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Big Data analytics involves the assembling, arrangement, and analyzes of enormous data sets with varying contents, to reveal or establish existing trends, relations, or patterns. Big data analytics requires tools for data mining, forecasting, optimization, and analysis.

Enterprises involved in big data analytics receive insights not only on their company's functionalities but also on customer preferences and interactions. With the right understanding of the data in-hand, businesses can make informed decisions that lead to customer retention, increase in sales, and improvement in operations. 

Big data analytics

The method of collecting, organizing and analyzing wide-ranging data sets, or big data, to find previously unnoticed correlations, patterns, market trends and other information that aids organizations in making informed business decisions is called big data analytics. Big data analysis helps organizations understand the information contained in the data better, thus helping them identify data that influence business decisions. The analysts studying Big Data usually seek the knowledge one gets from analyzing the data.

Big Data analytics is executed by specialized software tools and applications which are used for data mining, data optimization, predictive analysis and text mining. These processes are individualized but highly integrated functions of high-performance data analytics. The use of Big Data software and tools allows the organizations to sort huge volumes of data which a business collects and find out the relevant data whose analysis will be beneficial to the business.

Benefits of Big Data –

  • Errors are noticed more quickly, and corrected faster.

  • Quicker recognition of new strategies of competitors

  • Helps understand customer needs and thus optimize customer experience.

  • Security and fraud analytics seek to shield the physical, financial, and intellectual data from being mishandled. Effective data and analytics abilities prevent fraud and ensure the overall security of the organization’s data. The integration and correlation of data from all over the enterprise offers an integrated view of fraud from across businesses, products, and transactions.

  • Aids in recognizing trends and turning out relevant products.

Challenges of Big Data Analytics –

  • Standard Hadoop version is unsuitable for real-time data analysis, so it requires specialized tools.

  • Breaking down data silos to access data stored across the organization in multiple systems.

  • Requires platforms that can store unstructured data with the same ease as structured data.

  • Protecting the vast quantities of data being stored and analyzed.

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