The top 10 blogs from the first half of 2013 show you are interested in learning how to get started with big data – especially if you’re in the banking industry – and looking at just what it is that gives a data scientist that elusive star power. It’s also clear that good blog posts have long legs
We’re continuing our look at the most popular content on IBM Big Data Hub for the first half of 2013. Here are the most-watched videos.
Big Data, Big Opportunities for Communications Service Providers
Big Data, Big Opportunities: Energy & Utilities
T-Mobile: Network Engineering Success
With one half of 2013 behind us, let’s take a look at what has been on the top of your mind over those six months. All week long, we will be reviewing the most read, most watched and most listened to content on IBM Big Data Hub. Today, we’ll look at the top 10 most popular podcasts.
Top 5 Big
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
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
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
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
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.
Big Data Bytes is a weekly videochat where we look at some of the hot articles, blog posts, and social chatter about topics related to big data. For Friday, May 17, 2013, our guests were Frank Fillmore (@ffillmorejr), Founder and President of The Fillmore Group, and Tom Deutsch (@thomasdeutsch),
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
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
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 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
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
When talking about big data, the terms "structured" and "unstructured" often arise. Data scientists must boldly break out of the structured data world to consider not only unstructured data, but also unstructured processes and governance, and collaboration models in big data applications.