Over the past several articles, I’ve shared how my teams and I have navigated deployments and consulting projects with Fabric, Power BI, and Azure, where using AI has become essential for efficiency. The rapid adoption of tools like Claude for updating Power BI Semantic Models, the FabricNotebook agent for notebooks, and custom agents for enabling data chat has made it clear that GenAI is now a core part of the data landscape.
However, these advances also highlight the growing importance of DataOps principles. Without strong DataOps practices, AI can just as easily introduce new risks and undermine trust in your data solutions as it can accelerate productivity. I’ve compiled nine practical tips to help teams reduce GenAI risk and improve data quality. Read the full article on Simple Talk to learn more.