business Data Leadership, a firm committed to enhancing effective data and AI management practices through training, writing, coaching, and consulting. Her expertise includes enterprise data strategy, data and AI governance, business value realization, organization and change management, and ESG and Sustainability. Recognized as a leader in the data and AI industry, Villar is a frequent speaker and author.

Her accomplishments include co-authoring the book “Managing Your Business Data from Chaos to Confidence” with Theresa Kushner, developing online master classes, e-learning modules, and webinars, contributing to “Latin Business Today” since 2010, and serving as the WLDA Ventures Program Manager for an accelerator program focused on data and AI startups.

Mike Alvarez is a data and AI transformation leader with over 20 years of experience driving innovation at the intersection of data science and commercial product development. He helps organizations unlock transformative value from their data, technology, and human resources. His career spans pioneering data leadership roles at Fortune 20 companies where he delivered hundreds of millions in business value through data/AI initiatives.

As CTO and Head of Product at NeuZeit, he is focused on accelerating the value and adoption of AI for organizations with acceleration frameworks. Alvarez is passionate about helping companies navigate their data and AI transformation journey by establishing robust data foundations, deploying scalable AI solutions, and creating platforms that democratize insights to drive competitive advantage.

Elizabeth (Beth) Hiatt is Head of Global Data Governance at PayPal. She has close to 30 years of experience building and deploying enterprise-wide data management and governance programs. Beth has held various data management and governance roles across business and technology in financial services, telecommunications, and hospitality. She has implemented enterprise data management programs end-to-end, developing and enabling critical functions such as data governance, data quality, and master and metadata programs. She has deep technical expertise in enterprise data architecture, helping organizations “connect the dots” across the data lifecycle.

Beth is a strong, results-driven leader with experience managing large, complex organizations specifically focusing on growing a company’s data management maturity while changing the organization’s data culture. She has written articles including “Time to Level Up: The Evolving Role of the Chief Data Officer” published by TDWI, spoken at many conferences including the Women Data Leaders Global Summit in 2021, and was on CDO Magazine’s Global Data Power Women List in 2022.

Christine Legner is a Professor of Information Systems at the Faculty of Business and Economics (HEC), University of Lausanne, in Switzerland. Her research fields are data management, enterprise architecture, and business software. She is the co-founder and academic director of the Competence Center Corporate Data Quality (CC CDQ), an industry-funded research consortium and expert community dedicated to advancing the field of data management. In this role, Legner leads a research team that collaborates closely with industry experts from 20 Fortune 500 companies (BASF, Bayer, Bosch, Nestlé, Schaeffler, SAP, Siemens, and Tetrapak, among others) to develop innovative concepts, tools and methods for data management.

Together with Dr. Richard Wang, Legner also serves as the Co-Chair of the annual CDOIQ European Symposium, which brings together CDOs, CAOs, CAIOs, and senior leaders shaping the data, analytics, and AI landscape in Europe.