Data science democratized

With new tools arriving, data science may soon be in the hands of non-programmers.

I am not a data scientist. Nor am I a programmer. I’ve got an inclination toward technology, but my core skill set very much resides in the humanities domain.

I offer this biographical sketch up front because I think I have a lot in common with the people who work around and near tech spaces: academics, business users, entertainment professionals, editors, writers, producers, etc. The interesting thing about data science — and the reason why I’m glad Mike Loukides wrote “What is data science?” — is that vast stores of data have relevance to all sorts of folks, including people like me who lack a pure technical pedigree.

Data science’s democratizing moment will come when its associated tools can be picked up by tech-savvy non-programmers. I’m thinking of the HTML coders and the Excel power users: the people who aren’t full-fledged mechanics, but they’re skilled enough to pop the hood and change their own oil.

I’m encouraged because that democratizing moment is close. I saw a demo recently that connects a web-based spreadsheet with huge data stores and cloud infrastructure. This type of system — and I’m sure there are many others in the pipeline — takes a process that once had immense technical and financial barriers and makes it almost as easy as phpMyAdmin. That’s an important step. Within a year or two, I expect to see further usability improvements in these tools. A data science dashboard that mimics Google Analytics can’t be far off.

During that demo, Datameer CTO Stefan Groschupf told me about a fun Twitter inquiry he instigated. Groschupf had previously gathered around 45 million tweets and fed them into EC2. Later, over the course of a two-beer evening, Groschupf poked at that data to see if any interesting patterns turned up when comparing two vastly different hashtags (#justinbieber vs. #teaparty). He used his company’s system to parse the data, then he fed results through a free visualization tool.

Here’s the #justinbieber cluster:

Justin Bieber hashtag visualization

And here’s the #teaparty cluster:

Teaparty hashtag visualization

As you can see, the #teaparty folks are far more connected then their distant #justinbieber cousins. That’s interesting, but not really surprising. The political world has more connective tissue than of-the-moment entertainment.

But that specific conclusion isn’t what’s important here. Even if your end-point is inevitable, a data-driven conversation has more power and resonance than an anecdotal observation. Groschupf didn’t tell me the Tea Party movement is more connected. He showed me.

Significant implications emerge when you can bounce a question, even an innocuous one, against a huge storehouse of data. If someone like me can plug questions into a system and have it do the same kind of processing once reserved for a skilled minority, that will inspire me to ask a lot more questions. It’ll inspire a lot of other people to ask questions, too. And some of those questions might even be important.

That’s a big deal. Myself and others may never become full-fledged data scientists, but having access to easy-to-use data tools will get people thinking and exploring in all sorts of domains.

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