3V's of Big Data

What are the 3V's of Big Data?

Doug Laney, a Gartner analyst, introduced the 3V’s (volume, variety, and velocity) of Big data under the name of 3D Data Management.

Volume: The size of data has been collected is growing day by day in line with the increased usage rate of social media, e-commerce, IoT, AI and more. Therefore, those drive data complexity through new sources and forms of data. As supporting evidence of how the volume of data has been expanded day by day, Jacobson (2013) states that “90% of the data in the world today has been created in the last two years”. It is an outstanding estimation that, according to Desjardins (2019) “By 2025, it’s estimated that 463 exabytes of data will be created each day globally”. 

Variety: It refers to all structured, semi-structured and unstructured data. Numeric, structured data is not the only reliable type of data source as it was in the past. With the rise of big data, the data generated from emails, photos, videos, the audio started to gain importance as new applications are introduced. According to Forbes, “ There are 2.5 quintillion bytes of data created each day”. Furthermore, PCMag illustrates as an outstanding fact that 90% of the big data collected is coming from unstructured data gathered from various sources like social media, apps, IoT, customer purchase history, even customer service call logs.

Velocity: It is the speed of the data generated. As a consequence of the growth of the big data with large volume and variety in short time periods, it should be generated at a dramatic level of velocity.  The data comes into the server in real-time and it should be in a continuum to prevent the delays especially in case of real-time and near-time processing while real-time processing requires a continual input, constant processing, and steady output of data and near-time processing is when speed is important, but processing time in minutes is acceptable instead of seconds (Wilson, 2015).

 

References

Jacobson, R. (2013). 2.5 quintillion bytes of data created every day. How does CPG & Retail manage it? - IBM Consumer Products Industry Blog. [online] IBM Consumer Products Industry Blog. Available at: https://www.ibm.com/blogs/insights-on-business/consumer-products/2-5-quintillion-bytes-of-data-created-every-day-how-does-cpg-retail-manage-it/ [Accessed 31 Jan. 2020].

Griffith, E. (2018). 90 Percent of the Big Data We Generate Is an Unstructured Mess. [online] PCMag UK. Available at: https://uk.pcmag.com/news-analysis/118459/90-percent-of-the-big-data-we-generate-is-an-unstructured-mess [Accessed 4 Feb. 2020].

Laney, D. (2001). Application Delivery Strategies. [online] Blogs.gartner.com. Available at: https://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf [Accessed 23 Jan. 2020].

Marr, B. (2018). How Much Data Do We Create Every Day? The Mind-Blowing Stats Everyone Should Read. [online] Forbes.com. Available at: https://www.forbes.com/sites/bernardmarr/2018/05/21/how-much-data-do-we-create-every-day-the-mind-blowing-stats-everyone-should-read/#74d2d79660ba [Accessed 4 Feb. 2020].

Desjardins, J. (2019). How Much Data is Generated Each Day?. [online] Visual Capitalist. Available at: https://www.visualcapitalist.com/how-much-data-is-generated-each-day/ [Accessed 31 Jan. 2020].

Wilson, C. (2015). The Difference Between Real Time, Near-Real Time, and Batch Processing in Big Data. [online] Syncsort Blog. Available at: https://blog.syncsort.com/2015/11/big-data/the-difference-between-real-time-near-real-time-and-batch-processing-in-big-data/ [Accessed 4 Feb. 2020].