Storage costs have plummeted, leading to an explosion of data accumulation, particularly with the rise of Lakehouse architectures. Enterprises are now storing 2.5 times more data than in 2019, reaching a projected 175 zettabytes by 2025, with 90% of it unstructured and largely unanalyzed. This phenomenon, termed "data obesity," occurs when organizations gather data faster than they can extract value from it. The Lakehouse model, while solving the storage problem, has removed barriers to data collection, resulting in a glut of ungoverned and poorly maintained datasets that hinder decision-making rather than enhance it. The financial impact of this data overload is substantial. While storage itself accounts for only 8% of total data ownership costs, the vast majority stems from the human and computational efforts required to manage and analyze this data. Dark datasets demand ongoing maintenance and create operational debt, complicating analytics and increasing compute costs. Research shows that model performance improves with higher signal density rather than sheer volume. For example, a curated 100TB dataset outperformed a raw 1PB set, highlighting that more data doesn't equate to better insights. AI systems grappling with conflicting or low-quality data produce unreliable outputs, as seen with phenomena like "hallucinations," where models generate erroneous conclusions based on poor input. The article introduces a predator-prey framework to understand the dynamics of data systems. It draws parallels to ecological models, where unchecked data accumulation (the prey) leads to a decline in the ability to derive value (the predators). When ingestion rates outpace consumption, organizations face a situation where dark data proliferates, leading to cognitive debt and reduced analytical efficiency. The "Data Sustainability Index" is proposed as a metric to measure this imbalance, allowing organizations to assess and manage their data ecosystems more effectively. By recognizing dark data as a systemic issue rather than a mere storage problem, companies can better navigate the complexities of modern data management.