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Cutting Through Data Science Hype
An anonymous reader writes: Data science — or "big data" if you prefer — has evolved into a full-fledged buzzword, thanks to marketing departments around the world. John Foreman writes that part of the marketing blitz has been focused on how fast big data analysis can be. Most companies offering some kind of analytic service try to sell you on how it'll make it easy for you to quickly find and fix the problems with your business. But he points out that good, robust models need a stable set of inputs, and businesses often change far too quickly for any kind of stable prediction. He takes IBM's analytic services as an example, quoting Kevin Hillstrom: "If IBM Watson can find hidden correlations that help your business, then why can't IBM Watson stem a 3 year sales drop at IBM?" Foreman offers some simple advice: "Simple analyses don't require huge models that get blown away when the business changes. ... If your business is currently too chaotic to support a complex model, don't build one."
>> Catastrophe is a critical factor in most evolutionary history.
> Citation, please.
Wikipedia has a fairly good entry on "Catastrophism", and another on "Punctuated equilibrium". But even without large scale events such as dinosaur killer asteroids or the evolution of photosynthesis poisoning most species with much higher concentrations of volatile oxygen, the are much smaller and more frequent effects. Forest fires are a crtical factor in breeding jack pine trees, floods are vital to the fertility of the ecosystem near river banks, and hurricanes spread species throughout their trail and profoundly affect the ecology and evolution of areas that are likely to endure hurricanes. And catastrophes can and do create a "founder effect", where a small number of introduced species members become a new species quite quickly in their new environment.
Do I need to find individual links links for each of those?