Application Case of Big Data: Medical Industry
SetonHealthcare is the first customer to use IBM's latest Watson technology to analyze and predict healthcare content. This technology allows enterprises to find a large number of clinical medical information related to patients and better analyze patient information through big data processing.
In a hospital in Toronto, Canada, there are more than 3000 premature babies' data readings every second. Through the analysis of these data, hospitals can know in advance which premature babies have problems and take targeted measures to prevent premature babies from dying.
One of Big Data Application Cases: Energy Industry
Smart grid has now reached the terminal in Europe, which is called smart meter. In Germany, in order to encourage the use of solar energy, solar energy will be installed at home. In addition to selling electricity to you, you can buy back the surplus electricity from your solar energy. Data is collected every five minutes or ten minutes through the power grid, and the collected data can be used to predict customers' electricity consumption habits, so as to infer how much electricity the whole power grid needs in the next 2-3 months. With this forecast, you can buy a certain amount of electricity from power generation or power supply enterprises. Because electricity is a bit like futures, it will be cheaper to buy it in advance and more expensive to buy it in stock. Through this forecast, the procurement cost can be reduced.
Vestas wind system relies on BigInsights software and IBM supercomputer, and then analyzes meteorological data to find out the best location for installing wind turbines and the whole wind farm. Using big data, which used to take several weeks to analyze, can now be completed in less than 1 hour.
One of Big Data Application Cases: Communication Industry
XOCommunications has reduced the customer churn rate by nearly half by using IBMSPSS predictive analysis software. XO can now predict customers' behavior, discover behavior trends and find out defective links, thus helping enterprises to take timely measures to retain customers. In addition, IBM's new Netezza network analysis accelerator will help communication enterprises make more scientific and reasonable decisions by providing an extensible platform with a single end-to-end network, service and customer analysis view.
Telecom operators can analyze a variety of user behaviors and trends through tens of millions of customer data and sell them to enterprises in need. This is a brand-new information economy.
Through big data analysis, China Mobile conducts targeted monitoring, early warning and tracking of the whole business operated by enterprises. The system automatically captures the market changes at the first time, and then pushes them to the designated person in charge in the fastest way, so that he can understand the market situation in the shortest time.
NTTdocomo combines the location information of mobile phones with the information on the Internet to provide customers with information about nearby restaurants, and provide the last bus information service when the last bus time approaches.
One of Big Data Application Cases: Retail Industry
"One of our customers, a leading professional fashion retailer, provides services to customers through local department stores, the Internet and its mail-order catalog business. The company hopes to provide differentiated services for customers. How to position the company's differentiation? By collecting social information from Twitter and Facebook, they have a deeper understanding of the marketing model of cosmetics. Then, they realize that they must retain two types of valuable customers: high consumers and high influencers. I hope that by accepting free makeup services, users can conduct word-of-mouth publicity, which is a perfect combination of transaction data and interactive data, providing a solution to business challenges. " Informatica's technology helps retailers use data on social platforms to enrich customer master data and make their business services more targeted.
Retail enterprises also monitor customers' in-store walking and interaction with goods. They combine these data with transaction records for analysis, so as to give suggestions on which goods to sell, how to place them and when to adjust the price. This method has helped a leading retail enterprise to reduce the inventory of 17%, and at the same time, it has increased the proportion of its own brand products with high profit margin on the premise of maintaining market share.