Glasgow’s Wonka Fiasco is a Scary Case of AI Abuse
Enhance your data warehouse with a logical data warehouse, for unbridled agility Business depends on actionable intelligence, and for years this has been furnished by business intelligence tools, which pull the data from a data warehouse. Business stakeholders have come to expect that all of the relevant data is copied to the data warehouse, even if they sometimes have to wait a day for the latest data to arrive. The problem is, some of the newer sources of data, such as data from social media platforms, data from in-process transactions, or raw data about the movement of machines, is just not formatted in such a way that it can be accommodated by a traditional data warehouse. It could be stored there, it could be reformatted, but this is prohibitively costly. The solution to this quandary is the logical data warehouse. Download this solution brief to learn more about the following: Why business analysts continue to experience limitations in their ability to access business intelligence despite technological advances. What is a logical data warehouse? How does data virtualization work to enable companies to create a logical data warehouse? What are the six most common logical data warehouse use cases? How do logical data warehouses compare with traditional warehouses, in terms of processing queries?