The Dark Data Tax: How Hoarding is Poisoning Your AI (www.dataengineeringweekly.com)

A recent analysis discusses the concept of "data obesity" in enterprises, emphasizing that while data storage costs have plummeted, the rapid accumulation of data—often unstructured and underutilized—has led to a significant decline in actionable insights. With global data storage projected to reach 175 zettabytes by 2025, yet 90% of unstructured data likely remaining unanalyzed, the focus shifts from merely storing data to effectively managing it. The transition to Lakehouse architectures, despite their advantages in cost and flexibility, has inadvertently encouraged data hoarding, creating "dark data" that clogs operational efficiency and hinders decision-making. The implications for the AI/ML community are critical. Research indicates that model performance is more about signal density than sheer volume; a curated dataset can outperform larger, less relevant ones. Dark data introduces "hallucination vectors," causing AI systems to generate errors and inefficiencies due to conflicting information retrieval. To combat this, a proposed Data Sustainability Index (DSI) aims to measure the metabolic health of data ecosystems, emphasizing the need for organizations to prioritize data curation and management over accumulation. By developing metrics and automated processes, businesses can improve their data utilization and decision-making efficiency, steering the future of data operations towards sustainability and health.