Big data refers to the vast volume of structured and unstructured data that inundates businesses and organizations on a daily basis. This data comes from a variety of sources including business transactions, social media, sensors, devices, and more. Big data is characterized by its volume, velocity, variety, and veracity, often referred to as the "4Vs":

1. Volume: Big data is massive in size. It exceeds the storage and processing capabilities of traditional database systems. The volume of data generated continues to grow exponentially with the proliferation of digital devices and online activity.

2. Velocity: Data is generated at an unprecedented speed. Social media updates, sensor data, and other streaming sources continuously produce new data points that need to be processed and analyzed in real-time or near-real-time.

3. Variety: Big data comes in various forms, including structured, semi-structured, and unstructured data. Structured data is organized and easily searchable, like data in traditional databases. Unstructured data, on the other hand, includes text, images, videos, and social media posts, which can be more challenging to analyze.

4. Veracity: Veracity refers to the reliability and accuracy of the data. With the vast amount of data being generated, there is often uncertainty about its quality. Big data analytics processes must account for inaccuracies, inconsistencies, and errors in the data.

Big data analytics involves the use of advanced technologies and techniques to extract insights, patterns, and trends from large and complex datasets. Organizations leverage big data analytics to make informed decisions, improve operational efficiency, enhance customer experiences, and gain a competitive edge in their respective industries.