Today, with big data becoming a trend and a national strategy, how to maximize the value of big data has become a question for people to think about. Whether for Internet companies, telecom operators or a large number of start-ups, the realization of big data is particularly important. Whoever finds the password first can seize the market and win development. While exploring the business model of big data, big data is accelerating its application in all walks of life. Big data can not only help people shop, travel and make friends, but also play a role in important events such as the college entrance examination. Big data industry has the characteristics of pollution-free, eco-friendly, low investment and high added value, which is of strategic significance for China to change the past resource-based economic growth mode, promote the "internet plus" action plan and realize the 30-year development goal of the national manufacturing industry. In the past few years, there were many discussions in the domestic big data industry, and the business model was in its infancy. The industry was at two extremes: one was that overheating and impetuousness brought certain bubbles and industrial risks; One is to suspect that big data is just hype and still adhere to traditional management concepts and business models. However, after entering 20 15, the big data industry bid farewell to the bubble and entered a more pragmatic development stage, from the embryonic stage of the industry to the growth stage. At present, how to realize big data has become an important direction for the industry to explore. B2B big data exchange companies at home and abroad are promoting big data transactions. At present, China is exploring the "national team" B2B big data exchange model. 2065438+On February 20th, 2004, Zhongguancun Big Data Trading Industry Alliance, the first domestic data trading industry organization, was established. On the same day, Zhongguancun Digital Sea Big Data Trading Platform was launched, positioning the big data trading service platform. On April 201May 15, Guiyang Big Data Exchange was officially put into operation, completing the first batch of big data transactions. The sellers of the first batch of data transactions completed by Guiyang Big Data Exchange are Shenzhen Tencent Computer System Co., Ltd. and Guangdong Digital Guangdong Research Institute, and the buyers are jingdong cloud Platform and Admiralty Data System Co., Ltd. May 26th, 20 15. At Guiyang International Big Data Industry Expo 20 15 and Guiyang Summit in the era of global big data, Guiyang Big Data Exchange issued the White Paper on China's Big Data Transaction 20 15 and the Convention of Guiyang Big Data Exchange 702, which pointed out the direction and laid the foundation for the nature, purpose, transaction objects and information privacy protection of big data exchanges. Most of the data in the domestic consulting report comes from the statistical data of the National Bureau of Statistics and other ministries and commissions. Professional researchers analyze and mine the data, find out the quantitative characteristics of various industries, and then draw qualitative conclusions, which are common in "market research analysis and development consulting reports". For example, 20 15-2020 China Communication Equipment Industry Market Research, Analysis and Development Consultation Report, 20 15-2020 China Mobile Phone Industry Sales Situation Analysis and Development Strategy, and 20 15 Optical Fiber Market Analysis Report are all sold to the society, which is actually the big data trading mode of O2O. The analysis reports from all walks of life provide data reference for a large number of enterprises in the industry in terms of intellectual achievements, enterprise operation and marketing, which is conducive to optimizing the supply chain, avoiding overcapacity and maintaining market stability. These are professional studies based on structured data and unstructured data of statistical departments, and they are traditional one-to-many industry big data business models. The emergence of data mining cloud computing software cloud computing provides a cheap solution for small and medium-sized enterprises to analyze massive data, and SaaS model is the greatest charm of cloud computing. SaaS software in cloud computing services can provide third-party software and plug-ins for data mining and data cleaning. Some experts in the industry pointed out that big data = massive data+analysis software+mining process, and providing diversified data mining services through powerful analysis software is its profit model. Some big data companies in China have developed these cloud-based big data analysis software: it integrates statistical analysis, data mining and business intelligence. Users only need to import data into the platform, and they can use the rich algorithms and models provided by the platform for data processing, basic statistics, advanced statistics, data mining, data mapping and result output. Data is managed by the system in a unified way, which can distinguish private data from public data, ensure that private data is only used by holders, and support access to various data sources. It is suitable for analyzing data from all walks of life, easy to learn and use, and the operation interface is simple and intuitive. Ordinary users can use it with a little understanding, and it is also suitable for high-end users to model themselves for secondary development. The bigger the big data consulting and analysis service institutions and enterprises, the more data they have. However, few companies have their own big data analysis teams like large Internet companies, so there must be some professional big data consulting companies that provide big data modeling, big data analysis, business model transformation, marketing planning and other services based on management consulting. Based on big data, the conclusions and consulting results of consulting companies are more convincing, which is also the transformation direction of traditional consulting companies. For example, the vice president of a large foreign IT research and consulting company once said in public that big data can save 60% investment and increase 80% output in Guizhou agriculture. Of course, the company can make such an assertion based on its own accumulated data on agriculture, weather and soil in Guizhou and its modeling and analysis capabilities. The Decision of the Central Committee on Several Major Issues of Comprehensively Deepening Reform, which was adopted by the Third Plenary Session of the 18th CPC Central Committee, clearly proposed to strengthen the construction of new think tanks with China characteristics and establish and improve the decision-making consultation system. This is the first time that China's central document put forward the concept of "think tank". In recent years, a number of think tanks oriented to building modern think tanks and aiming at serving the national development strategy have been established rapidly. The number of think tanks in China has jumped from global 12 in 2008 to the second place at present. Big data is the core of think tanks. Without data, the prediction and analysis of think tanks will be passive water. In the case of massive information or even flooding, think tanks must rely on big data analysis to improve their ability to sort and integrate information. Studies show that 93% of behaviors are predictable. If events are digitized, formulated and modeled, in fact, how complicated events have predictable laws to follow, and the development trend of events is extremely predictable. It can be seen that the application of big data will continuously improve the efficiency of the government and the scientific nature of decision-making. As the value of big data is gradually recognized by all walks of life, large and medium-sized enterprises with huge customer groups have also begun to develop and build their own platforms to analyze big data. These big data are embedded in the information flow of ERP systems within enterprises, and the data will guide internal decision-making, operation, cash flow management, market development and so on. And play a role in increasing the internal value chain of enterprises. In the era of analysis 1.0, data warehouse is regarded as the basis of analysis. In the 2.0 era, the company mainly relied on Hadoop cluster and NoSQL database. The new "agile" analysis method and machine learning technology in the 3.0 era are providing analysis results at a faster speed. The company will set up a chief analyst in its strategic department, and organize personnel with rich knowledge structure and marketing experience to conduct mixed analysis of various data. The securities market behavior and various indexes of big data investment tools have a great relationship with investors' analysis, judgment and emotions. In 2002, the Nobel Prize in Economics was awarded to Kahneman, a behavioral economist, and Smith, an experimental economist. Behavioral economics began to be accepted by mainstream economics, and behavioral finance theory integrated psychology, especially behavioral science theory into finance. In real life, Internet companies with a large amount of user data link their forums, blogs, news reports, articles, users' emotions and investment behaviors with the stock market, study Internet behavior data, pay attention to hot spots and market emotions, dynamically adjust their investment portfolios, and develop big data investment tools, such as big data funds. These investment tools directly transform big data into investment and wealth management products. The data analysis results of online trading platform for directional procurement are often the business foundation of other industries. At present, China's real economy e-commerce has achieved B2C, C2C, B2B and so on. Even O2O is becoming more and more popular. However, there is no special online trading platform for virtual goods such as data. For example, garment manufacturing enterprises need the median and average data of the height and weight of customers in a certain province, so the hospital physical examination department and professional physical examination institutions are the providers of these data. By obtaining these data, garment enterprises will be able to carry out fine production and produce garments that meet the market demand at a lower cost. Imagine if there is such a "big data-oriented procurement platform", just like Taobao shopping, it can launch buyers' needs and sellers' products. Through this model and a third-party payment platform, the conclusion of commodity "data analysis" will emerge quietly. This kind of goods does not occupy logistics resources, does not pollute the environment, and has a fast response speed, but there is a huge market for both "supply" and "demand". And through this platform, the security of basic data can be guaranteed. The big data-oriented procurement service platform trades not the underlying basic data, but the data results modeled by cleaning. All sellers and buyers should have real-name authentication, establish a credit file mechanism and connect with the national credit system. Before the protection of citizens' information was brought into the scope of criminal law by non-profit data credit agencies, citizens' personal information was often sold publicly at a clear price tag, forming a "gray industry". Therefore, the crime of selling, illegally providing citizens' personal information and the crime of illegally obtaining citizens' personal information were added to the Criminal Law Amendment (VII) passed on February 28, 2009. The law specifically refers to the staff of state organs or financial, telecommunications, transportation, education, medical and other units, and may not sell or illegally provide citizens' personal information to others. However, citizen information is still sold in various examination institutions, real estate agents, phishing websites and website forums. Fraudulent calls, harassing calls and selling calls not only increase the telephone traffic of operators, but also undermine the credit system of the whole society and citizens' sense of security. Although the exchange cleaned the data before the transaction, the exchange staff could not monitor the massive data of the whole country in essence. Data cleaning only cleans up data that does not meet the format requirements, mainly including incomplete data, incorrect data and repeated data. Therefore, it is very necessary to establish a non-profit data credit evaluation institution. As a part of the national credit information system, it is necessary to incorporate data credit information into the credit information system of enterprises and individuals to prevent black market transactions from becoming the normal behavior of the market. In addition to credit rating agencies, the national public security department may set up a data security bureau in the future, which will be included in the category of network police, focusing on cracking down on the sale of basic data that infringe on trade secrets and citizens' privacy. Conclusion: Big data has gradually moved from forum to national governance system construction, marketing management, production management, securities market and other aspects, and its business model is also diverse. Market experience shows that there is a commodity economy when there is buying and selling, and the specific business model will be determined by the market. The final facts will prove that the commodity economy of big data transactions will inevitably become an important part of the "internet plus".