Wrong but useful. Working @Twitter, tweeting about data science and people management.
"Wait, it's all her views?" 
"Always has been"
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The procedure of experimentation is a way of ensuring quality and (at least in theory) will make comparison of results across experiments meaningful.
Another source of unproductive variance in operations is people coding in subtly different environments. In a worst case scenario, different versions of software packages can generate different results.
In a best case scenario, a DS tries to replicate an analysis, realizes their set up is different from the author's, and decides it's too much work to build off someone else's work, instead spinning up their own version of the thing they're trying to replicate.
Maybe they'll do a fine job reproducing it, maybe they won't, but in either case it's a huge waste of time. To really make sure everyone is contributing to the same body of work, it should require minimal effort for a DS to start where another analysis left off
This is all maybe a long-winded way of saying that the key to standardization might just be code! It's natural to think of data as the most important resource we have at our disposal, but that's not quite right
As DS, we earn our keep by using our expertise to extract value from data. Code is the way we formalize and scale our expertise, whether it's SQL, Scala or something in between
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