The New Economy: Big Data
Continuing on the theme of equality from the last post, Elance and Airbnb both promote equality in that they allow their users to interact on a more personal – and certainly less constrained – manner, since the setup of both websites reduces the participation to one individual on either side of the equation.
This equality is also carried along by collaboration of a different sort; Elance, while normally expecting one client to hire one worker, also allows for the creation of groups of workers for various purposes. These groups can be formed by either the client or the worker – the client may advertise that s\he is looking for several workers to collaborate together on a project, or individual workers may come together in order to offer their collective services out to the wider audience of clients on the site. In either case, the equality is present because the workers are equals to one another and would presumably support each other in the work they have been hired to do, whether that work takes place on exactly the same level, with both workers as writers or as a mini-organisation wherein one worker acts strictly as a writer while the other acts as an editor.
Collaboration between companies and workers gives a new edge to the data available to sites such as Elance, and companies such as American Express, as it gives them what have been labelled ‘economic clusters’ or ‘peer-to-peer collaboration’ to analyse for new information on how these companies\workers and their customers operate. To go back to the original blog post that inspired this series, by collaborating with Uber, American Express now has more access to data on the movements of their customers, and could use this as a springboard for launching more customer initiatives, either on their own or in collaboration with yet more companies.
Big data, as this is known, is not necessarily more data on individual people; it generally involves the same amount of data, but simply on a greater number of people than before. American Express would not have data on a greater number of people than before, because the offer was only open to people who already had a card with them; they would just have more data on those specific people to work with. To give an example, UPS has recently been using big data to upgrade and improve on their vehicle use – since the data gathered from UPS trucks includes the braking time, average speed, miles covered, fuel consumption and so on, the company was able to take that information and use it to reduce the miles covered, thereby saving both fuel and money. They are currently employing the same technique on the flights they run per day, hoping to perhaps have the same results.
The UPS example is one where a single company utilises its own resources in order to accumulate big data. Economic clusters allow for a greater variety of data from a greater variety of sources, as can be seen to great effect on the Airbnb website, where the information provided by quite literally thousands of people is used not only to facilitate the experience of the other site users on an individual basis (through the use of reviews and profiles, for example) but also to help the site as a whole by using those experiences when writing the articles in their help centre (how-to’s, recommendations and so on) and writing their neighbourhood guides for various locations.
While Airbnb is, to a degree, a self-selecting environment for the people using it, big data also comes into play in the economic clusters of Owyang, which brings us back to the beginning of this series: cluster collaborations allow not only for the sharing of resources, but also for the sharing of information. If UPS can overhaul its shipping services to such a great extent using only the information it can glean from itself, imagine what a group of businesses can do if they share their data.