When Requirements Fail: The Reality of Data Projects and How to Survive Them.

Since I began my journey as a data professional, I have been intimately acquainted with the rigorous, often painful process of validating data—especially after integrating new data sources. It’s a task that demands precision, patience, and an almost obsessive attention to detail. Yet, no matter how meticulously we prepare, the road to seamless data integration is rarely smooth.

The engineering team often works tirelessly, seeking domain knowledge from those who understand the business intricacies. Their goal is to implement the appropriate business logic into the data stream. But here’s the catch: no matter how thorough the requirement-gathering phase may seem, it’s only when the rubber meets the road that we truly discover whether those requirements hold water.

This phase of uncertainty often slows down projects. It reminds me of a saying from my mother tongue: “Only the gourd can tell where the rope placed around its neck would fit.” No matter how diligently we try to anticipate every need, it’s only when the project begins that we uncover what’s missing. The gourd, in this case, is the data project itself—its shape, its contours, its unique challenges—revealing itself only as we proceed.

The Challenges We Face

1. The Ghosts of Old Reports One of the most persistent challenges is dealing with legacy reports that have not been audited or properly maintained. Often, the original developers have moved on, leaving behind a trail of undocumented logic and assumptions. These reports, once the backbone of decision-making, now sit like relics in a museum—difficult to interpret and even harder to trust.
2. The Curse of Poorly Annotated Reports and Dashboards As data professionals, we sometimes fall into the trap of assuming that everyone speaks our language. We generalize terms, skip annotations, and overlook the importance of context. But the truth is, not everyone understands the nuances of data. A dashboard that seems crystal clear to us might be a labyrinth of confusion to someone else.

A Call to Action

The challenges I’ve outlined are not insurmountable, but they do require a shift in mindset. As data professionals, we must prioritize documentation, foster collaboration between technical and business teams, and embrace the iterative nature of data projects. We must also remember that our work is not just about numbers and algorithms—it’s about enabling better decisions, clearer insights, and ultimately, driving value for the business.

So, the next time you find yourself knee-deep in data validation or wrestling with a poorly documented report, remember the gourd and the rope. The path may be uncertain, but with patience, collaboration, and a commitment to clarity, we can navigate the chaos and deliver results that truly matter.