I've encountered the same challenge—summarizing data becomes extremely difficult when users need to drill through to transaction-level detail. In practice, it's not always feasible to aggregate data meaningfully without losing the granularity required for decision-making. And regardless of company size, data models can quickly become complex, especially when we need to calculate nuanced scenarios or layer in business logic that doesn’t fit neatly into standard measures.
What surprised me most was learning that even filtering data in the Power BI Desktop app can dramatically impact service capacity. I was pulling time-sensitive data for our company—just applying filters, not transforming or refreshing—and the entire environment slowed to a crawl. When IT investigated, I turned out to be the top resource consumer in the tenant. It’s hard to believe that a single user performing basic filtering could spike capacity usage so drastically, but this is the second organization where I’ve seen the same issue.
This highlights a broader need: we need better ways to accommodate complex modeling and transactional drill-through without compromising performance or overloading shared capacity.
