Instead of building data systems optimized for stability, we need systems architected for change.
The conventional wisdom about modern data governance sounds reassuringly methodical: treat data like a product, build for reuse, establish clear ownership, and watch costs fall as adoption rises. It's the kind of advice that feels both obvious and actionable—and yet often falls short in practice for one simple reason: By the time you've built the perfect solution for today's requirements, tomorrow's requirements have already changed.
Data use cases refuse to behave like the static requirements that common frameworks assume. Yet most scaling advice treats use cases like building blocks that can be stacked methodically, one after another. In reality, they're more like living organisms—constantly evolving as new data sources emerge, regulations shift, business models pivot, and technologies create possibilities that didn't exist even a month ago.
This volatility breaks down the many nice-sounding and seemingly logical data management frameworks because striving for "reusable data"—often celebrated as the holy grail of efficiency—carries hidden costs that are often overlooked. Every shared dataset embeds assumptions about how it will be used. Every supposedly universal model introduces integration debt as teams bend it to their specific needs. The push for reuse can easily generate as much complexity as it eliminates, creating rigid systems that optimize for yesterday's requirements.
Generative AI intensifies these dynamics rather than resolving them. Yes, it makes engineers more productive at building pipelines, but it also democratizes data exploration. Non-technical teams can now articulate needs directly and prototype solutions in hours instead of weeks. This accessibility is powerful, but it also means the volume and variety of data demands will explode. If anyone can generate transformations on demand, then static datasets become less valuable than dynamic capabilities.
What emerges from this is the need for a new design principle: Optimizing for constant change and adaptability. This means creating adaptive feedback loops that can test, validate, and retire data pipelines and use cases as quickly as they appear. It means treating consolidation and pruning with the same discipline we apply to creation. Most importantly, it means recognizing that in a world of moving targets, adaptability itself becomes the product.
Scaling data isn't about building better pipelines. It's about building organizations that can bend without breaking—where change isn't just managed, but mastered.