What is Data Management? Continuing the Discussion with Melissa Tsalicoglou & William Evans

Executive Summary

This webinar outlines key concepts and evolving practices in Data Management and governance, emphasising the critical role of Data Stewardship and the intricate nature of the Data Life Cycle. William Evans discusses the proposed enhancements to the DAMA Wheel and underscores the importance of information architecture in managing data complexity.

Melissa Tsalicoglou highlights essential strategies for integrating Data Management across operational frameworks, product development, and asset management, while addressing challenges related to security and future-proofing Data Governance. Collectively, Howard Diesel and the attendees discuss these elements and explore the multifaceted landscape of Data Management and its vital role in organisational success.

Webinar Details

Title: What is Data Management? Continuing the Discussion with Melissa Tsalicoglou & Willian Evans
Date: 2025-07-31
Presenter: Howard Diesel
Meetup Group: African Data Management Community
Write-up Author: Howard Diesel

The Potential Design for the DAMA Wheel

Howard Diesel opened the webinar and shared that it would continue from last week’s talk. Howard then shared that William Evans and Melissa Tsalicoglou had reached out to collaborate on a proposal, focusing on redesigning the DMBoK. As William was finishing his preparation for training in Saudi Arabia, he agreed to start sharing ideas for the new concept.

Figure 1 Proposed Data Management Diagram

 William Evans: Role and Responsibilities of Data Stewardship

Over the past 14 years, William Evans has developed a framework that aligns well with the DMBOK, highlighting the roles of technical and Business Data Stewards. Technical Data Stewards primarily operate within the “data as stuff” domain, while Business Data Stewards focus on the information space and coordinate across Knowledge Areas.

William shared that his diagram outlined the key components of Data Governance, Data Quality, and Data Security, collectively represented by a purple background to signify their pervasive influence throughout the framework. Although visual elements may not fully convey this, the intent is to showcase that these principles apply universally within the structure, highlighted by the blue section at the top.

Figure 2 “Submissions”

Figure 3 The DAMA Spiral

Figure 4 Data Lifecycle

The Data Life Cycle and Information Architecture

The Data Life Cycle encompasses several key components, including Data Architecture, Data Modelling and Design, Data Dictionaries and Catalogues, and Database Management, all of which contribute to effective data utilisation and enhancement. This process involves various stages such as data integration, interoperability, operations, lineage, and the management of data warehouses, lakes, and lake houses, alongside archiving, disposal, and recovery strategies.

Transitioning from data to information, the focus shifts toward information architecture, which encompasses modelling and design that emphasises the development of invoices, taxonomies, and ontologies. This area also integrates business reference and Master Data, business Metadata, and Business Intelligence, ultimately linking Data Management with decision-making processes and AI applications to maximise information value.

The current challenge involves integrating information and data, as highlighted in recent discussions with industry experts. It’s crucial to recognise that many are approaching information management from various angles, which may lead to a disconnection between data and its broader context.

Historically, frameworks like Data Management Body of Knowledge (DMBoK) version 2’s DAMA Wheel and its earlier iterations faced little competition in this space, prompting the need to address gaps in the information domain. The Data Management field is evolving, and others are beginning to contribute. William expressed that we must unify these perspectives to leverage the full potential of our information ecosystems and avoid falling behind.

William emphasised the need for collaboration and inclusivity to maintain relevance in a rapidly evolving landscape. He highlighted the importance of a structured approach, as illustrated by a spiral representation that outlines the progression in Data Management. The spiral begins with an aeroplane icon at the core, symbolising the starting point, followed by elements like trams, glossaries, taxonomies, and ontologies.

This organised framework helps bridge complex concepts in Data Management, making them more understandable and applicable to business stakeholders. By following this progression, stakeholders can enhance their understanding and effectively engage with the data landscape.

A discussion then began on the importance of Metadata as a foundational element within Data Management. William noted that Metadata encompasses both technical and business aspects, requiring clarity in its classification. The need to position Metadata centrally in the framework was agreed upon, as it influences various components of Data Management.

Figure 5 Use & Enhance (Information & Knowledge)

The Evolution and Importance of Information Architecture

William moved on to the concept of information architecture and its relationship to modelling and design, highlighting its importance in understanding the flow of documents rather than just data. He emphasised that, similar to Data Architecture, Information Architecture plays a crucial role in the initial stages of information development after defining terms and establishing records management. An example was provided, illustrating how an invoice is generated, processed through various systems, and tracked from issuance to payment, underscoring the focus on the information flow and context.

In the process of Data Architecture and Modelling, it is essential to connect data at a detailed level after completing definitions and records management. This iterative approach involves collaborating between data and information architects to ensure alignment between data needs and the overarching architecture. Key components include establishing a Data Dictionary that specifies fields such as first name and surname, while Database Management and development lead to the integration of necessary data, such as invoices. Furthermore, a well-defined glossary and taxonomy are crucial to clearly articulate the business context and intricacies associated with the data elements being utilised.

The concept of a Data Life Cycle encompasses a comprehensive process that spans from data acquisition to its eventual transformation into valuable information. William emphasised key stages within this cycle, including planning, design, enabling, and disposal, while noting that user acceptance falls outside this framework. By clarifying these stages, he deepens the understanding of the lifecycle approach and encourages further exploration and discussion among participants.

The Complexity of Data Life Cycle

William elaborated on his metaphor in his presentation that related to stage movement and the diverse roles within a project. He emphasised key topics such as the project life cycle, focusing on the processes of utilisation and enhancement, and the careful consideration of which elements to include or exclude.

By illustrating the flow of ideas from fundamental concepts to various modes of transportation—such as planes, buses, and cars—William highlighted the unique approaches individuals adopt in their work. He then encouraged the attendees to reflect on how their preferred methods of navigating project stages align with this flow.

The process of managing information begins with understanding the information architecture, modelling, and design, followed by organising the data into taxonomies and ontologies. This foundational knowledge informs records management and clarifies the approach to handling data. Subsequently, a connection is established between data and information architecture to determine the specific data required for invoices, linking it to the data lifecycle.

Development on the invoice’s information can then commence, identifying which components will serve as business references, alongside technical and market data. Additionally, considerations for business Metadata, intelligence, decision management, and AI are integrated, highlighting the business relevance of the right side of this framework in contrast to the more technical focus on the left.

Figure 6 Proposal DMBoK Wheel WIP

Melissa Tsalicoglou: Knowledge Areas and Their Evolution in Data Management

Melissa Tsalicoglou took over from William and took to discussing the categorisation of Knowledge Areas. Drawing parallels with the Project Management Institute (PMI), she highlighted the division of Knowledge Areas into categories such as time, cost, and HR management, which have evolved in response to industry trends, including technological changes. A notable shift in PMI’s approach was the separation of communication into distinct stakeholder management.

There was a suggestion from an attendee to adopt a similar method for data Knowledge Areas, where core areas are identified and periodically assessed for their relevance to community needs and organisational delivery. They felt that the focus should be on ensuring that the framework remains pertinent and reflects valuable data products and underlying data assets.

Figure 7 Rationalisation

The Proposed Changes to the DAMA Wheel

The current DAMA Wheel diagram outlines the existing architecture, with a specific focus on differentiating Data Security, Metadata, and Reference Data through the use of colour coding. A proposal is made to merge Metadata and Reference Data, while suggesting that operations be removed as the primary focus, and instead position it as a supportive function. The aim is to create a revised wheel that emphasises Data Architecture and design, reflecting a cohesive and updated framework for Data Management.

Melissa argued that effective Data Management involves organising various components, such as Metadata, Reference Data, Master Data, Data Warehousing, Business Intelligence, and document/content management, into logical conceptual areas. By categorising these elements as data assets, stakeholders can more easily understand their importance, despite differing opinions on the classification. Additionally, while project management and operations management serve as essential supporting functions within the Data Management framework, they may lack formal recognition as core Knowledge Areas.

Figure 8 WIP Knowledge Areas Rationalised

Data Management: Support Operations

Melissa emphasised the intricate relationship between Data Management and support operations, underscoring that while they serve distinct functions, support is vital for effective IT operations and service delivery. She identified the need to pinpoint discrepancies between the Data Management Body of Knowledge (DMBoK) framework and ITIL (Information Technology Infrastructure Library) within operational contexts. By mapping these frameworks, organisations can gain insights into these differences, paving the way for informed strategies to enhance operational efficiency. Ultimately, this approach not only clarifies roles but also guides improvements in service provision.

The Intersection of Data Architecture and Data Modelling

Melissa argued that integration of Data Architecture and Data Modelling could play a crucial role in effective Data Management. She highlighted their interconnectedness during a discussion, drawing from her experience in a previous organisation where the roles of Data Architect and Data Modeller were combined, suggesting that this approach could enhance overall efficiency.

An attendee highlighted that the evolving nature of Data Architecture, particularly with the emergence of concepts such as data lakes, fabrics, and meshes, which reinforces the argument for a more unified perspective on these functions. This could support Melissa’s argument, indicating that merging Data Architecture and modelling not only streamlines processes but also adapts to the dynamic landscape of Data Management.

The integration of traditional Data Warehousing and modern data platforms, such as data lakes, is crucial for effective Data Management. Melissa emphasised that rather than viewing these systems as independent entities, a cohesive strategy that accommodates both is necessary to eliminate the redundancy inherent in conventional approaches. Therefore, by recognising the need for a solid foundational architecture, whether through concrete designs or more adaptable frameworks like stick-and-frame models or shipping containers, organisations can optimise their data utilisation. Ultimately, a unified framework for data storage and architecture is essential for maximising the value derived from data assets.

Figure 9 Merging Data Architecture and Modelling

Data, Metadata, and Organisational Architecture

The classification of Metadata is crucial in distinguishing it from Master Data Management (MDM) and Reference Data Management (RDM), particularly in terms of data descriptors. The attendees in the discussion initially debated whether Metadata should be categorised independently or integrated within broader concepts, such as Data Quality and Governance. An analogy was drawn, likening Metadata to the grammar that structures a language, while a glossary serves a function similar to that of a dictionary.

Organisational architecture plays a crucial role in determining how data is structured and managed within an organisation. By distinguishing between organisational architecture, which defines the overall structure, and the organisational model, which details processes and interactions, we gain a clearer insight into their interconnection. This understanding not only enhances the integration of organisational architecture with operational practices but also underscores the significance of business architecture and stakeholder engagement in optimising these relationships. Ultimately, the synergy between structure and operational methodologies is essential for effective organisational management.

The alignment between data structure and architecture with the organisational structure is crucial for effective information management. It’s important to analyse the flow of data and information within the organisation, as many operational practices may not be formally documented. Additionally, change management plays a vital role by providing insights into the organisational structure, supporting the effective management of data and ensuring that information flow aligns with how the organisation functions.

An attendee expressed doubts about whether change management will effectively encompass all necessary elements, highlighting their own limited understanding of the underlying complexities. They then stressed the critical importance of validating workflows from both a data perspective and organisational practices, pointing out substantial gaps in these areas that must be addressed to achieve an effective design. Ultimately, a robust validation process is essential for ensuring the integrity and functionality of the proposed response.

Figure 10 Joining Metadata with MDM and RDM

Understanding the Challenges in Implementing Data Architecture in Organisations

The concept of “flow” helps to understand both business processes and Data Management. Specifically, it encompasses the overall organisational processes as well as the movement and sharing of data within those processes. For instance, consider a transaction at a shoe store, which illustrates this flow: it begins when a customer enters the store, progresses through the selection of shoes, and concludes with the completion of the purchase, including payment and the receipt of an invoice. Each step in this scenario serves as a critical data point, highlighting the interconnected nature of information and organisational processes. Ultimately, grasping the nuances of flow is vital for optimising business efficiency and data handling.

An attendee emphasised the importance of integrating data points in enhancing the sales process, as each step—from a customer entering the store to the final purchase—serves as a unique data point that can inform business strategies. They felt that it was essential to establish strong links between these data points and the comprehensive sales process to ensure accuracy and prevent errors in data matching.

Leveraging an existing information architecture is essential for the effective management and analysis of data points within the sales process. This structured framework enhances the ability to navigate the complexities arising from the interaction between manual and automated tasks, ensuring accurate data recording across various platforms, including databases and spreadsheets. Ultimately, this approach not only streamlines operations but also leads to improved sales outcomes.

Capturing all interactions during sales engagements is crucial, yet it presents challenges when integrating diverse data points into a unified system. This issue highlights the importance of effective information architecture, which seeks to transition from manual document workflows to automated digital processes. Ultimately, a comprehensive understanding of information flow is crucial for converting data into a structured and coherent format.

The integration of Metadata Management (MDM), Reference Data Management (RDM), and Data Architecture poses significant challenges within existing organisations. An attendee highlighted their ambivalence surrounding this integration, emphasising the difficulties faced when trying to align data strategy with business strategy in environments marked by entrenched cultural inertia. In contrast, they argued that establishing a new data-driven organisation from the ground up would be a more straightforward process. Nevertheless, despite their concerns, they strive to keep an open mind as they navigate these complexities.

The Intersection of Metadata and Data Quality Management

The relationship between Metadata Management and Data Quality management is crucial for effective Data Governance. An attendee emphasised that a comprehensive understanding of Metadata is vital for monitoring and managing Data Quality. They detailed that defining critical data elements requires a clear description of the data’s representation and the establishment of relevant business rules, which serve as essential inputs for Data Quality initiatives.

Integrating Metadata Management with reference and Master Data Management can significantly enhance data integrity and usability. An attendee highlighted Melissa’s perspective, emphasising that while these areas can work together, Master Data Management warrants dedicated focus. Ultimately, adopting a holistic approach to these disciplines ensures that organisations can leverage their data more effectively.

Melissa then proposed integrating Metadata into the Data Quality category, highlighting the interconnections among various components of Data Management. She highlighted the complexity of the DAMA wheel, particularly in relation to Business Intelligence (BI) and documentation, while also suggesting the potential for incorporating artificial intelligence (AI) into the framework. In her next slide, she plans to discuss data products and assets and believes that merging Metadata with Data Quality is a prudent strategy, underscoring her alignment with this viewpoint.

Data Management: Product Development

Data products are at the pinnacle of the Data Management triangle, supported by several foundational Knowledge Areas, including Data Governance, Data Quality, Metadata, Reference Data, Master Data integration, storage, interoperability, and security. In Melissa’s framework, Data Governance is fundamental, followed by Data Quality, which ensures the integrity and usability of data as it flows into data products. Ultimately, effective Data Management aims to enhance data products, including AI and BI applications, with the challenge being that Master Data Management depends on efficient storage and consolidation to achieve a reliable “golden record.”

Effective management of data requires a clear understanding of the distinctions between Metadata, Reference Data, and Master Data. By focusing on these categories, organisations can develop robust infrastructure solutions that enable real-time integration and operability, while also prioritising Data Quality through strong Master Data Management practices. Lastly, establishing solid Data Governance and design principles is crucial to ensuring that data resources are managed and utilised effectively.

Figure 11 Recommended Order for the 8 DAMA Knowledge Areas

Data Governance: Security and Future Proofing

The integration of security within Data Management is crucial to ensure that it is prioritised rather than treated as an afterthought. By positioning security above Data Governance in the hierarchy, organisations can emphasise its importance and ensure that it is incorporated from the very beginning of the architecture modelling phase. Placing Data Architecture and modelling between Data Governance and Data Quality further reinforces the necessity of considering security requirements during the design process. Ultimately, this iterative approach fosters a comprehensive framework that addresses security, governance, and quality.

The intricate nature of Data Management is underscored by its reliance on multiple feedback loops rather than a straightforward, linear flow. While raw data, data assets, and data products are fundamental elements, prioritising the categorisation of data products will significantly enhance an organisation’s adaptability to future technological advancements.

This approach becomes particularly crucial with the integration of AI, which requires extensive data resources in addition to traditional document and Business Intelligence (BI) systems. By acknowledging and adapting to these evolving categories, organisations can better position themselves to leverage future data products and technological innovations.

Future-proofing and adhering to industry standards are critical components of effective Data Management. The discussion emphasised the importance of established frameworks such as DAMA, PUSKY, PMI, and ITIL, which can provide valuable support to Data Management practices. However, they cannot fully integrate into every aspect. Additionally, concepts like enterprise architecture and the TOGAF framework were highlighted as relevant tools that further enhance the understanding and execution of Data Management strategies. Ultimately, embracing these standards and frameworks can lead to more robust and adaptable Data Management solutions.

Figure 12 Knowledge Area Data Products and Assets

Figure 13 Supporting Function Project Change and Operations Management

Data Management: Asset Management

The attendees emphasised the significance of aligning Data Management concepts with business value, recognising Melissa’s valuable contributions to the topic. Suggestions were made to refine the document’s structure and content management to highlight their connection to Business Intelligence, reinforcing the notion that data should be treated as a crucial asset.

Figure 14 Other Ideas

Data Management: Role Responsibilities in a Collaborative Project

Howard then emphasised the need for a clear and simplified framework for visualising roles and responsibilities in Data Management. He highlighted the importance of maintaining a clutter-free core model while extending it to incorporate related elements, such as data valuation and realisation. However, concerns were raised regarding the absence of crucial aspects, such as roles, expectations, and change management, in the current representation, suggesting that consensus on the best approach has yet to be reached. Ultimately, a refined model that strikes a balance between clarity and comprehensiveness is essential for effective Data Management.

Identifying areas of agreement and disagreement regarding the objectives was crucial to the discussion. Howard shared that he had effectively categorised the feedback into three distinct, colour-coded areas: those that are accepted, those needing realignment, and those that should shift from core to platform status. For example, as Melissa emphasised, the integration of Metadata with quality references and the alignment of Data Architecture with Data Modelling are essential components for ensuring project success. Ultimately, this structured approach will enable us to navigate our priorities better and enhance our strategic focus.

Figure 15 What’s in and what’s out (Digital Platform)

Figure 16 Foundational Activities (Functions)

Data Governance: Structure and Collaboration

Data Governance is a complex topic that can benefit from a more organised approach. To facilitate understanding, it may be useful to break down Data Governance into specific, manageable components or Knowledge Areas.

Howard encouraged the attendees to provide feedback on whether to categorise these components separately or to keep them unified under the overarching theme of Data Governance. This input will help determine the most effective way to present and discuss the topic moving forward.

Figure 17 Oversight Activities (Functions)

Data Management: Integration Strategies

Howard emphasised the critical need for realignment in the planning and design phases, particularly in integrating architecture with Data Modelling to create a cohesive framework. Participants highlighted the importance of pursuing the arts within this context, noting that a unified approach could enhance creativity and innovation. There was a consensus on the necessity of consolidating data storage solutions, including data warehouses and master and Reference Data Management, to strengthen the overall data platform.

The significance of Business Intelligence (BI) and its connections to predictive analytics as vital components of data science was underscored. Howard indicated an intention to incorporate these elements into future strategies. In conclusion, the integration of these aspects will not only streamline processes but also foster a more robust analytical environment as the project progresses.

Figure 18 Oversight Activities (Functions)

Figure 19 Lifecycle Management: Plan and Design

Figure 20 Lifecycle Management: Enable and Maintain

Figure 21 Lifecycle Management: Use and Enhance

Data Management: Transforming Information

The integration of various touchpoints within organisational frameworks presents significant challenges, as these elements currently function in isolation. Although equality among these touchpoints was initially emphasised, the lack of effective coordination has resulted in confusion and inefficiency. By utilising structured frameworks such as the software development life cycle (SDLC) or life cycle models, organisations can ensure that all components work together seamlessly.

For instance, Howard noted William’s proposed spiral model aims to create stronger links between practical elements, addressing the current fragmented approach. Ultimately, the strategic connection of assets, data, and information is essential for enhancing collaboration and improving overall efficiency.

To enhance Data Management, we need to transform information into knowledge and wisdom, which in turn informs our data strategy and planning for future initiatives. This iterative process involves designing new data products, enabling data acquisition, and leveraging insights and innovations, ultimately feeding back into our data repositories. As generative AI captures and incorporates our expertise and documented insights into these data systems, it creates a feedback loop that enriches our understanding and improves decision-making. Moreover, ensuring sustainability throughout this process requires effective orchestration of people, processes, and platforms across both business and technology domains, making collaboration essential for successful implementation.

Integrating new processes within an organisation poses significant challenges, particularly because pre-existing activities often complicate the implementation of new initiatives. The Peter Edkin diagram serves as a valuable tool for navigating this complexity, offering a practical framework for managing the orchestration and coordination among various people, roles, processes, and deliveries.

By focusing on a continuous cycle of development and improvement, this approach helps to identify and address the gaps in orchestration that traditional frameworks frequently overlook. Ultimately, understanding and leveraging these dynamics is essential for successful process integration within organisations.

Figure 22 Orchestration of Interdisciplinary Integration