The primary characteristics of Data are –
In large organizations, volume refers to the massive amounts of data that are collected and generated every second. This information comes from a variety of places, including IoT devices, social media, videos, financial transactions, and customer records.
Previously, storing and analyzing this massive volume of data was a challenge. However, distributed systems like Hadoop are now being utilized to organize data from all of these sources. Awareness of the usefulness of data requires an understanding of its scale. The volume can also be used to determine if a data set is Big Data or not. Data volume can vary. For example, a text file is a few kilobytes whereas a video file is a few megabytes.
One of the most essential properties of Big Data is its diversity. It refers to the various types of data sources and their characteristics. Over time, the data sources have shifted. Previously, it could only be found in spreadsheets and databases. Data can now be found in the form of photographs, audio files, videos, text files, and PDFs.
For data storage and analysis, variety is essential.
The speed at which data is created or generated is referred to as velocity. The rate at which data is generated is closely related to the rate at which it is processed. This is because the data can only meet the demands of the clients/users once it has been analyzed and processed.
Sensors, social media sites, and application logs generate massive volumes of data, which is constantly updated. There's no use in devoting time or effort if the data flow isn't continuous.
Value is likely the most crucial of Big Data's features. It doesn't matter how quickly or how much data is generated; it has to be dependable and valuable. Otherwise, the information is insufficient for processing or analysis. According to research, poor data quality can cost a corporation over 20% of its sales.
Raw data is initially converted into information by data scientists. The data is then cleansed to extract the most important information. This data set is subjected to analysis and pattern recognition. The data can be considered valuable if the process is successful.
This Big Data feature is linked to the previous one. It establishes the data's level of credibility. Because most of the data you see is unstructured, it's critical to filter out the irrelevant data and handle the rest.