Data Scientists vs. Business Analysts | Data Science Hangout Highlights
RStudio is joined by Frank Corrigan, Director of Decision Intelligence, to discuss how data scientists can become leaders within their organizations. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.rstudio.com LinkedIn: https://www.linkedin.com/company/rstudio-pbc Twitter: https://twitter.com/rstudio
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Transcript#
This transcript was generated automatically and may contain errors.
Concentric circles, of course, but I'll explain it one way. And there's probably other ways to think about this as well. In my eyes, the business analyst is there every day, trying to respond to the questions at hand for the day or the week, or maybe the two weeks, most of the stuff that they are working on is going to be relatively one off, right? Hey, can you help me figure out this carrier problem this week? Can you help me figure out why a vendor isn't shipping on time this week? And then those insights or those discoveries, ultimately, we want to be able to do that in a repeat fashion. So I kind of see that's where the technical problem solvers can bring their more technical skills in and say, Oh, that's great that we found that let's do that over and over again. So right, business analysts, more ad hoc one offs, and then the technical problem solvers, we're trying to scale it up and make it repeatable.
With that said, the technical problem solvers also sometimes are working on ad hoc, but maybe we need to figure out how many yard spaces do we need in our yards, because we're going to have the highest volumes that target is ever pushed through our supply chain network this fall. Should we get additional off site yard space at some yards? So yeah, again, concentric circles, but that's a little bit of the differentiation that I see.
