Rethinking Data Strategy for the Genie Ontology World

Traditional thinking is clear - data isn't "ready" until it's in a perfect data model, fully documented, tagged, bagged and presented to the business with a pretty little bow on top. But emerging tools like Genie Ontology are raising a different question: Are there some business questions that are so based on emerging, evolving concepts and trends that fixing them in governed KPIs actually holds them back? Can we not keep the formal, governed models for the most critical of our business KPIs and actually allow for an element of just-in-time contextual inference for the rest? In this video Simon digs into a key phrase Databricks have used "Model the head, infer the tail" and breaks it down into a framework for thinking about different levels of authority scoring across your data estate and how you can start to plot out this curation and governance framework. He also twists a metaphor further than is reasonable in a single video...