Analyzing lots of data is only part pf what makes big data different from previous data analytics. Learn what the other three aspects are.
Every day human beings eat, sleep, work, play, and produce data—lots and lots of data. According to IBM, the human race generates 2.5 quintillion (25 billion billion) bytes of data every day. That’s the equivalent of a stack of DVDs reaching to the moon and back, and encompasses everything from the texts we send and photos we upload to industrial sensor metrics and machine-to-machine communications.
That’s a big reason why “big data” has become such a common catch phrase. Simply put, when people talk about big data, they mean the ability to take large portions of this data, analyze it, and turn it into something useful.
Exactly what is big data?
But big data is much more than that. It’s about:
- taking vast quantities of data, often from multiple sources
- and not just lots of data but different kinds of data—often, multiple kinds of data at the same time, as well as data that changed over time—that didn’t need to be first transformed into a specific format or made consistent
- and analyzing the data in a way that allows for ongoing analysis of the same data pools for different purposes
- and doing all of that quickly, even in real time.
Read more about this here.