Data Observability – What is it and why is it important?

This session by internationally acclaimed analyst Mike Ferguson looks at the emergence of Data Observability and looks at what it is about, what Data Observability can observe, vendors in the market and examples of what vendors are capturing about data.

Data Lakehouse: Marketing Hype or New Architecture? (English spoken)

This session discusses all aspects of data warehouses and data lakes, including data quality, data governance, auditability, performance, historic data, and data integration, to determine if the data lakehouse is a marketing hype or whether this is really a valuable and realistic new data architecture.

Building an Enterprise Data Marketplace

This session looks at what a data marketplace is, how to build one and how you can use it to govern data sharing across the enterprise and beyond. It also looks at what is needed to operate a data marketplace and the trend to become a marketplace for both data and analytical products.

A Data Strategy for Becoming Data Driven

Becoming data driven will not be achieved by acquiring new technologies and tools alone. This seminar by Nigel Turner will outline the practical steps needed to produce an achievable data strategy and plan, and how to ensure that it becomes a living and agile blueprint for digital change.

Data Mesh & Fabric: The Data Quality Dependency

This session will briefly recap the main concepts and practices of Data Mesh and Data Fabric and consider their implications for Data Quality Management. Will the Mesh and Fabric make Data Quality easier or harder to get right? As a foundational data discipline how should Data Quality principles and practices evolve and adapt to meet the needs of these new trends? What new approaches and practices may be needed? What are the implications for Data Quality practitioners and other data management professionals working in other data disciplines such as Data Governance, Business Intelligence and Data Warehousing?

DataOps – best practices and lessons learned (Dutch spoken)

In practice, DataOps is not as common for data & analytics as DevOps is for software engineering. For the latter, Development and Operations are jointly responsible for developing a system, deploying it and maintaining the system. With the aim of delivering faster, being more agile and creating maximum business value. This is where DataOps is the same as DevOps: the objective is similar. But ‘How’ we do this, differs considerably.