Understanding the Data Death Cycle

The data death cycle refers to a recurring pattern where data and AI projects fail to deliver the expected value, often because they are not grounded in real user needs. This cycle begins with teams focusing on the technology without properly understanding the problem they aim to solve, leading to wasted resources and unsuccessful outcomes. The cycle perpetuates as teams attempt to enhance failed projects by adding more features or complexity, rather than reassessing the fundamental user requirements. Recognizing and addressing this cycle is crucial for creating impactful data solutions.