One way to think about the state of big data technologies is to draw an analogy with the business adoption of internet technologies. In Web 1.0, businesses busied themselves with getting the basic internet technologies in place so that they could establish a web presence, build electronic commerce capability, and improve operating efficiency. We can think of ourselves as being in the era of Big Data 1.0, with firms engaged in building capabilities to process large data. These primarily support their current operations—for example, to make themselves more efficient.
With Web 1.0, once firms had incorporated basic technologies thoroughly (and in the process had driven down prices) they started to look further. They began to ask what the web could do for them, and how it could improve upon what they'd always done. This ushered in the era of Web 2.0, in which new systems and companies started to exploit the interactive nature of the web. The changes brought on by this shift in thinking are extensive and pervasive; the most obvious are the incorporation of social-networking components and the rise of the “voice” of the individual consumer (and citizen).
- “SIMILARLY, WE SHOULD EXPECT A BIG DATA 2.0 PHASE TO FOLLOW BIG DATA 1.0. THIS IS LIKELY TO USHER IN THE GOLDEN ERA OF DATA SCIENCE.”
It is important to note that in the Web-1.0 era, some precocious companies began applying Web-2.0 ideas far ahead of the mainstream. Amazon is a prime example, incorporating the consumer's “voice” early on in the rating of products and product reviews (and deeper, in the rating of reviewers). Similarly, we see some companies already applying Big Data 2.0. Amazon again is a company at the forefront, providing data-driven recommendations from massive data. There are other examples as well. Online advertisers must process extremely large volumes of data (billions of ad impressions per day is not unusual) and maintain a very high throughput (real-time bidding systems make decisions in tens of milliseconds). We should look to these and similar industries for signs of advances in big data and data science that subsequently will be adopted by other industries.
- Foster Provost and Tom Fawcett. Big Data. March 2013, 1(1): 51-59. doi:10.1089/big.2013.1508 (an excerpt from Big Data journal ).