"Non-competitive": Open integration is more conducive to big data mining

In the fall of 2012, when CEO Su Mingtian, the chief executive of WPP Group, a global advertising giant, visited Google CEO Larry Page, Peiqi dispatched his driverless car to pick him up. It is a self-driving Lexus SUV car with a lot of high-tech equipment and radar, sensors and more than 1.5 million laser scanners per second. It self-service traveled 20 minutes, passing Interstate 280 and the busy State Route 85. The car autonomously cruises through the autopilot, corrects routes on its own, slows down when vehicles and pedestrians appear in front of it, and then accelerates out of the blind spot near the vehicle, eventually arriving at the Rose Hotel, about 32 km away from Google. (According to "Fortune" Chinese version March 2013 finishing)

This is a future vehicle that Google is developing. Such unmanned vehicles not only intelligently brake, route and overtake with traffic history and real-time calculations, but also save energy (because it makes traffic flow more smoothly) and increase productivity (which can reduce hours of commute For other transactions). After you reach your destination, your car can even automatically go to parking spaces that navigate through big data. If you still need to travel, just use mobile phones and other mobile terminals will be able to command the car to reach the scheduled location.

This is the magic of big data. Through the analysis of past and present data, it can accurately predict the future; through the integration of internal and external data in the organization, it can understand the relationship between things; through the massive data mining, it can replace the human brain, To assume the responsibility of social management.

Wisdom treasure of social decision-making

In 2007, Nobel laureate Jim Gray mentioned that data-intensive science is being separated from computational science and becoming the fourth paradigm of scientific research. At this time, all multinational companies have been concerned about the arrival of data-intensive science. For example, the Microsoft Research Institute publishes the Fourth Paradigm: Data-Intensive Scientific Findings and extends "Redefining Ecological Science with Massive Data," "Moving Us closer to Space: Discovery in Massive Data," Earth Scientific Research Tools: Next Generation Sensor Networks and Environmental Sciences "and other related research projects.

Like classical mechanics, quantum mechanics and computational science, data-intensive science is bound to affect social science research methods. Big Data Era: Big Changes in Life, Work, and Thinking put forward a big data thinking about the relationship. That is, one can manipulate all data rather than just extracting small samples; one can mine more promiscuous data rather than exacting the accuracy of the data; one just needs to know the "know it" relationship without going into " Know why they are "the causal relationship.

The paradigm shift in research paradigm eventually feeds people's mindset and decision-making paradigm. Google's driverless cars are based on big data analytics, using traffic engineering and artificial intelligence to enable traffic guidance and control. With ubiquitous computing and sensors, big data enables the resolution of complex network relationships that exist in the real world, in the virtual world, in the virtual world, and in making timely judgments and decisions. This decision-making model follows the process of transforming data into information, transforming information into knowledge, and intelligently emerging wisdom. Different from the previous strategic decisions made by experts, elites and authorities, the decision-making of big data has dimmed the light of industry experts and technical experts because of the emergence of statisticians and data analysts. A non-linear, decentralized, self- And on the discovery of group wisdom decision-making model gradually formed.

The power of big data to penetrate across regions, industries, and business units is disrupting the traditional, linear, top-down elite decision-making model that is shaping a nonlinear, uncertainty-oriented Bottom-up decision-making basis.

Non-competitive factors of production

As people hype big data, collaborative consumer or sharing economies are emerging. The sharing economy is the result of the joint action of social networks, mobile Internet and conservation-oriented society. It is also the typical application of big data in the real-life and distributed sharing. Sharing economy and the interaction of big data applications reveal three big data attributes.

First, the production factor attributes. For big data-hosting companies such as Google and Amazon, data is seen as a new factor of production that not only determines its sales and personalized service but also feeds back into production and R & D to create more accurate supply chain management mechanism. As a big data technology company, IBM summarizes the functions of big data production factors as four aspects: customer retention, IT and business integration, financial process transformation, risk prediction and avoidance.

Second, the temperature of the data. Although IBM sees veracity as the fourth V for big data, big data technology companies such as Microsoft and Oracle have taken data cleansing as a big step toward big data analytics and even Teradata has introduced multi-temperature data management Technology, but using past and current data to predict future characteristics makes the big data trade-offs tough.

Third, the potential value. Unlike material resources, the value of big data does not diminish as it is used, but can continue to be tackled and continually find new value. This creates a new problem that data owners may use the traditional data mining methods to realize the first release of big data value, and many non-owners in the value chain may mine the data by reorganizing the data and expanding the data. Times or even multiple values.

In a word, big data featuring distributed and interactivity features obvious "non-competitive" resources. More data integration and a more open data sharing platform are conducive to the discovery of the potential value of data.