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But the data repository options that have been around for a while tend to fall short in their ability to serve as the foundation for big dataanalytics powered by AI. Traditional datawarehouses, for example, support datasets from multiple sources but require a consistent data structure.
It provides rapid, direct access to trusted data for data scientists, business analysts, and others who need data to drive business value. Focus on Outcomes Analytics and AI hold the promise of driving better business insights from datawarehouses, streams, and lakes. Just starting out with analytics?
One option is a data lake—on-premises or in the cloud—that stores unprocessed data in any type of format, structured or unstructured, and can be queried in aggregate. Another option is a datawarehouse, which stores processed and refined data. Just starting out with analytics?
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