Hold tight. The big-data bubble is about to burst. And you may recall reading it here a few weeks ago: Editor in Chief Beth Schultz raised the issue in Big-Data Meltdown & 7 Other 2013 Predictions.
But plenty of others are also looking into similarly bleak crystal balls, at least as far as the fate of big-data is concerned.
In a New York Times post that questioned "the limits and shortcomings" of big-data technology, reporter Steve Lohr suggested that intuition is at least as valid as math modeling, predictive algorithms, and artificial intelligence:
In so many Big Data applications, a math model attaches a crisp number to human behavior, interests and preferences. Claudia Perlich, chief scientist at Media6Degrees, an online ad-targeting start-up in New York, puts the problem this way: "You can fool yourself with data like you canít with anything else. I fear a big-data bubble."
Perlich is worried too many people will start rushing to become "data scientists," do poor work, and give big-data a bad name.
Bill Franks, chief analytics officer for Teradataís global alliance programs, was even more emphatic in an International Institute for Analytics blog:
I predict that in 2013, the big data bubble will begin to spring some serious leaks. The bubble may not fully burst in the next 12 months, but it will begin to deflate...
While big data can (and will!) drive major change in the coming years, the fact is that every type of big data wonít drive change for every organization. Each organization will have to find its own path to success given its own unique business model and relevant big data sources.
Actually, people have been predicting the demise of big-data for a while now. More than a year ago, Eric Knorr, editor in chief at InfoWorld, wrote, "First there was the dot-com bubble; then the housing bubble. But nothing will compare to the big data bubble."
So what are some of the issues? Bruno Aziza, vice president of marketing at big data analytics company SiSense, argues that big-data is too expensive -- and not necessarily big. In a post titled "Why I donít buy the hype about 'big data'," he suggested that big-data needs to be refined and redefined. "We need to approach big data differently, and design solutions that allow smaller companies [to] take advantage of this opportunity," he wrote.
Part of the problem with big-data is that it is not necessarily big -- and badly needs a better definition, Aziza argued:
What if, instead of focusing of the proverbial 3 Vís (velocity, volume and variety), we tried something like this: "Big data is a subjective state that describes the situation a company finds itself in when its infrastructure canít keep pace with its data needs."
What do you think? Is big-data headed for a fall -- or is it just going through a natural state of evolution?