Consider the amount of data we produce in our daily activities in addition to our work. From social media posts to music playlists, doctor’s appointments to phone calls to the utility provider, online purchasing to online gaming etc. If you mix that with information from other people and organisations around the world, you will get disoriented.
The term “big data” refers to the vast volume of digital information that has been registered. The objective is to turn this flood of data into useful information. To gain more insight into Big Data, the system of the four Vs has been introduced. These Vs stand for the four dimensions of Big Data: Volume, Velocity, Variety and Veracity. In this essay, we examine these qualities in more detail.
The level of grip over these four V’s determines the analytical excellence of a Data Scientist.
VOLUME: The amount of the data sets that must be evaluated and processed, which are today frequently bigger than terabytes and petabytes, is referred to as the volume of data. The enormous amount of data necessitates processing methods that are separate from those used for traditional storage and processing. In other words, this means that the data sets in Big Data are too large to process with a regular laptop or desktop processor. Due to the massive volume of data we handle every day, new technologies and methods, such as multitiered storage media, have been developed to safely gather, process, and store it.
Example of a high-volume data set would be all banking transactions in a day in India, daily sensor data generated by commercial aircraft etc.
VELOCITY: The immense speed at which data is created, stored, analyzed, and visualized is referred to as the Velocity. Prior to a few years ago, it took some time to process the appropriate data to produce and present the relevant information. With the availability of Internet-connected gadgets, both wireless and wired, machines and devices can transmit their data as soon as it is created in today’s big data era. High-velocity data must be processed using certain (distributed) approaches because of how quickly it is produced. When it comes to time-sensitive processes like fraud detection, a few minutes can often be too late.
An example of data that is generated with high velocity would be Twitter messages or Facebook posts, daily call detail records of a telecom company in real-time etc.
VARIETY: Variety makes Big Data really big. Big Data comes from various sources and generally is one out of three types: structured, semi structured and unstructured data. The variety in data types frequently requires distinct processing capabilities and specialist algorithms. In the past, data was nicely structured—think Excel spreadsheets or other relational databases. A key characteristic of big data is that it not only is structured data but also includes text, images, videos, voice files and other unstructured data. We are able to use technology to make sense of unstructured data today in a way that wasn’t possible in the past. This ability has opened up a tremendous amount of data that have previously not been accessible or useful.
Examples of high-variety data sets would be the CCTV audio and video files that are generated at various locations in a city, exploit data growth in images, video and documents to improve customer satisfaction etc.
VERACITY: Veracity refers to the quality, reliability, and trustworthiness of the data that is being analyzed. To derive accurate insights. it’s crucial to comprehend the chain of custody, the metadata, and the context of the data when it is collected from a number of sources with high speed, different structures,s and in large volumes. Numerous records in high-veracity data can be used for analysis and make a significant contribution to the final findings. On the other side, a substantial proportion of meaningless data might be found in data with low veracity. Big data veracity refers to the biases, noise and abnormalities, ambiguities, and latency in data. These irrelevant, erroneous & meaningless part of data sets is referred to as noise.
An example of a high veracity data set would be data from a medical experiment or trial.
Even if the 4 Vs of data are the focus of this post, big data actually has a crucial fifth component that we must take into account. This is the need to turn our data into VALUE.
Effective big data analysis may help understand customers and their preferences, how to improve business operations and processes, and a virtually infinite number of applications. It is crucial that big data generates value in order to use it to develop new goods or services or to figure out how to reduce expenses. Due to the importance of this value, every organization, regardless of size, needs to have a data strategy in place to accomplish the adopted business objectives is being gathered and evaluated. Any business entity supported by high-level big data analytics has supremacy over their peers in strategic decisions and real-time implementation.
