Many brands and tech companies are turning to partnership marketing for better revenue-generating models, and understanding the 7 V’s of Big Data is crucial. The exponential growth of data has eliminated conventional record files and storage discs. Storing data in some database systems has been adopted by many businesses and has been tackling the problem of insufficient storage space. If you think that big data is just about data being big and in large volumes, then you need to go through this article. Big data consists of various types of data with different formats. However, the three main types of big data commonly recognized are Structured Data, Unstructured Data, and Semi-Structured data. This article will look at the 7 V’s of Big Data: Volume, Velocity, Variety, Variability, Veracity, Visualization, and Value.
Volume: Perhaps one of the most distinguishing features of Big Data, Volume here defines big data as “Big.” Big companies like YouTube, Meta, Walmart, Microsoft, Instagram, etc., have an unimaginable size of data incoming daily. Gigabytes and terabytes would not be sufficient to store this data, so now the data is stored in Yottabytes, Exabytes, and Zettabytes. The best way to understand Volume would be to look at an example of data on YouTube. Almost 50 hours of video is uploaded on YouTube every minute, so imagine how much storage space it would require to store this data.
Velocity: Velocity in big data is the speed at which data can be accessed and processed. Most of the data received are warehoused before analysis. Accessing this data was only possible after the concept of big data was discovered. The speed and rate of organizations moving, processing, and capturing data in real time have allowed their users to access this data. Real-time processing provides accurate and profitable responses and reduces storage requirements.
Variety: One of the challenges in the big data concept is the variety of data extracted from sources. Beyond massive volumes and increasing velocity, manipulation of data and deciphering it is crucial. Data consists of natural language, multimedia hashtags, geospatial data, sensor events, and more. It takes a significant amount of algorithmic and computing power to extract meaning.
Variability: Variability is very different from what we see in variety. Variability in data is defined as the data that keeps changing at regular intervals. The best way to understand variability is to look at an example of language processing by computers. Some words have different meanings, so variability allows us to add new meanings and discard old ones over time. In big data, variability is the inconsistencies in data and the speed at which the data is loaded into rational database systems.
Veracity: Understanding the significance of the veracity of big data is one of the primary and first steps in filtering the signal from the noise in big data. You can say that veracity helps you filter raw data. Once the data is filtered and sorted, it generates a deeper meaning and understanding, allowing its user to take appropriate action. Reliable and high-quality data has allowed businesses to make profitable decisions as the core business model is based on the dissemination of information. Many experts often debate that veracity is a secondary characteristic of big data as a vast amount of information is already available from different sources. However, many enterprises focus on veracity as it has the potential to derive data that can provide valuable insights. The typical use case can be seen in sales and marketing, healthcare, politics, finance, logistics, data scientists, and the research industry.
Visualization: Data visualization presents data and information using elements like charts, maps, and graphs. Visual elements make it easier for the human brain to understand and pull insights. One of the main goals of data visualization is to identify patterns and trends in large data sets. Data visualization is often synonymous with information visualization, statistical, and information graphics. Data visualization is also one of the essential steps in the data science process. In data science, once the data is collected, processed, and modeled, the last stage would be presenting the conclusion in a visual format. Data visualization is essential for most careers and always impresses a recruiter. While charts, maps, and graphs are traditional visual elements, people have adopted innovative visual elements such as bubble clouds, heat maps, fever charts, infographics, bullet graphs, and time series charts.
Value: Similar to Marvel Avengers, Endgame, Value is the big data’s end game. After having the 6 V’s in the account, there comes one more important V, which stands for value. If the data collected and processed has no value, the resources used in the process can go to waste, and a company might face a financial loss. Companies should have ample resources to convert a Tsunami of data into something useful for a business.
Wrap-Up
In a concluding presentation on the Impact of Big data, Oscar Herencia, General Manager of MetLife Iberia, an insurance company, and an MBA professor, said, “Big Data is like sex among teens. They all talk about it, but no one knows what it’s like”. Big data is becoming massive minute by minute, but most experts recommend understanding and learning the 7 V’s of big data to land a 6 figure job. Data nowadays is changing rapidly, and companies need to gain the best benefits of big data to achieve that; understanding the key characteristics of big data is vital.
