What Is Big Data Analytics?

Big Data Analytics

Big data has found application in numerous fields. From wind mills to business analytics big data is a force to reckon with. While it is changing lives and becoming a part of more lives every day, understanding what big data implies is crucial to stay ahead of the trend.

Understanding Big Data Analytics

Big data is so called due to the large sizes of the data sets. The software tools that are commonly used to understand and decrypt the data so to speak cannot be used on big data sets within normal time frames.

The data sets range from a few terabytes to the current petabytes. Each petabyte of data is equivalent to 1000 terabytes. Each terabyte is in turn equivalent to 1000 gigabytes of data. The normal hard drives and USB drives that people use have a storage capacity of a few gigabytes.

The size of a big data set is not limited to a universal unit. The size of a big data set ranges anywhere from a few terabytes to a few petabytes.

Gartner defines big data as, “Big data is high volume, high velocity, and/or high variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization.” Some institutions are also adding veracity to the definition of big data.

Big Data Analytics

Given the constantly changing size of the big data sets, analyzing and understanding the data requires new and updated software. Traditional software solutions like DBMS technology and newer solutions like NoSQL are constantly being updated to receive and analyze bigger data sets.

Big Data Analytics – The Application

Big data analytics has pervaded the world of economics in its entirety. The application of analytics can be help spot business trends, combat crime, prevent diseases, reduce downtime for machines like wind mills, link legal citations and can even determine real-time traffic conditions.

Sensors, mobile devices, cameras, microphones, software logs, radio-frequency identification readers and wireless network sensors are constantly gathering information. This data when analyzed and decoded can increase efficiency and help solve problems before they become large scale issues.

Big data may be observed in action through big science, large-scale e-commerce, RFID, video archives, sensor networks, photography archives, social networks, big social data analytics, internet documents, internet search indexing, astronomy, call detail records, atmospheric science, genomics, biogeochemical, biological and interdisciplinary scientific research, military surveillance and medical records. These are only some of the avenues that show a heavy reliance on big data analytics. The number of industries that depend on big data analytics is increasing by the hour.

Big Data Analytics – The Accomplishments

As of 2012, big data analytics was the most cost-effective method to process and understand data. This data analytics software has gone from expensive to affordable over time. The software today is accessible to even the smallest startups that operate out of garages.

With big data analytics, a company can detect and stop a malfunction before it costs billions of dollars in downtime required for servicing. The software has the potential to optimize operations in real time for machines and human operations.

Companies can depend on big data analytics to maximize on profits while eliminating the manpower that was relied on to clear inventory and keep track of records. Each and every avenue of commerce seems to have a need for big data. There is no avenue that cannot benefit from turning to analytics to optimize and create efficiency while being cost efficient.

Companies like IBM, Oracle and GE amidst numerous others have capitalized on big data analytics and the advantages that it brings. With companies engaged in a never-ending battle to gain a competitive edge over the others in the field, big data analytics has become the advantage to trump all others. The analytics can help make smarter decisions faster for an outcome that will certainly make a difference in the bottom line margins.

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