With analytics today, it is often the case that you can’t see the forest for the trees. There is simply too much data to physically move into a data warehouse, which ultimately inhibits business analytics.
Big Data is an obvious culprit, but one can’t dismiss the mountains of mainframe data that is continually being extracted, transformed and loaded into a data warehouse.
Data virtualization can eliminate the need to move data, allowing isolated silos of information to be represented as a single, logical data source. Data virtualization can be a strategic improvement that complements the data warehouse.
Things to Learn:
- How to enable analytics to be more self-service and discovery-based
- How to eliminate information bottlenecks – especially mainframe ETL/Batch
- How to provide a low latency option for higher priority information
- How to improve your risk profile for information sharing in regulatory environments