Keys data on Big Data summits
On Big Data, there are some summits every month in town from United States, Uk, Ireland, Germany and India.
In all of each, there are:
1. 80+ Industry Speakers,
2. 1000+ attendees,
3. 25+ hours of information given,
4. 50+ case studies proposed.
The last event in San Fransisco
The last conference in April 2013 gathered some of the greatest minds in the space; from Facebook , to LinkedIn , to Google , to Citibank, to the NYSE and eBay.
The event had attracted a fair amount of “Big Data Newbies,” lured by the prospects of a better performance or a better career. Statisticians wanting to get their hands around particular technologies or former database administrators curious about “what the business really cares about” came together to find out what was really hiding behind the “Big Data Hype”.
Big data is not so big
Gartner, In a recent podcast, explains how, despite the emphasis on large datasets, the term “Big” in “Big Data” could actually be irrelevant. “What is Big today, might be normal tomorrow,” Frank concludes. This is quite different from IDC’s definition, which puts the bar at a 100 Terabytes here. Frank’s point is difficult to debate. In fact, it broadens the spectrum for what should fall under the “Big Data” umbrella; and that’s a good thing. As an industry, if we want every company to realize their potential with data, we need to obsess less about the size of their databases, but rather, focus on their assessment of what “Big Data” is for them.
The 3Vs and Big Data: not married!
If you look at the genesis of the term “Big Data,” you’ll find that the industry often refers to the “3 Vs”: Volume, Variety and Velocity. The “3 Vs” have put a lot of emphasis on storage technologies to the detriment of the analytics field. If you look into the evolution of technologies supporting “Big Data,” you’ll find that storage is doing well but that analysis technology isn’t evolving as fast.Between 80s and 2010, Storage has seen a revolution: price divided by 30000 and capacity exponential. Analysis, at best, has seen an evolution but absolutely not revolution at all from now. The future revolution of big data is in its capacity to analyses the data automatically and without knowing completely what’s we search exactly, identify important links in between data and see new data inside big data. This is the challenge of 2010’s decade.