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Juha Korpela explores why semantics is the missing foundation of modern data and AI, and how conceptual data modeling helps organisations create shared meaning beyond tools and platforms.

Discover how integrating Vaultspeed and AutomateDV enables end‑to‑end Data Vault automation, aligning business‑driven design with efficient, production‑ready dbt execution.

When joins distort numbers and schemas hide essential relationships, even simple datasets can mislead. Francesco Puppini explains why this happens and presents a cleaner modeling method that eliminates duplication and improves analytical accuracy.

Organisations face huge modelling demands as AI evolves in transport and infrastructure. Adam Sobey reveals how automation can scale this work and deliver real emissions impact.

Unreliable AI insights usually stem from flawed data foundations. Stephen Nwoye outlines why this happens and shares a metadata‑based architecture that helps large language models deliver clearer, more trustworthy results.

Discover how templated modelling and automated pipelines reduce complexity and accelerate Data Vault delivery.

A grounded exploration of how day‑to‑day delivery, analyst involvement, and hands‑on learning help bridge the gap between technical detail and business value, guiding teams toward Data Vault solutions that support meaningful BI results.

Learn how Data Vault gives organisations a strategic edge in handling SAP’s complexity, enabling more flexible integration, better data quality, and faster analytical insights.

Blending decades of experience with his town‑planning metaphor, John Giles explains how robust conceptual models and reusable patterns can transform Data Vault into a business‑friendly, scalable approach that works far beyond analytics.

This session shows how smarter patterns, clearer alignment, and better collaboration can transform Data Vault from a technical framework into a high‑value, business‑first data platform.

In this real‑world story of rapid transformation, Tero‑Matti Kinnen shows how data and automation powered Southwest Finland’s overnight healthcare reform, delivering clarity, stability, and actionable insights under intense time pressure.

Neil Strange explores the real‑world obstacles that complicate Data Vault modelling and provides actionable insights to help teams balance abstraction, design effective hubs and links, and build more reliable data platforms.

With a focus on preventing problems instead of correcting them later, this session highlights how quality principles and upstream controls help teams produce cleaner data and more stable analytics.

A practical look at how smarter automation, clearer patterns, and hands‑on delivery experience can simplify Data Vault development and help teams build better data platforms with confidence.

A real‑world perspective on how modelling discipline, curiosity, and practical SQL experience shape the way LLMs understand data, showing how better structures and metadata enable more natural, conversational querying

A practitioner’s view of combining domain‑driven design, Data Vault methods, and Snowflake capabilities to shape reliable data platforms that scale with the organisation’s needs.

A practical reflection on how lessons from the field and clear engineering principles guide a more sustainable approach to building dbt‑powered Data Vault solutions.

A concise story of practice, partnership, and iteration, showing how the right automation supports modelers in navigating real‑world challenges and producing stable, scalable Data Vault designs.

How McDonald’s Nordics used Data Vault and automation to replace fragmented local reporting with a unified, business‑focused data platform. The session shows how standardisation improved consistency, scalability, and time to insight across the region.

How do we make decisions, and should we entrust them to AI built upon cloud data warehousing?

A public sector migration effort highlights how structured integration approaches improve data consistency, governance and operational reliability in sensitive environments.

Evolving BI systems are moving toward more automated, real-time and integrated architectures that enhance decision-making and reduce analytical latency across organisations.

Using the erwin Suite, organisations can speed up Data Vault adoption through automated design, stronger governance controls and more efficient modelling workflows.

A federated approach to data management enables distributed ownership while maintaining governance consistency across complex enterprise data environments.

Integrating Data Fabric with Data Mesh aligns centralised governance with distributed data ownership to create more adaptable and scalable enterprise architectures.

Continuously documenting database structures ensures evolving systems remain understandable, traceable and easier to manage across iterative development cycles.

While Data Vault highly supports agile development, Kimball-style Data Marts usually do not.

In this session, Erik Bouvin will discuss how data can be moved between your Data Vault and Data Marts in an agile way, without data pipeline dependencies and bypassing PITs using Twine.

Twine is an efficient set-based algorithm that can be applied when you have a table in which you have recorded a history of changes and some other table with related points in time, for which you want to know which historical rows were in effect at those different time points.

Join us to learn about Erik's innovative approach.

Extending CI/CD practices into Data Vault environments strengthens automation, improves data quality controls and accelerates reliable analytics delivery.

Cloud-based data warehousing removes traditional infrastructure constraints, allowing organisations to scale analytics systems more flexibly and efficiently.

Contrasting Data Mesh with Data Vault highlights tensions between decentralised ownership and structured enterprise modelling in modern data architectures.

Repeated iterations in Data Vault projects reflect evolving understanding of modelling principles and the gradual refinement of enterprise data architecture practices.

Applying agile principles to Data Vault 2.0 supports incremental delivery of structured information while maintaining governance and architectural consistency.

This session explores Data Vault performance on Snowflake, focusing on constraints, design choices, and optimisation strategies for scalable cloud data models.

Neil Sparrow explained integrating Kimball models with Data Vault, emphasising its role in preventing data silos and enabling incremental development. He touched on Data Vault architecture and innovative team concepts.

Petr Beles, CEO of Datavault Builder, discussed simplifying Data Warehouse projects and achieving maintainability. He emphasised the importance of the business data model as the foundation for Data Vault-driven data warehousing.

Jean-Francois Saluden and Stephane Vivien addressed the challenge of managing evolving business keys within business process. They discussed scenarios where business keys were constructed during processes, leading to multiple contact keys in the Data Vault system. The session explored strategies to consolidate these keys into a unified one.

Torsten Glunde explored the compatibility of Data Vault and Machine Learning in a model-driven architecture, emphasising the automation and operationalisation potential for machine learning models, including feature management, algorithms, parameters, and training scores.

Jennifer Stirrup presented the question, “Is your Data your Organisational North Star or Death Star?” Many businesses grappled with the transition from static reports to innovative analytics and are struggling to adapt. They have also recognised that implementing Business Intelligence projects demanded a different skill set and a shift in organisational culture. This presentation unveiled some of those answers.

Richard Adams discussed gaining senior leadership support for Data Vault initiatives, the “7 Steps to Maximise Data Value,” and business user perspectives on Data Vault initiatives.

Francesco Puppini highlighted the benefits of using the Unified Star Schema (USS) in building an Information Mart. He presented an alternative approach for handling large data volumes and included a live demo.

Alessia Pulieri shared the transformation journey from an outdated Data Warehouse to a scalable, flexible Data Vault 2.0 Enterprise Data Warehouse achieved through agile practices and strategic partnerships.

Dylan Roe presented a case study showcasing how Osiris Trading adopted and learned Data Vault. The presentation detailed the challenges faced during the implementation and provided insights into the status of the project.

Mustafa Rhemtulla presented a case study on Oodle Car Finance’s experience in implementing Data Vault.

This session simplifies cloud ML, focusing on practical setup, architecture choices, and moving smoothly from experimentation to production.

This session shows how embedding testing into Data Vault workflows helps teams deliver higher-quality data faster and with greater confidence in outcomes.

This session explains why even Data Vault implementations benefit from a governing layer, helping teams manage complexity, ensure consistency, and maintain long-term scalability.

This session shows how automation can simplify Data Vault modelling and speed up delivery while maintaining structure and consistency across projects.

This session explores how Business Intelligence is evolving with real-time analytics, modern data stacks, and more adaptive decision-making approaches.

Misaligned business requirements often lead to inconsistent analytics, while conceptual data modelling provides a shared structural blueprint that stabilises design decisions.

Through agile delivery methods in data warehousing and BI, organisations address long-standing integration, scalability and change management challenges.

Recurring implementation mistakes in Data Vault projects reveal where teams misapply modelling principles and how better delivery choices emerge.

As organisations adopt Data Mesh, data ownership is increasingly organised around business domains, changing how scalability, accountability and delivery are managed across teams.

By introducing ontologies into Data Vault design, teams can restructure evolving models to maintain shared meaning and reduce semantic inconsistency over time.

Rather than enforcing rigid oversight layers, agile non-invasive governance integrates control mechanisms directly into data processes to maintain compliance without slowing delivery.

Combining Data Vault automation with governance and data quality raises questions about how consistency and control can be maintained at scale.

Handling time correctly in distributed data systems introduces architectural challenges that directly influence data accuracy, sequencing and operational reliability.

From modelling complexity to integration overhead, recurring Data Vault challenges reveal where architecture decisions most affect long-term maintainability.

Comparing Data Fabric with Data Mesh highlights how architectural choices influence interoperability, decentralisation and enterprise-wide data coordination.

As data ecosystems expand, accelerating business taxonomy mapping becomes increasingly important for maintaining shared definitions and structural consistency.

Without integrated DataOps capabilities, Data Vault environments struggle to maintain operational efficiency, deployment consistency and scalable delivery over time.

New approaches to data sharing are changing how organisations collaborate, distribute access and create value from shared information ecosystems.

As organisations demand faster insights, integrating streaming data into Data Vault architectures changes how ingestion, latency and scalability are managed.

Combining Snowflake with Azure services creates a cloud-native foundation for Data Vault architectures built around scalability, orchestration and platform interoperability.

New perspectives on Data Vault methodology highlight evolving approaches to modelling standards, implementation strategy and enterprise data architecture.

As Data Vault continues to evolve, future developments centre on scalability, automation and adapting modelling practices to increasingly complex data ecosystems.

Early Data Vault implementations frequently expose avoidable design and delivery challenges that later inform more robust modelling and architectural decisions.

Recent Snowflake capabilities strengthen Data Vault implementations by improving performance, simplifying scaling and supporting more efficient cloud-based data architecture.

As dbt adoption grows, its role in Data Vault environments highlights how transformation tooling improves structure, testing and scalable analytics delivery.

A real-world insurance implementation demonstrates how Data Vault supports scalable integration and consistent data delivery across large, regulated environments.

In banking environments, Data Vault enables structured data integration that strengthens governance and improves consistency across reporting and analytics systems.

A real-world agricultural use case shows how Data Vault enables structured integration of complex datasets to support scalable analytics and operational insight.

Blending dimensional modelling principles into a unified structure improves consistency between analytics layers while reducing complexity in enterprise reporting systems.

Data Vault outcomes are shaped early by how well business concepts are structured, linking domain understanding to scalable architectural design decisions.

At its core, Data Vault is a modelling methodology designed to structure enterprise data for scalability, traceability and adaptable integration across systems.

Automation tools like erwin Data Intelligence streamline Data Vault design by reducing manual modelling effort while improving governance and structural consistency.

Early-stage data warehouse design emphasises rapid yet structured setup choices that influence long-term scalability, integration and analytics performance.

Cloud-native capabilities in Snowflake enable Data Vault implementations to scale efficiently while maintaining flexibility in modelling and enterprise data processing.

Data Vault strengthens risk management frameworks by providing structured, auditable data models that improve visibility and consistency across risk reporting systems.

Embedding business rules within Data Vault structures clarifies how enterprise logic is applied, supporting consistent interpretation and scalable data processing.

Structured Data Vault modelling provides a consistent framework for integrating complex enterprise data while maintaining scalability and traceability across systems.

A well-structured business case connects data initiatives to measurable value, helping organisations justify investment and align stakeholders on delivery outcomes.

Overcoming common adoption challenges enables organisations to better realise the benefits of Data Vault through clearer design practices and improved delivery alignment.

Automation in Data Vault accelerates delivery by standardising modelling workflows and reducing manual complexity in enterprise data architecture.

Data Vault 2.0 extends core modelling principles with stronger automation, agility and governance to support modern enterprise data ecosystems at scale.

Integration architecture in Data Vault defines how disparate data sources are systematically combined to support scalable and auditable enterprise data management.

Key advantages of Data Vault 2.0 include improved automation, enhanced scalability and better alignment between governance and modern data architecture needs.

A real-world healthcare implementation demonstrates how Data Vault 2.0 supports scalable integration and consistent analytics across complex, regulated data ecosystems.