13 February 2014
This was my last day at the Strata conference.
Buzzwords are the new stopwords.
The vast majority of the keynotes were nothing but buzzwords, again. The audience reacted logically; they ignored the presenters and checked email, Facebook, and Twitter. One guy was trading stocks.
The best keynote was James Burke)'s. He was illuminating, funny, and persuasive. His argument was history and discovery are messy and full of unexpected change.
Some memorable quotes:
Discovery and progress happens between disciplines, not through specialization. I see this all the time in software teams. The most creative work comes from groups of disparate people working together.
Our society rewards specialists more than generalists. The result is a larger number of narrower niches; we know more and more about less and less. Broad thinkers are desperately needed but not valued.
I enjoy thinking in systems. I was taught and raised this way, fortunately. Being able to see the trees and the forest comes in very handy. I encourage everyone to try this.
Technical change usually doesn't cause problems directly. Its biggest headaches are predominantly due to side effects.
Facebook is a great example. Posting your personal info to friends isn't controversial. What's controversial is when none of it's private anymore and visible to employers, parents, random strangers, and stalkers.
Society is reactionary to scientific, technical and industrial change. It's important to be mindful of that.
One of Hadley's points is that it is good to code when doing analysis.
The two projects Hadley is working on are dplyr and ggviz.
Dplyr is pretty amazing; it's a way to create query-like operations in R and have them work against data frames, data cubes, or even backends like RDBMS or BigQuery. I'm reminded of LINQ and lambda expressions.
One of the beautiful parts of dplyr is that it's declarative. You code what you want done, but not exactly how. Anyone familiar with SQL will feel right at home.
IPython notebooks are the de facto way to share data analysis, for several reasons:
Brian Granger gave a great series of demos about the upcoming IPython 2, which is going to be even more user-friendly. I'm looking forward to it.
The key takeaways:
Talking to dozens of people and attending many sessions led me to some unexpected conclusions...
Breakthroughs happen in 3 ways:
Those are in descending order of difficulty.
Data Integration is not a solved problem
Chris Re mentioned a study done for various CTOs. The result was stark: if you're a CTO faced with a big integration challenge, your best course of action is to quit.
People are messy
It seems like data professionals have a bit of OCD. We like things to be clean and orderly.
However, people are messy. They come in all shapes and sizes, with biases, irrational behavior and communication headaches. We have to accept people as they are or face a constant impedance mismatch with the very people we are supposed to serve.
Work on big problems
I met some amazing data scientists over the past few days. Most of them will never be famous, even if they're exceptionally smart.
They work on boring projects. Nobody cares if a brilliant data scientist works on online advertising, or a new kind of social media platform, or becomes yet another high-finance quant.
However, people do notice when the data scientist who changes how a city does building inspections. What matters is relative impact.
This isn't a new idea. Michael Lewis' Moneyball was about more than stats coming into baseball; it was a beautiful example that quantitative skill can have a dramatic impact in areas where it doesn't currently exist. For example:
Want to change the world? Find out where all the money goes in education (it's not to teachers). Build a platform to crowdsource finance for farmers and remove all the middlemen. Figure out how music affects the brain.
Build big things.