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What is big data's definition? aiming at this question, this article introduces the corresponding analysis and answer in detail, hoping to help more partners who want to solve this problem to find a more simple and feasible way.

What on earth is big data? Many people may still be a little confused. In this article, let's take a look at some of big data's main definitions. First of all, it should be noted that everyone in the industry generally agrees that big data is not just more data.

(1) the original big data

The characteristics of big data can be described in many words. In 2001, Doug Laney*** put forward the "3V" model, including quantity (Volume), speed (Velocity) and type (Variety). Since then, many people in the industry have extended 3V to 11V, including effectiveness, authenticity, value and visibility.

(2) big data: technology

Why is the old term of 12 years ago suddenly put in the spotlight? This is not just because we have more quantity, speed and variety now than we did a decade ago. But because big data is driven by new technologies, especially fast-growing open source technologies, such as Hadoop and other NoSQL ways to store and process data.

Users of these new technologies need a term to distinguish them from previous technologies, so big data became their choice. If you go to big data meetings, you will surely find that there are very few meetings involving relational databases, no matter how many Vs they preach.

(3) the difference between big data and data

The problem with big data's technology is that big data is so ambiguous that every supplier in the industry can jump in and claim that his technology is big data technology. Here are two good ways to help enterprises understand the difference between big data and big data in the past.

■ trading, interaction and observation: this is proposed by Shaun Connolly, vice president of corporate strategy at Hortonworks. Transactions are the main data we collected, stored and analyzed in the past. Interaction is the data obtained by people clicking on web pages and other operations. Observation is data collected automatically.

The ■ process mediates data, human-generated information, and machine-generated data.

(4) big data: signal

SAP's Steve Lucas argues that the world should be divided by intention and timing, not by the type of data. The "old world" is mainly about transactions, and when these transactions are recorded, we can no longer take any action on them: companies are constantly managing "invalid data". In the New World, companies can use new "signal" data to predict what will happen and intervene to improve the situation.

Examples include tracking people's attitudes towards brands on social media and predictive maintenance (using complex algorithms to help you decide when parts need to be replaced).

(5) big data: opportunity

This is Matt Aslett from 451Research, who defines big data as "data that was previously ignored due to technical limitations". (although technically, Matt uses "dark data" instead of big data, it is very close.) This is my favorite definition because it is consistent with what is said in most articles and discussions.

(6) big data: metaphor

In his book, Rick Smolan writes that big data is "a process that helps the planet generate the nervous system, in which we humans are just another type of sensor." It's profound, isn't it?

(7) big data: old wine in new bottles

Many projects basically use previous technologies, which used to be called BI or analysis, suddenly jumping into the ranks of big data.

Bottom line: although there is a lot of controversy about the definition of big data, everyone agrees on the fact that big data is a big event that will bring great opportunities in the next few years.

So much for the answer to the question about what big data's definition is. I hope the above content can be of some help to you. If you still have a lot of doubts to be solved, you can follow the industry information channel to learn more about it.

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