10 Signs Your Data Architecture Is Limiting Your Innovation: Part 3

When it comes to your database architecture, complexity can quickly lead to a drag on your productivity, frustration for your developers, and less time to focus on innovation while your team maintains the status quo. New feature rollouts take longer than they should, while your resources are consumed up by tedious tasks that allow your app to survive, but not truly thrive.

This complexity manifests in many different ways; as they accumulate, they can become a serious hindrance to your ability to bring innovative ideas to market. We think of the effect as a kind of tax — a tax that is directly rooted in the complexity of your data architecture. We call it DIRT — the Data and Innovation Recurring Tax.

We have identified ten symptoms that can indicate your business is paying DIRT. For an in-depth view, read our white paper 10 Signs Your Data Infrastructure is Holding You Back.

Sign #5: New features are rolled out in months, not days

With a complex data architecture, your developers have to switch constantly between languages and think in different frameworks. They may use one language to work directly with a database, another to use the object-relational mapping (ORM) layer built on top of it, and yet another to access search functionality. That becomes a major drag on productivity.

That slows down your individual developers, but it also has consequences for how they work as a team. If every application architecture is bespoke, it’s almost impossible for developers’ skills to be shared and put to use across an organization. Development slows down. When a key person leaves, there is no one who can effectively fill in and you end up hiring for very specific skills. That’s hard enough, but you also don’t know if you’ll still need those skills in a year or three.

Sign #6: It takes longer to roll out schema changes than to build new features

If you’re rolling out application changes frequently — or trying to — and you’re using a relational database, then schema changes are hard to avoid. One survey found that 60% of application changes require modifications to existing schema, and, worse, those database changes take longer to deploy than the application changes they are supposed to support.

Legacy relational databases require developers to choose a schema at the outset of a project, before they understand the entirety of the data they need or the ways in which their applications will be used. Over time, and with user feedback, the application takes shape — but often it’s not the shape that was originally anticipated. At that point, a fixed schema makes it very hard to iterate, leaving teams with a tough choice: try to achieve your new goals within the context of a schema that isn’t really suitable or go through the painful process of changing it.