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Big Data Analytics

Today there large and complex sets of raw facts and figures that cannot be dealt with ordinary data handling methods. Big data is this bulky amount of data and information that is being generated in various fields from globally, while big data analytics involves advanced analytic methods used to process this data. The data is in various forms such as unstructured, unstructured or semi-structured and comes in different sizes such as terabytes of zettabytes.  The social media, internet of things, artificial intelligence, and mobile devices are some of the things that have led to the development of these complex data. The big data analytics allows the business or researchers to processes these large volumes of data enabling them to make decisions more quickly and accurately.

Tools used in Big Data Analytics

Specialized analytic systems and high-powered computers and software are among the tools that are used in the collection of data from various sources. The various open source technologies such as have been developed to help the data analysts in the handling the big data and some of them include the Map Reduce, Hadoop and NoSQL databases. These databases process both the structured and unstructured data and hand over to advanced analytic tools such as such as data mining, which searches for relationships in the data; predictive analytics, which uses the data to model future behavior of elements; machine learning, which is used to tap algorithms for large data sets analysis as well as deep learning and text mining.

Application of Big Data Analytics

Big data analytics is being applied to almost all spheres of life. The advanced techniques have enabled businesses and researchers to make tremendous strides in their fields. In medicine, for example, a researcher can easily decode DNA strands in minutes, while security agents can predict terrorist’s attacks from the analysis of big data. Organizations can also determine the type of ads one is likely to view on social media using the big data analytics enabling the making of business decisions. There is a lot of efficiency in doing business and processing systems through the use of big data. Also, operations are improved and risks in various types of businesses and fields ae managed effectively.

Among the challenges faced in the big data analytics is lack of the skills to perform these complex analysis of data. The costs of hiring experienced analysts and engineers are also relatively high to most businesses and researchers. However, the latest developments in machine learning and artificial intelligence have led to the development of software that is easy to use by ordinary people.