Katrina Briedis

AI/ML solutions use data to provide the intelligence to support day-to-day business processes and decision making. The more data these solutions use, the more they learn, and the more accurate their predictions are. However, extracting data from multiple sources and then replicating it to a central repository is an old and inefficient way to achieve this and often results in 80% of the project time being spent on data acquisition and preparation tasks. Data virtualization is a modern data integration technique that integrates data in real-time, without having to physically replicate it. It can seamlessly combine views of data from a wide variety of different data sources and feed AI/ML engines with data from a common data services logical layer.

In this presentation, you’ll learn how data virtualization:

• Exposes data to the data science ecosystem
• Enables data science by providing all enterprise data in real time
• Provides Data Science teams with data discovery through a Data Catalog
• Can be leveraged to accelerate AI/ML initiatives