Big Data can be defined as the accumulated data sets that are humongous and complex in nature, so much so that it is difficult to process them without the implementation of an advanced database management tool, and often a data processing application as well. There are various challenges associated with big data, including capture, curation, storage, analysis, sharing, searching and visualization. These challenges have arisen as it became necessary to analyze huge quantities of data as one huge related set, as opposed to multiple smaller sets of the same data. The advanced analysis of these massive data sets has resulted in the ability to derive greater insights that initiate the spotting of business trends, determine the overall quality of research, prevent diseases, and can even help to combat crime.
In the year 2012, the upper limit of the data that would have been feasible to process was in the exabytes of data. The researchers and scientists would often be restricted by these limitations in the larger sets, especially in the area of meteorology, complex physics, biological research and other simulations. The data never stops growing and this can be accredited to the ever growing mobile ecosystem, aerial sensory technologies and other logs like software, microphones, and wireless sensor networks, etc. It has been estimated that there is a minimum of 2.5 quintillion (2.5 X 1018) bytes of data collected every day. The challenge for most of the bigger organizations is to determine how to utilize and keep up with the continuous flow of data.
Managing big data without the existence of a Big Data analytics tool is incredibly difficult. Such a tool will consist of state of the art relational database management systems, desktop statistics, and visualization packages that would be operating on thousands of servers. The term “big data” often raises a question about the ability of an enterprise to actually manage the data set and analyze it within their own domain. Most enterprises are overwhelmed by such challenges, leading to the consideration of data management. However, there are companies that would handle hundreds of terabytes of data before actually considering them a significant data set.
With 90 percent of data being created in the last two years alone, big data can be said to follow the Four Vs. The first of them is Volume: most organizations are daunted by the colossal amounts of data being created and stored every day. Next is Velocity, the speed at which data is created, collated, and needs to be analyzed. In sensitive issues, big data is analyzed to catch fraud as it streams within the enterprise, so timing is of the essence. The next V stands for Variety: big data, can be in any number of formats. Bringing together, analyzing, and comparing data across formats delivers new, more advanced insights. The last V stands for veracity; the pace at which this big data is generated. Most enterprises do not trust the information available to make accurate decisions at the speed required.
Effective handling of these huge volumes of collected data results in insights, feedback, and deeper understanding – intelligence that can crucially make a business more agile.
Using Big Data to Make Businesses Agile