You’d think by this point we might have settled on a definition of what a data scientist is, or at least on a general agreement that they’re important. It seems we have not.
Here are two blog posts published within the last day about the relative value of data scientists:
- One is from Miko Matsumura, CEO of database vendor Hazelcast, who calls data scientists glorified database administrators who will find themselves the kings and queens of “a rotting whale-carcass of data.” You can read it here.
- The other is from John Foreman, chief data scientist at MailChimp (and, full disclosure, a regular Gigaom contributor lately), who argues that the work of a good data scientist will never cost as little as $30 per hour. You can read it here.
We’ve covered this ground before, in posts trying to define the skills a good data scientist should have and whether massive open online courses can teach people the skills they need to really call themselves “data scientists.”
At Structure Data in two weeks, I’ll be sitting down with AnnaLee Saxenian, dean of the University of California, Berkeley’s School of Information, about how universities and other institutions can and should try to train the next generation of data analysts everyone seems to agree we’ll need. She’s overseeing a $60,000 a year graduate program in data science, so I’m really keen to hear what she has to say about it.
Given these two recent posts, though, I tend to agree with John’s take. Data scientists actually do a lot of complex work with data, sometimes at the intersection of data and business, and software can only automate so much of that. Even as software improves, one could argue, really skilled people will always be working ahead of the curve. Share your thoughts in the comments.