Part 5/14:
Vun advocates moving from pipeline-centric design to product thinking—treating data as a customizable, controlled asset. Instead of one-size-fits-all warehouses, organizations should focus on building data products tailored for specific use cases, like targeted marketing or supply chain optimization, with flexible, multi-format storage options.
He discusses how AI demands vast, high-quality, and contextualized datasets. For instance, traditional queries are now replaced by open-ended questions requiring nuanced understanding—implying that data must be enriched with metadata, domain knowledge, and contextual signals such as seasonality or market events.