For enterprises, data is the fuel and power for enterprise development, and the more data, the better.
For the public, data involves privacy and security. For individuals, the less people know, the better.
"The main contradiction of current data transactions is the contradiction between people's increasing attention to data security and the infinite pursuit of data by enterprises. "
However, this is only a superficial contradiction.
In fact, there are other problems and contradictions in data trading that need to be solved.
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Affirm rights
To trade data, you must first confirm the right. From a legal point of view, data is owned by individuals, enterprises or governments.
But in reality, there are many vague areas.
Such as car data (mileage, driving records, refueling records, etc.). ) theoretically owned by the owner, but the application is applied by the car brand. ...
For example, if you posted a message on Weibo, did it belong to you or the Weibo platform?
The most obvious is the data generated by the pickpocket party shopping on the e-commerce platform. Is this personal, online or platform?
Similar examples are too numerous to mention.
To be truly valuable, data needs quantity (volume), completeness (completeness) and unity (dimension).
Theoretically, many data are owned by individuals, but the single data owned by individuals is of little commercial value.
Therefore, many platforms will allow users to agree to some terms when registering, and transfer the right to use data to the platform, and most of them are free to use (users can't register without consent). At present, the problem of confirming the right is basically solved through the transfer of the right to use, just as land belongs to the state, and its right to use belongs to individuals and real estate developers.
In addition to confirming the right, the data is also private and secure.
As I said before, data is a description and record of people's behavior in the past (see: a picture takes you to understand big data), and it describes "who" and "how" in the past.
In the current compliant data transaction, the data transmitted is basically "don't tell you who it is, just tell what happened".
For example, according to the data, 26-year-old Zhang San bought a pair of 298 shoes from Taobao on 10. The telephone number is133 * * 3333, 1 1.
However, the data sent is dl 1, 26 years old. The telephone number is #shdhfnahgjjeu,165438+1October1I bought a pair of 298 shoes from Taobao. (It belongs to the metropolitan processing of personal information, and the technical term is: the supply and demand sides form different marks, which cannot identify specific individuals and cannot be recovered)
At present, compliant data transactions are basically to protect personal privacy, and the value of data is still there.
/02/
evaluate
The second difficulty in data trading is valuation.
As one of the core driving forces of enterprise development, data is both a commodity and an asset, and its attributes are very complex.
Say it is a commodity because it can be transferred, processed, circulated and traded;
It is an asset, because it does not exist alone, and it must be attached to some usage scenarios to be valuable;
It's easy to understand about goods, so I won't give examples.
For a simple explanation of assets, let's take automobile data as an example. Equipment manufacturers collect the operation data of automobile equipment, know the state of the equipment, how many years it can be used, and can repair and replace it in time, so as to solve the safety risk for the owner, reduce the maintenance cost and optimize the product upgrade for the manufacturer, and maintain the brand reputation for the enterprise. These data have great value and many uses.
Suppliers with automobile data, if they put the data on the trading platform for sale, may not be interested if they are not industry-related, such as e-commerce, and feel worthless.
However, the insurance company thinks it is very valuable. After the insurance company has big car data, he can recommend different auto insurance for different models and car ages.
This example is to let everyone know that there is no clear reference standard for the pricing and valuation of data. The gold mine in your eyes may be the waste mine in others' eyes.
At present, it can only be adjusted by the market, which is in the early stage of dynamic adjustment. We believe that, like commodities, with more and more data circulation and transactions, data pricing will gradually become clear.
At this stage, the seller should know clearly in his heart how much value this data can produce if it is sold to others.
Buyers should also know their psychology. I bought this data, and how much value it can create for me.
The more this time, the meaning and value of the data trading platform will come out. The biggest significance of the trading platform is to provide trust guarantee, promote the fast and efficient data trading, accelerate the maturity of the data trading market, and make the data generate more value. In the early stage of trading, it is best to have a highly reliable and easy-to-use data trading website.
The big data trading platform in the birthplace is also working in this direction.
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scale
In addition to data validation and data pricing, there is also a problem of data scale.
To put it simply, there is not enough data flow and transaction in this market at present, and the scale is not large enough. The data market is being cultivated, but everyone suddenly finds that there is not so much data.
There are two main reasons for this phenomenon:
1. There is enough demand in the market, but it is not fully expressed.
Most enterprises have data demand, which is a market where demand is greater than supply. But what is embarrassing is that most enterprises don't know what channels and ways to obtain data, many people don't know that data can be legally traded, and demanders don't know that there is a data trading platform, so they don't clearly express their data needs, which leads to the inability of data scale.
2. Data acquisition/collection is difficult.
For example, I want to open a shopping mall, I want to open a shop, I want to determine the flow of people in a certain place, the consumption level and consumption habits of people around me, which is difficult to count;
There is also the existence of data islands. For example, Taobao will not give its e-commerce data to Tencent, Tencent will not give its social data to Taobao, and Baidu will not give everyone's search data to Tencent or Ali.
In addition to data collection, there are a series of problems such as data cleaning, sorting and storage.
The combination of the two factors leads to the data scale is not large enough, and the number available for circulation transactions in the market is small.
Fortunately, this is not a vicious circle. We see that in terms of staffing, more and more universities have opened big data majors, more and more people are engaged in data collection, data analysis and data mining, and more and more collection tools suitable for ordinary people have become popular.
Understand data collection. See: Friday, let me tell you a secret. I never work overtime.
From a policy perspective, policies are also encouraging the promotion of big data transactions.
From the market point of view, data companies are also subdivided. Some people focus on collection, some focus on analysis, and some focus on data solutions. Our big data trading platform focuses on promoting the circulation of data transactions and cultivating mature markets.
/04/
abstract
Due to the above difficulties, the data transaction at this stage is not "you can buy it if you want".
However, these difficulties are all staged difficulties, not insurmountable obstacles. Confirmation can be solved through policies, regulations and technical means, valuation can be solved through the market, and scale can be solved through industry development.
Optimistically speaking, with the maturity of big data-related industries, the application of big data is gradually enriched, and people gradually pay attention to data collection and collation. The productization and standardization of a large number of data will gradually become a problem.
The data trading platform can also solve the problem of information asymmetry between supply and demand in the process of data circulation, and ultimately help everyone "buy if they want".