A data-first approach to decision-making often falls short due to several challenges, with untrustworthy data being a primary obstacle. Untrustworthy data, described as the "Data Wheel of Death" by Brian Balfour, leads to decreased use and prioritization, causing it to become stale and perpetuating a cycle of unreliability. This issue is categorized into four types: stale data, unclear data, inaccurate data, and no data, each presenting unique problems such as outdated information, multiple confusing data points, misleading data accuracy, and incomplete datasets. These challenges often arise from poor maintenance practices and manual tracking processes, making it difficult for product teams to rely on data-driven strategies. To address these issues, companies can either invest heavily in resources for data maintenance, spend significant time planning and updating tracking processes, or implement virtual datasets that capture all event data upfront, allowing for retrospective analysis without the need for extensive manual tracking. Heap aims to help organizations make informed decisions by addressing these data challenges.