Programma

Data Mesh is uitgegroeid tot een van de meest invloedrijke concepten in het moderne data management. Door gegevens te organiseren rond business domeinen, domeinteams zeggenschap te geven over hun eigen gegevens en alles te delen als dataproducten, kunnen organisaties hun data werkzaamheden eindelijk opschalen buiten het centrale team om, dat altijd het knelpunt vormt. Maar decentralisatie heeft een addertje onder het gras dat de meeste teams te laat ontdekken: wanneer elk domein zijn eigen taal spreekt en zijn eigen dataproducten bouwt, wordt het begrijpen van de gegevens binnen de hele organisatie het nieuwe knelpunt. Wat is een ‘klant’ in de verkoopafdeling versus de financiële afdeling? Wat bevat dit dataproduct eigenlijk, en kan ik het vertrouwen? Hoe vind ik het überhaupt? Dit zijn geen technologische problemen: het zijn inhoudelijke problemen, en geen enkel technisch platform lost ze op zichzelf op.

Dit is waar informatiearchitectuur en datamodellering hun plaats verdienen in het hart van een Data Mesh. Datamodellering wordt vaak afgedaan als een trage, technische backoffice-activiteit. In werkelijkheid is het de meest betrouwbare manier om vast te leggen wat de business moet weten, in de taal die de business daadwerkelijk gebruikt. Vervolgens kunnen we dit gedeelde begrip vertalen naar goed ontworpen, herbruikbare dataproducten. Een conceptueel model beschrijft de realiteit achter de data: de zaken die voor een domein van belang zijn en hoe deze met elkaar in verband staan. Een logisch model zet dat begrip om in een concrete structuur die geschikt is voor een specifieke use case. Als dit modelleringswerk goed wordt uitgevoerd, vormt het de brug tussen de business realiteit en de technische implementatie, en de basis voor semantische interoperabiliteit tussen onafhankelijke domeinen.

In deze workshop van een hele dag doorloopt u dat traject van begin tot eind. We beginnen met de basisprincipes van Data Mesh — de vier principes, domeinen en dataproducten — en de uitdaging op het gebied van interoperabiliteit die hieruit voortvloeit. Vervolgens leert u de basisprincipes van conceptuele modellering en past u deze toe in een praktische oefening, waarbij u een echt domein voor een fictieve online retailer modelleert en de bijbehorende glossary opstelt. Van daaruit gaan we verder met logische modellering als onderdeel van het ontwerp van dataproducten, en met de metagegevens, datacontracten en glossaries die de betekenis van een domein over de grenzen heen blootleggen. Ten slotte kijken we nog eens naar het operationele model: de rollen, feedbackloops en structuren op ondernemingsniveau die ervoor zorgen dat gefedereerde teams autonoom kunnen blijven en toch aan hetzelfde touw trekken. De nadruk ligt daarbij op de praktijk en toegankelijkheid: u hoeft geen modelleringsspecialist te zijn om de cursus te volgen, en na afloop kunt u deze ideeën in de eigen organisatie toepassen.

Leerdoelen

• Data Mesh en de belangrijkste uitdaging ervan begrijpen: Krijg inzicht in het Data Mesh-paradigma, de vier principes ervan en waarom gefedereerd domeineigendom een semantisch interoperabiliteitsprobleem veroorzaakt op de domeingrenzen.
• Betekenis vastleggen met conceptuele modellering: Leer hoe je een domein in zakelijke taal beschrijft met behulp van entiteiten, relaties en attributen – en hoe je de veelvoorkomende valkuilen vermijdt.
• Domeindefinities en glossaries opstellen: Begrijp hoe u duidelijke definities in zakelijke taal schrijft die de taal van een domein weergeven en gegevens begrijpelijk maken voor anderen.
• Dataproducten ontwerpen met logische modellering: Leer hoe logische modellen dienen als use-case-specifieke ontwerpen die zijn afgeleid van het conceptuele model van een domein.
• Maak context zichtbaar over domeingrenzen heen: ontdek hoe dataproductdefinities, datacontracten en metadatastandaarden (ODPS, ODCS) de betekenis van een domein vindbaar en interoperabel maken binnen de hele onderneming.
• Ga om met taalproblemen: leer omgaan met synoniemen en homoniemen (polysemen) door gebruik te maken van voorkeurs- en alternatieve labels, domein glossaries en gedeelde enterprise glossaries.
• Informatiearchitectuur op schaal beheren: Begrijp de rollen, verantwoordelijkheden, feedbackloops en het Enterprise Knowledge Plane die ervoor zorgen dat de semantiek binnen autonome domeinteams op één lijn blijft.

Bestemd voor ú

Deze workshop is bedoeld voor iedereen die verantwoordelijk is voor het begrijpelijk, betrouwbaar en herbruikbaar maken van data in een gedecentraliseerde of domeingerichte omgeving. Er is geen diepgaande achtergrond in modellering vereist: de concepten worden vanaf de basis geïntroduceerd.

• Data-architecten en datamodelleerders
• Chief Data Officers en personen in Data Office-functies
• Data-product-owners en domeineigenaren
• Professionals op het gebied van databeheer en datagovernance
• Data-engineers en platformteams die werken met domeinen en dataproducten
• BI- en analytics-specialisten die afhankelijk zijn van goed gedefinieerde, betrouwbare data
• Businessanalisten die een brug slaan tussen bedrijfsbehoeften en data
• Data- en IT-consultants die advies geven over Data Mesh, dataproducten of informatiearchitectuur.

Gedetailleerde Programma inhoud

1. Introduction and Objectives

• Welcome and introductions
• Overview of the day’s goals and structure

2. Data Mesh Basics

• The general idea and background of Data Mesh
• The four principles: domain-driven ownership, data as a product, self-serve platform, and federated computational governance
• Domains and domain teams: what a “domain” is and how to define one
• Data products: definition, anatomy, and types (source-aligned, aggregate, consumer-aligned)
• The interoperability challenge: technical vs. semantic interoperability at the domain boundary

3. Conceptual Models for Cross-Domain Understanding

• Why data needs business context to be useful
• How data models capture context
• The three levels of modeling: conceptual, logical, and physical
• Basics of conceptual modeling: entities, relationships, and attributes
• Identifying the real business objects and common pitfalls to avoid
• Building entity definitions and domain glossaries

4. Hands-On Exercise: Modeling a Domain

• Introducing “Storefront”, a fictional online retailer
• Defining domain boundaries: who owns what
• Identifying entities within a domain
• Building a conceptual model and named relationships
• Creating definitions and a Domain Glossary

5. Data Modeling as Part of Data Product Design

• The data product design process
• Understanding product scope within the domain model
• Logical models as product-level design and documentation
• Deriving logical models from the conceptual model
• Connecting the data product to its business context and maintaining links to the domain model

6. Ensuring Semantic Interoperability at the Domain Boundary

• Exposing metadata from domains and data products
• Data Product Definitions as collections of business and technical metadata
• Data Contract basics: promises, machine-readability, schema compliance, and versioning
• Example standards: Open Data Product Standard (ODPS) and Open Data Contract Standard (ODCS)
• Domain glossaries vs. shared enterprise glossaries
• Dealing with polysemes: synonyms, homonyms, and the Enterprise Knowledge Plane

7. Data Mesh Information Architecture Operating Model

• How information architecture creates and scales value
• Roles and teams: data product owner, domain owner, platform team, and domain DevOps team
• Product ownership, backlog management, and the data product lifecycle
• Modeling at the design stage and the importance of feedback loops
• Organizing data modeling on two levels: product and enterprise/domain
• Cross-domain interoperability and the Enterprise Knowledge Plane
• The goal: context-aware data utilization for AI, applications, and people

8. Conclusions and Next Steps

• Key takeaways
• Where to start in your own organization
• How to learn more
• Open Q&A and discussion

Rapid requirements gathering using the Information Product Canvas

The Information Product Canvas is a pattern template for capturing the data and information requirements for a single Information Product, in 30 minutes, in a language both stakeholders and data teams understand. In this hands-on, full-day workshop Shane Gibson, the creator of the canvas, teaches you the twelve areas of the canvas, how to complete it interactively with your stakeholders, and how to use it to prioritise what to build first. You spend half the day completing canvases in small groups on a realistic case study, and finish with a hands-on session on using an AI assistant alongside the canvas. You leave able to run the canvas pattern storming workshop with your own stakeholders the next working day.

Course description

Ask a data team what slows them down most and the answer is rarely the technology. It is the gap between what stakeholders ask for and what the team ends up building. Stakeholders and data teams speak very different languages. Requirements take weeks or months to gather, and nobody can say when they are done. They arrive full of ambiguity, describe a dashboard when the real need is a decision, and focus on a specification of what is wanted rather than why it is needed. The data team cannot work out what to build from the requirements, so they guess, build, and then hear the words every data professional dreads: “That’s not quite what I wanted.” Meanwhile the detailed requirements document nobody reads sits in a folder, and the data team is treated as an order taker rather than an enabler by stakeholders.

The Information Product Canvas closes that gap with one shared language and one conversation. It is a pattern template, twelve areas on a single canvas page, for defining a single Information Product: the business questions it must answer, the actions and outcomes those answers drive, who will use it and how, what data it needs, what is in and out of scope, and how big a job it is.
The stakeholder and the data team fill it in together, in one 30-minute session, so the requirements are validated in real time rather than weeks later. Because the canvas captures the why as well as the what, it also becomes the basis for prioritising across many Information Products and for feeding the rest of the Information Value Stream: the concept model, the transformation logic, the metric definitions, the acceptance tests and the build.
It is a pattern, not a framework: pick it up, use it, change what doesn’t fit, keep using it. Shane created the canvas and has iterated it with data and analytic teams for more than a decade. It is documented in his book “An Agile Data Guide to Information Product Canvas” and extended by more than ninety companion articles.
In this full-day workshop you learn the canvas by using it.

After an overview of the pattern template and the problems it solves, we work through the twelve canvas areas step by step with a worked example, explaining what is captured in each area and why, with Q&A after each one. Half the day is spent in small groups populating canvases based on a realistic case study organisation, so you practise the pattern rather than watch slides about it. You then learn how to facilitate the canvas live with stakeholders using the Pattern Storming workshop format, review real-world canvases from a range of industries, and see how completed canvases are used to prioritise and to drive delivery.
The day closes with a hands-on session on using an LLM assistant such as Claude or ChatGPT alongside the canvas, bring your laptop and your own LLM access to take part. No prior experience with the canvas, or with agile ways of working, is required.

Learning objectives

• Capture the requirements for one Information Product in 30 minutes: Deliver the data elephant one bite at a time, defining requirements in small, well-formed chunks the data team can deliver in days or weeks rather than months.
• Speak a shared language: Learn a data requirements language that both stakeholders and data teams understand, so one conversation produces requirements the stakeholders recognises and the team can build from.
• Collaborate visually with stakeholders: Fill in the canvas in front of stakeholders and get real-time validation, instead of writing a document and waiting weeks for feedback.
• Capture the what and the why: Elicit the actions stakeholders will take and the business outcomes they expect, turning the data team from order taker into business value enablers.
• Make scope and priority visible: Use Will / Won’t, T-shirt sizing and stack ranking so stakeholders can make trade-off decisions and see at a glance what gets built first.
• Facilitate the canvas and teach it to your team: Run the structured Pattern Storming workshop with your own stakeholders, avoid the common anti-patterns, and leave with a repeatable pattern your whole team can run the next working day.
• Use AI alongside the canvas: Apply practical techniques for using an LLM assistant to draft, challenge and refine canvases, while keeping the stakeholder conversation at the centre.
• Use the completed canvas downstream: See how a well-formed canvas feeds concept models, acceptance testing and prototyping, so the requirements keep paying off long after the workshop.

Who is it for?

This workshop is for anyone who gathers, defines, prioritises or builds from data and information requirements, on either side of the conversation. No prior experience with the canvas, or with agile ways of working, is required.

• Business Analysts who gather data requirements on behalf of a data team
• Data Product Managers and Product Owners who need to define and prioritise data work
• Data Analysts who want to agree outcomes with stakeholders before starting work
• Data Engineers who are frustrated with requirements they cannot build from
• Data Architects and Data Modellers who need well-formed requirements as input to their models
• Data Leaders looking for one repeatable requirements pattern for their team
• Agile Coaches and Scrum Masters who want to add a data-specific pattern to their toolkit
• Business stakeholders who commission Information Products and want to be understood the first time.

Detailed Course Outline

Bring your laptop and access to an LLM assistant of your choice (Claude, ChatGPT or similar).

Introduction and Objectives

• Welcome and introductions
• Team up: forming the small groups you will work in for the day
• Overview of the day’s goals and structure

Why Data Requirements Keep Going Wrong

• The language gap between stakeholders and data teams
• The seven symptoms: too slow, never done, ambiguous, unbuildable, the what without the why, unused documents, order taking
• What an Information Product is, and why we define one at a time
• Where the canvas sits in the Information Value Stream: from discovery to delivery

The Information Product Canvas: An Overview

• The canvas as a pattern template, not a framework
• The twelve areas at a glance and how they fit together
• The difference between a canvas and a requirements document

The Twelve Canvas Areas, Step by Step

• Business Questions: the questions the Information Product must answer, how many, how long, how much, why
• Actions and Outcomes: what people will do with the answers, and what changes in the organisation as a result
• Personas: who will use the Information Product
• Delivery Type: how they want it delivered, dashboard, report, data service, API, AI Agent, or something else
• Data Sync: how fresh the data needs to be, from monthly to real time
• Core Business Events: the data needed to answer the questions, captured as core business concepts and events
• Feature Stories: the specific features and behaviours the Information Product needs
• Will / Won’t: what is in scope, what is explicitly out, and how that enables trade-off decisions
• Information Product Name: giving the work a name everyone uses
• Product Owner: who makes the trade-off decisions on behalf of the stakeholders
• Vision Statement: the elevator pitch that distils the canvas into a few sentences
• T-Shirt Size: how big a job this is, without false precision
• Cross-checking the areas: using the canvas as a quality check on the completeness of the requirements

Hands-On: Completing a Canvas for the Case Study

• Introducing the case study organisation, its stakeholders and its problems
• Working in small groups to populate each area of the canvas as it is introduced
• Playing the roles of stakeholder and data team
• Group playback: comparing canvases across groups and spotting the differences
• Feedback and coaching on each group’s canvas

Facilitating the Canvas with Stakeholders: Pattern Storming

• The Pattern Storming Canvas Workshop: a structured, collaborative session for completing a canvas with stakeholders
• Sequencing the areas, timeboxing, and keeping the conversation moving
• Getting to a completed first canvas in 30 minutes
• Common anti-patterns and how to avoid them
• Adapting the pattern: what to change and what to keep

Real World Examples

• Real Information Product Canvases from a range of industries and use cases
• What a strong canvas looks like, and what a weak one looks like
• Define Once, Reuse Often: how canvases compound across a portfolio of Information Products
• Prioritising across canvases: stack ranking to decide what to build first

Hands-On with AI: Using an LLM Alongside the Canvas
Bring your laptop and access to an LLM assistant of your choice (Claude, ChatGPT or similar).

• Where an LLM helps in the canvas process, and where it doesn’t
• Using the canvas as structured context for an LLM
• Techniques for drafting, challenging and refining a canvas with an LLM
• Keeping the human conversation at the centre

From Canvas to Delivery

• Using the completed canvas as the input to creating a concept model
• Turning business questions, actions and outcomes into acceptance tests
• From canvas to prototype: getting something in front of the stakeholder early
• Keeping the canvas alive as the Information Product evolves

Conclusions and Next Steps

• Key takeaways
• Running your first canvas with your own stakeholders next week
• Continuing the journey: the book, the companion articles and the AI Coach
• Open Q&A and discussion