3VS AND BIG DATA

BIG DATA AND 3VS

Big Data has become one of the most popular buzzwords in the field of digital technology. Big Data, consisting of structured, unstructured and semi-structured data, refers to enormous data sets that surpass the processing capacity of the traditional database systems. It comprises of large data sets with the complex and diverse structure that are difficult to store and analyze with the use of conventional tools and techniques (Banit and Bandyapadhyay, 2016). In other words, when we say Big Data, we are talking about an array of data set whose size, complexity and rate of growth has made them challenging to be processed and analyzed with traditional tools and technologies like visualization packages, software/hardware tools or data statistics within the time required to make them valuable (Chen et al, 2014). This has made Big Data to be labeled by three Vs, volume, velocity, and variety, also known as the 3vs of big data. The 3Vs of Big Data (volume, velocity, and variety) was first proposed by Doug Laney, an analyst at the META Group in 2001 to describe the technical difficulties for current database management tools and how they can be addressed (Laney, 2001).

Data Volume

The volume of data being generated is continually on the increase. The term “volume” can be used to describe data size and is often used to measure the amount of data accessible to an organization. Volume is one of the major characteristics that distinguish data from Big Data and is also one of the reasons why Big Data cannot be handled using the conventional relational database management systems (Vorhies, 2014).

Data Variety

According to De Mauro et al (2015), variety simply means the different data structures which are produced. While the data produced in traditional data are usually structured, in the case of Big Data, the generated data are usually unstructured or semi-structured in the form of audio, texts, images, videos, etc (Owais & Hussein, 2016). Data variety also symbolizes the richness of the data representation and is often referred to as the major impediment to using the enormous volume of data because of the variability of inconsistent and incompatible data formats and structures.

Data Velocity

Al Nuaimi et al (2015) defined velocity as the speed with which data is produced, kept, examined and processed. It measures the speed of data formation, streaming, and accumulation. Data velocity also epitomizes the pace at which data is used to support interaction. For Big Data, the data is created at a very high velocity, which makes it very difficult to analyze.

Originally, these 3Vs were regarded as the three key extents of Big Data. Today, these three features have become generally acknowledged and have been recounted in academic literature and by Big Data practitioners.

References

Al Nuaimi, E., Al Neyadi, H., Mohamed, N., & Al-Jaroodi, J. (2015). Applications of big data to smart cities. Journal of Internet Services and Applications, 6(1), 25.

Banik, A., & Bandyopadhyay, S.K. (2016). Big Data- A Review on Analysing 3Vs. Journal of Scientific and Engineering Research, 3(1), 1-5.

Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile Networks and Applications, 19(2), 171-209.

De Mauro, A., Greco, M., & Grimaldi, M. (2015). What is big data? A consensual definition and a review of key research topics. AIP Conference Proceedings, 1644(2015), 97-104.

Laney, D. (2001). 3D Data Management: Controlling Data Volume, Velocity, and Variety. Gartner, file No. 949. 6 February, http://blogs.gartner.com/douglaney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf

Vorhies, B. (2014). How Many “V”s in Big Data-The Characteristics that Define Big Data. Data Science Central.