Exploring the Data Management Body of Knowledge (DMBoK) for Data Executives
Executive Summary
This webinar focuses on the broad spectrum of data management, which encompasses concepts, principles, and challenges, reflecting its critical role in technology and organisational success. It involves defining data governance, architecture, and modelling while addressing the integration of big data, data ethics, and change management. The continuous evolution of data management underscores the importance of the data lifecycle in decision-making processes and the implementation of strategies that promote effective data management practices. This comprehensive approach promotes meaningful discussions around the inclusion and exclusion of knowledge areas within data management, ensuring the discipline adapts to the dynamic landscape shaped by advancements in AI and generative technologies.
Webinar Details
Title: Exploring the Data Management Body of Knowledge (DMBoK) for Data Executives
Date: 2025-07-24
Presenter: Howard Diesel
Meetup Group: African Data Management Community
Write-up Author: Howard Diesel
Contents
The Debate Surrounding the Definition and Scope of Data Management
The Concept of Data Management in Technology
The Comprehensive Nature of Data Management
The Challenges of Encompassing all Knowledge Areas
Big Data, Data Ethics, and Change Management Integration and Educational Resources
Knowledge Area Debate: Data Governance and Metadata Management
Knowledge Area Debate: Data Architecture and Data Management Challenges
Knowledge Area Debate: Data Modelling and Data Architecture
Knowledge Area Debate: Data Science, AI, and Data Integration
Definition and Interpretation of Data Management
Defining Data and Information Management
Decision-Making Principles in Data Management
Principles and Implementation of Data Management
Data Management and the Process of Agreement
Debate on the Inclusion and Exclusion of Knowledge Areas in Data Management
The Data Lifecycle and its Role in Data Management
The Data Lifecycle: Its Role in Data Management and AI
Data Initiative and Data Management Strategies
Data Governance in the Context of Documented Content and Generative AI
The Data Lifecycle: Its Implications
Layered Use of Technology and AI in Business
The Data Lifecycle: The Importance of Data Management
The Evolution and Challenges of Data Management
Academic Positioning of Data Management
The Debate Surrounding the Definition and Scope of Data Management
Howard Diesel opened the webinar and shared that it would focus on the concept of data management, stemming from a recent, intense conversation with the editorial team that lasted about an hour and a half without reaching a conclusion. The group acknowledged the need for further preparation and contemplation on the topic. Some surprising and interesting suggestions were made during the discussion, which prompted Howard to seek feedback from the webinar attendees.
Figure 1 DMBoK Version 2 & Version 3 SWOT Analysis
The Concept of Data Management in Technology
The webinar focused on the definition and understanding of data management as a complex and often debated topic. Howard compared it to foundational mathematical concepts, such as proving that 1 + 1 = 2. Many professionals, such as industrial engineers, frequently engage in these discussions, reflecting on various frameworks, including the Daima wheel, to structure their thoughts. While some individuals may feel familiar with the principles of data management, they acknowledge that their understanding can be influenced by existing frameworks, which might not always capture the full intricacies of the subject. Engaging in these debates enriches the understanding of data management beyond its surface-level definitions.
Howard highlighted the differing perspectives on data and information between platforms like Google and Copilot, emphasising how these variations can be surprising. One key insight is the importance of understanding the meaning of data, which is vital for effective information management. Additionally, over the past 5-6 years, there has been a trend towards separating the technical aspects of data management from its meaning, particularly in the context of records management and information governance.
This disconnect can undermine the value of data management, as it fails to integrate meaning with technical processes. The DAMA Wheel is recognised as a framework that effectively encompasses both aspects, providing a more comprehensive understanding of data management.
In a discussion about data management challenges, an attendee shared their experience working with a customer’s records management department, which identifies itself as focusing on information governance and distinguishes its role from data management by deeming the latter too technical. They noted the departmental separation, suggesting that while one side handles information governance, another is responsible for data management.
This raised questions about the definition and scope of data management, particularly concerning its relation to database management systems, prompting others to reflect on similar experiences in justifying data management roles within businesses. An attendee emphasised the importance of viewing database administration as part of broader data operations. They mentioned using coasters with visuals to illustrate the various disciplines encompassed within data management, which he distributes during meetings to help explain their approach.
Figure 2 "What is Data Management?"
The Comprehensive Nature of Data Management
In their previous project, an attendee shared an encounter with a common misconception: data management was primarily regarded as either data migration or not data management at all. They highlighted that these notions are just a fraction of the broader concept of data management.
Howard then referenced the DAMA Wheel diagram, a comprehensive diagram that encompasses various aspects of data management. Initially met with scepticism, my colleagues began to agree once they reviewed the book, recognising that data management involves a wide range of components beyond just migration and traditional views.
The Challenges of Encompassing all Knowledge Areas
The attendees were invited to discuss the DAMA wheel and its relationship to knowledge areas within the DMBoK 2 framework. Howard shared that he planned to gather insights regarding varying responses to questions about merging knowledge areas, specifically addressing the additional chapters related to big data, data science, change management, and organisational.
Figure 3 "What KAs can we MERGE in the DAMA Wheel"
Big Data, Data Ethics, and Change Management Integration and Educational Resources
Howard shared that there were considerations regarding excluding change management as a separate chapter, given that it is addressed within the Project Management Body of Knowledge (PMBoK). Furthermore, he noted that there are also concerns about potential confusion arising from the number of chapters in the study materials compared to what is covered in the exam. There is an emphasis on focusing on content that is significant for the exam, noting the disproportionate number of questions related to data management and big data versus ethics, which has fewer questions. The relevance of the big data chapter in the context of the curriculum is also being questioned.
The importance of enhancing decision-making processes through the integration of artificial intelligence (AI) and business intelligence (BI) with knowledge graphs is emphasised. Howard expressed a keen interest in exploring how data ethics can be effectively incorporated, questioning whether it should be merged with data governance or data risk frameworks, highlighting the significance of data ethics as a foundational aspect in the field.
Ethics is a fundamental aspect that permeates various domains, particularly within data governance. While it shares connections with foundational areas such as risk management, metadata, and data quality, its application in IT and business contexts raises significant questions. Although businesses may engage with ethical considerations, there is often a disconnect in how these principles are operationalised in the IT space. Additionally, the data side emphasises the necessity of ethics by demonstrating clear cause-and-effect relationships, highlighting that while technology itself may not inherently address ethical concerns, it is crucial for effective governance and responsible data management throughout the data lifecycle.
Figure 4 "What KAs can we Merge in the DAMA Wheel" Pt.2
Knowledge Area Debate: Data Governance and Metadata Management
Howard focused on integrating data governance and metadata, suggesting the potential merger of these areas or the elimination of certain chapters from the core Data Management (DM) framework. An attendee highlighted the necessity for alignment between chapters and the DM wheel, advocating for either their inclusion in the wheel or removal. Another key point raised was the role of change management in projects, debating whether it should be classified as a foundational element or part of data governance. Additionally, there was a call for the inclusion of extended knowledge areas, such as rationalised terms, definitions, and taxonomy, acknowledging their significance in managing both information and data.
The topic of integrating ethics into foundational elements versus data governance was raised. Howard highlighted the inclusion of various new knowledge areas within the life cycle management framework, such as master data management and master data usage. He acknowledged the complexity of these concepts, noting the diverse elements impacting life cycle management and the need for clarity in organising these components effectively.
Howard then focused on the DAMA Wheel, which features governance at its centre, serving as a key framework for considering various knowledge areas and their integration. It was noted that there is an enhanced version of the DAMA Wheel, and a comparison was made to its structure, resembling a clock. The importance of data governance was emphasised, indicating that it should be addressed first in the overall framework, suggesting a potential shift in approach seen in DMBoK 2.
Figure 5 "What KAs can we Merge in the DAMA Wheel" Pt.3.
Figure 6 Data Lifecycle Management Diagram
Knowledge Area Debate: Data Architecture and Data Management Challenges
A discussion then emerged around data management, highlighting the importance of data architecture and modelling, initially causing confusion with a clock analogy to illustrate the sequence of components, such as data quality positioned at 10 o'clock. Although there is a structured framework where elements build upon each other, flexibility is emphasised, allowing teams to implement solutions in a manner that suits their unique needs.
This introduces challenges, as often the guidance provided does not clarify how to construct the architecture effectively. Peter Aiken's phases of development further illustrate the iterative process, leading to ongoing conversations about best practices for building data systems.
Figure 7 The DAMA Wheel
Figure 8 Purchased or Built Database Capability
Knowledge Area Debate: Data Modelling and Data Architecture
During the continued discussion about the DAMA Wheel, Howard and the attendees debated the potential removal of knowledge areas, ultimately concluding that none should be eliminated. One notable suggestion came from a data architect who proposed merging data modelling into data architecture, sparking further conversation.
An attendee shared their perspective prevalent in their environment, advocating for the integration of modelling and engineering into unified roles, a notion that others found unconvincing. The discourse highlighted the importance of maintaining distinct roles, particularly emphasising the value of conceptual modelling in the data architecture process.
Figure 9 "What KAs can we REMOVE from the DAMA Wheel?"
Figure 10 "What KAs should we ADD to the DAMA Wheel?"
Knowledge Area Debate: Data Science, AI, and Data Integration
The discussion on the DAMA Wheel then shifted to a suggestion of dropping the format in favour of an enhanced version. Howard noted that Sue Gorens supported this notion, as concerns had been raised about misinterpretations of the wheel's purpose.
A point was raised on the perspective that big data should not be viewed as a distinct category but rather as another form of data, aligning with the principles of data science and artificial intelligence, which utilise various data types. There was a mention of the importance of data integration and interoperability, particularly in relation to streaming and low-latency data ingestion and engineering.
Figure 11 "What KAs should we ADD to the DAMA Wheel?" Pt.2
Definition and Interpretation of Data Management
Howard then redirected the debate to focus on establishing a shared definition for vetting items for inclusion on the "wheel." Key considerations include the criteria for adding, merging, or removing items, as well as reaching a consensus on these decisions. Participants are encouraged to reflect on current industry practices and organisational objectives to guide the discussion. Additionally, a specific issue was raised regarding challenges encountered when using Google for information, highlighting difficulties in finding relevant lifecycle data, particularly in relation to companies such as Copart.
In a debate on the definition of the data lifecycle, Howard noted that the traditional definition encompasses obtaining, storing, utilising, and disposing of data. However, he noted that this perspective raises questions about the relevance of various associated concepts within the knowledge area, leading to a consensus that nothing should be excluded from consideration. Howard then challenged the attendees to consider the criteria for defining what aspects should remain included and how to establish measurable standards for assessment.
Figure 12 “Purpose of the Debate”
Defining Data and Information Management
To effectively determine the inclusion or exclusion of elements in data and information management, it is crucial to establish a clear and agreed-upon definition. The underlying challenge stems from cultural shifts that can impact relevance and understanding within the field.
It is essential to recognise that perceptions of data management are influenced by cultural context, which shapes how people interpret and engage with it. Without a unified definition that aligns with current cultural understandings, efforts to communicate and sell data management concepts may fail, as audiences may misconstrue the intent and value of these initiatives.
Decision-Making Principles in Data Management
Howard then steered the discussion onto the criteria for decision-making regarding inclusions and exclusions in a global context, particularly in relation to cultural influences on current behaviour. An attendee suggested that any adjustments should reflect existing practices. In contrast, others raised concerns about the variability of cultural perspectives, noting differences in data management experiences between regions, such as the UK and South Africa. Howard emphasised the desire for a unified yardstick to determine what should be included or excluded, focusing on the need to clarify the rationale behind these decisions.
The importance of defining the value of data is emphasised, as highlighted by Larry Burns, who emphasised that value is derived from data in motion. Howard suggested that building a strong business case around quality and metadata is crucial, as these elements are closely tied to value creation. However, concerns have been raised by figures like Tom Redmond and Bob Signer about the effectiveness of data governance, questioning whether efforts in this area are yielding results.
An attendee proposes that a foundational principle to assess data quality is its timelessness, posing the question of whether the relevance of a data field remains consistent across legacy, current, and future systems. Howard added that while technological advancements may evolve, certain foundational principles remain constant and essential.
Howard supported the argument for recognising data as an asset and argued for a principle-based approach to data management. However, he noted that differing interpretations of these principles can arise. He then highlighted the importance of clarity and agreement on what constitutes these core principles to ensure effective data governance.
An attendee emphasised the importance of focusing on the robustness of knowledge areas. For instance, the quality of data is deemed essential regardless of technological advancements. There is an agreement on the significance of these principles, suggesting that regardless of debates over methods, the core concepts remain crucial to success in any technological endeavour.
Principles and Implementation of Data Management
Howard focused on defining the components of a data management framework, particularly examining the relationship between data quality and data management principles. The attendees were encouraged to consider how data quality fits within this framework and how it aligns with the broader data management principles.
A comment emphasised that assessment criteria should align with the enterprise data strategy, ensuring clarity of scope and compatibility with existing knowledge assets, while also being responsive and proactive to future trends. Additionally, the importance of establishing data covenants and effective change management was emphasised. Howard noted that it was also important to consider the consolidation of elements within a central framework.
Howard then touched on the challenges presented by experts regarding the DMBoK 3 and its reliance on a limited perspective of data management, particularly from Certified Data Management Professionals (CDMPs). Critics argue that this narrow approach restricts the understanding of data management practices.
Data Management and the Process of Agreement
The discussion highlighted the complexities of data management, with a key issue raised being the lack of consensus on terminology. Definitions of terms such as "customer lifetime" and "customer churn" can vary based on different perspectives.
This divergence prompts a need for agreement, much like the challenges encountered when constructing a business glossary. Howard noted that there has been express interest in merging concepts like data governance and metadata, but the focus remains on finding common ground rather than removing elements. The central question is how to achieve consensus on these definitions and combinations effectively in practice.
To develop a business glossary, a collaborative process is essential, where individuals from various roles contribute their definitions of terms. By analysing these contributions, the team can identify commonalities and areas of consensus. Achieving a shared understanding requires bringing together a diverse group, defining their roles, and iteratively refining the definitions through feedback and discussion. This systematic approach fosters agreement among participants, ensuring clarity and alignment in terminology.
Howard shared that at the end of the editorial meeting, each participant acknowledged that they were unable to reach a consensus. They then decided that each participant would document their individual perspectives, which would later be analysed to identify commonalities and differences. The editorial group confirmed this approach, and members will return to their respective chapters to further develop their ideas.
Debate on the Inclusion and Exclusion of Knowledge Areas in Data Management
The discussion emphasised the importance of analysing definitions rather than relying solely on voting outcomes to determine inclusion in a taxonomy. Howard highlighted that aspects like change management, while relevant to business environments, should be categorised outside of data management, as the latter must integrate external factors to ensure success.
The attendees were encouraged to establish clear rules for taxonomy inclusion, suggesting that if a concept pertains to the business realm, it should remain external. The conversation also touches on the debate around whether data management should be taught in MBA programs. However, this topic is set aside as a more complex issue with varying opinions.
Howard emphasised the need for a criterion to value knowledge areas within a conceptual framework to be established. He raised the question of whether certain elements should stand independently, be integrated with others, or be excluded entirely, emphasising the need for a use case to demonstrate their value.
The central inquiry is how to measure and assess what keeps these elements within the framework, suggesting that the key consideration is whether they add value. This is illustrated by the debate surrounding data governance, where figures such as John Ladley and Bob Signer have argued that it is ineffective and fails to demonstrate its usefulness.
The discussion highlighted the challenges of demonstrating the value of metadata management within an organisation, as some team members view it as lacking importance and akin to a "big black hole." The responsibility to prove its significance falls on one individual, emphasising that perceptions of value vary widely based on personal experiences.
The attendees and Howard acknowledged that there are differing opinions on business intelligence (BI) and artificial intelligence (AI), with some suggesting these areas should be excluded from core data management processes, as they do not align with the fundamental tasks of creating, obtaining, storing, and maintaining data. This situation reflects the complexities of reaching a consensus on the importance of metadata management amidst diverse perspectives on data practices.
Figure 13 "Key Questions"
The Data Lifecycle and its Role in Data Management
Howard raised a question about whether data management should also incorporate business decision-making and the role of data in feeding into business intelligence (BI) and artificial intelligence (AI). He then made a distinction between data and information, noting that once data is analysed through BI, it transforms into information.
The DIKW triangle illustrates the progression from data to information, then to knowledge, and ultimately to wisdom, which feeds back into planning. Howard noted that there is a debate regarding whether data management should encompass all four elements—data, information, knowledge, and wisdom. Additionally, the key questions are whether there is agreement on this approach and what the full lifecycle of data management should entail, including the types of data being managed. Howard added that addressing these fundamental questions is essential for defining effective data management strategies.
The Data Lifecycle: Its Role in Data Management and AI
The discussion emphasised the distinction between Business Intelligence (BI) and data warehousing within data management. While both are often treated as a single chapter, they serve different purposes; data warehousing focuses on the storage and management of data, enabling its availability, whereas BI is concerned with actively utilising that data to derive insights and value. This clarification underlines the importance of understanding the roles each plays in effectively leveraging data throughout its lifecycle.
Howard then stressed the importance of distinguishing between data storage and effective data management, particularly in the context of business intelligence (BI). An attendee then argued that while a data warehouse serves as a storage solution, the analytics component—encompassing both predictive and descriptive analytics—should be treated as a separate discipline.
The necessity of establishing clear definitions and principles to guide the placement of various data management elements was underscored. Howard advocated for a holistic approach that considers the entire life cycle of data, resulting in a more structured framework for data management.
The discussion returned to the concepts of data, information, knowledge, and wisdom in the context of a comprehensive data lifecycle. It suggests that traditional models, such as the one outlined by Google—which typically includes stages like creation, storage, and disposal—lack critical elements like planning and enhancement through AI. Furthermore, the aim is to establish a full lifecycle for data that encompasses various phases, from initial generation to advanced applications like decision support, emphasising that understanding these stages is essential for effective data management. By aligning on these concepts, stakeholders can better navigate and implement operational practices that reflect the dynamic nature of data evolution, akin to the transformation of a butterfly.
An attendee then highlighted the significant gap in understanding and defining life cycles within organisations, as illustrated by Google's insights, indicating that 99% of people align with a specific interpretation. This suggests that many organisations lack educational resources and proper definitions to clarify these concepts. Furthermore, Howard emphasised the need for experts to anchor the definition of life cycles, despite the DMBoK's prevailing but disputed framework.
Data Initiative and Data Management Strategies
Howard emphasised the importance of aligning data initiatives with an organisation's overarching strategy to effectively manage and govern the continuously generated data in any business. As managers recognise the need for a structured approach to handle this data, it becomes essential to develop a plan that aligns with the company's strategic objectives, ensuring that data management supports the organisation's overall goals.
The attendees and Howard then went about highlighting the distinction between data warehousing as a storage solution and business intelligence, noting that the latter can refer to both systems and data visualisation tools. Howard then suggested that businesses commonly incorporate visualisation into data initiatives, driven by analytics and big data trends. They underscore the importance of evaluating whether certain data should be included or excluded based on fundamental rules for effective data management.
Data Governance in the Context of Documented Content and Generative AI
The importance of understanding various data types—structured, semi-structured, and unstructured data— was discussed in the context of enhancing documented content with generative AI. Howard highlighted the challenge posed by Google's dominance in shaping perceptions of data validity and emphasised the need to establish a clear framework for data governance.
The proposed model involves visualising data as a layered structure, where data governance forms the core, surrounded by information and knowledge, ultimately leading to a message that is easily consumable by audiences. To effectively navigate this complexity, a collective agreement on data categorisation and lifecycle management is essential.
The Data Lifecycle: Its Implications
Howard emphasised the need for a cohesive data strategy that encompasses various components, including data governance, architecture, and modelling. He highlighted the importance of integrating data—whether in motion or at rest—while maintaining robust operational practices and security measures throughout the process.
A cohesive data map is essential for adding or removing elements as needed, facilitating the thoughtful organisation of where specific components should be positioned. Additionally, an attendee noted that warehousing should align with business intelligence as part of the overall storage framework, which includes both small and big data within a unified area.
The discussion then moved on to defining the components that should be included in the data management wheel, specifically regarding the integration and management of various knowledge areas. Howard noted that there was an ongoing debate about whether data warehousing belongs in the big data lifecycle and whether business intelligence (BI) should be incorporated in the same context or treated separately.
To address these questions, it was suggested that a small group, including key participants, convene to review the MDDMF framework, which outlines these components, to understand better how to integrate and optimise the relevant elements of data management cohesively.
Layered Use of Technology and AI in Business
To effectively analyse and categorise technology and its applications, we can conceptualise it through a layered framework. The first layer addresses the technological aspect itself, the second focuses on human usage, and the third encompasses the utilisation of advanced systems, such as artificial intelligence. For business considerations, the emphasis is primarily on the middle layer—human interaction with technology—while AI sits within the foundational technological layer. This framework highlights the relationship between various components, such as business intelligence and reporting, underscoring the complexity of positioning technology within organisational structures. Further discussion on measuring these layers can enhance our understanding and implementation.
The Data Lifecycle: The Importance of Data Management
The discussion revolves around the concept of life cycles in relation to technology usage and data measurement. It is clarified that the life cycle includes various stages from planning to storage and disposal, structured in a spiral model rather than a traditional layered approach. The inner layer pertains to technology use, followed by a usage layer, and then enhancement. Participants acknowledge the importance of integrating life cycle considerations, drawing parallels with established frameworks like PMBOK, which emphasises the life cycle as a foundational element in
The attendees, along with Howard, shifted their focus to project management practices, specifically the phases of initiation, planning, execution, and closing, as well as the overall project lifecycle. The discussion aimed to clarify these concepts and explore the connection between the DIKW triangle and the project lifecycle. Howard then raised a question about whether data management encompasses all types of data, including records management, thereby challenging the notion that records management is a separate discipline from data management.
The focus then shifted to the complexities of regulatory data management and the challenges organisations face in adhering to established processes. Howard raised the question of why some organisations seem to bypass critical steps without facing consequences, suggesting a need for tailored approaches—perhaps a more rigorous method for heavily regulated environments and a lighter one for others.
The Evolution and Challenges of Data Management
Howard noted the need to update and adapt the DAMA Wheel framework in light of current realities, particularly regarding concepts like security, which may no longer be relevant. There’s a consensus on the potential benefits of merging certain chapters, but a clear agreement on the criteria for merging or removing content is essential.
The discussion emphasised the importance of establishing a core set of rules to guide these changes, especially as the knowledge landscape evolves rapidly over time. Enhancing understanding and utilising AI for knowledge enrichment are highlighted as key areas that can contribute to improving the lifecycle of information.
The attendees and Howard then focused on the definition and management of data throughout its life cycle, emphasising the need to consider both data in motion and data at rest. Two of the attendees raised important questions about achieving sustainable, ethical, and scalable data management practices. They debated whether to consolidate certain stages of the data life cycle, such as planning and storage, into a single category or to maintain them as distinct phases. This highlights the ongoing need for consensus on effective data management strategies within the framework established by the DMBoK training.
The discussion then touched on the complexities of data management and the current challenges posed by Google’s dominant presence, which shapes public perception. Howard emphasised the importance of addressing these realities while ensuring that efforts to manage data evolve in tandem with information, knowledge, and wisdom. There was a consensus on the need to present a cohesive understanding of data management to the public, which can be aided by outlining a clear life cycle. Maintaining this clarity is viewed as essential for fostering comprehension and supporting informed decision-making.
Academic Positioning of Data Management
Howard suggested that Data Management be recognised as an academic discipline that merges business, information science, and computer science. This interdisciplinary approach has raised questions about its placement within university programs—whether it should align more with computer science or emerge as a distinct undergraduate degree focused on its business applications.
The final part of the discussion focused on the potential for developing an interdisciplinary academic program that combines business management, information science, and computer science. The importance of gaining academic support for this new discipline was emphasised, as it could provide a solid foundation for further progress.
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