In life, slow and steady often wins the race. But in high tech, it typically goes to the quick but wide-reaching.

That certainly seems to be the case these days with cloud computing, especially now that it owns the hottest industry around: Big Data.

Big Data has reached a tipping point. It is now officially the hot new technology industry of the second decade of the 21st century. And there were 70,000 witnesses to the moment it happened.

How do you know when a new technology becomes cool? When everybody who is anybody in tech feels obliged to attend its trade show.

According to Gartner's recent 2012 Hype Cycle for Emerging Technologies, Big Data is just about to hit its "peak of inflated expectations." For those unfamiliar, Gartner's hype cycle purports to "highlight the common pattern of over-enthusiasm, disillusionment and eventual realism that accompanies each new technology and innovation”. Meaning, when a technology is just nearing the top of the hype curve, such as Big Data is, we're all about to be disappointed. With the big buzz around big data, what can we truly expect? Big returns or truly big hype?

The hottest tech trend of the year is Big Data, the application of powerful new analytical tools to the mountains of data now being created by the Cloud and by millions of new sensors embedded into the natural world.

Or “why a dimensional warehouse is an analytical imperative.” Give me some credit. The latter is at least a marginally creative title, whereas the former sounds like a session at TDWI and I know how many line-of-business executives attend conferences like TDWI. But, if you are a line-of-business executive, here’s why you should care: your business changes almost daily. Simply put, a slowly changing dimension is the mechanism used by a real analytics platform to accurately measure an attribute that changes over time.

Big Data, the application of a new breed of analytical tools to the vast caches of data being produced by computers and other forms of technology, is on the brink of becoming a household word – thanks to new books, conferences and articles that are slated to appear over the next few months.

But as impressive as these Big Data stories will be – and many of them, like mapping the insides of F5 tornados, tracking every patient heartbeat over a lifetime and predicting the behavior of consumers from millions of store purchases – the real story is unlikely to be told.

For decades we have been focusing on the architecture of data; now the time has come to turn our attention to the economics of data. . .

It is a tired cliché that Silicon Valley and high tech revolution resemble the 1849 California Gold Rush. But, at the dawn of the Big Data era, that analogy may be more true than ever. . . not to the Gold Rush itself, but to what came immediately thereafter.

As a marketing professional, I’ve been working with industry analysts my entire career.  Industry analysts play an important role in educating the market and often provide guidance for organizations seeking advice on technology directions.  Similarly, vendors work closely with the analyst community keeping them abreast of their companies’ progress, new offerings, and strategic directions.  Analysts can have a lot of influence on technology adoption and a result, vendors with new technologies or different approaches to industry issues, will often spend considerable time with the analyst comm

Nucleus Research, a boutique analyst firm based in Boston which focuses on “investigative information technology research,” released their Technology Value Matrix for Analytics for 2012.  The matrix seeks to evaluate vendors that have a global presence and provide functionality in the four core analytics areas: business intelligence (BI), performance management (PM), predictive analytics, and big data.   We were delighted to learn that Birst was named a “Leader” in its Analytics Value Matrix that published today.  Birst joins the company of some larger (and better known) vendors in the busi

One of the great things about the blogosphere is that you are allowed to change your mind.

In my last blog I defined Big Data as any body of information that is so big it cannot be analyzed directly for profitable use in its raw form. Since I wrote that I’ve had a number of conversations with Big Data providers, tool-makers and users, and I’ve come to the conclusion that my definition was a bit too facile – and insufficiently empirical. So let’s try it again:

Big Data is any data that requires massively parallel computational techniques to handle.