Downloads

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?

The IOPC were developing a new Case Management System migrating data and documents using the Enterprise Data Warehouse

Can there be a role for Data Vault? Where we might be heading over the next few years?

This presentation will highlight the flexibility and acceleration of Data Vault implementation using the latest versions of erwin Solutions.

Chad tackles the necessity of modern data management in an age of hyper iteration, experimentation, and AI.

This presentation introduces a hybrid data architecture that combines data mesh and data fabric elements, automating Data Vault processes.

In this talk, Alexey will show you how Minimal Modelling can help you reduce project onboarding time, supports better communication with business stakeholders, and reduce project costs.

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.

In today’s data-driven world, the quality of data can be the difference between success and failure for businesses. At the same time, focusing on data quality and governance in the wrong way can paralyse an organisation.

This insightful session delves into how combining simplified use of powerful CI/CD with the right approach to data modelling provides an answer to balancing quality and agility.

Hung Dang, CEO & Founder of Y42, will delve into the complexities of 'bad data,' exploring its sources, impacts, and the shortcomings of traditional approaches to managing it while touching upon the need for flexibility in adding new use cases. Join us for a comprehensive session that not only identifies the problems but also offers practical, frontier solution approaches.

The past five years has seen an explosion of innovation and new technical forms as cloud data warehousing has gone mainstream. But confusion has grown too. After all, the business needs are largely unchanged. So, why are there so many options and approaches? How do they differ? Which one may be the best choice? And why?

In this talk, Dr. Barry Devlin—a founder of the entire data warehousing industry—offers high level thoughts on these questions. Drawing lessons from the long history of data warehousing, he defines an all-embracing architecture and draws specific architectural design patterns for each of these modern approaches.

Data Mesh is the latest and much talked about data platform architecture to emerge. The Data Mesh approach is more broad-scoped than other methods out there, and deliberately doesn’t provide details of exactly how to build some of the components needed for a Data Platform solution.

It includes recommendations on data governance, data product management, federation, and self-service. It focuses on what a business needs to put in place for effective analytics, providing an architecture, and identifying the interfaces that components need to satisfy to make the architecture work.

It is early days yet, however, there has been a lot of industry interest and there are companies trying to implement a Data Mesh. For some of these, a Data Vault is part of the solution.

This presentation will assess the Data Mesh method and explain how a Data Vault can be used to underpin success.

Bringing Data Vault to a new company can be a daunting task. Its proof of concept work is likely going to be built by you, and no one else. There is a lot to learn and I'd like to walk folks through what feels like my fifty first dates with Data Vault. There were some failures (not fifty!) between now and when I started, and I'd like to help folks avoid those mistakes by walking through my journey with the methodology.

In this presentation, expert database architect, Bruce McCartney, explored various alternatives for taking a raw Data Vault and building useful information, so that you can select the most appropriate method(s) for your use-case.

Bruce also examined the various informational toolkits available, to develop the raw data into the information needed.

Bruce has been working with Data Vault solutions for over 15 years, and leading Certified Data Vault Practitioner certification classes and consulting with PerformanceG2 for over 4 years. His professional background is extensive, including 30 years of involvement with Oracle.

Patrick Cuba, one of the world’s leading thought-leaders on the use of Data Vault, and author of “The Data Vault Guru: A Pragmatic Guide on Building a Data Vault”, explains how to make your Data Vault sing using Snowflake’s unique Data Cloud Architecture.

The Snowflake Senior Solutions Architect shares different techniques and ideas that you could use in your own Data Vault, as well as exploring the new workshop he has developed, which contains add-on information regarding new features introduced by Snowflake.

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.

Machine Learning can be difficult, and not everyone knows how to get started with it. Some people wonder if you need to go back to school to relearn mathematics and statistics, whereas others ask if you need to study all the processes, algorithms and hyperparameters?

There’s no need to panic!

In this presentation Heli and Matias Helskyaho show you how to get started with Machine Learning, whilst discussing AutoML and AI services.

In this presentation, Chris Fisher, experienced data engineer at Datavault, explains how Test-Driven Development in a Data Vault implementation can deliver business value to your organisation or clients. Chris describes how to test cloud infrastructure, data loading patterns, data transformations, business rules, and many other aspects of the Data Vault 2.0 methodology.

As a bonus, Chris will demonstrate how development and deployment frequency can be increased and become more efficient, leading to faster delivery of value.

Senior data architect Christopher Siegfried explains why you need a Data Vault for your Data Vault. In this presentation, Christopher examines how Data Vault knowledge and metadata can be captured and leveraged by adding a MetaVault layer to the Data Vault.

He explains that as a Data Vault grows, it is a challenge to track the organisational knowledge of the Data Vault itself, showing the importance of a MetaVault.

Petr Beles, CEO of Datavault Builder addresses: the complexity of Data Warehouse projects, the problem of Data Warehouse implementations and provides insights on approaching complexity and maintainability.

Petr explains that the business data model is not only more constant over time but should be the foundation of your Data Vault driven data warehouse automation.

In this presentation, Wayne Eckerson, President of the Eckerson Group, international thought leader and best-selling author, tells you about his views on the future of business intelligence.

This presentation examines the latest trends in business intelligence and data management, with the aim of helping attendees make better decisions about how to deploy data and analytics resources, to their organisations.

Juha Korpela, a leading data modelling expert at Ellie Technologies explains the use of conceptual modelling as the starting point of a Data Vault project.

In his presentation Juha explains the value of being business driven and why you should always start a Data Vault project with conceptual modelling. He highlights why it is a must have and how it fits in with the rest of the process.

What can we do when our uses can’t tell us what data they need? In this presentation, Scott Ambler the consulting methodologist with Ambysoft Inc, addresses challenging questions such as – how can we proceed without modelling everything up front?

Scott explains how the world is moving at a rapid pace and how we must be able to respond to these changing conditions within the agile data warehousing and business intelligence industry.

Data and analytics leader Veronika Durgin takes us on a Data Vault driving test, as we identify and learn the different stages of understanding, using and building a Data Vault warehouse. She covers what things should be considered when choosing Data Vault and the steps to success based upon her varied experiences working in different organisations.

Jacek Majchzak co-author of ‘Data Mesh in Action’ is a lead architect in the drug discovery area. Here he explains his insights on Data Mesh using a simple example using a strong focus on domain driven design.

Richard Strange explains, one of the many advantages of the Data Vault 2.0 approach is that warehouses can be refactored quickly and easily, but it can be challenging to know where to begin. An ontological approach might be a good place to start. By putting business processes and concepts into a semantic framework, you can translate business users’ experiences into a skeleton of your core data model. But this ontological approach can go further than supporting a refactor!

Dominic Cahill, specialist in data governance describes the key features of implementing data governance in an agile and practical way. Using real-world examples, he presents to the Data Vault User Group about the underlying principles of delivering effective data governance and data quality in a non-invasive way.

Richard Adams and Paul Kinnier, from erwin talk through the key features of erwin’s Data Intelligence Suite.
They discuss combining Data Vault Automation with Data Governance and Data Quality. Data governance functionality ensures regulatory requirements are met while also making the data available for multiple teams across the business. The ability to generate Data Vault structures gives development teams additional productivity.

Christian Kaul chair of the knowledge gap, talks about the complexity of time in your data warehouse, what issues may arrive and which timelines you should be using.

Christian explains the advantage of Dan Linstedt’s Data Vault method which helps when it comes to good data governance and data lineage. he discusses this and more in his presentation.

A selection of challenges were covered by Neil in this presentation, ranging from complex issues to the simplest misconceptions that occur when engaging in a Data Vault method. The reality of implementing a Data Vault was addressed with solutions from bottom-up modelling to improving communications. The value of business engagement in the model is examined in depth. Real world examples were used to create some key takeaways.

Dr Barry Devlin author of ‘Business UnIntelligence’ presented two new frameworks, data fabric and data mesh. He discusses the benefits and draw backs and how they can relate to the data warehouse, lake hub or lakehouse. They aim to address the issues created by legacy data warehouses and heavily centralised data platforms, which can seriously limit an organisation’s ability to react to the rapidly changing data landscape.

Automation is one of the great benefits of embracing the Data Vault method. Dirk presented a persuasive case for how to map your business into your data warehouse using Data Vault principles to make the most of these automation features. He highlights some of the issues with both creating a source system Data Vault and a pure Enterprise data model. He explains his approach a how he uses a business taxonomy with automation to deliver results quickly and effectively.

Guy and Justin of DataOps.live made a compelling case for using DataOps principles in any data warehouse or data platform project, not just Data Vault. DataOps brings some of the principles established in the DevOps movement in the software engineering world into the data world. They then went on to explain how the DataOps.live product can help users to embrace DataOps.

Steven, Executive Director at Link Digital and Co-Steward at CKAN Association, presented a fascinating look at how the open source community has been building and refactoring a data management system, CKAN, to meet the evolving needs of Open Government Data programs, Data Sharing initiatives and corporate data catalogues. Open data is becoming more and more important especially public sector organisations sharing data at scale. Users of CKAN include the UK National Health Service, New South Wales Government, the Government of Canada, data.gov in the USA and opendata.swiss.

Bruce McCartney presented the concepts and considerations needed when populating the Data Vault data warehouse in near real-time using streaming technology. The presentation covered the basic architectural choices and loading/processing alternatives available to populate business vault and information marts in near real-time.

There is a plethora of choices when building a Data Vault on Snowflake with Azure. Neil has now been involved in deploying a number with a range of different clients. In this presentation he shares some of the design decisions taken and the thinking behind them.

Patrick introduced his book “The Data Vault Guru – a pragmatic guide on building a data vault”. During the presentation he introduced the book, why he wrote it and why he is so enthusiastic about Data Vault before walking through the scope of the book and the topics covered.

Dan presented his vision for the future of future of Business Intelligence and Analytics with seven key predictions for the next five years. Then, as the creator of the Data Vault method, he put this into context with Data Vault 2.0 and why the Data Vault method will become even more important going on to make some prediction for how implementations of Data Vault will evolve over the next 5 years.

During this meeting some experienced Data Vault professionals presented their top three “things I wish I knew before I started my first Data Vault Project”. This meeting included five presentations with their invaluable top tips for those planning to implement a Data Vault. The contributors, generously sharing their expertise, were Gábor Gollnhofer of Metaconsulting in Hungary, Iliana Iankoulova and Ana Narciso of Picnic in the Netherlands, Mike Magalsky of infoVia in the USA, Dirk Vermeiren of VaultSpeed in Belgium and Neil Strange of Datavault in the UK.

Kent and Dmitry presenting some of the new and upcoming features in Snowflake and specifically how these features can be used in a Data Vault project. These include Materialised Views, Continuous Data Pipes, Dynamic Data Masking and use of unstructured data. Finally new ways to consume data with exciting new possibilities for exchange data globally within and importantly outside your organisation.

Drew Banin, Co-Founder and Chief Product Officer at Fishtown Analytics, the creators of the dbt, explained what dbt is. How it works and why he believes that it is seeing such a rapid growth in popularity. He gives some hints where dbt is going and exciting trends he sees the marketplace over the coming years.

Adam introduced an on-going project at Tokio Marine HCC, a specialist insurance company, to replace their existing Data Warehouse with a Data Vault Data Warehouse. He detailed their reasons for selecting the Data Vault method and presented the architecture and tools being used to implement their new Data Warehouse.

Tim, Programme Manager at Argenta Bank in Belgium and Jonas the CEO of VaultSpeed presented their experiences implementing a complex Data Vault project at a retail bank. They explored the business drivers for the project and why Data Vault was a good fit. They then explored the architecture, the tools used, the use of virtualisation and made recommendations to others starting on the journey.

Veronika discussed the introduction of a greenfield Data Warehouse into an agricultural technology business, Indigo AG, in the USA. She shared some of her experiences with adopting Data Vault via the good, the bad and the ugly of their initial implementation and their plans for the future.

The Data (or Information) Mart used in a Data Vault implementation often use the Kimball Dimensional Modelling techniques. The star schema approach is easy to use. But the Kimball approach has a big limitation: it does address what happens when a query involves multiple fact tables. How do we merge two star schemas? It usually results in an “ad-hoc approach”, where a data analyst writes an SQL query, specific for a business requirement. But this means that the business users do not have full access to a self-service analytics experience: for every new requirement they need to go through a data expert. What if we could build a special data structure that involves all the required facts and dimensions, all connected together in a single star schema, easy to use, and always available for self-service analytics?

Subsequent to this presentation Francesco and Bill’s book “The Unified Star Schema: An Agile and Resilient Approach to Data Warehouse and Analytics Design” is now available on Amazon and at https://technicspub.com/uss/.

John Giles author of “The Nimble Elephant: Agile Delivery of Data Models Using a Pattern-based Approach” and “The Elephant in the Fridge: Guided steps to Data Vault success through building business-centered models” gave an entertaining presentation on why the business model is key to making your Data Vault project a success. He even answers the questions – what is an ontology? And why do I need one!

Neil presented an introduction to Data Vault 2.0 explaining and when it is a suitable approach and why. He gave a quick outline of Hubs, Links and Satellites, illustrated by a simple example, followed by an explanation of why Data Vault lends itself to automation for standardisation and increased operational efficiency.

Alan presented an introduction to the erwin product offerings and how they help organisations bring order to their “data chaos”. He set the scene for a hands-on demo of how the automation tools within the erwin Data Intelligence suite can be used for Data Vault 2.0.

Alex Higgs, lead developer of dbtvault, presented and demonstrated dbtvault, a new quick start, open source tool for building a Data Vault data warehouse using dbt and Snowflake.

Dmytro Yaroshenko, Senior Solutions Architect, explained some of the benefits of using the Snowflake cloud data platform for building highly scalable Data Vault Data Warehouses.

Highly experienced data expert, Paul Ramsay, talked about his experiences of a corporate risk management use case and why Data Vault proved a great solution.

How to add business rules to your Data Vault project The area of how to use Business Rules in Data Vault has been evolving over the last couple of years and some of the latest approaches are much more robust. This presentation will bring you up to date with the latest thinking.

Neil Strange, CEO of consultancy Datavault UK, explained how to Data Vault 2.0 data modelling to produce a powerful and scalable data warehouse system.

In his presentation to the User Group Neil Strange explored the issues around building a business case, and provided some ideas for successful approaches to getting your business case approved.

Neil Strange, chairman of the User Group, presented a short introduction on DataVault and Wherescape.

Terry Mooney, Senior Solutions Architect at Wherescape, presented a short introduction on DataVault Automation.

Neil Strange, CEO of consultancy Datavault UK, presented an introduction to Data Vault 2.0 entitled Data Vault 2.0 what’s it all about during which he talked about some of the principles behind Data Vault 2.0 and some of the design ideas.

Simon Dimaline, Lead Technical Designer at Sainsbury’s, presented the Data Vault based Integration Architecture that they are using to deliver a cloud based analytics platform. This is part of a group wide programme to reduce risk and democratise access to data using a Data Vault architecture on Snowflake.

Dan Linstedt, the author and inventor of the Data Vault 2.0 method, presented the background to Data Vault 2.0 and highlighted the benefits of the approach. This was followed by a some examples of Data Vault project successes.

Kent Graziano, Chief Technical Evangelist at Snowflake, presented an in-depth case study of Aptus Health’s adoption of Data Vault 2.0. This went from the initial inception of the project through to the architecture before and after with the tools that made it successful including the Snowflake database and WhereScape automation.