Original article was published on Deep Learning on Medium
How do companies achieve data monetization, and how do companies use big data to win?
In the current economic development of the data core, data is a strategic asset for companies and data monetization, and is also a top priority for many companies.
In the contemporary era, the focus of most companies has shifted from increasing the cost of products to improving the user experience with products. Showing a stronger user experience is especially important to create customer satisfaction with products and limit customer loyalty. The complete advent of the big data era and the big data learning tools have made companies understand that data is the most important strategic asset for the steady development of their companies. When it comes to monetization of big data, companies are looking forward to making considerable profits by lowering costs and fees in today’s operating model. Big data generally refers to customer-specific data and personal behavior data that need to be obtained when contacting customers. Therefore, big data is neither public data nor exclusive data, but is a kind of data that can be reasonably and legally used to improve business processes and personal behavior of web pages, social network supervision and participation.
Today’s companies have a lot of abnormal data, including Internet data, customer introduction, machine equipment data, location information, main use methods, click stream data, program operation data, etc. The company has a lot of data in hand, and an international transfer company I know has changed the international financing model from key industries according to the data and the gold ore received every day, and the company can use this method. According experts attracting, discovering, analyzing, storing, inspiring and disseminating big data, data monetization has become a process that can be profitable from the available resources. According to data monetization, everyone can endanger data related to company services and product operations.
According to the Internet Technology Data Management Center, the increased profitability of various information content products may double the asset allocation of other products. Increase in use value and initial data will be based on multilateral trading or trading on the sales market. In 2015, the world created 180 Billion USD of data, and in 2015 it was only 10 Billion USD. The company will compete for independent innovation methods to increase the value of data use. International data analysis laboratory said that cloud data dealers will compete with traditional data analysis dealers. With the rapid development of cloud service platforms, customers will just begin to cause harm to key dealers in the 19-year data analysis system. The Internet Technology Data Management Center predicts and analyzes that by 2020, the new price of data analysis labor will be 5 times higher than the analysis and processing cost based on cloud space.
Most executives think that the role of top data officers is big data, which has defensive effects and needs to comply with actual regulations. However, with the development trend of big data, the company’s high-rise residential must create a data culture art with the core of innovative ideas. According to customer regulations, data translator staff promotion may exceed expectations. Microphone Kensney Worldwide organizes data storage based on quantitative analysis using in-depth professional skills abroad. Now they also predict and analyze that in the entire process of monetizing big data, tens of millions of Chinese translation staff will be required. In order for Chinese to translate data language, it seems that this authoritative person has professional knowledge at the business service level in the whole process of participation. The data translator must have the professional skills of getting along with others and have sufficient knowledge of data expertise.
The scientific research on IOT technical debt done by Wade Regen shows that nowadays, even if understanding the data constitutes a greater threat, most companies are also actively investing in big data. In addition, the technological development trend has stimulated the charm of the data manufacturing industry and has spawned a series of reasonable countermeasures, such as: satisfaction management methods, avoiding customer decline, attracting customers, creating hedge funds, accelerating the resolution of difficulties, optimizing the network and Risk management and so on. The key concerns of the data manufacturing industry in 2020 are three top priorities, namely overall goals, independent innovation and improvement. Based on caring about a large number of products, increasing the scope of online marketing, and using big data analytics, everyone can make the customer experience have their own characteristics, improve their satisfaction with the product, and can also improve work efficiency, thereby improving customer management efficiency.
After mastering, according to how to optimize the network through creation, invention and adjustment, the company can clarify new industries, invest in project development capabilities, and make diligent development trends and business processes under moderate conditions. They can reasonably develop and design products that comply with customer satisfaction. . In this way, it is possible to complete the monetization of big data, formulate new independent innovation countermeasures and implement new methods in industries where the commercial service potential is constrained by the Soviet Union. That is to say, as a gold ore, big data has sufficient working capacity to jeopardize all service project chains, making it a comprehensive main purpose. In fact, with this development trend, the company quickly not only showed distributors of basic services and products, but also became independent and reasonable distributors. In the same way, because artificial intelligence technology is very profitable, by this year, everyone will also develop a lot of new special tools for artificial intelligence technology to collect and analyze big data, creating a large number of characters and obligations.