Executive Summary

This webinar outlines the critical components of developing a Data Stewardship Training Course designed to enhance understanding of data’s impact on business operations while addressing challenges in Data Governance and student engagement. Howard Diesel shares on his new course, which aims to clarify objectives related to Data Stewardship and the role of artefacts, emphasising the importance of effective implementation in organisations.

Key focuses of this webinar include formalising the role of Data Stewards in business compliance, navigating the intricacies of the data management RACI matrix, and employing strategies for data implementation and stewardship. Lastly, Howard explores the curriculum, which addresses the significance of data quality training, effective communication methods, and impact assessments that prioritise innovation within the data management framework.

Webinar Details

Title: Unlock the Power of Data Stewardship for Data Managers
Date: 05 June 2025
Presenter: Howard Diesel
Meetup Group: African Data Management Community
Write-up Author: Howard Diesel

Developing a Data Stewardship Training Course

Howard Diesel opens the webinar with a reflection on a recent training for CDMP certification. He came to realise the challenges faced by Data Stewards who were attending the training in retaining complex theoretical and technical information.

This intensive four-day course, attended by various companies, noted that participants had difficulty digesting the material focused on the “why” and “what”, rather than practical applications. Additionally, Howard shares that following the first iteration of training with one customer, the need for a more effective approach to improve retention and applicability of the content was recognised as training progressed into subsequent iterations.

Business Data Stewards face significant challenges in effectively engaging with Data Governance and management concepts, particularly due to their technical nature. Howard adds to this by noting that terms like Data Modelling and the role of Data Architects play can be intimidating, leading to difficulty in grasping essential information.

A notable case shared involved a major oil and gas company struggling to implement its Data Stewardship program, reflecting a broader issue of inadequate training that lacks relevance to their specific contexts. Howard then emphasised that Business Data Stewards require foundational training tailored to their industry, as generic data concepts often do not resonate. Practical experience is deemed essential for conveying these concepts successfully, underscoring the gap between hands-on data management and theoretical understanding.

Figure 1 How-to Steward Data

Figure 2 Data Stewards in Charge?

Understanding Data and Its Impact on Business Operations

An attendee reflects on a discussion about the challenges of training and engagement within DAMA’s senior leadership, adding that the experience emphasised the importance of recognising the extensive practical experience that leadership possesses. This can often be taken for granted. They noted the significant transition for employees, such as becoming a Data Steward, and acknowledged the difficulties in conveying complex concepts, including data quality and formalisation.

Howard added his observations from training sessions, where he has witnessed participants disengage, with some expressing confusion and frustration. An example was given of a legal Data Steward who initially felt overwhelmed but ultimately experienced a breakthrough in understanding the relevance of data in their field. This experience contrasted with the more common occurrences of participants walking out without grasping the material, highlighting the need for trainers to provide practical examples and maintain engagement.

Figure 3 “Data Stewards in Charge?”

Challenges in Data Governance and Student Engagement

Another attendee shares their approach to training, emphasising the importance of understanding participants’ backgrounds through pre-training research, such as LinkedIn. They focus on engaging influential participants seated at the back of the classroom by encouraging them to create personal stories that relate to the training content.

Howard then shares more on the course he has developed, which aims to prepare students for the CDMP exam, with pre-assessments to identify those at risk of falling behind. Howard asks the audience for feedback on successful programs in relation to the current curriculum, which reveals gaps in Data Stewardship. He added that Data Stewardship is crucial for Data Governance. They reference their success story at EDF Energy, where they implemented effective Data Governance practices that lacked initial traction.

The discussion highlighted the challenges faced in engaging participants in Data Governance initiatives, particularly their initial reluctance and misconceptions about the process. An effective understanding of the interconnectedness within their roles was identified as crucial for fostering a commitment to quality improvements. Despite enthusiasm for data-driven approaches, there remains a significant gap in effectively addressing the human and stewardship aspects, as many are quick to adopt technological solutions like AI without considering the necessary groundwork.

Figure 4 Final Results for “Data Stewards in Charge?”

Figure 5 Meme on the State of “Data-Driven” Companies

Objectives of a Daily Student Course in Data Stewardship

Howard then moves on to outline the key objectives for the initial day of the Data Steward course. The course aims to enhance understanding of the business impact of data quality through an impact analysis, emphasising the benefits and risks associated with effective Data Stewardship.

Participants will learn about critical artefacts and Metadata that facilitate proper data usage, enabling them to address common business inquiries such as data availability, relationships, inconsistencies, and duplication. The goal is to equip Data Stewards with the knowledge needed to respond effectively to questions regarding data management and integrity.

Figure 6 Learning Objectives

Figure 7 Data Stewardship Learning Objectives

Data Stewardship and Artefacts in Business Operations

The discussion focuses on the importance of creating and maintaining key data artefacts for effective Data Stewardship and Governance. It emphasises the need to identify responsible parties for these artefacts, such as the Business Glossary and subject area model, and highlights the role of Data Stewards in answering questions about data resources.

The ultimate goal is to develop a comprehensive governance framework, ideally facilitated through a user-friendly interface, such as a chatbot or Copilot, that enables users to interrogate data effectively. This approach aims to prevent misuse of data by ensuring users understand its context and definitions before application, avoiding situations where time is wasted on data that proves unsuitable.

Implementing Data Stewardship in Organisations

The implementation of Data Stewardship requires a clear understanding of roles, artefacts, and their impact within the organisation. Effective stewardship involves cataloguing data sets, which must be comprehensively understood rather than superficially categorised. A crucial aspect of successful Data Stewardship is identifying data owners who can provide support and authority to the stewards.

By appointing a steward for specific data sets, such as an audit data set, data owners can delegate responsibilities, allowing stewards to manage tasks effectively. This collaborative approach, where stewards and owners work closely together while engaging with the governance council, has proven to enhance Data Stewardship practices.

Data Stewardship and Its Importance in Business

Bob Seiner, a previous speaker, emphasised the importance of formalising roles and responsibilities in non-evasive Data Governance, countering the misconception that everyone must be a data expert. Howard adds that Bob highlights three key roles regarding data interaction: defining, producing, and consuming data. By clearly identifying these roles, organisations can enhance efficiency and improve decision-making regarding data usage.

According to Bob, rebranding “data owners” as “data investors” fosters a culture of shared responsibility and collaboration rather than restriction. For successful Data Stewardship, it is essential that individuals recognise the value in managing their data, rather than viewing it as an additional burden. A lack of understanding of the business and processes can lead to disengagement and underperformance.

Figure 8 “Big Cat has got a PLAN”

Figure 9 Short Video on Data Stewardship

Step one, what’s the biz gonna lose?

Business Impact — gotta light that fuse!         

Know the stakes, what’s at play,
Data drives the moves we make each day!       

Now we dig in deep

The secrets we keep

Who, what, where? Yeah we define,

So the data’s clean and looking fine!

Now we set the scene

Formalise roles – keep it lean!

Clear roles mean no place to hide!

Who’s the boss? Who’s the guide?

Now we lock it down

Implement practices – wear that crown

Stewards in place, rules in check,

Data so smooth you’d think it’s tech!

Data Game Plan

The data game plan involves four crucial steps designed to enhance business impact through effective data management. First, it’s essential to identify and understand the stakeholders involved in data-driven decisions. Secondly, a thorough analysis of critical Metadata and its implications is necessary to ensure clarity and precision.

Thirdly, establishing formalised processes allows for clear guidance and accountability within the organisation. Lastly, implementing robust practices ensures that Data Governance is upheld, keeping the necessary rules and standards in place. This structured approach helps organisations navigate their data landscape efficiently.

Business Impact and Data Management Challenges

The primary objective is to assess the business impact, focusing on the risks associated with poor policy and the current challenges faced by the organisation. Collaborating with team members, a mnemonic rap was developed to emphasise the importance of structured approaches in addressing these issues.

Data Stewards must understand the stakes and the challenges faced by downstream users and decision-makers, including the critical need for appropriate data management and quality expectations. Questions surrounding data availability, location, meaning, and interconnections underscore the importance of critical Metadata, promoting a deeper understanding of data’s role in daily operations, rather than relying solely on applications. Additionally, the effectiveness of existing technology is also called into question, as many may struggle to navigate and relate to the available data.

Figure 10 Business Impact

Figure 11 Critical Metadata

Data Lineage and Data Stewardship

Howard focuses on the distinction between horizontal and vertical data lineage. While horizontal lineage tracks data movement through applications, staging, and data warehouses, vertical lineage focuses on tracing data back to its physical data model upon transformation. It’s crucial to define and assess various levels of data models, including logical, conceptual, and subject area models, as well as the Business Glossary.

This involves collecting and utilising well-defined artefacts to answer critical questions about data assets. Howard adds that there is a challenge in engaging the right audience, as many users often return to IT for answers, which can lead to dissatisfaction and loss of trust. Despite the effort put into developing resources like Business Glossaries and data strategies, it can be disheartening to see limited engagement and usage, prompting a reflection on the overall value of these efforts.

In the context of business operations, it is crucial to recognise and address issues promptly, which is facilitated through effective Data Stewardship and risk management. By focusing on essential terminology and processes, we can link these elements to potential risks, ensuring they are managed and mitigated proactively.

As stakeholders begin to understand the interconnectedness of their actions—from capturing applications to data entry—they become more engaged in the process. This understanding fosters an environment where critical Metadata is valued, prompting a shift in how information is handled and shared. Additionally, the goal is to transform our approach to data management, enhancing clarity, cleanliness, and overall effectiveness in utilising data assets within departments.

Data Stewardship and Role Formalisation in Business

To effectively establish Data Stewardship within an organisation, it is crucial to clearly define roles and responsibilities for Data Stewards. Initial confusion can arise when individuals are asked to sign documents committing to such roles without a thorough understanding of their specific duties. To address this, a “Day in the Life of a Data Steward” framework was developed, outlining typical commitments and interactions with peers, Technical Data Stewards, and Data Governance personnel.

This clarity helps potential stewards feel more comfortable in taking on responsibilities, while also ensuring that the workload is manageable. Additionally, establishing clear lines of communication and accountability, including identifying Subject Matter Experts (SMEs) for specific data sets, can prevent unnecessary confusion and streamline data-related inquiries.

The discussion emphasises the crucial role of Data Stewardship in empowering individuals within the organisation to take responsibility for their data. It highlights that true buy-in occurs when people recognise the personal benefits of engaging with Data Governance, including time savings in data analysis and improved decision-making.

The importance of establishing clear roles and responsibilities is underscored, along with the need for strong stewardship to foster a culture that positively influences how data is utilised. Ultimately, it’s about enabling individuals to lead the change, ensuring they are equipped to guide the organisation in effectively managing and utilising data.

Figure 12 Formalise the Roles and Responsibilities

Figure 13 Implement Data Stewardship

Figure 14 Outro: Recap

Implementing Data Governance Processes in Companies

To enhance data management within organisations, the implementation of specialised roles, such as executive process architects and departmental process specialists, has proven effective. These roles provide direct points of contact for employees seeking assistance with data-related inquiries, thereby fostering clearer communication and understanding of Data Governance.

Training these specialists involves not only technical knowledge but also emphasises the importance of accessibility and prioritisation based on departmental needs. A successful approach encourages each department to identify and address its specific data challenges, creating a supportive environment that promotes responsibility and ownership over data management. Additionally, when individuals, such as Data Stewards, recognise the benefits of understanding and visualising data relationships, it fosters a culture of accountability and efficiency, ultimately leading to improved organisational data practices.

Role of Data Stewards in Business Compliance

Howard then addresses the role of Data Stewards in ensuring business compliance and data quality, particularly in assessing the financial and operational impacts. During training sessions, participants will provide feedback on these impacts, enhancing their data capabilities and fostering a data-driven culture.

A crucial aspect involves identifying necessary artefacts from the 293 available in the DMBoK, understanding their significance, and translating concepts into usable database elements. The implementation and communication of artefact information will be streamlined through large language models and conversational AI, allowing for direct engagement with documents and facilitating focused discussions.

Figure 15 Business Impact of Data Stewardship

Figure 16 Critical Artefact Identification and Purpose Outcomes

Understanding the Data Management RACI Matrix

The RACI Matrix serves to clarify the roles and responsibilities of various stakeholders involved in data management, emphasising the need for a practical framework tailored to specific data assets rather than relying on generic definitions. It facilitates collaboration among key participants, including data investors, domain Data Stewards, business Data Stewards, and technical Data Stewards, who work together to create artefacts and provide informed insights. Additionally, continuous evaluation and improvement of the Matrix are essential to identify gaps and overlaps, ensuring accountability and ownership among all involved.

Figure 17 RACI Matrix Development for Data Artefacts Outcomes

Data Implementation and Stewardship Strategies

The implementation of Data Stewardship involves a comprehensive understanding of data principles, followed by the development and execution of a tailored implementation plan. This process may include iterative approaches or focusing on one unit at a time, prioritising the most valuable data assets that contribute to revenue generation.

The goal is to transition from initial challenges to measurable progress, thereby fostering recognition of successful data practices. As these data assets demonstrate value, data champions will emerge, promoting a culture of stewardship. The foundation laid during this phase will pave the way for further exploration of policies, procedures, Data Governance, Master Data, Metadata, and data quality.

Figure 18 Data Stewardship Implementation Planning Outcomes

Figure 19 Data Stewardship Learning Outcomes

Figure 20 Data Stewardship Learning Outcomes Pt.2

Impact of Data Quality Training in Business

Howard emphasises the importance of achieving the right impact through effective practices and strategies, particularly in data management. A notable example was shared about a report writer who, during a fundamentals course, realised her dissatisfaction stemmed from persistent data quality issues, leading to frustration and inefficiency in her work.

This turning point occurred when she discovered the existence of a dedicated data quality team, highlighting the significant role of proper data management in enhancing employee satisfaction and performance. Similarly, another training experience illustrated how data scientists also struggle with inadequate data quality, which hampers their ability to meet deadlines. These stories underscore the broader impacts of prioritising data quality on staff morale and overall operational effectiveness.

Additionally, this highlights the importance of using relatable examples to illustrate complex challenges faced by retailers, particularly regarding the treatment of data. A powerful analogy is made between selling meat past its sell-by date and the way some organisations handle outdated data, underscoring the potential financial consequences of both actions. The emphasis is on the necessity for educators to employ specific and impactful metaphors that resonate with their audience, making the implications of poor data management tangible and relatable. This approach requires active listening and a commitment to identifying relevant comparisons, enabling a deeper understanding and engagement.

Figure 21 Learning Objectives

Figure 22 Business Impact of Data Stewardship

Figure 23 Higher Education Impacts

Effective Communication and Retention Strategies

Howard highlights the value of asking probing questions when entering a new industry to stimulate insights among business professionals. A case is made for a facilitation technique used by Remco Broekmans, who assigns “homework” to participants before training sessions, encouraging them to define key business concepts and identify challenges related to data issues, such as definitions and duplicates.

This approach not only opens their eyes to overlooked aspects but also emphasises the importance of personal retention, as poor treatment can lead to employee turnover. The overall goal is to foster a positive mindset and demonstrate that investing time in tools like Power BI can yield significant returns in productivity and organisational benefit.

Impact Assessments, Prioritisation, and Innovation in the Data Engine Room

The framework for prioritisation emphasises that higher criticality leads to greater priority and stewardship. Engagement highlights the application of AI tools, specifically ChatGPT, in enhancing innovation within the field, with some expressing a desire for resources to advocate for more innovative practices to their leadership. Additionally, the attendees shared their own past experiences in sourcing applications and offered unique insights gained from working closely with business needs.

Howard then highlighted the multifaceted roles of the team, including business analysis, application support architecture, Data Architecture, and Business Architecture, which evolved from data support to broader business functions without a formal role description. A program manager in charge of data management operations emphasised the high volume of inquiries about data, illustrating the key contact points for information.