Big Data : Making sense of Social Media Madness
Social media usage among consumers is growing at a humongous pace resulting in huge amount of data getting created every minute. The growth in usage of smartphones, location based apps and more "Internet of Things", the data is multiplying at a faster pace. The following statistics provides a glimpse of the amount of data we are talking here and no guesses that it is going to move in upward direction only. Few statistics on the social data growth,
· More than 250 million tweets are generated in a day and it is increasing at a tremendous speed.
· 30 billion pieces of content shared on Facebook on a monthly basis.
· 40% project growth in global data generated per year.
· Data will grow over 800% in the next 5 years and 80% of these data will be unstructured.
While handling such huge volumes of data pose a significant challenge, at the same time provide huge opportunities and competitive edge for the enterprises, which are ready with a strategy to handle them. Social Media data has tremendous wealth of information which analyzed properly can make significant impact to both the top line and bottom line. That is where BigData technology and Infrastructure is coming to the rescue.
Big Data processing can be defined in simple terms as fast and reliable analysis of complex and huge volumes of data in near real time with commodity hardward. Big data technologies helps in combining Social World data streams with Enterprise IT systems in a powerful way using which one can derive meaningful and actionable insights. Big data technology is not new, it has been adopted by technology companies like Yahoo, Google, Facebook, Amazon and many more for quite some time now , it has come a long way in terms maturity and now it is ready for mainstream adoption.
Having a scalable BigData Processing Infrastructure to analyze Social Data will help enterprises in,
· Responding faster to a social outburst before negative sentiments go viral the in social world.
· Creating powerful Recommendation Engines for customers to enable cross-sell and up-sell.
· Providing a complete Personalization experience for users through data collected from across the channels.
· Taking key decisions on products and services based on consumer feedbacks.
· Identifying key influencers who are impacting the increase or decrease of sales of a particular product.
Mckinsey Quarterly has projected that there is 60% potential increase in retailers operating margins possible with big data analysis. Though it is too early to put definitive numbers on the impact of BigData, this is the next Big thing which enterprises need to be prepared to get an definitive edge among the competition.
