What is the difference between the era of big data and the era of traditional data?

1, there is no doubt that the big explosion of data information constantly reminds us that the future will change due to big data technology. Big data (big

Data) is usually used to describe a large number of unstructured and semi-structured data produced in the digital age. Big data is undoubtedly one of the most striking technologies that will affect the development of all walks of life in the future. In 2009, the global research projects on big data were still very limited. From 20 1 1, more and more managers began to realize that big data will be an unavoidable problem in the future development, and by the end of 20 12, it will be one of the top 500 fortune companies in the world.

90% of powerful enterprises have started big data projects. IDC research shows that by 20 15 years, the market prospect of big data will reach 169 billion US dollars. At present, the operating data of all enterprises will double every 1.2 years.

So why does big data become the focus of attention? What kind of essential changes has big data brought? Therefore, we interviewed Professor Du, the leader of big data discipline of China Computer Federation and the dean of School of Information, China Renmin University.

The Internet is a magical big network, and big data development and software customization are also a model. Here is the most detailed quotation. You can come here if you really want to do it. The starting number of this skill is 187, with three children in the middle.

The last one is 14250, which can be found by combining them in order. What I want to say is, don't come unless you want to do or understand this content, if you just come to join in the fun.

Professor Du believes that big data has brought about three fundamental changes: First, big data has freed people from dependence on algorithms and models, and data itself can help people get close to the truth of the matter; Second, big data weakens causality. Big data analysis can mine the associations between different elements. People don't need to know why these elements are related, so they can use the results. In the modern society with complex information, such application will greatly improve efficiency. Third, compared with previous database-related technologies, big data can handle semi-structured or unstructured data. This will make the data range that the computer can analyze expand rapidly.

2. The difference between traditional data and big data

First of all, before big data appeared, computer science relied heavily on models and algorithms. In order to get an accurate conclusion, people need to establish a model to describe the problem, and at the same time, they need to straighten out the logic, understand the cause and effect, design a sophisticated algorithm, and draw a conclusion close to reality. Therefore, whether a problem can be solved best depends on whether the modeling is reasonable, and the competition of various algorithms becomes the key to success or failure. However, the emergence of big data has completely changed people's dependence on modeling and algorithms. For example, suppose there is an algorithm A that solves a problem.

For algorithm B, the result of algorithm A is obviously better than that of algorithm B when running with a small amount of data, that is to say, as far as the algorithm itself is concerned, algorithm A can bring better results; However, it is found that when the amount of data is increasing, the result of algorithm B running in a large amount of data is better than that of algorithm A running in a small amount of data. This discovery has brought landmark enlightenment to both computer science and computer derivative science: when the data is getting larger and larger, the data itself (rather than the algorithms and models used to study the data) ensures the validity of the data analysis results. Even if there is no accurate algorithm, as long as there is enough data, we can get a conclusion close to the fact. Therefore, data is known as the new productivity.

Second, if there is enough data, we can draw a conclusion without knowing the specific causal relationship.

Like Google.

When helping users translate, they are not setting various grammars and translation rules. Instead, we use the vocabulary habits of all users collected in the Google database for comparison and recommendation. Google checks the writing habits of all users and recommends the most commonly used and commonly used translation methods to users. In this process, the computer may not know the logic of the problem, but when there are more and more recorded data of user behavior, the computer can provide the most reliable results without knowing the logic of the problem. It can be seen that massive data and analytical tools for processing these data provide a brand-new way to understand the world.

Third, because it can handle various data structures, big data can make maximum use of human behavior data recorded on the Internet for analysis. Before the emergence of big data, all the data that computers can handle need to be structured in the early stage and recorded in the corresponding database. The big data technology greatly reduces the structural requirements of data. The information of various dimensions left by people on the Internet, such as social information, geographical location information, behavior habit information, preference information, etc., can be processed in real time, and the various characteristics of each individual can be outlined in a three-dimensional and complete way.