Data Scientist: Master the Basics, Avoid The Most Common Mistakes

July 2, 2013 | by James Kobielus, Big Data Evangelist, IBM
Data science is a human craft, demanding just as much nuanced judgment and intuitive technique as you’d expect from any skilled artisan. One of the downsides of using the word “science” in this context is that people think that statistical analysis is just some sort of cut-and-dried laboratory...

Single Version of the Truth: Ground it in Data Science, Not Data Faith

June 20, 2013 | by James Kobielus, Big Data Evangelist, IBM
Is data a religion? I think that’s a ridiculous notion, but it has recently gained credence in the popular mind. Some people seem to believe that a powerful elite regards data-driven management as an absolute faith. Here, for example, is a Washington Post article arguing that the current president...

Monetizing Your Big Data: A Grand Dream, A Constrained Reality

June 13, 2013 | by James Kobielus, Big Data Evangelist, IBM
Everybody these days wants to monetize their big data—and why not? You know that on some level your data is valuable. If it weren’t, you wouldn’t be investing so much in the acquisition and analysis of it all. But is big data truly monetizable? This utopian vision can break your heart if you let it...

Data Scientist: Strike a Balance Between Quantitative & Qualitative Exploration

June 6, 2013 | by James Kobielus, Big Data Evangelist, IBM
Life is stubbornly qualitative on every level. But we wouldn’t be modern and scientific if we didn’t try to constantly reduce it to numbers that we can calculate, manipulate and extrapolate. Even when we’re trying to parse the mess into particular entities and interactions that we can analyze...

Big Data Philosophies: More Practical than You Might Think

May 30, 2013 | by James Kobielus, Big Data Evangelist, IBM
People often treat philosophy as an intellectual pastime for the unemployable. That’s absolutely not the case. Philosophy is the most practical of disciplines. Essentially, it is an examination into basic principles, cultivating minds that can critically examine problems down to their very marrow....

Data Scientist: Bias, Backlash and Brutal Self-Criticism

May 16, 2013 | by James Kobielus, Big Data Evangelist, IBM
Data scientists such as Nate Silver have recently begun to receive rockstar status in the big-data universe. That’s a tricky status to sustain for long, because it inevitably inspires popular backlash. You can already see that backlash gaining force, as evidenced through the growing volume of...

The rise of the data scientist: Recap of IBM Twitterchat

May 14, 2013 | by James Kobielus, Big Data Evangelist, IBM
Big data is not just about scaling your data analytics processing platforms to keep up with the onslaught of new information. Just as important, big data is about bringing together your best and brightest minds—your data scientists—and giving them the tools they need to interactively and...

New Big Data Zone on IBM developerWorks

April 19, 2013 | by Barbara Wetmore
We’ve got a new zone on developerWorks, dedicated to big data and to architects and developers looking to build analytics applications to derive insight from that data. It turns out developerWorks was already covering big data to some extent, just not in a classic developerWorks “zone” format. And...

Data Journalism: Big Data, Data Science, & the Art of Non-Fictional Storytelling

April 12, 2013 | by James Kobielus, Big Data Evangelist, IBM
Data journalist? Something about that nouveau term feels a bit pretentious—and unnecessary. Every journalist is a “data journalist” of one sort or another, in the same way that every scientist is at heart a “data scientist” (see this blog for my take on the latter). After all, the core function of...

Big Data On the Move: Everywhere You Need It To Be

March 7, 2013 | by James Kobielus, Big Data Evangelist, IBM
Most of us don’t think of big data as a personal resource for mobility, but, clearly, that thinking will need to change. Smarter mobility depends on the ability to serve all of our mobile devices from an intelligent big-data infrastructure