These costs include the initial investments, maintenance and upgrade fees, and the related manpower costs. High costs of computing infrastructure: Another key challenge is the potentially high costs of setting up and maintaining an organization’s computing infrastructure, including both hardware and software.For example, running a data science workshop or statistics class can be unwieldy if everyone is working within their own separate environments. Obstacles to collaboration between organizations or groups: If a team is restricted to operating within their organization’s firewall, it can be very difficult to support collaboration or instruction between groups that don’t normally interact with each other.These burdens tend to fall either on the individual data scientists or on DevOps and IT administrators who are responsible for configuring servers. New hardware might be needed for developing data science analyses or for sharing interactive Shiny applications for stakeholders. Long delays and high startup costs for new data science teams: When you bring a new team of data scientists onboard, it can be costly and time consuming to spin up the necessary hardware for the team.
There are many reasons why organizations are looking to use cloud services more widely for data science. Why Do Organizations Want to Move to the Cloud? In this blog post, we discuss the various ways RStudio products can help you along that journey. As they do, they naturally want to bring along their favorite data science tools, including RStudio, R, and Python. Over the last few years, more companies have begun migrating their data science work to the cloud.
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