The popularity of superheroes is timeless. And no wonder: It’s fun to think about a world populated by heroes with super-human intelligence, the ability to manipulate substances at the molecular level, and supersonic speed.
I’ll bet you can guess where I’m going with this. Maybe the summer movie season has put superheroes on my brain, but lately the things that Cray products are doing have reminded me of the superhero feats that excite kids (and grownups – you know who you are).
Some of this you’ve read in our blog posts over the last few months. There’s the “Beagle,” a Cray® XE6™ supercomputer at the University of Chicago, which is allowing researchers to test potential new drugs on the entire human genome, rather than only a portion of it – as they had previously been limited to. And they compressed 37 years of theoretical CPU time to 50.4 real-time hours. Kapow!
August 7, 2014 by adnan
In previous articles, I discussed some common big data problems and causes of the problems. In the final part of this series, we can now get to the icing on the cake – your big data strategy. Read on, my friends.
When do you have a big data problem?
Since our main interest here is in big data, a fitting question is when do you have a big data problem? The answer is not as straightforward as we’d all like but mostly because we need to have a paradigm shift in terms of how we think about the problem. This HBR article has some really good insight into how data visualization is helping companies understand complex consumer behaviors. The key is to think in aggregates and this is harder than it first appears because finer obvious details are lost. However, with enough data, more complex patterns begin to emerge. A good analogy is emergent theory, where patterns only become apparent and somewhat predictable due to the collective interactions of multiple different elements. On their own, each element exhibits very random behavior so you have to look at lots and lots of data together.
July 17, 2014 by adnan
In my last blog, I shared some interesting articles and hopefully got you thinking, “Why can’t I solve my big data problems with all this progress in technology?” But let’s not get ahead of ourselves. Let’s start with the cause of the problem.
Why all the problems?
Technologies fail for reasons that are as wide-ranging as those that challenge almost any human endeavor. Sometimes the technology is complicated and misunderstood, resulting in its incorrect application. Other times the reasons are far more mundane and bureaucratic. Let’s look at some of the common patterns of failures so you don’t have to repeat them.