Profiting with Practical Supervised Machine Learning

Practical half day seminar on getting started with Supervised machine learning by Keith McCormick. How do you decide when to use Supervised versus Unsupervised machine learning? How to properly prepare data for different kinds of supervised models and how can data preparation be automated in parallel with the model if deployment is to succeed? [Video introduction]

How to Revamp your BI and Analytics for AI-based Solutions

In this half day virtual session, Dr. Barry Devlin explores the challenges and potential benefits of moving from BI and Analytics to AI. We explore its use in data management; its relationship to data warehouses, marts, and lakes; its emerging role in BI; its strengths and weaknesses at all levels of decision-making support; and the opportunities and threats inherent in its two main modes of deployment: automation and augmentation.

Would you let AI do your BI?

In BI and analytics vendors are adding artificial intelligence possibilities with the promise of better or faster decisions. But how far could AI go? Barry Devlin explores in this session the challenges and potential benefits of moving from BI to AI. Also he will discuss the dangers of thoughtless automation and ethical considerations in adopting AI.

Data Strategy according to DMBoK [Dutch spoken]

Autonomous systems are increasingly deployed for problem solving but are strongly dependent on the data in your organization. Therefore a data strategy is needed to enable your organization to make fact based decisions. Peter Vieveen will help defining a data strategy using the Data Management Body of Knowledge, and explain how to use gamification and data literacy to explain the data strategy to your organization.

The data mesh: a distributed data architecture [Dutch spoken]

Centralized and monolithic data architectures have a number of problems according to Rick van der Lans and he proposes the datamesh as a data architecture solution. Contrary to datawarehouses, datalakes and datahubs that are centralistic and monolithic, the datamesh is a distributed solution. Classical responsibilities within an IT organisation will shift dramatically.