Programma

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.

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.