Big Data and Business Analytics

The introduction of web, newer technologies and mobile devices has caused a fundamental change to the nature of the data. Big data has its own distinctive and unique qualities that has differentiated and distinguished it from conventional corporate data. It is the information that is no longer centralized, highly structured and well manageable now as compared to the traditional form of loosely structured and distributed data. In general, it contains the following mentioned attributes.

·         Volume – The volume of data developed both inside and outside the firewall and corporations through web, infrastructure, mobile devices and other sources is rising exponentially every year.

·         Speed – The speed at which this new data is developed and the requirement for real time analytics to fetch business from it is rising to the digitization of mobile computing, transactions and the number of mobile device users and internet.

·         Type – The types of data are increasing at a speedy pace mainly in form of semi structured and unstructured text based form including log file data, location based data and social media data.

On a wider note, big data could be generated from a large number of resources like mentioned in brief in below.

·         Social Media – At present, there are about 150 million public blogs, 260 million twitter users and 700 million Facebook customers. Every tweet, blog post, comment and Facebook update creates multiple new data points available in the form of structured, semi structured and unstructured also known widely as data exhaust.

·         Mobile Devices – There are approximate 5 billion mobile phones that are in use across the world. Each text, call and an instant message is treated as data. Mobile devices especially tablets and smart phones make it simpler to use data generating and other social media applications. Mobile devices also gather and transmit location data.

·         Internet Transactions – Billions of stock trades, transactions and online purchases used to happen on daily intervals comprising innumerous automated transactions. Each develops a wide number of data points collected by banks, retailers, credit agencies and credit card service providers.

·         Networked Devices and Sensors – Electronic devices of all kinds comprising IT hardware, servers, smart energy meters and temperature sensors develop semi structured updated log data that records every action.

Traditional data management and other data warehousing tools are not up to the mark of analyzing and processing big data in a cost effective and timely manner. To be precise, data must be handled and managed effectively into relational tables having neat rows and columns prior to conventional enterprise data warehouse can absorb it. Due to the requirement of manpower and time, incorporating such structure to bigger chunks of unstructured data is quite illogical. In addition, scaling up a traditional enterprise data warehouse to accommodate potentially Megabytes of data would need unrealistic and authoritative financial investments in new as well as oftentimes proprietary hardware requirements too. There could be many different ways used and applied to analyze and process big data. They take benefit in general of commodity hardware to enable scale out parallel processing strategies, bestow non relational data storage capabilities in order to process semi structured and unstructured data and apply modern data analytics and visualization technology to convey deep insights to end users worldwide. Last but not least, IT firms and organizations like IBTechnology assist others in determining the most reliable and practical big data use cases and create products and services by making use of latest technologies and big data recommendation engine easier to implement, use and manage along with helping out the customers in giving the flexibility required to experiment with new big data tools and technologies.