How to alleviate the embarrassment caused by big data

How to alleviate the embarrassment caused by big data

With regard to big data, a joke broke out recently: At an industry conference within the film industry, a "Big Mac" film industry spokesman said: Through data mining, we found the relevant box-office preferences of different audiences. For example, the audience of Fanghua consumed more hot drinks than the audience of Wolf Warriors. These are all things we didn't know before, and they are unpredictable.

The above conclusion, based on the analysis of the viewing data of the two films, seems to be objective and correct, but in fact it is a joke because of the imperfect model (lack of consideration of the viewing season) and other reasons.

When we took stock of financial technology recently, we found that big data itself was an embarrassment. We searched the news and found nothing particularly worth saying about this word. Only a little current political information can make up the inventory of this keyword.

20 17, big data is so important, but it is so unexpected.

The big data model is not perfect because the foundation is not strong. Big data has always been tepid and has a lot to do with its development defects. Although everyone is optimistic about it, it failed to usher in the outbreak of the industry.

Chatting with some friends who do big data, they even spit out their data models directly.

"Those so-called data models, such as ghosts, you can have a headache all day as long as you take a look. The data in the model is huge and the clue logic is complex. Many data seem to be very important but extremely boring, and the evaluation results are meaningless. It is a pity to eat tasteless and abandon it. "

"To be honest, the root cause is not the backwardness of technology, but the development foundation of the whole industry is too shallow to make irregular induction and reasonable explanation of the validity of data."

"Generally speaking, a reasonable big data architecture is a perfect data model, which can carry out comprehensive and reasonable data reduction according to specific fields, eliminate irrelevant data and interference data, sort out reasonable objective suggestions, and summarize reasonable conclusions according to the subjective judgment and errata of data analysts, and make accurate predictions for related industries."

"Now? Originally, there were loopholes in the data model, but I still thought about the complete automation of data processing. "

"It is nonsense to rely entirely on objective data to complete the so-called artificial intelligence calculus."

"The jokes of" Teenager "and" Wolf Warriors "just mentioned are actually a seemingly objective but ridiculous analysis conclusion."

"This is because when people talk about big data, they take it for granted. If we only rely on this awareness to do data analysis in the field of consumer finance, there must be many investors who have been pitted! "

"So it is the data company that makes money by buying and selling user data. A data packet, add some water, sell it everywhere, and the income is unlimited. "

"However, it seems that it is not so easy to rectify recently, because the government is getting stricter and stricter, and some so-called big data companies can't move. I am afraid it will be cold."

The Internet of Things may be a real opportunity for big data companies. "In addition to the accumulation of industry experience, more data is needed for online support."

"Of course, it is not that the more data, the better, but that the richer the online data, the better it is for us to organize effective data."

"The core problem is how to generate a large number of effective data."

"Effective data, simply put, refers to the relevant data in a certain field, such as a small piece of consumer goods in the field of consumer finance. After reasonable combination and deconstruction, it makes a reasonable forecast for the development of the industry and is responsible for the expectations of investors. Otherwise, the bigger the data, the heavier the burden, and the more impossible it is. "

When is the end of accumulating experience?

"Maybe we have to wait until the real arrival of the Internet of Things era."

Why?

"The Internet of Things can make more consumer financial data and logistics data lines online, personal consumption credit will be further online, and data collection and processing will be more efficient and comprehensive."

"However, with the rapid development of mobile payment, more people's financial consumption ability is basically presented online, including personal consumption habits and personal credit information, as well as the resulting logistics information, housing and loan information. It is gradually completing the ultimate online, which is an excellent opportunity for big data. "

"The big data industry has great opportunities, but big data is an unstable industry, because all data comes down to machines, and machines are controlled by people. The related operational risks depend entirely on their own risk awareness and personality. Large-scale risks break out in the industry at any time. Good luck only affects data security. Unfortunately, the credit of enterprises and individuals will go bankrupt. This will bring huge disasters to the industry and even the whole society. "

"Therefore, the relevant standards of enterprises need to be further refined and standardized, and people also need to be controlled by professional ethics."

What kind of people use data, its purpose and effect are different.

This has something to do with a big data-related paragraph, just at the beginning and end of the paragraph, which is quite satisfactory.