Data scientists have three requirements for efficiently performing data science and advanced analytics: (1) access to wide-ranging enterprise data, (2) flexible modeling, and (3) easy data preparation. It is easier if all the data is normalized and stored in a single repository, but, in reality, data is stored across multiple systems and applications on premises or in the cloud, in diverse formats—structured, unstructured, and semistructured, and in different latencies—at rest or in motion.
Logical data fabric powered by data virtualization promises to provide access to all enterprise data in real time, in a normalized format, and with the ability to create multiple logical data models, all in a fraction of the time it usually takes with physical architectures such as data lakes. In a recent ROI study, data virtualization reduced the modeling time for data scientists from three months to one week, an 83 percent reduction in time to value.
Don’t believe it? Watch this on-demand Solution Spotlight and learn how a customer company enabled predictive analytics for its data scientists in a shorter amount of time.
Join Dr. Sathyan Munirathinam, expertise manager at ASML, David Loshin, president of Knowledge Integrity, and Ravi Shankar, senior vice president and chief marketing officer at Denodo, to learn about the role of logical data fabric and data virtualization to aid advanced analytics and data science.