What miracles can big data create? Not for data but for data!
With the in-depth application of big data technology, the public resources that are in short supply in the future should be more rationally allocated.
In 1844, Morse issued the first telegram in human history: "What miracle God created!" The invention of the telegraph opened the first page in the history of telecommunications. Nowadays, with the continuous development of information technology and the rapid improvement of AI data analysis capabilities, what kind of miracles can big data create in the future? In the media group interview of 2017 China Big Data Technology Conference (BDTC2017), Zhou Xiaofang, Professor of Queensland University of Australia, Liu Tieyan, Principal Investigator of Microsoft Asia Research Institute, Qian Weining, Professor of East China Normal University, and Zhang Wenxi, Senior Vice President of Didi Travel Discussed the future application of big data.
Big data, not for data
Most of the big data systems implemented by enterprises today show a very cool panel and some data analysis that looks very attractive, but the actual application value of the data is not exactly reflected. On this issue, the four experts expressed their views on current big data:
President Zhang Wenzhao first expounded his point of view: "Not for data and data, first of all, what are the problems that need to be solved with big data? How to define these problems? If you sort out the problems and then based on these problems, look at the data. Can you do a lot of revelation, really want to solve the problem and not the data for the data."
Professor Zhou Xiaofang then conducted an in-depth analysis of this issue: The first aspect is just said, we have a problem, then look at what kind of data is needed, and how to use this data to solve our problem. The second aspect, after we have the data now, how to emancipate the mind and create new application value. The business community and the research community can work together to solve this problem. In the case of big data, the Chairman’s speech just before the General Assembly also said that it is a feature of our time. Big data is already a reality, and the technology of data application is there, and various application scenarios are now on the way. Therefore, CROs can't big data for big data, but what things you should do, what kind of data do you have, what data you need, how to use this data to do better with our existing work, use these How data can drive some of our new applications.
Dean Liu Tieyan: Data is a part of what we have. We get the data is a foundation. We must find value from the process. The process is the process of doing machine learning. Let's talk about big data. We must combine data and intelligence. Get up, have a full plan. In this perspective, what data do we want to take in order to achieve the purpose of achieving the purpose of the data we just said, instead of getting all the data, it feels like a baby stays, there are many good aspects of the data. It is very rich, and there are many bad aspects. It is noise. Some may be wrong. If there is no effective algorithm and method, the noise is removed and the information is mined. In fact, big data is not necessarily valuable.
Finally, Professor Qian Weining made a summary of this problem: one of our views is that the data is actually very similar to our previous electricity. Before the power is available, the application is different. If I have electricity, I still In the use of steam opportunities, I think that this electricity does not have much effect. Now that we have data again, we may say that a business model or a decision model is going to change, so that is, maybe you just said that after the data, I just A presentation is just a report. This is still a traditional thinking mode, but more about how we can reconstruct this data based on this data, and then redesign the process of our entire business, so that we can put the data Use better.
Data cleaning, how to distinguish sand from gold
In the last question, the value of data is mentioned. In terms of good data, there are also many bad aspects. It is interference. How can noise pass effective methods to remove noise and mine information? The following experts have conducted a more in-depth discussion on the issue of data cleaning.
Professor Zhou Xiaofang: Data quality management is a problem of big data, and it should be viewed from the aspect of data integrity. For example, the opinions of some people on the Internet cannot represent the whole society because the data is incomplete. There are also timeliness, accuracy, consistency, etc. This is a traditional issue. The issue of data cleaning has been studied for many years. Now our own research team has been pushing this data quality management. Now the glory has been taken away by AI, but the data management, data analysis and various big data differences supported by AI are silently done behind. . If you don't do this underlying data support, then it's cool.
President Zhang Wenzhao: The quality of the data is very critical. If the data is not allowed, any conclusions you may have are problematic. Some of the problems we face here, the solution involves the process of data collection, production, and transmission. We must verify the data at all levels, which check some data is not allowed, or some links The data is lost. Take the most important vulnerability in the case of Didi, the user comes in that bubbling to express his destination. After bubbling, we have a lot of actions, then the user is a bill, whether the platform sends a bill, and the post is executed. Pay until the end of the order. This funnel model is actually verified at all layers. Therefore, this data can help us locate which buying points are right, and which links are caused by the bugs in our network transmission software processing. This is a layer-by-layer verification. Just like financial verification, every link must be verified. To ensure data accuracy and software correctness.
Dean Liu Tieyan: I have been in contact with many traditional enterprises. When I talk to us, I don’t talk about big data, but AI. In the case of in-depth communication, solving the first problem is a big data problem rather than a smart one. Their vision is to use intelligence to solve an application. In fact, the data in the entire company is not managed and organized. It is really important to return to big data. First, digital, then traditional management and then digital cleaning. From the perspective of machine learning and AI, what is data cleaning and what is data management, we want to start with the most primitive data, and then connect to the final application through a particularly complex link. Feedback may be provided at each link. If we don't do end-to-end processing, what might happen when splitting? When we did data processing in the early stage, we thought it was a noise problem. It might be a signal. What we thought was a signal might be noise. So how do you give everyone an opportunity in the process of intelligentizing the entire data link? What used to be a noise-cleaning thing is the opportunity to re-excavate and repeatedly match what has been considered useful by you. Where is the baby, where is the problem, this is a supplement.
Data application, resource allocation and people's livelihood
In the following discussion activities, several experts also conducted in-depth communication on the changes in people's lives driven by intelligent driving, and how to reduce the gap between the rich and the poor in the east and the west. Raise the technical discussion to the height of people's livelihood issues.
We believe that with the in-depth application of big data technology, the public resources that are in short supply in the future should be more rationally allocated. For example, the train ticket of Spring Festival can give more purchase opportunities to ticket buyers who have practical home needs. Patients who are in urgent need of medical treatment can be more conveniently registered, and public transportation can also be adjusted. The future of big data will make people's lives better.
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