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Cloud Versus On-Premises Architecture for Science

Analytics in life sciences and healthcare today is extremely data-intensive. Most research projects require extensive collaboration and utilize data from many locations generated by a large number of researchers. As IT infrastructure becomes harder to manage, science organizations are trying to decide between going to the Cloud or investing in on-premises resources for scientific computing. Unfortunately, the decision isn’t that straightforward. There are many important factors to consider when deciding where to invest your time and money for your science. During the pandemic, and with a significant and persistent supply chain problem for purchasing hardware, many organizations have decided to make more use of the Cloud. However, many have started to see rising costs and lower availability due to that decision. At the Bio-IT World Conference and Expo, Ari Berman, CEO of BioTeam, discussed the use cases for scientific computing (types of analytics, data storage, hardware capabilities, collaboration), the primary personas in most organizations that use and manage the technology, and the consequences and outcomes of making either the choice of Cloud or on-premises architectures as a solution to the needs of an organization.



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