Top Menu
Big Data Blog

With the overwhelming rate of data inflow at all times and increasing market competition and development to meet customer needs in the most efficient manner, there has risen the need to put a name on the age-old process of collection, management and analysis of data, consequence of which: ‘Big Data’ analysis

Big data is a term that describes the large volume of data – both structured and unstructured – that floods a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what the organizations do with the data that matters. Big data logistics can be analyzed for insights that lead to better decisions and strategic business moves, all leading to increased efficiency of the organization, i.e how well they run it! The concept of Big Data gained momentum when industry analyst Doug Laney constructed, the now mainstream definition of ‘Big Data’ as the three Vs:-

Volume Organizations collect data from a large variety of sources; business transactions, social media and information from machine-to-machine data. In the past, storing it would’ve been a problem – but the advent of new technologies (such as Hadoop) have diminished the burden.

Velocity Data floods in at an unprecedented rate and must be dealt with in a quick, timely manner. RFID tags, sensors and smart metering are fueling the need to deal with the gushes of data in near-real time.

Variety Data comes in all kinds of formats –structured, numeric data in traditional databases, unstructured text documents, email, video, audio, and financial transactions.
Types of Big data that aid business intelligence include

  • Prescriptive analysis of big data decides reveals actions to be taken next and assists in compiling rules and regulations for the next step.
  • Predictive as the name suggests is the analysis of likely scenarios.
  • Diagnostic analysis is a look at what went wrong in the past and why. It results in corrective measures for future efficiency.
  • Descriptive Is the analysis of real-time incoming data, i.e. analyzing the present.

What goes into carrying out a mammoth task of this magnitude is surprisingly smooth to understand. The whole process of analyzing the Big Data logistics, comprises of a succession of three steps.

  • Recognizing the sources of big data inflow. Is it streaming data, social media data or from publicly available sources?
  • Once the sources have been recognized; How to store and manage it (collection)? How much of it to analyze? (curating) and finally how to use the insights revealed (application).
  • No amount of analysis can help an organization unless they know how to put the information to good use. So, unarguably it is vital to find technologies that help you make the most of Big Data and its analytics.

Big Data is employed in banking, government, healthcare, retail, education and manufacturing. It can be analyzed for insights that lead to better business decisions and strategic business moves.

A region to stress upon in this process of analyzing such massive amounts of logistics, is the fact that in spite of having such huge amounts of unprocessed, raw data to be analyzed, only a very small percentage of it is actually analyzed and put to use for increased efficiency. How can organizations make better use of the raw information that flows in every single day. This issue of the efficient application can be addressed by agencies managing Big Data, who can help their clients understand the most of their Big Data Analytics and apply it to their Business Intelligence. It is essential to realize the massive potential Big Data Analysis holds in leading organizations to make better decisions and result in optimum efficiency.

About The Author

Shrey Patel works with Intransure Technologies as Search Engine Optimizer. His passion for helping people in all aspects of Digital Marketing. You can find him on Twitter and LinkedIn.

Leave a Reply

Your email address will not be published. Required fields are marked *

You may use these HTML tags and attributes: <a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <cite> <code> <del datetime=""> <em> <i> <q cite=""> <s> <strike> <strong>