In the past couple years the term “Web 2.0″ has come to mean a lot of different things to different people. So, what exactly is Web 2.0?
To understand where the term Web 2.0 came from, we have to go back to the dot com bubble burst in 2000 and 2001. The dot com burst left the Internet industry in shambles, but there were a few people that managed to survive. Google, Amazon, and EBay each emerged from the ruins of the burst and have seen remarkable success. As Internet gurus analyzed why such companies succeeded they realized that the web was going through a change–a series of improvements–much the same way software does as it moves through versions. So, the term “Web 2.0″ was initially used to describe what set those companies that survived the crash from those that didn’t.
That is a little trickier question to answer, as Web 2.0 means a lot of different things to different people now. So, we’ll try and break things down into a few different categories:
Web 2.0 in design has been characterized by a variety of trends and shifts in web design theory. Trends have included the prominent use of bright colors, rounded corners on DIVs and other containers, heavier use of gradients, incorporating more bubbly fonts, adding faux reflections on images, etc. Some of these design trends have already seen a decline in their use, while the shifts in web design theory are ongoing. Sites now tend to be less cluttered as white space is better used. They tend to be more legible as more thought is given to typography. Design throughout a site now tends to be more consistent as designers are striving for consistency and effective use of complementary color palettes.
Web 2.0, for some, simply means that users are now able to interact and network with one another. This has lead many website owners to try to integrate social networking aspects (friend connections, user ratings, user-generated or user-provided content, etc.).
Now that the term “Web 2.0″ is so pervasive and is such a hot buzz word, some people have begun describing any new tech related startup company as “Web 2.0″ whether their site or company embodies any of the other definitions of the term.
Tim O’Reilly gets the credit for this definition. O’Reilly found that in each of the aforementioned survivors of the bubble burst, collective intelligence was the driving factor behind the sites’ success. In the case of Google, collective intelligence was seen in the development of their Page Rank algorithm. The algorithm was based off of the idea that the more sites that link to a page the better resource that page is. This is allowing the community (of website) to determine (collectively) which sites really deserve top rankings. In the case of eBay the pricing and use of the website was determined largely by its users and the collective intelligence they supplied by way of their interactions. Finally, Amazon used the collective intelligence of its users to not only provide simple improvements (like book reviews), but also more complex marketing strategies (like making product recommendations based off of the purchasing habits of similar users).
At this point, the majority of the definitions above are pretty widely accepted. So, I would hesitate to call any one of them “wrong”. I do, however, tend to gravitate towards O’Reilly’s definition for the simple reason that it seems the one that will make the biggest impact on the Internet and it’s users.
At the recent Web 2.0 Expo in San Fransisco, O’Reilly illustrated the difference between Web 1.0 and Web 2.0 by using the following example. Consider a bank. A bank has at it’s disposal massive amounts of information because of all the transactions it processes on a daily basis. But, the bank doesn’t do anything with the collective intelligence it has laying everywhere. Contrast this with a website like WeSabe. WeSabe allows users to input daily financial expenses and to categorize those expenses as they see fit. In his presentation, O’Reilly used the example of a BMW repair. He entered an average car repair, performed at a prominent BMW repair shop in the Bay area into WeSabe. WeSabe searched through its records and returned an average repair cost, repeat customer percentage, and recommendation percentage for the popular repair shop which was pretty helpful. What was more helpful is that WeSabe also returned stats on an alternative (and far less known) garage whose average repair cost was lower, had a higher return rate, and higher recommendation percentage. So, the site was able to put the collective intelligence to good use by making a relevant and useful suggestion.
My hope is that as more data becomes available through the web we start to think about how it can be linked and combined to provide us with greater intelligence and understanding. Maybe we can call that World 2.0.
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