Big data – the professional and the amateur

In my last post on big data I hypothesised that really we are just beginning to understand some of the implications.

Within this evolving ecosystem we have an interesting tension developing between the role of the professional and a community of amateurs. Three models of engagement seem to be emerging between the two communities, all of which deliver value, regardless of any apparent conflict:

  1. Where there is an immense volume of data, how does a professional get to the core data they need as quickly as possible? Possibly by setting a simple classification process and using crowd sourcing to triage data in a similar manner to the Galaxy Zoo experiments. Effectively the professional can work on the needle once the haystack has been removed.
  2. In the second model, a community of interest is created. An example of this is Patients Like Me – a data-oriented social network bringing together people with a shared interest (in this case chronic illness). They share how they feel, their condition, the symptoms they are exhibiting and the treatments they are receiving. What’s interesting is when the community of interest can support and in some cases challenge the opinion of the professionals.
  3. Model three is concerned with gamification, in that it encourages loyalty, participation and a particular behaviour through rewards. In reality it’s rewarding people or making a game of something that most of us would consider a chore. Another post examined the concept of gamification in the workplace and in another example, the Search for Extra-Terrestrial Intelligence (SETI) is looking to add game-play mechanics to continue its quest (which I’m sure we all did ten years ago, but got bored with it when ET did not come to visit!).

These three models offer a means to harness society and resolve real problems for real people in a timely and collaborative way. The non professionals amongst us contribute through triage to allow the professionals to operate upon the nugget that makes a difference.

So what am I trying to say here is that social networks are not only a means of creating massive data sets but can also be used to extract value for the common good. The interest may be as part of a hobby, a personal interest in the subject, or participants might just be looking to earn some rewards, but engaging amateurs and professionals in a collaborative manner could lead to an exponential growth in ideas, reduced time to market for products, validation of products and services (and the list goes on), all because “we did our bit” on the big data mountain.