Observability 2.0 is emerging as a critical advancement in monitoring systems and applications, focusing on utilizing raw data effectively. By developing a dual-capability database framework that can handle both raw (unprocessed) and cooked (processed) data, organizations can extract maximum value from observability data. A concept such as the Dynamic Distributed Dimensional Data Model (D4M) can serve as an inspiration for creating such a framework, enabling more flexible and insightful analysis of observability metrics. This approach could revolutionize how organizations implement observability, leading to better decision-making and operational efficiency. Key opportunities lie in designing systems that adapt seamlessly to diverse data types, presenting a challenge to traditional database models.