Institutional memory, often plagued by issues termed as "Institutional Alzheimer’s," is a significant challenge in analytics, as it encompasses a company's collective understanding of metrics and their evolution, which is crucial for maintaining consistency and accuracy in data interpretation. Discrepancies in defining metrics like "active user" or "revenue" can lead to errors in analysis, highlighting the need for standardized definitions and documentation. The maintenance of institutional memory is often hindered by fragmented documentation and the reliance on individual knowledge, leading to inefficiencies such as double-checking numbers and reconciling mismatched data. Tools like Metabase aim to address these challenges by centralizing data models and providing user-friendly interfaces that empower non-technical users to engage with data effectively. By focusing on key metrics, documenting them accurately, and ensuring easy access to this information, organizations can improve their institutional memory, helping avoid chaos and inefficiencies in analytics processes.