Hype Big Data: "Thunder, Rain"

May 29, Baidu Chairman and CEO Robin Li said at a summit of the Baidu Alliance, the Internet field has two things is bat (Baidu, Alibaba and Tencent abbreviation) do not do or do not do things: First, enterprise-class software; Two are big data. Although the word "big data" has been hotly fired by people from all walks of life, there are few projects to really "cash in" the big data.

Hype Big Data

Five years ago, a Google research team announced a remarkable achievement in the world's top science magazine Nature. The team can track the spread of flu in the United States, which does not depend on any medical examination. They are tracking even faster than the CDC. Google's tracking results are only a day's delay, and the CDC needs to summarize a large number of physicians ' diagnoses to get a spread of the trend chart for more than a week. Google can count so fast because they find that when people have flu symptoms, they tend to go to the web and search for something relevant.

Google's success in predicting flu trends soon became a symbol of the latest trends in business, technology and science, and raised concerns about an industry concept-big data.

Back in 1997, NASA researcher Michael Cox and David Elsworth, for the first time, used the term "big data" to describe the huge amount of information generated by supercomputers. Today, people's understanding of the content of large data is already different from that of Cox and Elsworth. The definition of large data is translated into: the need for new processing mode to have more decision-making power, insight and process optimization capabilities of the vast, high growth rate and diversified information assets.

Finally, in 2012, the term "big data" was mentioned on a large scale. The year, including the New York Times and the FT, have focused on the topic of big data, and books on big data are voluminous.

"Big Data" hype Cycle chart data source: Gartner

"Big Data" was nearing the top of the hype in 2012, and the era of "Big Data bubbles" is coming to an end this year, according to the latest survey by Gartner, an international authoritative it research consultancy.

As you can see from Gartner's new technology "Hype Cycle Diagram": When a new technology appears, if the developer claims that the technology can "change everything," it will certainly occupy the headlines of major newspaper websites, and then there will be a lot of investigative agencies to investigate; When this hype reaches a certain stage, Will not go on, that is, to reach the point of anticipation, the focus began to fall sharply; in the next year or two, some enterprises entered the actual combat phase, summed up in practice that can "change everything", while negative speech is becoming more and more serious; 18 months later, the new technology flourished and developed steadily, increasing productivity.

From 2012 to 2013, the frenzied discussion of big data from all walks of life pushed the concept of big data to the top. And in 2014 years, the heat of public opinion fell, how to "change the big Data", it becomes the real problem that the big enterprises really need to face.

Big Data problem

Gartner surveyed 720 companies across the United States, and the results showed that only 8% of the companies had already launched large data projects, and most of the rest were still in the preparation stage.

The heat of big data contrasts strongly with the hard business model. Li said at the Baidu Alliance summit that while human production has accounted for 90% of all the data in the history of human civilization over the past two years, "but a lot of the data we produce every day is basically worthless data." "Even in companies like Baidu, where the technology for big data is in place, Robin Li still thinks that the data he really wants is not being collected, and that" the data collected are basically worthless. "

Dr. He Lingnan, a psychologist at Sun Yat-sen University, has been tracking the development of large data in recent years, and the domestic Kaidi Data Service center and other data Services organizations to cooperate, he told reporters: "For large data, the data itself is not valuable, mining data to be valuable, and, and not simply statistical method can analyze data , it requires more in-depth theoretical mastery. ”

At present, in addition to a few large enterprises, data acquisition and analysis of large projects are often separated, collected by commercial companies to do, and analysis to the research institutions. This industry situation directly leads to a disconnect between the application of large data and the business plan.

However, Dr He Lingnan is still optimistic about the future of big data projects, "at present, the input-output ratio of large data is relatively low." In the short term, do data collection, analysis, the cost is indeed relatively high, but in the long run, large data applications gradually mature, can greatly save costs, large data business model will be more mature. ”

Ma Shaoping, a professor of computer science at Tsinghua University, said: "Although the big data has been hyped up a bit too much, but the current situation is that the data has been multiplying exponentially, the essence of large data will not change," data always have value.

Jundongxing, director of the Education Informatization branch of China Higher Education Association, agreed with Professor Ma Shaoping's view that: "At the current stage, large data must be ' fried paste ', and then surf the sand, this is an objective law." As one of the most important factors in it, data will be valued and utilized in the long run.