Much has been made at the Strata Conference in Santa Clara, CA this week about Gartner Research Director, Svetlana Sicular‘s January 22, 2013 blog post that “Big Data is Falling into the Trough of Disillusionment.” Ms. Sicular has a very impressive resume. She served as VP of Global Data Standards atVisa prior to joining the ranks of Gartner about a year ago.
The problem with her analysis is simple. Big Big Businesses, like Visa, and major financial institutions, have had the resources to invest in Big Iron for Big Data Processing. Big Data, however, isn’t just about Big Business. Big Data, as Neuralytix defines it, is the ability “to bring together complete internal and external data sets to create business value and innovation”. While her background gives her a great perspective on the upper echelon of Big Big Business, it is not representative of the business environment at large.
Gartner and other leading market research firms have stuck with the 3 V’s definition: requiring volume, variety and velocity. In full disclosure, when I led the IDC team to the 23 words that IDC uses to define Big Data back in the middle of 2011, it took us around eight weeks, nearly 40 analysts (including several Ph.D’s) to come up with a definition that we all did not vehemently ot object to. The IDC team recognized (and i speculate, still recognize) that the definition that we came up with is an academic definition. That said, we all recognize that the V’s are all relative: relative to vertical, geography, the size of the organization and frankly any other demographic or segmentation that one could define.
Any way you
define Big Data, it’s not new. Big Big Business and even Big Business have been doing it for years.
So, the “new” element of Big Data is about “democratizing” or enabling non-Big Business to perform business intelligence and analysis in a cost effective manner to generate new business value and innovation. I agree with the attendees and vendors at Strata, with whom I met. Ms. Sicular, and by extension, in her capacity as a Research Director at Gartner, is simply wrong. Her perspective, and again, by extension, Gartner’s is myopic and skewed, and I declare, wrong! Well, if not wrong, at least very biased and unfounded for the IT community at large.
I believe that in the next three to five years, most businesses will be implementing some form of Big Data. Enterprises with data and information that are presently managed within an enterprise data warehouse (EDW) will leverage data integration technologies, such as those offered by large established vendors like Informatica, newly recognized vendors like Hadapt or emerging start-ups like Splice Machine, with whom I spoke this morning, and begin a migration towards Big Data, or Big-Data-like techniques to gain insight, and ultimately business value and innovation from data and information.
Even Microsoft is getting into the game with its Parallel Data Warehouse (PDW) that enables SQL Server customers evolve their SQL databases to Massively Parallel Processing (MPP) architecture to take advantage of commodity hardware.
Let’s get real. Even if we use Gartner’s own hype cycle analogy (an analogy that I believe is outdated and irrelevant), we’re still at the “technology trigger” part of this cycle. While Big Big Businesses are usually the most prestigious opportunities for any emerging technology, Big Data is attempting to break all the rules. Big Data is enabling organizations of any size, geography or frankly any demographic to be able to gain competitive advantage from leveraging its (and others’) data, analyzing it, and accelerating innovation. What this means is that “traditional” views of technology, cycles, etc. are no longer applicable. A new analogy needs to be created. Am I putting forward an alternative, not yet, I’m still formulating it. I’m not too proud to say that the velocity of change is faster than my ability to model this change.
Gartner Hype Cycle (Source: Gartner)
Lastly, whatever model gets created the “cycle” is not likely to be a 10 year cycle as we have observed in the past; instead, I believe that maturity for Big Data will be reached in about three years (two if you count 2013). But in these three years, the “hype” element (or at least the “excitement” element) will be very active.