Schedule

In today’s rapidly evolving digital environment, organisations must continuously evolve their Data Governance practices to stay ahead and remain competitive. The explosion of data and the rise of transformative technologies such as AI and machine learning are reshaping the landscape, demanding adaptive and changing approaches to Data Governance. Join Nicola as she shares invaluable insights from her extensive Data Governance journey to date. Learn how organisations can transform their frameworks and strategies to not only address emerging challenges but also harness the full potential of data in this rapidly changing environment.

• The Shifting Data Landscape: Understanding how advancements in technology are reshaping Data Governance.
• Integrating AI and Machine Learning: Addressing the unique governance challenges posed by intelligent technologies.
• Building Adaptive Data Governance Frameworks: Practical strategies for creating flexible, future-ready governance models.
• Lessons from the Field: Key takeaways from real-world successes and challenges in evolving Data Governance practices.
• Future-Proofing Your Data Strategy: How to align Data Governance with long-term business goals and innovation.

In an era where data and artificial intelligence play a central role in business strategies, it is crucial to understand not only the new opportunities but also the latest European legislation in this field. This session offers an engaging and accessible overview of the three most influential European laws of the moment: the AI Act, the Data Governance Act, and the Data Act. What do these laws mean for your organization, and how can you foster innovation while complying with complex legal requirements? With a unique combination of technical and legal expertise, the key aspects of these laws will be explained, offering practical insights to help organizations future-proof themselves. You can expect an informative session filled with concrete tips, potential pitfalls, and real-world examples.

• An overview of European legislation in the digital domain
• The AI Act – what is prohibited and what is high-risk AI?
• The Data Governance Act – how can we share data in a reliable, transparent, and ethical way?
• The Data Act – how do we regulate access to data, especially for IoT, to create a fair and competitive data economy?
• Practical tips to remain legally compliant without limiting innovation.

In his LinkedIn article ‘Data is Dead,’ Wouter van Aerle sparked a significant debate: many organizations manage their data in ways that are fundamentally inadequate. This includes a lack of clear responsibilities, an overly technological approach, or the absence of a strategic vision for data use. As a result, ambitions to become ‘data-driven’ often fail before they can truly take off.

Wouter offers a forward-looking perspective: how can organizations break entrenched patterns in data management, what fundamental changes are required, and what first steps can they take immediately?

This session will cover the following topics:

• Enhancing knowledge and skills: Practical tools to set up an internal curriculum and offer training.
• Establishing data management as a business function: Applying a use-case-driven approach to solve specific data management challenges. This helps to gradually define roles, processes, and responsibilities.
• Decoupling data and functionality in software development: Both for custom and COTS solutions. Practical steps organizations can take to begin addressing this.
• Communication and change management techniques: How to effectively communicate change and engage employees. Practical tips and examples of change messaging will be shared.
• External collaboration: How to leverage market innovations and learn from (government) challenges and collaborative initiatives.

Our data processes and systems are becoming increasingly complex, and dynamic. Many companies are struggling with maintaining data quality and increasing trust in the data landscape.

Testing offers insight into risks and quality of the data, the systems, and the dataflows. It investigates for instance the performance, the data integrity and the business logic. Much more than finding issues and bugs, testing is about providing confidence and building trust for end-users in the solution that is being built. Testing should therefore be a critical component in any business intelligence and data environment.

In this talk, Suzanne addresses testing knowledge targeted to data environments using TMAP and the VOICE model. She will address DAMA quality characteristics you can adopt and encourage you to communicate the level of confidence you have in the quality of your systems and data. Gain insight and tips on how to test BI & Data solutions.

Key points:

• The importance of testing
• The TMAP and VOICE model of testing
• Building confidence by providing insight into the level of quality
• Testing in a BI & Data environment by looking at:
  • Data flows; looking at how the data moves through the system
  • Data quality; what KPI’s can be used?
  • Data profiling; how to find bugs even before the solution has been built.

How can you truly harness data as a business asset? We will explore the central pillar of Data Mesh: Federated Computational Data Governance. Gain insights into structuring data teams to meet your needs both centrally and locally, and learn how federated data governance can ensure accountability across the organization. We will dive into some Data Governance challenges concerning data products and establishing data contracts to align expectations and responsibilities across teams.

Topics and discussion points:

• Data as a business asset – what does that entail?
• Structuring data teams for flexibility and impact.
• Ensuring data accountability with clear ownership.
• Implementing federated data governance that balances control and autonomy.
• Maintaining long-term sustainability in data management practices.

Erasmus University and TU-Delft joined forces in 2023 to start a new era of research collaboration through an innovative open data sharing platform. Built on the foundations of seamless user experience, robust security, and modern infrastructure, this platform makes sharing and discovering research data effortless. Researchers benefit from intuitive dataset management with automated Digital Object Identifier (DOI) creation, while sophisticated security ensures GDPR compliance without compromising accessibility. The platform features automated dataset synchronization and unique compute-to-data capabilities, allowing secure algorithm execution while protecting sensitive information. Built as an open-source solution, the platform encourages community participation and continuous improvement. Whether you’re a bank analyzing market trends, an insurer seeking risk insights, or a retailer exploring customer behavior patterns, discover how this platform enables secure data collaboration while protecting your intellectual property and maintaining full control over your sensitive information.

This session will highlight the following:

• Platform Architecture: Discover the building blocks of a modern data sharing platform focusing on security and user experience.
• Practical Application: Learn how organizations can share data while maintaining full control over their sensitive information.
• Technical Implementation: Explore the implementation of security measures and automated functions for efficient data sharing.
• Community Building: Understand how to build an active data community between knowledge institutions and businesses.
• Future-Proofing: See how open-source development ensures continuous innovation and AI-readiness of the platform.

• Cloud-Native Architecture: Leveraging the power of cloud platforms for scalable and flexible data solutions
• Data Mesh: Implementing a decentralized approach to data ownership and governance 
• Data Fabric: Exploring metadata-driven solutions for unified data management across diverse environments 
• Data Lakehouse: Combining the best of data lakes and data warehouses for optimized storage and analytics 
• Future Trends: Examining emerging concepts such as AI-powered automation, edge computing, and real-time data processing.

All data modellers want to translate the business needs into a logical data model. Yet, communication gaps between business and IT have historically hindered the development of efficient, aligned solutions. In this presentation Remco will explain the journey towards the Ensemble Logical Model and how to engage the business on this path. The use of the 6 ELM artifacts to be used in the workshops will guide both data modelers and business. As a bonus Remco will discuss the option to have a GPT based upon a specific LLM help in the whole process.

In today’s fast-paced and data-driven world, effective communication between business and IT is no longer optional—it’s essential. Yet, the gap between these two critical functions often leads to misaligned goals, inefficiencies, and lost opportunities. Adapting existing modelling and model storming techniques were not a direct fit for the goal – an Ensemble Logical Model (fit for Data Vault, Anchor, Focal Point, etc). That is why Remco will always use the 6 ELM artifacts to map any business towards a documented, agile and adaptable data model. Small steps are best if you do not want to lose the people in your business when you go from a business case / challenge towards a logical data model which is understood by both business and IT. During this presentation Remco will walk the entire path and explain not only the use of the ELM artifacts itself but also the challenges on the road and why it is important not to skip a step. As a bonus he will discuss the option to have a GPT based upon a specific LLM help in the whole process. 

• A practical step-by-step approach to capturing business stories and translating them into logical, actionable data models.
• Tools and artifacts such as the CBC-List, Event-Canvas, and NBR-Matrix that guide teams in mapping business concepts and refining relationships.
• Proven methods for running workshops that foster collaboration and mutual understanding between business and IT stakeholders.
• Using the “Willibald” case study, showcasing how the ELM approach addresses common challenges in organizational data modeling.
• Showing a data modelling GPT which is following the ELM approach as an assistant for both business as well as data modelers.

When implementing enterprise software packages, the single most common reason for dissatisfaction, or even total failure, is a “data model mismatch.” That’s a bold statement, but it is backed up by the speaker’s 40 year consulting career and many “project recovery” assignments. In those cases, an organisation has spent tens or hundreds of millions or even billions of dollars (or euros) on implementing purchased software, and it simply doesn’t work or works so poorly the organisation is worse off than before. This presentation will share the common factors in these failures, and also some success stories and how data modelling fits in helping solving these problems.

• How one of the world’s most admired companies spent $1B on an implementation and achieved worse performance.
• The public institution that spent $80M configuring cloud-based HR and Payroll software, had nothing functional to show for it, and how the situation was resolved.
• On a brighter note, how a manufacturer applied the techniques we’ll discuss (including data modelling!) over the software vendor’s objections and became a global showcase account.
• “Types vs. Instances,” “Challenge the ones,” and other simple patterns that can make a huge difference.
• Why the “data-process connection” is essential, and the end-to-end business process perspective works so well with the data perspective

In today’s distributed and dynamic data landscapes, traditional approaches to governance and team organization can no longer keep pace. To unlock the full potential of data as a strategic asset, organizations must rethink how they manage, govern, and structure their data functions. This course, rooted in the principles of Federated Computational Data Governance, explores how to balance centralized oversight with distributed autonomy while ensuring accountability and alignment across teams.

Why We Need a New Approach

In many organizations, data governance is struggling to find its place, providing static policies focused on compliance rather than enablers of innovation. However, modern organizations need governance frameworks that are flexible, computational, and adaptive to distributed ecosystems. Federated data governance provides the balance needed to: 

• Enable innovation through decentralized decision-making while maintaining control.
• Foster collaboration and alignment between central oversight and distributed teams 
• Ensure accountability and ownership, even in complex, multi-team environments.

By introducing computational models and distributed governance principles, this course shows how to create a scalable, adaptable data team and framework.

The Three-Dimensional Approach to Structuring Data Teams

Data teams today must operate across three key dimensions to meet the demands of strategic alignment, operational execution, and distributed autonomy. Participants will learn how to organize their teams to: 

1. Strategic and Tactical Levels: Align data initiatives with organizational goals and ensure compliance with overarching governance frameworks.
2. Operational Efficiency: Build robust processes, tools, and workflows to maintain data quality, security, and accessibility.
3. Distributed Autonomy: Embed data functions into business units or regions, empowering them to act independently while adhering to shared principles.

This multi-layered approach ensures that data teams can balance innovation with foundational stability, creating a system that supports agility without sacrificing control.

Ensuring Data Accountability in Distributed Landscapes

As data becomes more distributed, accountability is critical to maintaining trust, quality, and compliance. The course will cover: 

• Data Ownership and Stewardship: Defining clear roles and responsibilities for maintaining data quality and ethical use. 
• Data Contracts: Establishing agreements between producers and consumers to clarify expectations, autonomy, and responsibilities. 
• Creating a Culture of Responsibility: Ensuring that every team member understands their role in the data ecosystem, fostering a sense of ownership and trust.

Key Topics Covered

This course closely aligns with the workshop outline and includes practical, actionable insights into: 

1. Federated Data Governance: How to implement distributed authority while maintaining centralized oversight.
2. Data Products and Data Contracts: Why design reusable, scalable data products and establish clear data contracts to streamline collaboration and accountability.
3. Team Structures for Impact: Organizing data teams across strategic, operational, and distributed dimensions to maximize flexibility and innovation.
4. Sustainability in Governance: Drawing lessons from long-term projects like NASA’s Mars Global Surveyor to ensure that governance systems are adaptable and maintainable over time.

Learning Objectives

• By the end of this course, participants will have a deep understanding of how to: 
• Build and manage federated governance frameworks that balance autonomy and alignment
• Structure data teams to meet the dual needs of transformation and stability
• Embed accountability into every level of the organization through clear roles, data contracts, and a culture of ownership
• Implement sustainable practices that ensure long-term success in data management and governance.

Who is it for?

This course is designed for data leaders, managers, and governance professionals who want to create scalable and effective data organizations. Whether you’re responsible for strategy, compliance, or operations, you’ll gain tools and insights to navigate the evolving data landscape with confidence.

Detailed Workshop Outline

1. Introduction

Overview of Workshop Goals: Explain the importance of data as an asset and why organizations must move beyond treating data as just a service.
Solar System Metaphor: Introduce the concept of the data organization as a solar system, with data teams, governance, and accountability as key planetary bodies that need alignment for optimal performance.Key Points:

• Data as a core asset vs. a service
• The relationship between data, digital, and AI – why they aren’t interchangeable
• The balance between transformation and strong foundational structures in data management.Key Learning: Participants will understand why it’s essential to treat data as a core asset, setting the stage for exploring how to structure data teams and governance effectively.

2.  Data Accountability: Creating a Culture of Ownership and Responsibility

Why Data Accountability Matters: Without clear accountability, data quality, security, and data availability suffer.

• The need for clarity in data ownership
• Creating a culture where team members feel responsible for data
• Defining clear data accountability and responsibility roles across the organization (Data Stewards, Data Owners, etc.).

Practical Steps to Ensure Accountability:

• Setting up reporting structures for data quality
• Understanding the value of Data Products and Data Contracts to codify accountability
• Implementing checks and balances for data privacy and security
• How to align individual accountability with organizational data goals.

Activity: Scenario-based discussion where participants identify where accountability is lacking in a fictional data-driven organization, and propose solutions for creating accountability.

Key Learning: Participants will gain insights into what data accountability entails, ensuring each team member knows their role in maintaining data quality and governance.

3. Data Governance Models: Federated Governance and Distributed Authority

Introduction to Data Governance: Why data governance is essential to manage risk, ensure compliance, and drive effective data use.
Federated Data Governance: What it is and how it works – balancing centralized oversight with distributed ownership across data hubs.

• The Gravitational Pull of strong governance: Central authority ensures alignment, while decentralized teams maintain autonomy.
• How to harmonize data governance policies across departments without losing agility.

Key Components of a Data Governance Framework:

• Roles and Responsibilities
• Data access controls and security measures
• Compliance with legal and ethical guidelines (e.g., GDPR)
• Continuous governance process for maintaining standards.

Activity: In groups, participants will design a federated governance model for a hypothetical organization, ensuring alignment between distributed teams and central governance.

Key Learning: Participants will learn how to implement a federated data governance model that balances control with autonomy, ensuring alignment across the organization.

4. Structuring Data Teams: Balancing Centralized and Distributed Needs

Discussion: Challenges in organizing data teams.

• Centralized vs. decentralized data functions
• Roles and responsibilities: What does a modern data team look like?
• Data Science, Data Engineering, DataOps, Data Management, etc.
• Balancing Innovation and Foundation: How do you organize a team that is both transformative (innovation-focused) and foundational (infrastructure-focused)?

Activity: Group exercise where participants design an ideal data team structure that addresses both distributed and centralized organizational needs.

Key Learning: Participants will learn how to create a data team structure that is flexible enough to meet both innovation-driven and operational demands.

5. Navigating Long-Term Sustainability: Lessons from NASA’s Mars Global Surveyor

Reflection: Insights from NASA’s Mars Global Surveyor and NASA’s Mars Climate Orbiter.

• Long-term data management challenges
• The importance of human involvement (Human-in-the-loop) in managing complex systems
• Sustainability in data practices: How to ensure that your data organization remains agile and maintainable over time.

Key Learning: Participants will leave with strategies for ensuring long-term sustainability and scalability in their data governance and team structures.

6. Wrap-Up and Key Takeaways

Summarizing the Journey: Recap of the solar system metaphor and how the workshop’s concepts apply to real-world data challenges.

Key Takeaways:

• How to structure data teams for maximum flexibility and impact
• Ensuring data accountability through clear roles and ownership
• Designing a federated data governance model to balance distributed autonomy with central oversight
• Practical steps to create a sustainable, future-proof data organization.

Q&A and Next Steps: Open the floor for final questions and discussions about how participants can implement the lessons in their own organizations.

In today’s data-driven world, organizations struggle to maintain a single, trusted view of their data. This half-day workshop provides an essential introduction to Master Data Management (MDM) and the critical role of Data Governance in ensuring data accuracy, consistency, and value. Through interactive discussions and practical insights, participants will explore key concepts of MDM, learn how to identify valuable data domains, and understand why mastering reference data and implementing data governance strategies are essential for business success. By the end of the session, you will be equipped with the knowledge and tools to drive your organization toward trusted, well-governed data.

Learning Objectives

• What is Master Data Management (MDM): Understand the purpose and benefits of MDM in delivering trusted data.
• Why MDM Matters: Learn the business benefits of having a single, authoritative source of truth.
• Identifying Key Data Domains: Recognize the types of data that can be mastered and assess their value to your organization.
• Reference Data Management: Explore what reference data is, how it differs from master data, and why mastering it is crucial.
• The Role of Data Governance in MDM: Understand why Data Governance is critical to the success of any MDM initiative.
• Practical Insights: Learn actionable strategies for getting started with MDM and Data Governance.

Who is it for?

• Business people with a strong interest in data and how to improve it
• Chief Data Officers and all people working in Data Office roles
• Data Management and Data Governance professionals
• IT professionals who are dealing with master data management and reference data
• BI and Analytics specialists who want to ensure that the quality of data they rely on is fit for purpose
• Business Analysts
• Data Architects
• Data and IT consultants.

Detailed Course Outline

1. Introduction and Objectives

• Welcome and introductions
• Overview of course goals

2. Understanding Master Data Management (MDM)

• Definition and purpose of MDM
• Business benefits of a single, trusted source of data

3. Identifying Key Data Domains

• Overview of data domains in MDM
• Determining the value of mastering specific data domains

4. Reference Data Management

• What is Reference Data?
• Differences between Reference Data and Master Data
• Importance of mastering Reference Data

5. The Role of Data Governance in MDM

• Why Data Governance is critical for MDM success
• Understanding the relationship between Data Governance and MDM

6. Key Takeaways and Next Steps

• Recap of critical learning points
• Practical steps for applying MDM and Data Governance principles
• Open Q&A and discussion

Whether you call it a conceptual data model, a domain model, a business object model, or even a “thing model,” the concept model is seeing a worldwide resurgence of interest. Why? Because a concept model is a fundamental technique for improving communication among stakeholders in any sort of initiative. Sadly, that communication often gets lost – in the clouds, in the weeds, or in chasing the latest bright and shiny object. Having experienced this, Business Analysts everywhere are realizing Concept Modelling is a powerful addition to their BA toolkit. This session will even show how a concept model can be used to easily identify use cases, user stories, services, and other functional requirements. 

Realizing the value of concept modelling is also, surprisingly, taking hold in the data community. “Surprisingly” because many data practitioners had seen concept modelling as an “old school” technique. Not anymore! In the past few years, data professionals who have seen their big data, data science/AI, data lake, data mesh, data fabric, data lakehouse, etc. efforts fail to deliver expected benefits realise it is because they are not based on a shared view of the enterprise and the things it cares about. That’s where concept modelling helps. Data management/governance teams are (or should be!) taking advantage of the current support for Concept Modelling. After all, we can’t manage what hasn’t been modelled!

The Agile community is especially seeing the need for concept modelling. Because Agile is now the default approach, even on enterprise-scale initiatives, Agile teams need more than some user stories on Post-its in their backlog. Concept modelling is being embraced as an essential foundation on which to envision and develop solutions. In all these cases, the key is to see a concept model as a description of a business, not a technical description of a database schema. 

This workshop introduces concept modelling from a non-technical perspective, provides tips and guidelines for the analyst, and explores entity-relationship modelling at conceptual and logical levels using techniques that maximise client engagement and understanding. We’ll also look at techniques for facilitating concept modelling sessions (virtually and in-person), applying concept modelling within other disciplines (e.g., process change or business analysis,) and moving into more complex modelling situations. 

Drawing on over forty years of successful consulting and modelling, on projects of every size and type, this session provides proven techniques backed up with current, real-life examples.

Topics include:

• The essence of concept modelling and essential guidelines for avoiding common pitfalls
• Methods for engaging our business clients in conceptual modelling without them realizing it
• Applying an easy, language-oriented approach to initiating development of a concept model
• Why bottom-up techniques often work best
• “Use your words!” – how definitions and assertions improve concept models
• How to quickly develop useful entity definitions while avoiding conflict
• Why a data model needs a sense of direction
• The four most common patterns in data modelling, and the four most common errors in specifying entities
• Making the transition from conceptual to logical using the world’s simplest guide to normalisation
• Understand “the four Ds of data modelling” – definition, dependency, demonstration, and detail
• Tips for conducting a concept model/data model review presentation
• Critical distinctions among conceptual, logical, and physical models
• Using concept models to discover use cases, business events, and other requirements
• Interesting techniques to discover and meet additional requirements
• How concept models help in package implementations, process change, and Agile development

Learning Objectives:

• Understand the essential components of a concept model – things (entities) facts about things (relationships and attributes) and rules
• Use entity-relationship modelling to depict facts and rules about business entities at different levels of detail and perspectives, specifically conceptual (overview) and logical (detailed) models
• Apply a variety of techniques that support the active participation and engagement of business professionals and subject matter experts
• Develop conceptual and logical models quickly using repeatable and Agile methods
• Draw an Entity-Relationship Diagram (ERD) for maximum readability
• Read a concept model/data model, and communicate with specialists using the appropriate terminology.