For decades, companies have tried to break down silos by copying data from different operational systems into central data stores for analysis, such as data marts, data warehouses and data lakes. This is costly and prone to error. Most struggle to manage an average of 33 unique data sources, which are diverse in structure and type and are often trapped in data silos that are hard to find and access.
With data virtualization, you can query data across many systems without having to copy and replicate data, which reduces costs. It also can simplify your analytics and make them more up to date and accurate because you’re querying the latest data at its source.