Data Warehousing & BI Summit - Mike Ferguson - Ron Tolido - Alec Sharp Data Warehousing & Business Intelligence Summit

Testing in a BI & Data landscape [Dutch spoken]

As data systems grow more complex, maintaining data quality and trust is essential. This session explores how to test BI and data solutions effectively with TMAP and the VOICE model, offering strategies to assess data flows, quality, and profiling to build confidence and ensure reliable insights.

Building Data Warehouses with Gen-AI: A Glimpse into the Future [Dutch spoken]

Discover how you can build a fully functional data warehouse in just 45 minutes, no deep technical expertise needed! Using Gen-AI, Victor de Graaff will demonstrate how to set up, populate, and visualize data in a BI dashboard with the help of Azure, ChatGPT, and public APIs, making advanced data engineering accessible to all.

Innovation within Regulatory Frameworks: the AI Act, Data Governance Act, and Data Act [Dutch spoken]

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.

Hybrid Query Processing in MotherDuck [English spoken]

MotherDuck is a new service that connects DuckDB to the cloud. It introduces the concept of “hybrid query processing“: the ability to execute queries partly on the client and partly in the cloud. The talk covers the motivation for MotherDuck and some of its use cases.

Data Governance as Keystone for Compliant AI and Digital Trust [English spoken]

In this keynote, we will discuss how data governance can serve as a keystone for building ethical AI and digital trust. We will explore the challenges and opportunities of data governance in the context of AI, and present some best practices and frameworks for implementing data governance in AI projects. We will also share, examples and case studies, recommendations and future directions.

Democratisering van Data: Het Kwadrantenmodel in Actie [Dutch spoken]

Met de snelle ontwikkelingen in data-democratisering en AI wordt het integreren van privacy by design in de architectuur essentieel. Het moet niet langer worden gezien als een hindernis, maar eerder als een katalysator voor deze vooruitgang. Het kwadrantenmodel van Damhof biedt hierbij een leidraad.

Mixed Source Data Engineering & Analytics: a best of both worlds approach [Dutch spoken]

This session will highlight the strategic choices made at Erasmus Data Collaboratory consisting of a mix of open source and proprietary solutions, both on-premise and in the cloud, and guided by modern software engineering principles.

Data Mesh Light – getting there, step by step, avoiding the Mess [English spoken]

The transformational impact of Data Mesh is potentially big, but many organizations have found it difficult to implement the approach. In this talk, Ron Tolido, CTO of Capgemini’s global insights & data business, dives into the Data Mesh rabbit hole.

Knowledge Graphs – pragmatic approach and best practices [English spoken]

This seminar explores the strategic implementation of Knowledge Graph initiatives within organizations, offering a comprehensive framework that blends cutting-edge techniques with real-world case studies. It equips participants with the crucial understanding needed to make informed decisions, optimize initiatives, and unlock the transformative potential of Knowledge Graphs in today’s data-driven landscape.

Data Products – From Design, to Build, to Publishing and Consumption [English spoken]

This half-day workshop looks at the development of data products in detail. It also looks at the strengths and weaknesses of data mesh implementation options for data product development. Which architecture is best to implement this? How do you co-ordinate multiple domain-oriented teams and use common data infrastructure software like Data Fabric to create high-quality, compliant, reusable, data products in a Data Mesh. Is there a methodology for creating data products? Also, how can you use a data marketplace to share and govern the sharing of data products?