The Petabyte Age is different because more is different. Kilobytes were stored on floppy disks. Megabytes were stored on hard disks. Terabytes were stored in disk arrays. Petabytes are stored in the cloud. As we moved along that progression, we went from the folder analogy to the file cabinet analogy to the library analogy to — well, at petabytes we ran out of organizational analogies.
At the petabyte scale, information is not a matter of simple three- and four-dimensional taxonomy and order but of dimensionally agnostic statistics. It calls for an entirely different approach, one that requires us to lose the tether of data as something that can be visualized in its totality. It forces us to view data mathematically first and establish a context for it later. For instance, Google conquered the advertising world with nothing more than applied mathematics. It didn’t pretend to know anything about the culture and conventions of advertising — it just assumed that better data, with better analytical tools, would win the day. And Google was right.
Exponential Growth
http://www.ideacouture.com/blog/the-only-constant-thing-in-life-is-change-embracing-the-petabyte-age-part-i/
The petabyte is a unit of information or computer storage equal to one quadrillion bytes, or 1000 terabytes:
1 PB = 1,000,000,000,000,000 or 1015 bytes.
Translation: A lot of data. That’s about 250 trillion MP3 downloads from iTunes, 1.5 billion DivX Movies, or 100 billion 20 megapixel superfine photographs.
One year ago, Chris Anderson – the editor of Wired Magazine who is often (understandably) mistaken for the curator of the beloved TED conference – wrote an article called “The End of Theory” in his widely cherished magazine. He wrote of emerging opportunities and trends that will result from immense data compilations and subsequent manipulations.
Google operates with a particular philosophy that it applies to virtually all of its activities: spare humans from the burden of tasks that machines are simply better equipped to accomplish. For example, it does not make sense for humans to maintain a dictionary for Google’s seemingly flawless “did you mean _____?” corrective search feature. Google uses pattern recognition to identify (with remarkable accuracy) typographical errors and likely intentions of users. Data knows best. Numbers don’t lie. This is a great example of how data – and intelligent manipulation of data – can drive useful solutions.