Big Data Will Change Human Thinking

When it comes to big data, four Vs are usually mentioned: large volume (Volume), high accumulation speed (Velocity), multiple sources of data generation (Variety), and large generalized noise of data (Voracity). However, these are just descriptions of the phenomenon of ultra-high-speed growth of big data in the Internet era. The real meaning and value of Big Data is that it changes the way we think. This is big data thinking.

Big data thinking enables us to transcend the limitations of our original thinking framework in the decision-making process. Everyone makes decisions about actions based on their own perceptions and judgments of reality rather than reality itself. Intelligent data-based decision-making has two steps. The first is to understand and judge things, and the second is to make an action decision (inaction is also a decision). The decision to act can be influenced by the decision maker's value orientation. For example, at the end of World War II, the United States reached the coast of Japan and mobilized more warships in the Pacific Ocean than had landed in Normandy during the attack on Germany in preparation for an attack on the Japanese mainland. Based on an analysis of casualty data from the capture of several Japanese islands, the U.S. military predicted that the capture of the Japanese mainland would cost 500,000 U.S. casualties. Based on this judgment, U.S. President Harry S. Truman made the decision to drop the atomic bomb on Japan. The result was a reduction in U.S. casualties but hundreds of thousands of Japanese civilian deaths and radiation hazards that continue to this day, and the value proposition is clear.

People's understanding and judgment of things can be limited by the confines of their own frame of mind. A physicist analyzing something will naturally apply the laws of physics to his thinking, understanding and judgment. The concepts and language used will also have physical characteristics (time, velocity, field, weight, mass, action, reaction, etc.). When a social scientist analyzes something, the frameworks that come to mind are interpersonal relationships, social status, historical context, societal benefits, and so on. The concepts and language used are socio-humanistic in character. The frame of mind of those who engage in theoretical work and those who engage in practical work are also very different, with the former attaching importance to logic and systematicity, while the latter attaches more importance to timeliness and feasibility. Even people in the same profession may have different thinking frameworks depending on their age, experience, environment and education. When the same phenomenon and information enters different people's minds, it will be filtered by different thinking networks, processed by different ways of thinking, and the final result will be different interpretations of the same reality. Without a frame of mind, we cannot understand and judge a thing. But the frame of mind itself creates a limitation on our perception that is difficult to overcome.

Big data thinking is not from someone's frame of mind, but to let the massive data collision, looking for correlation, first see the results and then analyze the reasons. This breaks through the limitations of the original thinking framework. For example, a U.S. retailer in the massive sales data processing found that every Friday afternoon, the sales of beer and baby diapers rose at the same time. Through observation, it was found that after work on Friday, many young men wanted to buy beer for the weekend and wives often called to remind their husbands to buy diapers for their children on the way home. After discovering this correlation, the retailer put beer and diapers together to make it easier for young dads to shop and greatly increased sales.

Big data thinking can spark new approaches to city management. Since the U.S. Embassy published a daily PM2.5 index, the issue of urban air pollution has gained the attention of governments and citizens in various Chinese cities. The collection of daily PM2.5 test data has become an important task for environmental protection and management. If a statistician follows the original frame of mind to design the collection of test data, he will start from the statistical principle to collect and report the data at different representative locations in the urban area at regular intervals. The result is limited data volume, high cost, and low detection coverage and accuracy. Applying big data thinking, a city's environmental protection department is considering distributing tens of thousands of handheld detectors to citizens in the diaspora to detect and upload data via cell phones. Through cell phone positioning, the environmental protection department can determine the location and time of measurement of each data, greatly improving the coverage and accuracy of data collection.

Big data thinking can provide new ideas for analyzing historical data. The Chinese say that to learn, one must "read ten thousand books and travel ten thousand miles". With big data thinking, reading thousands of books is not difficult today. The U.S. Library of Congress is digitizing all the books in its collection. In the future, through the computer "book" search keywords, analyze the relevant entries and data will be very easy to read thousands of books may be just a few hours of "small task". The University of Pittsburgh School of Public **** Health will be recorded in newspapers, reports, microfilm across the United States since 1888 on the occurrence of infectious diseases and deaths of the pluralistic, fragmented, massive data collection, organization and digitization. Through data modeling and analysis, we brought to life more than 100 years of historical "dead" data and created an electronic data archive of more than 50 infectious diseases in the United States from 1888 to 2010. The historical data proved that the invention and use of immunization vaccines have prevented more than 100 million Americans from dying from infectious diseases. (see below)

Big data thinking can help pioneer new business models. The emergence of the Uber taxi service in the United States and the later emergence of China's DDT (formerly known as DDT taxi) are classic 020 (a perfect combination of online and offline) new business models generated by big data thinking. The popularization of smartphones in the mobile Internet era has made real-time location-based data transfer and information communication possible. It provides a brand-new platform for commercial exchange between passengers and drivers, changes the traditional telephone taxi or roadside taxi, reduces the communication cost and idling rate, and greatly saves the resources and time of both drivers and passengers. A steady stream of electronic data on ridesharing transactions and time and place is being accumulated and stored at high speed. Data scientists can analyze the huge amount of data to find patterns to enhance and improve the experience of passengers traveling by taxi, find new business opportunities and launch new services.

At the heart of big data thinking is the realization that we already live in a world where the Internet is almost everywhere. The Internet connects all kinds of information devices (cell phones, computers, sensors, cameras, camcorders, etc.) into one (the Internet of Things), and digitized data and information are transmitted, stored, and accumulated all the time on this vast network. Digital data can be processed at high speeds and have become a new, if not the most valuable, means of production. While minerals can be smelted into metals and crude oil can be refined into gasoline, the challenge is how to process data into information, generate intelligence, solve old problems that could not be solved in the past, and create new management and business models to generate new value. The first step to meet this challenge is to know and understand big data thinking.

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