Creating data that is trustworthy and reliable

For data to be truly valuable, the data must be trustworthy and reliable. In this session Hugo will share 3 concrete steps for working with Data Quality for ensuring that you, your team and your data earns and keeps the trust from your consumers. 

1. Prioritise Data Quality Dimensions together with your consumers
2. Measure the prioritised quality expectation and use cases, and continously share the results of the tests
3. Have a strategy for acting on data quality incidents.

The smallest of RAGs for your GenAI

The Retrieval Augmented Generation (RAG) architecture is one of the fastest approaches to customize your LLM with your own data. So in this talk I will take you through what a RAG is, show an example architecture together with a live demo, and finally present some use-cases beyond "chat with your data”.