Predictive analytics and Hadoop

 

Predictive analytics is the next big thing. It is being used widely and has found many applications in the software industry. Predictive analytics enhances user experience by suggesting products or rather data before the user actually starts looking for it or rather wants it. Many business models are already actively using predictive text for their products. Google happens to be the greatest example. The Google Now app is so predictive that it may seem sneaky an you may start wondering about how does Google know all that! It goes to the extent of suggesting movie timings in a theatre near you if you search for a particular movie on its search engine. It provides the traffic status from work to home and vice versa at the exact hours. Is not that really cool? And so convenient. Amazon uses the browsing history to predict what the user may buy next. Why? So that it can optimize the shipping time. Imagine the revolution that this can cause in the healthcare industry. It can increase the accuracy of diagnosis, it will be able to prevent diseases: instead of curing it could be predicted how they can be prevented. This will have a huge impact in insurance agency. Costs will be optimized as better solutions and alternatives be available. Analyzing such data can help provide better solutions for the masses. How did we evolve to this stage where organizations are able to predict what users may want in the future?

This has all been made possible by the amount of data created every day. The term coined for this amount of data was coined as Big Data. Gartner defines Big Data as high volume, velocity and variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making. Earlier there was now means to use it. This data that is created every day needs to be categorized in order to be used. With emergence of technologies like Hadoop this has been made possible. Hadoop has gained popularity in the recent times owing to the benefits offered by it. Hadoop can use both structured and unstructured data. This plays in its favor because most of the data that enters the systems is unstructured. It needs to be converted in a usable format so that the data then in turn can be used for predictive analytics. Hadoop is a cost effective tool to store both structured and unstructured data. Hadoop uses distributed parallel processing approach to read and process data. Hadoop also allows you to store huge amounts of data and it does so very gracefully. It uses multiple systems as nodes to store data. It is also fail proof as if one node fails, there’s always a node available to replace it. There’s practically no loss of data with Hadoop.

Hadoop has the capacity to analyze any kind of data, be it text, image, Facebook comments, tweets, the list is endless. It allows you to query data on the basis of various parameters so that sense can be made from the enormous amounts of data it has the capacity to process. Hadoop uses MapReduce and HDFS(Hadoop Distributed File System) to perform its job. The former breaks the process into smaller tasks. It uses the concept of distributed processing, using a number of computers forming a cluster. The latter uses several computers to store the data also ensuring reliability.

With its powerful features Hadoop is being adopted as the software used for this rapidly growing sector called ‘Predictive Analysis’. This field has immense potential and is expected to expand dynamically in the future.

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