What challenges will marine tourism industry face?

Challenge 1: The data source is complex.

Rich data sources are the premise of the development of big data industry. However, the total amount of digital data resources in China is far lower than that in the United States and Europe, and the amount of newly added data is only 7% in the United States and 12% in Europe, among which the accumulation of data resources in government and manufacturing industry lags far behind that in foreign countries. As far as the limited data resources are concerned, there are still low standardization, low accuracy, low integrity and low utilization value, which greatly reduces the value of data.

In the era of big data, we need more comprehensive data to improve the accuracy of analysis and prediction, so we need more convenient, cheap and automated data production tools. In addition to the browser we use on the Internet to record personal information and data intentionally or unintentionally, various wearable devices such as mobile phones, smart watches and smart bracelets also generate data all the time; Even our home routers, televisions, air conditioners, refrigerators, water dispensers, purifiers and so on. Become more and more intelligent and have networking functions. These household appliances are not only serving us better, but also generating a lot of data. Even when we go shopping, merchants' WIFI, operators' 3G networks, ubiquitous cameras and electronic eyes, self-service screens in department stores, ATMs in banks, gas stations and card readers all over convenience stores are generating data.

Challenge 2: Establish a data mining analysis model

Stepping into the era of big data, people are talking about big data, which seems to have evolved into a new trend. Data is more rooted in every corner of our lives than ever before. We try to use data to solve problems, improve welfare and promote new economic prosperity. People have expressed their high expectations for big data and their special optimism about big data analysis technology. However, regarding big data analysis, people are clamoring for its magical value, but there are few models and methods for its practical application. There are two main reasons for this dilemma: First, there is still a lack of in-depth insight into the value logic of big data analysis; Secondly, some important elements or technologies in big data analysis are still immature. The massive growth of data in the era of big data, the lack of this big data analysis logic and the development of big data technology are the challenges we face in the era of big data.

The size of big data is generally considered to refer to its massive data scale. With the technological revolution in data recording, acquisition and transmission, and the convenience and low cost of data acquisition, the limited data originally obtained in a high-cost way, describing human attitudes or behaviors, has become a huge and massive data packet. This is actually a one-sided understanding. In fact, before the era of big data, there was a huge data set, but because of its single dimension, it was divorced from the organic activity of people or society, and its value of analyzing and understanding the truth was extremely limited. The real value of big data lies not in its size, but in its comprehensiveness: the overlapping and reproduction of multi-angle and multi-level information in the spatial dimension; Continuously present information related to the activities of people or social organisms in the time dimension.

Challenge 3: Trade-off between data disclosure and privacy

The premise of data application is that data is open, which is already a knowledge. Some professionals pointed out that China has the largest population in the world, but the newly stored data of 20 1 1 in China is 250PB, which is only 60% of that of Japanese and 7% of that of North America. At present, some departments and institutions in China have a large amount of data, but they prefer not to use it themselves or provide it to relevant departments, resulting in incomplete information or repeated investment. In 20 12 years, the data storage capacity in China reached 64EB, of which 55% needed some protection, but at present, less than half of the data was protected.

How to balance openness and privacy is also a big problem. Any technology is a double-edged sword, and big data is no exception. How to effectively protect the privacy of citizens and enterprises while promoting the full openness, application and enjoyment of data, and gradually strengthen privacy legislation will be a major challenge in the era of big data.

Challenge 4: Big Data Management and Decision-making

The technical challenges of big data are obvious, but the decision-making challenges it brings are even more arduous. The key to big data is that it will directly affect how organizations make decisions and who will make them. In the era of limited information, high acquisition cost and not being digitized, people who make major decisions in organizations are usually high-powered people, or high-priced external think tanks with professional skills and excellent resumes. However, in today's business world, executives still rely more on personal experience and intuition than data.

The fundamental purpose of the development of big data is to help people make more informed decisions and optimize the operation of enterprises and society based on data analysis. According to Harvard Business Review, big data is essentially "a management revolution". Decision-making in the era of big data can't rely solely on experience, but really "speak with data". So, big data can really play a role. At a deeper level, we need to improve our management mode, and we need to adapt to the management methods and architecture of big data technology tools. This is perhaps the most difficult obstacle for us to overcome.

Challenge 5: Big Data Talent Gap

If big data represented by Hadoop is a baby elephant, then enterprises must have animal trainers who can tame it. When many companies enthusiastically embrace this big data technology, the relevant talents who are proficient in big data technology have also become a big gap.

Every link of big data construction needs professionals to complete. Therefore, it is necessary to cultivate and bring up a professional team of big data construction who understands command, technology and management.