Caiyong: The application of large data in the financial field

2014 Zhongguancun Large Data day on December 11, 2014 in Zhongguancun, the General Assembly to "aggregate data assets, promote industrial innovation" as the theme, to explore data asset management and transformation, large data depth technology and industry data application innovation and ecological system construction and so on key issues. The Conference also carries on the question of the demand and practice of the departments in charge of the government, the finance, the operators and so on to realize the path of transformation and industry innovation through the management and operation of data assets.

On the afternoon financial @big Data Forum, the CEO of the Beijing Software Exchange Caiyong a keynote address on the role of large data in data trading platforms.

The following is the full text of the speech:

Now also become the Beijing Exchange Group under the Beijing Software Exchange CEO Mr. Caiyong, for everyone to do on the data trading platform role, applause welcome.

Caiyong: Because today my topic is a bit awkward, the earliest let me talk about the topic, and then let me talk about the application of large data in the financial field. I think I am not an expert in this field, I can say in two sentences in this area, first I am big data transaction, but I am not a big data expert. The second I did some financial services, but I'm not a financial expert. But today, with two words stacked in one piece, I can't talk Big data in finance, and I can't talk about finance in big data. The trading platform that released the big data just yesterday, including the big data and credit areas, we have a topic to do, so try to put two things together and talk about it. First, let's talk about the big data trading platform we released yesterday, and what we think and do in this platform. It's about the platform. We do big data trading, but we don't do big data.

Soft Exchange is a company, it is the government-led enterprises, the establishment of the time is to build a transparent software trading market, to promote the development of the industry, large data is within the scope of the software. Our corporate goal is simply to make software transactions easier, and this is a clear explanation, first of all, that we focus on the entire industry's trade problems in the entire information field of software. Second, we do not think in the field of trading for each enterprise to solve the transaction problem, is to solve the whole industry transactions, so that the transaction becomes a little easier. The complexity of the trading links in the software domain is specific, unlike traditional software, I often tell you a thing, we electric business is very fire, electric business fire is supported by software. But selling software on a software platform has been done by many people since 2000, but not successfully, including the fact that the cat may not be able to sell the software, because there are too few software transactions. But we have to do this on the soft hand, so we also face some peculiar problems. For example, trading information is discrete, because the transaction is too strong, no software people can not find the people who sell software, the price of software cannot be priced, software requirements are difficult to express and so on, we have to solve these common problems.

We have all the models of SoftSwitch, including the large data trading model behind, and basically set up an exchange model. Why is it that we do big data transactions but not big data? We are building a place, a tangible and intangible place for the Internet, and the whole system of supporting trading. We still have some might look at a leather company, where he can find big data to sell to people who need it. The thing we do is this piece, the trader we are constantly going to standardize. Under this model, the soft exchange has also developed faster, this year's turnover is 5 billion, I have done some financial services, but I do not do finance. I'm a trader. I'm certainly not an expert on this.

Large data I mainly want to make clear a question, why do we do the soft trading platform? From this picture, I would say that the volume of the big data deal is not quite there yet, this is the famous (English) graph, our country Big data 2013 almost reached this peak, this year has gone down, has been fooled almost, now the real knife gun dry things, We found the big data. It's too hard to do. There are a lot of problems to be solved. So our big data trading platform is under construction. The government wants us to launch this big data trading platform as soon as possible, and we want to get involved as soon as the industry matures.

There are a lot of problems in the big data market, and there's a classic thing that happens. In the first two days China's standard working group on large data was established, process set up 10 standard groups, in the preparation of the Working Group I also participated in, this large data standards, from the technical standard point of view we know will set up cloud data, how data collection, storage, format how to transmit, and so on, from the technical problems. I asked a sentence, I said I was a trade, I do big data I want to pay attention from the transaction point of view. I asked a question, who can tell me what a big data trader is? Or what is the product? No one can make a clear answer. We are in the investigation of large data in fact also encountered this problem, we encountered large data to do better, especially the letter-type enterprises, there is really enterprise data, personal data, as well as evaluation reports. But some enterprises are packaged with the concept of large data later in the sale of some traditional software, services and so on. In fact, from the commodity point of view there is no precise positioning, many people say to buy large data, what is the big data? What is a big data product?

After I mentioned this question, the standard working Group put the problem back, now set the standard inside there are two soft pay is responsible for, the first is a large data trading platform under the statement of the transaction. The second is what the general features of the big data-trading platform are. In our soft hand to launch this platform when the market has a lot of so-called large data trading platform, it is true. But these trading platforms can see the basics of selling their own products and selling their own trading services, thus lacking a third-party neutral market. This is the definition of our soft exchange is to do the transaction, but not big data, so we and all the manufacturers are not competitive relationship. A user wants to look for big data vendors to do what things will be able to find, yesterday there is a big customer to find a academician said to find the Hague large data research manufacturers to do, I said this kind of thing to us. Perhaps now our partners do not have such a factory, but I will be able to find, as long as the world will be able to find.

Big data in this field, our trade is about securing transactions, the strategy of safeguarding the trade we do it in a standardized way, and we have a guest who talked about the trading things that we are dealing with on the exchange that are basically non-standard transactions, so we have to get ahead of ourselves by standardizing, including when we're doing a big data trading platform. , we have a lot of manufacturers willing to come in, that is, what is the transaction, what is the standard, so that everyone can easily understand. Users say a few numbers, party B can understand, or party B's goods can be listed according to the user's procurement standards, which is our first role.

The second is the right to the sun, then the multiplication service, the multiplication service is we cooperate with all manufacturers, so the soft exchange can see, in our big data trading platform inside, our trading model is the same, but the transaction may become the data or data application, the data development, the data talented person and so on, or data tools, and so on, these are all, so we'll step through it. What is easy to deal with the above to deal with, I must be done first easy after the difficult work, including soft sex to do some of the products, the latter to have the proliferation of services to support it. This is a big data trading platform thing, the first topic I made clear, we are trading, but not big data experts.

We have also done something in the financial sector, at least in the software sector has a relatively large breakthrough, is to take intellectual property as a mortgage, this matter to solve the light asset enterprises, especially the software business loans difficult problem. Now we have for more than 200 enterprises directly with software registration patents, certificates and so on to the loan. This year, Zhongguancun commissioned us to soften the exchange of software and information in the field of the credit system, our ideal is the industry characteristics of the credit system plus intellectual property can be loans. Our credit system model, the enterprise model inside the first we respect the traditional credit system, the traditional credit system is the industry and commerce data, statistical data and so on. We have done one thing in 2012 years, published a special index of software transactions, the core of which is the activity of the transaction, how the transaction activity definition is not to say the formula, the formula is a bit complicated. is to take two companies, the same enterprise, two of enterprises income and turnover is a billion, a 10 single annual income of 100 million, there is a business turnover is 10,000 times, so you can see the two enterprises so simple that you can obviously feel that the back 10,000 of the enterprise more reliable and stable, do 10 single, As long as one less income is down, 10,000 times the ability to resist risk is relatively strong. So 2012 we put this coefficient, is the transaction frequency and the amount of the algorithm, there is a ratio of quantification in, so that the 2012 of more than 8,000 enterprises, the largest number of enterprises of the amount of turnover and the total amount of money to calculate a value, the final calculation of the results are very reliable. The first three names are Sina, the second is Odyssey, and the third is the gold map. There is a company I do not know, and then checked, the three companies are listed companies are Nasdaq, so the transaction data is very large, Internet enterprises each a few dollars add up, trading volume is large, the three companies are in front. The back is the east soft and so on, we this data take into account turnover, turnover, transaction data to do.

Soft hand to do the credit system, the first is the Apple family system we want, our transactions will be added, as well as intellectual property rights, the formation of software-specific credit system. This may be the only thing we can do with big data. Our goal is to let Enterprises get our credit rating, plus his intellectual property direct mortgage can borrow. We also have one advantage, that is, the AA level can be clearly known as the number of companies listed in this industry. This is the two things I've introduced, and it's not really financial professionals. I'm sorry, thank you.

(Responsible editor: Mengyishan)