What are the 4V characteristics of big data?

The 4V characteristics of big data are: 1. Volume; with the rapid development of information technology, data begins to grow explosively. 2. Velocity. 3. Variety; mainly reflected in multiple data sources, multiple data types and strong correlation between data; 4. Value.

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Big data (big data), an IT industry term, refers to a collection of data that cannot be captured, managed, and processed with conventional software tools within a certain time range. It requires new processing models to make stronger decisions. Massive, high-growth and diversified information assets with powerful capabilities, insights and process optimization capabilities.

4v Characteristics of Big Data

Characteristics of Big Data, in "The Era of Big Data" written by Victor Mayer-Schonberg and Kenneth Skye It is proposed that the 4V characteristics of big data are: Volume, Velocity, Variety, and Value.

(1) Scale

With the rapid development of information technology, data has begun to grow explosively. Data in big data is no longer measured in gigabytes or terabytes, but in PB (1,000 terabytes), EB (1 million terabytes), or ZB (1 billion terabytes). .

(2) Diversity

Diversity is mainly reflected in three aspects: multiple data sources, multiple data types and strong correlation between data.

① There are many sources of data. The traditional data faced by enterprises is mainly transaction data. The development of the Internet and the Internet of Things has brought data from multiple sources such as social networking sites and sensors.

And because the data comes from different application systems and different devices, it determines the diversity of big data forms. It can be roughly divided into three categories: the first is structured data, such as financial system data, information management system data, medical system data, etc., which is characterized by strong causal relationships between data; the second is unstructured data, such as videos, pictures, audio etc., which is characterized by no causal relationship between data; third, semi-structured data, such as HTML documents, emails, web pages, etc., is characterized by weak causal relationship between data.

②There are many data types, and they are mainly unstructured data. In traditional enterprises, data are stored in tables. 70%-85% of the data in big data are unstructured and semi-structured data such as pictures, audios, videos, web logs, link information, etc.

③Data are highly correlated and frequently interacted with each other. For example, the photos and logs uploaded by tourists during their travels are closely related to the tourists’ location, itinerary and other information.

(3) High speed

This is the most significant feature of big data that distinguishes it from traditional data mining. The important difference between big data and massive data lies in two aspects: on the one hand, the data scale of big data is larger; on the other hand, big data has stricter requirements on the response speed of processing data. Real-time analysis instead of batch analysis, data input, processing and discarding are performed immediately with almost no delay. The growth rate and processing speed of data are important manifestations of the high speed of big data.

(4) Value

Although enterprises have a large amount of data, only a very small part of it exerts value. The value hidden behind big data is huge. Since the proportion of valuable data in big data is very small, the real value of big data is reflected in a large amount of irrelevant data of various types. Mining valuable data for prediction and analysis of future trends and patterns, and conducting in-depth analysis through machine learning methods, artificial intelligence methods, or data mining methods, and applying it to various fields such as agriculture, finance, and medical care, in order to create greater value.

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