Did you know that 2.5 quintillion bytes of data get created globally every day? Even more shocking is the fact that 90 percent of the data we have today was created in the last few years. That’s how fast data generates. When data scientists capture and analyze data, they discover that it is categorized into four sections. They are the four Vs of Big Data.
There are hundreds of sources from where data comes. Digital pictures, videos, social media posts, and phone signals are just a few data-generating sources that we may know about. There are many others, such as purchase records, climate information sent out by sensors, government records, etc. Big Data means when data generates in colossal volumes.
A more distinct definition of Big Data reads as data not captured, managed, or processed with commonly used software tools within a reasonable time frame.
There are two types of Big Data. A small portion of the Big Data generated is classified as structured data. This type of data secures in databases spread across various networks.
Unstructured data makes up nearly 90 percent of Big Data information. Unstructured data generally comprises human information such as emails, tweets, Facebook posts, online videos, tweets, Facebook posts, mobile phone texts and calls, posts, conversation content, website clicks, and more.
Many may believe Big Data is all about size. In a sense, it is, but Big Data is also an opportunity to find insights into new and emerging types of data and content. Big Data can help organizations make their business more agile and make processes more efficient.
Data accumulation and analysis are challenging tasks because data is available primarily to users in an unstructured form. Here are the four Vs of Big Data.
Volume
The first of the four Vs of Big Data refers to the volume of data. This means the size of the data sets an organization has to analyze and process. The volume of data is generally more prominent than terabytes and petabytes. Big Data requires a different approach than conventional processing technologies, storage, and capabilities. In other words, you need specialized technology and setups to manage the vast volumes of data generated in Big Data.
Organizations can scale up or scale out to manage significant volumes of data.
Scale-up involves using the same number of systems to store and process data but migrating each system to a larger structure.
Scale-out involves increasing the number of systems. But, they are not migrated to larger systems.
Velocity
Velocity means the speed at which data is to be consumed. As volumes surge, the value of individual data points may diminish rapidly over time. At times, even a couple of minutes may seem too late. Some processes, such as data fraud detection, may be time-sensitive. In such instances, data needs analyzed and used as it streams into the enterprise to maximize its value. An example of such events is scrutinizing millions of trade events each day to identify potential fraud. It could also be about analyzing millions of call detail records daily within a fixed time frame to predict customer churn.
Variety
Variety makes Big Data a colossal entity. Big Data comes from a number of sources but generally is one out of three types.
- Structured
- Semi-structured
- Unstructured data
The frequent changes in the flow of data variety entail distinctive processing capabilities and dedicated algorithms. The flow of data from unlimited resources makes velocity a complex thing to deal with. Traditional methods of analysis cannot be applied to Big Data. Also, new insights are detected while analyzing these data types.
With Variety, you can monitor hundreds of live video feeds from surveillance cameras to focus on a specific point of interest.
Veracity
Veracity refers to the accuracy and reliability of data. It is effectively a measure of the quality of the data. Many factors influence the quality of data. But a key factor includes the origin of the data set. When an organization has more control over the data-gathering process, the veracity is likely to be more significant. When they have the confidence that the data set is accurate and valid, it allows them to use it with a higher degree of trust. This trust will enable them to make better business decisions than those sourced from an unvalidated data set or an ambiguous resource.
The veracity of data is an essential consideration in any data analysis due to its strong connection to consumer sentiment. One of the most accepted social media analytics capabilities used by large companies is analyzing consumer sentiment based on keywords used in their social media posts.
Analysts take into account the accuracy and reliability of the particular platform when taking a call on how to perform analysis on Big Data. This ensures sound output and value to the end client. A data set must score high on the veracity front to fit into the Big Data classification.
Conclusion
There is a fundamental principle driving the use of Big Data. An organization must be able to decode the patterns of data behavior. They can then accurately and effortlessly predict how people will behave in the future. This has enormous business implications for all kinds of industries.
Big data is no longer a series of numbers on large spreadsheets. Today’s data can flow into an organization from various sources – some fully reliable, others not. Businesses need cutting-edge big data analysis techniques to accept and process vast amounts of data quickly and effectively. Hopefully covering the four Vs of Big Data helps your business thrive.
