Kunshan Keyiyou Hardware Machinery Co., Ltd.
Contact person: Mr. Yin
Seating machine: +86 512-57270580
Address: No. 55, Changshun Road, Zhangpu Town, Kunshan City
Nowadays, with the arrival of the information age, more and more traditional mold industry has realized the importance of change. Through the era of big data, it can help the traditional mold industry to break through. The mold industry, which is the mother of industry, will undoubtedly need to use the convenient conditions of the information age to arm itself, so as to truly keep up with the pace of the times and embark on the road to progress.
The origin of big data is attributed to the Internet, e-commerce, telecom operators, and financial industries. Due to the characteristics of these industries, they can naturally obtain large amounts of data during the production and operation process. They are the pioneers of the big data industry.
However, it can be asserted that the greater demand for big data and the broad application prospects are still in the traditional industries, and big data will be the best combination of traditional industries adapting to the Internet age.
Well, traditional industries need to be big data teams and must be prepared. Since you are building a big data team, you must have a background in big data. Big data cannot be separated from the industry and the enterprise itself to talk about technology. It is a castle in the air; the analysis from big data thinking will lead to the dead application of data. Therefore, the traditional mold enterprise sings the big data team of the enterprise component, not only needs the big data technical talent, but also needs the brave changer who has deep industry background and big data thinking.
When we build a big data team in our mold industry, the following must be done for the automotive die tilt rods:
1. Try the "taste" of big data before you talk about how to do it. It is recommended that you spend a little time trying to make big data before you build a big data platform.
2, first internal "team", experts can only do "foreign aid." In simple terms, it is this team that must have "data" people, "analysis data" people, and "data" people.
3, big data has to be "private customization." Data can't be obtained, processes can't work, and systems and systems can't interact. These seemingly insignificant problems are the underlying foundation for whether big data can be effective in the future. Only by taking the pulse of a good company and discovering potential problems can it be effective.