We’ve arrived at a point where the data landscape is a maze of tools, each serving a very specific purpose but often leading to a tangled web of integrations.

• The result? An overwhelming number of back-office processes that need to be managed, maintained, and understood just to keep things running.

In traditional data workflows, data cleanup and structuring often happen as a back-office process—an expensive, time-consuming endeavor that demands constant attention.

• But what if we could flip the script? What if the messy, unstructured data could be cleaned, transformed, and structured the moment it enters your system, right at the edge?

Instead of building a complex ecosystem of tools that need constant upkeep, what if we frontloaded more of these processes directly into our applications?

• By simplifying the architecture and placing the emphasis on front-loaded processes, we can create a more direct path from data to decision-making—without the detour through a dozen different platforms.

What if, rather than relying on a mess of back-office data tools, we designed our systems to handle data transformation and integration closer to the user-facing side of things?

We all should rethink our approach and bring data processes closer to the application layer, we can cut through the clutter and complexity of the data tool market.