Big Data Analytics: A Solution to Business Problems

In past few years, business people have seen tremendous explosion in data that is being generated through daily transactions. Corresponding to this, the heterogeneity in data volumes have grown by an even larger amount. Organizations with high-performance computing infrastructures and data centers are finding ways to deal with the voluminous data. Various tools are also used to bring in significant benefits in data mining, machine learning and knowledge discovery but still there is a big hole.

Many traditional data-centric applications lack in their ability to handle the most important aspect that is data but if focused utilizing emerging trends in computing, the possible solutions of business problems lies with big data analytics. Although there are many obstacles in dealing with the huge amount of data, Big Data Analytics tools have potential to reform the data analytics. Big data analytics comes to the rescue for business problems by deploying, integrating and operationalizing Big Data environment required to realize the value of their existing business resources.

Recent innovations in technology and affordable price of electronic gadgets have become the common reasons for data explosion. In age of Big Data today the variety and amount of digital data generated is quite high and it is too big to process conventionally or to transport anywhere. Big Data Analytics act as a driver in optimizing the primal processes and functions for the organization. It involves aggregating all types of data whether internal or external. The use of big data tools allow organizations to manage massive data, manage risk, satisfy stakeholder’s expectations and improve their overall performance by transforming data into information, information into knowledge and knowledge into intelligence. In short, the main objective for any organization for using big data analytics is to improve the efficacy decisions taken by business people.

Big data analytics is classified in the following categories:

  • Text Analytics: It refers to the process of analyzing unstructured text to extract relevant information by using statistical analysis, computational linguistics, and machine learning. E.g. Extracting textual information from social network feeds, emails, blogs, online forums, survey responses, and news.
  • Audio analytics: These are used to analyze and extract information from unstructured audio data like call centers and healthcare companies. It can be used in various video surveillance applications.
  • Social Media Analytics: It includes the analysis of structured and unstructured data from various social media sources including Facebook, Linkedin, Twitter, YouTube, Instagram, Wikipedia, and others.
  • Predictive Analytics: It includes techniques for predicting future outcomes based on past and current data using NNs, SVMs, decision trees, linear and logistic regression, association rules, and scorecards.

Big data can bring about considerable cost reductions and substantial improvements in the time required to perform a computing task because of existing technologies. These technologies would allow organizations to achieve an edge over the competitors.

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