Big data applications must solve three key points

Big data application must solve three key points

The key points of big data application are data source, productization and value creation; data resources are unevenly distributed, and big data application in data Intensive fields are more likely to achieve breakthroughs; inappropriate industry management models must be reformed to promote the application of big data in various existing industries.

Big data is valuable in its application. Currently, at the national level, the State Council has issued the "Action Outline to Promote the Development of Big Data"; at the local level, big data is used as a strategic engine for regional development; at the corporate level, various big data concept companies are in the ascendant and booming. We focus solely on big data applications, focusing on where the data comes from, how the data is used, and who pays for the results, which are the three key points of data sources, productization and value creation. A good big data application may be technically complex, but its business model should be simple, straightforward, and effective. We are also concerned about whether there are several "data-intensive" industries or fields in which big data applications may be easier to develop. In terms of industrial policy, we are concerned about big data as an emerging industry. Will the tried and tested methods in the past, such as giving land, money, and projects, etc., continue to be effective?

Three keys to the application of big data Click

The State Council's "Action Outline for Promoting the Development of Big Data" (referred to as the "Big Data Outline") positions big data as a "new generation of information technology and service formats" and empowers big data to "promote economic transformation and development" Reshape the strategic function of "national competitive advantage" and "enhance government governance capabilities" and define data as "national basic strategic resources". In terms of application, the "Big Data Outline" proposes many development directions in the public sector, such as scientific macro-control, precise government governance, convenient commercial services, efficient security and people's livelihood services; at the industrial level , mainly divided by industry fields into industrial big data, emerging industry big data, agricultural and rural big data, innovation big data, as well as big data product system and big data industry chain. These directions are only the potential and space for big data application. Whether it can be applied and whether it can play a role depends on whether there are feasible models and actual effects. Whether in the public sector or at the industrial level, big data applications are inseparable from data sources, processing technologies and methods, and value creation models. This is our focus. In summary, the following three seemingly simple but critical questions need to be answered. (1) Where does the data come from? Regarding the source of data, it is generally believed that the Internet and the Internet of Things are the bases for generating and carrying big data. Internet companies are born big data companies, accumulating and continuing to generate massive amounts of data in their respective core business areas such as search, social networking, media, and transactions. IoT devices are collecting data every moment, and the number of devices and the amount of data are increasing day by day. These two types of data resources, as big data gold mines, are constantly generating various applications. Most of the successful foreign experiences on big data are classic cases of the application of this type of data resources. There are also some companies that have accumulated a lot of data in their business, such as real estate transactions, commodity prices, consumption information of specific groups, etc. Strictly speaking, these data resources are not considered big data, but for commercial applications, they are the most accessible and easy-to-process data resources, and they are also relatively common application resources in China. In China, there is another category of data resources held by government departments, which are generally considered to be of good quality and high value, but with a low degree of openness. The "Big Data Outline" takes the open and open sharing of public data as the direction of efforts and believes that big data technology can achieve this goal. In fact, for a long time, information data between government departments have been closed and separated from each other. This is a governance issue rather than a technical issue. The desire to open public data to the society is very good, but I am afraid that it will be out of reach for some time. In terms of data resources, the application of "small data" and "medium data" in China is insufficient. Trying to step into the era of big data and take the opportunity to solve problems that could not be solved in the early informatization process, the prospects are not optimistic. In addition, since the business of Chinese Internet companies is mainly domestic, their big data resources are not global. Where the data comes from is the first focus when we evaluate big data applications.

Of course, if you can withstand the "Three Questions of Big Data" mentioned above, you may not necessarily be considered excellent, but you are not far from an excellent big data application. Looking for data-intensive areas Since big data is regarded as a resource, the issue of resource distribution must be considered. Generally speaking, the distribution of resources is extremely uneven, such as water, minerals, arable land, energy and other natural resources; the distribution of human resources and knowledge is even more uneven. Does big data also have the problem of uneven distribution? Can the development of the big data industry really overtake others in a corner? These issues are worthy of in-depth consideration. Unlike natural resources that can be detected, the distribution of data resources is difficult to locate and characterize. However, the distribution of big data human resources can be used to indirectly reflect the differences in big data applications between regions and industries. Which industries and regions have intensive big data human resources can be regarded as data-intensive. We screened the recruitment information released by two mainstream recruitment websites, "51job" and "Zhilian Recruitment", since the second half of 2014, and found that the two websites released the most relevant information in the past two years involving 227,000 companies and 1.007 million positions. The amount of data is indeed "large" enough. Through summary analysis by region and industry, the results show that the distribution of big data human resources is extremely uneven, with great differences among regions and industries. However, to be precise, what is reflected through recruitment websites is the demand for talents, not the distribution of human resources stock in the strict sense, but the two are closely related. From the perspective of where big data-related positions work, Beijing, Guangdong, and Shanghai are highly densely populated, far ahead of other regions. Adding up the three places, the number of companies publishing recruitment information accounted for 52.35 and 47.48 on the two websites, and the number of positions accounted for 61.23 and 56.74. It can be speculated that half of the big data human resources are concentrated in these three places, which is highly consistent with our usual intuitive feelings. In addition to these three places, we are concerned about whether local governments attach great importance to the big data industry and use big data as an engine for regional economic development, which can promote the concentration of human resources and surpass other regions with similar economic development levels. Judging from the data, at least for now, we cannot see such a result. This reveals that the human resources structure is the shortcoming that needs to be made up for and the difficulty that is most difficult to overcome in the development of the big data industry in late-developing regions. Changing the composition of human resources in a place is far more difficult than changing the appearance of buildings on the ground. It either requires a long-term process or a unique system. Even within the same province, the distribution of big data human resources is extremely uneven. For example, in Guangdong, Shenzhen alone accounts for roughly half of the province. Coupled with Guangzhou, it can reach 90%. Even though other places have good economic strength, compared with Shenzhen and Guangzhou, they are far behind in terms of big data human resources. This once again shows that the distribution of big data human resources is extremely uneven. Obviously, the foundation for developing the big data industry in areas with intensive big data human resources is better than that in areas with poor human resources. From the perspective of city rankings, Beijing, Shanghai, Shenzhen and Guangzhou can be regarded as first-tier cities with intensive demand for big data human resources, while Hangzhou, Nanjing, Chengdu, Wuhan, Xi'an, etc. can be regarded as second-tier cities. The distribution of big data human resources is generally consistent with the city's economic strength, vitality and even housing price levels. From the perspective of industry distribution, the demand for big data human resources is even more unevenly distributed, mainly concentrated in the Internet, information technology and computer-related industries. This fully demonstrates that big data is a part of the Internet or IT industry and a new development based on the original basis. These industries are typical "data-intensive" industries and are the cradle of the development of the big data industry. Finance is another "data-intensive" field of particular importance. The financial industry is not only the base for generating data, especially valuable data, but also the demander and application place for data analysis services. More importantly, the financial industry has sufficient payment capabilities and will be an important battlefield for competition in the big data industry. A lot of big data has radiated to various industries through its application in the financial field. In addition, telecommunications, professional services (such as consulting, human resources, accounting), education and training, film and television media, online games, etc. are also relatively data-intensive industries.

The "Big Data Outline" plans out the broad prospects of big data applications in almost all industries and fields. However, the distribution of data resources is extremely uneven. Big data applications in "data-intensive" fields are more likely to achieve market success. . What kind of industrial policies are needed for big data? What kind of industrial policies are needed for big data applications? From an application perspective, big data is not a brand-new industry, but an integration with existing industries, the transformation, upgrading and transformation of existing models. substitute. What restricts the development of big data is often not the big data itself, but the existing problems in the industries and fields where big data is applied, such as industry regulation, administrative monopoly, the inability of free flow of factors, etc. Therefore, promoting the development of big data by giving land, money, and projects cannot solve the fundamental problem. From the perspective of big data application fields, it is necessary to reform inappropriate industry management models and adjust the existing interest pattern so that big data applications have the necessary conditions. Even within an enterprise, big data application is not just a technical issue, but involves business process reorganization and management model change, which is a test of enterprise management capabilities. "Data-intensive" industries such as finance, telecommunications, education, film and television media, etc. are not only areas with huge potential for big data application, but also key areas that urgently promote industry reform. On the other hand, the application of big data can also provide technical support for industry reform and achieve industry development goals with more effective technical routes.

The industrial policies needed for big data applications are actually the policies that should be required for the development of various industries under a market economy, such as liberalizing access, fair competition, reducing corporate burdens, eliminating corporate ownership discrimination, and eliminating corporate size discrimination. ,etc. Only in an open industrial environment can big data be effectively used in these industries. If a place wants to vigorously promote the application of big data in finance, medical care, education and other fields, the most effective policy is to carry out powerful reforms in these industries.