After a very busy day, I’m going to keep it brief up top today and just link to five really interesting items from today. They range from an assessment of the CAP theorem to a former Uber/Twitter exec joining the Democratic National Committee, so never say I don’t give readers a balanced informational meal ;-)
Democrats have hired Raffi Krikorian, a former Uber exec, as their chief technology officer (Recode):
Raffi Krikorian was previously senior director of Uber’s Advanced Technology Center, and before that was VP of platform at Twitter. He knows a heck of a lot about building and operating cutting-edge web infrastructure at scale, and I’ll assume a thing or two about doing so securely, so it will be interesting to see how this plays out in his CTO role with the DNC.
The limits of the CAP theorem (Cockroach Labs):
This is obviously a self-serving blog post, but it raises some good points that more companies are going to be considering in a world now populated by commercially available, globally distributed databases. Things get a little trickier to parse when you factor in that companies like Google own their own networks (which can improve availability) but that anybody can run a database like CockroachDB on the Google cloud.
Hedge funds love data and automation: OK, this is technically three separate items, but at least two of them stem from the same Future of Fintech conference panel. Long story, short: Hedge funds are investing heavily in data-savvy employees and machine learning, but still see a lot of benefit in human intuition:
No one really knows what AI will mean for the economy: What everyone seems to agree on is that AI will boost productivity and raise GDP for companies that can leverage it effectively. Who benefits from these increases is the question everyone is trying to determine, because a fast-widening inequality gap could be a very bad situation. Here are two stories addressing the issue:
What if the data science “skills gap” is just a hiring hot mess? (Fast Company):
I’ll be honest, I would have guessed that companies had already given up searching for those unicorn data scientists who have every conceivable skill and qualification. They certainly exist, but they probably have very cushy gigs already—especially if they have some AI know-how, as well. The author here argues companies should focus a lot more on finding people who can apply data to business problems (still not a solved problem) than on finding people with the right résumé.