Practical Data Stewardship with Dave Wells

Executive Summary

This webinar outlines the essential components of Data Stewardship, emphasising its significance in effective Data Management. Dave Wells covers the core principles, various roles of Data Stewards—including leaders, managers, and facilitators—and the organisational structures that support their function. He explores key responsibilities related to culture, governance, Data Quality, and Metadata management, while also examining the interplay between AI governance and Data Governance. Additionally, the webinar highlights the importance of acquiring executive buy-in and equipping Data Stewards with the necessary knowledge and capabilities to drive cultural change and strategic discipline within the organisation. Finally, it presents a roadmap for implementing a Data Stewardship program, focusing on team building, aligning with organisational realities, and fostering grassroots engagement for sustainable impact.

Webinar Details

Title: Practical Data Stewardship with Dave Wells
Date: 2025-11-12
Presenter: Dave Wells
Meetup Group: DAMA SA User Group Meeting
Write-up Author: Howard Diesel

Introduction and Opening Discussion

In the recent webinar featuring Data Management expert Dave Wells, the topic of Data Steward certification was critically examined. With over 30 years of experience influencing Data Stewardship practices, Dave provided a balanced perspective on this debate. He recognised that while industry veteran Bob Seiner has often been sceptical about generic certification, there has been a shift in their viewpoints over time. Dave pointed out the necessity for Data Stewards to possess not only an understanding of specific business data but also fundamental knowledge in Data Quality, Metadata management, and essential human skills such as problem-solving, conflict resolution, and consensus building.

Dave introduced his Data Steward Body of Knowledge (DSBOK) as a complementary resource to the Certified Data Management Professional (CDMP) certification. He argued that DSBOK addresses specific gaps in practical stewardship skills that are becoming increasingly important as data plays a crucial role in AI and business operations. This emphasis on a well-rounded skill set for Data Stewards highlights the evolving demands of the Data Management field and underscores the importance of combining theoretical knowledge with practical application.

Overview of Data Stewardship in Practice

Dave’s recently released book, “Data Stewardship in Practice,” provides a comprehensive framework for establishing Data Stewardship as a formal discipline within organisations. The book explores six major areas, including fundamental concepts, organisational structures, roles and responsibilities, key knowledge areas, effective problem-solving approaches, and strategies for program implementation. Additionally, he highlights two valuable resources that complement the book: the Data Steward Body of Knowledge, which encompasses around 60 topics related to technical, data, business, and industry knowledge, and the Data Steward Field Guide, a diagnostic tool with over 20 years of proven use in addressing Data Management challenges.

In the book, Dave emphasises the necessity for Data Stewardship to transition from its current ad-hoc, informal practices to a well-structured and supported program. By formalising Data Stewardship, organisations can ensure more effective Data Management and accountability, ultimately leading to improved outcomes. The inclusion of the Field Guide as an appendix further supports his argument, as it offers practical tools readily accessible through Technics Publications, underscoring the importance of establishing a robust Data Stewardship framework.

Figure 1 Data Stewardship in Practice (and more)

Figure 2 Table of Contents

Core Principles of Data Stewardship

Dave articulates a comprehensive approach to Data Stewardship by defining it as “responsible Data Management through the life cycle.” He identifies four fundamental principles, drawn from the realm of natural resource stewardship, which serve as the bedrock of effective Data Management.

These principles include Sustainability, focused on maintaining data over the long term without risk of depletion or loss; Conservation, aimed at ensuring the ongoing health of data and preventing its misuse; Protection, dedicated to safeguarding data from potential harm; and Advocacy, which promotes best practices while addressing harmful behaviours.

These concepts are universally applicable, whether managing forests, wildlife, or data assets, highlighting their widespread relevance. Ultimately, Dave emphasises that these principles should establish the philosophical foundation of any Data Stewardship program, guide decision-making and fostering a culture of responsible and ethical Data Management.

This culture of stewardship extends beyond mere technical implementations. It requires a commitment to ethical and sustainable practices that enhance the integrity and value of data assets. By adopting these principles, organisations can create a robust framework for managing data responsibly, ensuring its availability and utility for future generations.

Furthermore, addressing poor practices openly is equally important, as it cultivates transparency and accountability within Data Stewardship efforts. In essence, the integration of these stewardship principles ensures a holistic and responsible approach to Data Management that benefits both current and future stakeholders.

Figure 3 What is Data Stewardship?

Five Types of Data Stewards

Dave presents a comprehensive framework for categorising Data Stewards, emphasising their roles based on the scope of data managed and their positioning within the organisation. At the highest level, the Enterprise Data Steward oversees company-wide policies and coordinates stewardship across all data domains.

In contrast, Domain Data Stewards focus on specific entities, such as customers or products, aligning closely with Master Data domains. Business Unit Stewards are tasked with managing critical data within individual departments, such as finance or legal, while Process Data Stewards handle data that crosses multiple organisational units, exemplified by processes like employee onboarding that involve HR, payroll, benefits, facilities, and IT. Lastly, Project or System Data Stewards take on temporary roles for specific initiatives, highlighting the diverse stewardship requirements within an organisation.

The interplay among these various stewardship roles underscores the necessity for clear communication and collaboration in Data Management. Since multiple stewardship types may have overlapping interests in the same data, it is crucial to define the scope of responsibilities explicitly. This clarity fosters effective teamwork, ensuring that Data Stewardship activities are aligned and coordinated, ultimately enhancing the organisation’s ability to manage its data assets efficiently.

Figure 4 Five Kinds of Data Stewards

Figure 5 Who Becomes a Data Steward?

Who Becomes a Data Steward?

Data Stewardship is best achieved by recognising and empowering individuals who have already displayed a passion for effective Data Management within their organisations. According to Bob Seiner’s “non-invasive” approach, Data Stewards can come from diverse backgrounds such as Data Governance, business operations, Data Management, or IT, each contributing unique skills and perspectives. The critical aspect is aligning the right individuals with specific stewardship roles that complement their existing expertise and organisational positions, rather than creating entirely new roles from scratch.

This method of leveraging existing talent not only formalises and enhances the ongoing Data Management efforts but also ensures that dedicated individuals receive the necessary resources and recognition to thrive. By supporting those who are already engaged in Data Stewardship activities, organisations can foster a more robust Data Management culture. Ultimately, this approach enhances the overall effectiveness of Data Stewardship, resulting in improved Data Quality and governance throughout the organisation.

Data Stewardship Organisational Structures

Dave examines three distinct organisational models for structuring Data Stewardship teams, each with its own advantages and challenges. The Hierarchical Organisation provides a clear top-down structure that ensures accountability but risks creating silos and bureaucratic barriers.

In contrast, the Matrix Organisation fosters cross-functional collaboration, allowing stewards to report through both governance and functional channels, albeit with added complexity due to multiple reporting relationships. Meanwhile, the Circular Organisation is particularly beneficial for startups, facilitating decision-making that flows from Data Stewards to functional executives and ultimately to the executive level for formalisation.

While these models can stand alone, Dave highlights that they are not mutually exclusive; organisations can successfully implement hybrid structures that leverage the strengths of each model. Regardless of the chosen structure, the ultimate objective remains the establishment of a Community of Practice, where all Data Stewards engage in transparent collaboration, share innovative ideas, and participate in active problem-solving to enhance Data Governance.

Figure 6 Hierarchical Organisation

Figure 7 Matrix Organisation

Figure 8 Circular Organisation

Figure 9 Data Steward Communities

Data Steward Roles: Leaders, Managers, and Facilitators

Data Steward certification is essential due to the multifaceted roles that stewards must fulfil within organisations. They serve as Leaders by spearheading quality improvement projects and semantic modelling initiatives, while embodying best practices in Data Management.

In their capacity as Managers, they set objectives, strategize initiatives, and implement programs aimed at enhancing Data Quality or introducing new policies. Perhaps most crucially, as Facilitators, they enhance collaboration by guiding diverse groups, encouraging participation, and helping teams reach shared goals.

These varied responsibilities necessitate a broad skill set that extends beyond mere data comprehension. Data Stewards must possess expertise in people processes, best practices, and organisational dynamics to be effective. This complexity highlights the importance of formal training and certification programs, which equip stewards with the knowledge and skills required to excel in their critical roles within data-driven organisations.

Figure 10 Primary Roles of a Data Steward

Additional Data Steward Roles

Data Stewards play a crucial role in organisations by embodying multiple responsibilities that extend beyond traditional leadership and management. As Problem Solvers, they are often the first to identify data-related issues, framing problems effectively, analysing root causes, and implementing solutions to both remediate existing damage and prevent future occurrences. Additionally, as Coaches, they guide individuals who work with data daily, fostering best practices, enhancing data literacy, and promoting teamwork while ensuring accountability for responsible data use.

Moreover, Data Stewards function as essential Change Agents and Collaborators, effecting transformation across teams and bridging gaps between technical and business stakeholders as Communicators. They act as Consensus Builders, facilitating agreement and developing a shared understanding of data ethical practices.
Their role also involves Critical Thinking, as they assess the benefits and risks associated with policies and procedures. By embracing the roles of Influencers and Relationship Builders, Data Stewards create connections that enable effective teamwork and promote responsible data usage. The wide-ranging nature of these roles underscores the necessity of teamwork, utilising diverse skills and capabilities, to achieve successful Data Stewardship.

Figure 11 More Roles of a Data Steward

Figure 12 Data Steward Responsibilities: Data Culture & Data Governance

Data Stewardship Responsibilities: Culture, Governance, and Strategy

Data Stewards play a crucial role in bridging the gap between high-level data strategy and everyday execution within organisations. Their responsibilities span several dimensions, including Data Culture and Governance, where they serve as knowledgeable advisors and contributors rather than creators of the data strategy. By fostering Data Literacy through regular interactions with data users, stewards engage in coaching and facilitating problem-solving activities, ultimately promoting a strong understanding of data across both individuals and the organisation.

In addition to fostering a data-driven culture, Data Stewards play a crucial role in shaping and enforcing Data Policies and ensuring Data Protection. They provide oversight by collaborating with governance teams to identify and draft necessary policies, overseeing their maintenance, and monitoring compliance.
Furthermore, in partnership with data owners and IT teams, stewards work to translate protection requirements into effective management practices. Through these efforts, Data Stewards ensure that governance frameworks are not just theoretical constructs but are effectively implemented within the organisation.

Figure 13 Data Steward Responsibilities: Data Management

Data Quality and Metadata Management Responsibilities

Data Stewards play a vital role in ensuring Data Quality by collaborating with various stakeholders, such as data creators, consumers, data engineers, and governance teams. They are responsible for establishing quality standards, conducting assessments, implementing monitoring systems, and driving continuous improvement initiatives. This collaborative effort is essential for organisations to enhance the reliability and usability of their data.

In addition to their role in Data Quality, Data Stewards are key players in improving Metadata Management by addressing the issues caused by fragmented Metadata systems across different tools. Dave highlights a common challenge: many organisations often maintain multiple data catalogues, which results in Metadata silos and complicates the establishment of a unified source of truth.

By guiding Metadata practices, Data Stewards help organisations gain clarity on their data assets, including their location, meaning, and flow throughout the data lifecycle. Ultimately, effective Metadata management is crucial for enhancing data discoverability, understanding, and governance within organisations.

Data Stewards as Product Managers and the Metadata Challenge

Data Stewards and data product managers play vital roles in managing data as a product, but their functions can sometimes be misunderstood. Dave emphasises that applying product management practices to Data Management is essential; viewing data as products produced by processes for consumption by customers allows Data Stewards to leverage product management thinking effectively. However, he warns that the term “data product” has become ambiguous, particularly in the context of data mesh, which can lead to potential communication challenges among professionals.

Moreover, the persistent issue of Metadata fragmentation presents significant challenges for organisations attempting to manage their data effectively. While initial hopes were placed on data catalogues to serve as comprehensive enterprise Metadata repositories, the reality has led to a proliferation of multiple catalogues—such as Collibra, Tableau, and Azure Data Catalogue—operating without coordination or conflict resolution. This situation mirrors historical issues in the industry where proprietary Metadata systems ignored established standards, highlighting the critical need for Data Stewards to establish robust enterprise Metadata practices to prevent the repetition of past mistakes.

Figure 14 Bridging Business and Technical Knowledge

Knowledge Areas for Data Stewards

Dave presents a comprehensive framework for the Data Steward Body of Knowledge, highlighting the vital role stewards play in bridging business and technical domains. This framework encompasses essential components, including the fundamentals of Data Stewardship, core processes in Data Management, data culture, and foundational aspects of Data Governance. It emphasises the necessity for stewards to possess a blend of knowledge across various areas, including Data Quality, Metadata management, and data integration.

Moreover, Dave clarifies that while not every individual steward needs to master all these topics, stewardship teams should collectively cover these important domains by leveraging each member’s expertise. The “Data Stewardship in Practice” book provides a high-level overview, whereas the “Data Stewardship Body of Knowledge” book delves deeper into each specific area. This structured approach ensures that organisations have well-rounded Data Stewardship practices that contribute to effective Data Management and governance.

Figure 15 The Data Stewardship Body of Knowledge (DSBoK)

AI Governance vs. Data Governance

In the evolving landscape of Data Management, it is crucial to distinguish between AI Governance and Data Governance as distinct yet interrelated disciplines. Data Governance centres on the protection and quality of the data itself, while AI Governance focuses on the methodologies employed to utilise that data effectively and ethically.

Dave emphasises that conflating these two areas could undermine the essential responsibilities of Data Stewards, leading to a dilution of their roles. Therefore, Data Stewards must approach AI and machine learning from a perspective that prioritises responsible data consumption and clearly delineates their responsibilities from those of AI governance.

Maintaining a separation between these disciplines is vital for the integrity of Data Stewardship in an increasingly AI-driven environment. Dave proposes that the Enterprise Data Steward role acts as a crucial intermediary, fostering collaboration between Data Stewardship and AI stewardship. While there may be potential for these disciplines to converge or evolve in the coming years, preserving their distinction in the present allows for focused and effective stewardship that meets the challenges posed by advanced technologies. By doing so, Data Stewards can ensure they uphold their critical work in safeguarding data integrity and promoting ethical practices in AI utilisation.

Prioritising Data Steward Knowledge and Capabilities

In prioritising extensive knowledge areas in Data Management, Dave emphasises a structured framework that recognises the limitations of individual mastery. He recommends starting with the fundamentals of Data Stewardship and an understanding of Data Governance, as these foundational elements create a strong base for effective management. Following this, he suggests focusing on three primary areas: Data Quality, Metadata management, and data protection, which together form a comprehensive approach to Data Stewardship.

While Dave personally values the importance of fostering a data culture—believing that data literacy should be taught alongside language skills from an early age—he acknowledges that many organisations do not currently prioritise this aspect. To avoid overwhelming individuals and organisations, his practical recommendation is to first establish a solid foundation in stewardship and governance before tackling the operational areas. This step-by-step approach provides a realistic roadmap for developing Data Steward capabilities, enabling organisations to build expertise effectively without setting unrealistic expectations.

Getting Executive Buy-In and Addressing AI Risks

The challenge of securing executive attention for Data Stewardship is compounded by the prevailing MBA-focused mindset, which views AI as a “silver bullet” solution that fails to adequately address the foundational work required to ensure effective AI deployment. Dave highlights a troubling trend: significant failures that capture media attention may be the only way for executives to recognise the risks associated with implementing AI without a strong data foundation. He emphasises the dangers of agentic AI—autonomous systems operating in poorly understood environments—where both leadership and technical teams are overlooking critical risks.

Reflecting on past technological bubbles, a participant draws parallels to the dot-com era, where blind enthusiasm led to a crash and a subsequent, more cautious approach to technology. Dave concurs, acknowledging that while AI presents significant opportunities, it also harbours substantial risks that make effective Data Stewardship increasingly vital. He firmly argues against the belief that AI can replace Data Stewards, labelling such thinking as “absurd” and “dangerous,” and insists that recognising the role of data experts is essential for harnessing AI’s promise responsibly.

Data Stewards as Problem Solvers and the Field Guide

The Data Steward Field Guide serves as an invaluable resource for Data Stewards, empowering them to effectively navigate complex Data Management problems. Over the course of two decades, Dave has developed this practical diagnostic tool, emphasising a systematic approach to problem-solving that includes Awareness, Diagnosis, Analysis, Synthesis, and Facilitation, with Communication and Process Improvement underpinning each step. By starting with the symptoms they encounter, stewards can refer to symptom tables and pinpoint issues within various Data Management processes, allowing for a deeper exploration of root causes.

This structured methodology not only addresses immediate data-related challenges but also fosters a culture of collaborative problem-solving among stewards. The guide incorporates essential facilitation techniques—such as open questions and brainstorming—that encourage diverse perspectives, ensuring that all voices are heard, not just the loudest ones. As a result, Data Stewards evolve from being reactive problem-handlers to proactive, systematic problem-solvers, ultimately leading to more effective and inclusive solutions within their organisations.

Figure 16 Problem Solving Skills

Figure 17 Facilitation Skills

Figure 18 A Diagnostic Guide

Implementing a Data Stewardship Program: The Roadmap

Developing a formal Data Stewardship program requires a strategic roadmap that outlines both initial and ongoing activities. To successfully launch such a program, organisations should begin by defining their vision and objectives, conducting a thorough data assessment, and establishing initial Data Stewardship roles, recognising that not all stewards will be in place initially.

Additionally, adapting or creating a Data Governance framework that includes stewardship is essential. Many programs start by addressing quality and compliance issues, as these are often the most visible to executives. It’s also critical to consider tools and infrastructure from the outset to support the program effectively.
Ongoing activities are just as crucial for the sustained success of a Data Stewardship program. These include providing ongoing training and education, managing the day-to-day stewardship tasks, and implementing robust monitoring and improvement processes.

Importantly, monitoring and scaling efforts should continually inform and refine the program, reinforcing the idea that Data Stewardship is not a one-time event, but rather a continuous journey of evolution. The key takeaway is to avoid attempting to launch a perfect program all at once; instead, organisations should focus on foundational aspects, demonstrate value, and expand their efforts through measured and systematic stages.

Figure 19 Data Stewardship Roadmap

Guiding Principles for Data Stewardship Programs

Dave outlines essential principles that should guide the implementation of any Data Stewardship program, emphasising the importance of clear ownership and transparent decision-making. Key components, including accountability, compliance with regulations, ethical practices, and the protection of sensitive data, provide a strong foundation for effective Data Management. Additionally, fostering collaboration across functions, embedding a stewardship culture in everyday practices, and maintaining a user-centric focus on stakeholder needs are critical for success.

Two principles stand out as particularly significant: Stewardship by Example and Stewardship as a Service. By positioning stewards as visible role models of good Data Management and as helpers rather than enforcers, organisations can fundamentally change how stewardship is perceived.

This shift not only enhances the stewards’ ability to influence positive change but also transforms stewardship into a practical, value-creating program that stakeholders are excited to support. In essence, these principles collectively help bridge the gap between abstract concepts of Data Stewardship and tangible, actionable practices.

Figure 20 Guiding Principles for Data Stewardship

Building Data Stewardship Teams

Data Stewardship is fundamentally a collaborative endeavour that necessitates the collective strengths of a diverse team. Given the extensive scope of responsibilities, no single individual can effectively fulfil the myriad expectations associated with Data Stewardship.

Stewards are required to undertake various actions—such as advising, advocating, coaching, and consulting—while also embracing multiple roles, including change agent, communicator, and consensus builder. Furthermore, they must possess a broad range of knowledge across technical, business, governance, quality, and Metadata domains, along with essential human skills such as communication and conflict resolution.

Building a successful Data Stewardship program hinges on organising teams that leverage these complementary strengths. It is crucial to identify the specific actions, roles, knowledge areas, and human skills that each team requires based on their responsibilities.
Additionally, the configuration of teams should facilitate effective interaction, ultimately leading to a robust community of practice. Acknowledging the inherently collaborative nature of Data Stewardship will significantly enhance the program’s evolution and overall success.

Figure 21 Build Stewardship Teams

Balancing Data Stewardship Roadmap with Organisational Reality

The challenges faced by new Data Stewards highlight a critical tension between immediate problem-solving and the long-term goal of institutional program development. New stewards often find themselves in unfamiliar environments where there is a pressing expectation for expert solutions to existing issues.

Dave points out that organisations typically employ a haphazard approach to Data Stewardship, assigning stewards in challenging situations and anticipating miraculous results. To navigate this, he proposes the necessity of balancing current stewardship efforts with the establishment of a more organised framework that includes defined roles, responsibilities, and adequate resources—such as tools, training, and incentives.

To achieve effective Data Stewardship, it is essential to pursue two parallel tracks: maintaining current efforts while simultaneously formalising the stewardship structure. This dual approach allows stewards to receive proper support and preparation, ultimately leading to a more robust Data Stewardship program.

As organisations work towards solidifying these frameworks, they must secure sponsorship and commitment to define the desired characteristics of the stewardship organisation, recognising that meaningful transformation will not occur overnight. Ultimately, the goal is to ensure that Data Stewards are valued and equipped to succeed, rather than merely being “thrown into” their roles.

Data Stewardship as a Strategic Discipline

The lack of appreciation for Data Stewardship as a critical discipline presents a significant challenge for many enterprise organisations. A participant highlighted that success in addressing this challenge is rooted in helping business sponsors recognise Data Stewardship as a strategic enabler, which necessitates appropriate resourcing.

To foster collaboration, they established a community of practice that promotes co-creation and cohesive working relationships. However, difficulties persist, as some Data Stewards struggle to effectively communicate their technical expertise in business terms, while others may excel in business language but lack the necessary technical depth. This community plays a vital role in nurturing both aspects—offering mutual support and advocating for Key Performance Indicators (KPIs) that illustrate the value of stewardship.

Incorporating these strategies can lead organisations to better Data Stewardship maturity. Dave emphasises a five-stage progression: Initiating, Developing, Defined, Measured, and Optimising. Most organisations currently find themselves in the Initiating or Developing stages and must focus on systematically advancing through each level. This structured approach not only enhances Data Stewardship practices but also aligns them with broader business outcomes, ensuring that stewards have a voice that contributes to organisational success.

Creating Cultural Change Through Consistent Messaging

Organisational change can be effectively facilitated through clear communication and a commitment to aligning messages across all levels of an organisation. One participant shared their experience working on a digital twin system for a nuclear power plant, highlighting the importance of a consistent “drumbeat” message in meetings.

This approach not only helped individuals recognise their roles within the larger framework but also fostered a deeper awareness and commitment among team members. By maintaining this persistent messaging, the team ensured that everyone, regardless of their background in data, could connect with the project’s goals and significance.

As organisations navigate the evolving landscape of artificial intelligence, the roles of Data Stewardship and ethical considerations become increasingly important. Dave points out that a new dimension of objectivity must be considered in Data Quality, emphasising the need to monitor and eliminate bias.

Additionally, understanding data ethics and responsible use requires a cultural shift where good judgment prevails over strict rules. To initiate this change, fostering open conversations about ethics can help build a foundational understanding, paving the way for the development of meaningful policies that resonate with the organisation’s core values.

Figure 22 Data Stewardship Maturity

Figure 23 Closing Slide

Final Q&A: Program Structure, Sponsorship, and Grassroots Engagement

The relationship between Data Stewardship and Data Governance is integral to effective Data Management within organisations. Dave emphasises that Data Stewards function as Data Governance professionals, establishing a strong overlap between stewardship and governance programs. While traditional governance outlines high-level strategies and policies, stewardship focuses on day-to-day governance practices, highlighting the need for a clear distinction or integration based on an organisation’s culture.

Securing sponsorship and funding for data initiatives is another crucial aspect of successful implementation. Finding an executive champion who recognises the significance of data can facilitate initial support. Starting with a proof-of-concept allows for incremental demonstrations of value, enabling organisations to request resources for manageable phases rather than overwhelming large-scale budgets.

Furthermore, grassroots engagement can generate enthusiasm and interest, fostering bottom-up momentum that helps secure additional manager support when pursuing board funding. As Dave notes, cultivating grassroots buy-in is essential, highlighting the importance of human engagement skills for successful stewardship.