Responses two postings

Student 1:
As we learned in class, Volume refers to the magnitude of data, Variety refers to the structural heterogeneity of the data, and Veracity to the unreliability of data (Gandomi & Haider, 2015). These are all affected by the Velocity of data, which refers to both the speed at which the data is generated and the speed at which it should be processed & analyzed to make something useful out of it.
For example, when we think about the Volume of data we must think about where to store this data. It is not the same to store one terabyte of data generated every day versus a terabyte of data generated every hour. The Velocity at which the data is being generated will directly impact the Volume of data and the space where this data is stored. The rate at which data is generated can also affect the Veracity of data since it’s not the same having to deal with a certain percentage of uncertainty in a terabyte of data produced every day versus a terabyte of data produced every hour. Think about Google Maps dealing with the uncertainty of your location every second versus a bank having to deal with the uncertainty of the legitimate use of your credit card only every time you use it. Finally, you will have to deal with the Variety of data differently at different Velocities, especially if this data is to be analyzed in real time.
If you increase the variety of your data, you might have to integrate data from various sources, and by doing this you’re already increasing the volume of data and the uncertainty from these different sources. The reality is that these 4 Vs are intertwined and any change in one will affect the other, which also means that to understand one you must understand the other.
References
Gandomi, A., & Haider, M. (2015). Beyond the hype: Big Data Concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144. https://doi.org/10.1016/j.ijinfomgt.2014.10.007

Student 2:
The prevailing consensus of the day is that big data has distinct characteristics. These are known as the four Vs in big data circles: volume, variety, velocity, and veracity. The magnitude of data is referred to as volume. The size of big data is measured in terabytes and petabytes. variety refers to the structural heterogeneity in a dataset. velocity refers to the rate at which data is generated and the speed at which it should be analyzed and acted upon, and data veracity refers to the unreliability inherent in some data sources. Because high-velocity data is created at such a rapid rate, it necessitates the use of separate processing procedures.
The prior discussion of the relativity of enormous data volumes applies to all dimensions. As a result, there are no common benchmarks for the amount, variety, and velocity of big data. The defining boundaries are determined by the firm’s size, sector, and location, and these constraints change over time. It’s also worth noting that these dimensions are not mutually exclusive. As one-dimension changes, the likelihood of another dimension changing as well increases. As data velocity rises, a higher volume of data can be handled more quickly. The status of the data, which is Veracity, might change depending on the pace with which it is handled. The speed of data will vary depending on the Variety of data, its volume, and its veracity.  Every company, however, has a “three-V tipping threshold” beyond which standard data management and analysis methods are no longer effective for extracting timely knowledge.
The data to be studied is described by the four Vs. The process of extracting value from data is known as analytics. When you put it all together, you have the opportunity for either remarkable insight or worrying oversight. Big data, like any other tremendous power, comes with both enormous promise and significant responsibility. Today’s world has a large and constant flow of data, and the speed with which it can be retrieved has a direct impact on decision-making. The basic goal is to collect, process, and show data as near to real-time as possible, because even a tiny quantity of real-time data can offer businesses with knowledge and insights that lead to better business results than vast volumes of data that take a long time to process.
Reference:
Nina Tudor, Importance of Big Data: understanding the 5 Vs of big data. (2020, June 1). Bornfight. Retrieved November 5, 2021 from https://www.bornfight.com/blog/understanding-the-5-vs-of-big-data-volume-velocity-variety-veracity-value/ (Links to an external site.)
Gandomi, A., & Haider, M. (2015). Beyond the hype: Big Data Concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144. Retrieved November 5, 2021 from https://doi.org/10.1016/j.ijinfomgt.2014.10.007