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Data Virtualization and Digital Agility

This Article originally appeared in ITProfessional Magazine.
"Data lakes” has become the new buzzword for organizations dealing with never-ending and increasingly complex and costly requirements for using all of their data sources to produce value. Whether that value is in analyzing past performance, predicting customer needs, automating processes, or creating new products and services, the building block of any of these value streams is data—in all of its shapes, sizes, speeds, formats, quality, and richness...

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Earley Information Science Team
Earley Information Science Team
We're passionate about enterprise data and love discussing industry knowledge, best practices, and insights. We look forward to hearing from you! Comment below to join the conversation.

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Recently I chaired the Artificial Intelligence Accelerator Institute Conference in San Jose – in the heart of Silicon Valley.  The event has brought together industry innovators from both large and small organizations, providing a wide range of perspectives. For example, the CEO of AI and ML testing startup of Kolena, Mohamed Elgendy and Srujana Kaddevarmuth, Senior Director, Data & ML Engineering, Customer Products, Walmart Global Tech discussed productization of AI solutions and ways to increase adoption. I especially liked the idea of a model catalogue from which data scientists can retrieve data sets and machine learning models that others have built rather than starting from scratch.