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LinkedIn's use of data analytics
Generally, I'm quite turned off by the fact that apps and sites can access Big Data and use it in ways I'd probably disagree with. Ironically, I never take the time to look at the policy and learn about what kind of data different sites take or why. When it comes to the superhighway robbery we see every day, I wish governments would simply say no to big companies (once again) unfairly accumulating Big Data for their commercial interests, making a huge profit out of data theft.
One 'exception' to my disapproval of Big Data might be Linkedin. Here's an example of a more or less symbiotic relationship between an ostensibly free provider and its clients. And here is a brief explanation of where and how LinkedIn uses Big Data to achieve its impressive results without necessarily ripping people off.
On the one hand, LinkedIn uses Big Data to very closely analyze what its members do when they are accessing its site so that it is able to provide the identity, learning, and networking services that comprise Linkedin's unique brand and drives its popularity. The features Linkedin offers would not be possible without the data its users provide. Of course, the huge amount of data also guides its future business decision-making.
As mentioned above, there are three wings of the Linkedin service. On the networking side, its algorithms prime the searches through comparatively small items of data to identify “people you may know” and to make suggestions for users to add to their personal networks. Machine-learning techniques enable LinkedIn to refine its algorithms based on user feedback (e.g. user uptake of suggestions) and this in turn is enabling better suggestions to be made. Customers benefit from this data usage by being able to build better and better personal networks that benefit them, socially, professionally, or educationally.
One of the ways Linkedin manages to keep re-designing itself, balancing its business and user interests is by its constant data collection and transformation of data into end-user displays. The data that keeps LinkedIn users engaged includes contacts' job, profile and connection updates. This drives the keeping up with the Jones impulse that drives users' need to further engage and connect. For this to work best, data needs to be live-streamed and analyzed simultaneously, using real-time stream-processing technology. The direct from source information LinkedIn is interested in includes items that are 'liked' shared, clicked, and contact who are messaged.
In short, the (open) secret of LinkedIn's success and the success of many other businesses using Big Data to drive success is its use of timely, on the fly, and personalized suggestions and recommendations.