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I have been researching on Big data analytics for social networks for sometime now. I would like to share what I have seen and known. May be some discussion on it.I would like to share the insight I have gained to the larger audience at VMWare.
Measuring IT efficiency varies significantly as the data capture, data processing and storage is viewed differently than the traditional systems in Big data analytics.
Big data is all the voluminous and unstructured data from a wide ranging sources in the form of click stream data from websites, social media data like ‘Likes’, Tweets and ‘Blog posts’ etc. and from video entertainment as well. Just to give you an idea, Google processes about 24 petabytes of data and not all of this in rows and columns. The consumers as well as working professionals in the organizations have begun to realize the potential value and the intelligence that can be derived from the vast amount of data that is generated through social media conversations.
The challenges of Big data analysis
Big data technologies rely on their ability to handle large amounts of unstructured data. The server infrastructure capability depends on their ability to handle geometric growth of social networks. Data is generated all the time and in real time in social networks.
The challenges for mining such huge voluminous unstructured data are of two kinds. Firstly, this requires use of emerging technology such data mining grid and Map reduce infrastructures such as Hadoop and a non-linear and non-deterministic software architecture. This actually changes the way we think about data capture and processing.
Secondly, it is known fact that ‘what we measure is what we manage’. We need to know ‘What we are looking for’ and the timing ‘When to ask the question’ is important. ‘Spotting trends’ is one emerging area in social media analytics. Then the question, Do you know what you are looking for? Still lingers on.
Practical application of Big data in social networks
Recently, in an industry talk, leaders emphasized on external data. Organizations need to focus on it. All this while, they have focused on the internal data generated by systems like ERP. Analyzing internal and external data needs to go hand in hand with understanding the business needs. Once we know the business needs, insight gained from the data can be applied. We also need to understand that with so much importance given to Data analytics, The data we input needs to be clean and accurate. We need to be responsible for our data. More on the Garbage in, Garbage out on this page.
Now Social networks like Facebook uses big data to understand all about you and provide personalized information. Personalized information comes in the form of serving the post you like and introducing you to friend's friends and so on. Other companies analyze information to understand their customers. With the introduction Web 3.0, the applications for Big data for social networks are enormous. Analysis on pattern matching and personalization are the biggest trends in the coming years.
For example, after a big event like a football match, they want to know where people head to so that they tap into it for more opportunities to serve their customers.Today, even many mining companies have started using Big data analytics for gaining operational efficiencies. They also look at Big data for work force management and real time planning. Companies on the retail trade want to understand what their consumers are thinking about their brand and what is influencing their buying decision. They collect enormous amount of data from the point of sale location at the retail stores across the country.
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Cheers.