Dashboards are a sacred artifact in the syncretic religion of data analysis. At various points I've used them for reporting, automating analyses, making basic tools, storing groups of thematically related queries, generating viz in EDA when I'm too lazy to fire up a notebook ...
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They're tremendously useful, and as this article says, way better than previous tooling. I remember once spending days debugging a giant financial report that was broken because someone accidentally overwrote a cell in the Excel workbook with a space https://towardsdatascience.com/dashboards-are-dead-b9f12eeb2ad2 …
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Even so, I'm skeptical that a drag-and-drop tool is what we'll need to replace dashboards. Removing the need to write SQL will help a lot (and will also save data scientists a shitton of time) with getting new people in the funnel, but it won't automatically produce GOOD work.
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Replying to @imightbemary
legitimate question, because I see this semi-regularly: why do some data scientists see writing SQL as a bad thing to be avoided, or something that takes a lot of time?
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Writing SQL queries is like knife skills for a chef. It's table stakes for doing the craft and it may even been fun to do sometimes, but it doesn't require critical thinking or creativity. Writing too much SQL means they're not doing the higher level, satisfying parts of the job
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