I've been working on a project called Antonlytics and, while building it, I've found myself thinking a lot about how we structure data in analytics. Most of the work I’ve seen (and done so far) is pretty dashboard-driven tables, funnels, cohorts, KPIs, all that. But recently I’ve been exploring a different way of thinking where instead of flattening everything into metrics, you model the data more like connected entities and relationships, almost like an ontology or knowledge graph. What I’m trying to understand is whether this actually helps in real analytics work or if it just ends up making things more complicated than they need to be. In theory it feels useful because you can trace relationships between things like users, events, and systems more naturally, and maybe get a better sense of why something is happening instead of just what is happening. But I’m not sure how practical that is once you get into real-world data, scale, and business constraints.…