The key and necessary condition for big data application is the integration of "IT" and "operation". Of course, the connotation of operation here can be very broad, ranging from the operation of a retail store to as large as The management of a city. The following are cases about the application of big data in various industries and different organizations. I would like to declare that the following cases are all from the Internet. This article is only for reference, and is briefly sorted and classified on this basis.
Big Data Application Case: Medical Industry
Seton Healthcare is the first customer to use IBM's latest Watson technology for healthcare content analysis and prediction. This technology allows companies to find large amounts of patient-related clinical medical information and better analyze patient information through big data processing.
In a hospital in Toronto, Canada, there are more than 3,000 data readings per second for premature babies. 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.
Big data application case: energy industry
Smart grid has now reached the terminal in Europe, which is the so-called smart meter. In Germany, in order to encourage the use of solar energy, solar energy will be installed in homes. In addition to selling electricity to you, you can also buy it back when your solar energy has excess electricity. Data is collected every five minutes or ten minutes through the power grid. The collected data can be used to predict customers' electricity consumption habits, etc., thereby inferring how much electricity the entire grid will require in the next 2 to 3 months. With this prediction, a certain amount of electricity can be purchased from power generation or power supply companies. Because electricity is a bit like futures, it will be cheaper if you buy it in advance, but it will be more expensive if you buy it in spot. After passing this prediction, the procurement cost can be reduced.
Vestas Wind System relies on BigInsights software and IBM supercomputers, which then analyze weather data to find the best location to install wind turbines and the entire wind farm. Using big data, analysis work that used to take weeks can now be completed in less than an hour.
Big Data Application Case: Communications Industry
XO Communications has reduced its customer churn rate by nearly half by using IBM SPSS predictive analysis software. XO can now predict customer behavior, identify behavioral trends, and identify deficiencies, helping companies take timely measures to retain customers. In addition, IBM's new Netezza network analysis accelerator will help communications companies make more scientific and reasonable decisions by providing a single, end-to-end scalable platform for network, service, and customer analysis views.
Telecom operators can analyze a variety of user behaviors and trends through tens of millions of customer data and sell them to companies in need. This is a new data economy.
China Mobile uses big data analysis to conduct targeted operations on all business operations of the enterprise.monitoring, early warning, and tracking. The system automatically captures market changes at the first time and then pushes them to the designated person in the fastest way, so that he can learn the market conditions in the shortest time.
NTTdocomo combines mobile phone location information with information on the Internet to provide customers with information about nearby restaurants. When the last train time is approaching, it provides last train information services.
Big data application case: retail industry
"One of our clients is a leading professional fashion retailer that operates through local department stores, the Internet and its mail-order catalog business. Provide services to customers. The company hopes to provide differentiated services to customers and how to position the company's differentiation. They have a deeper understanding of the marketing model of cosmetics by collecting social information from Twitter and Facebook, and then they realize that they must retain two categories of products. Value customers: High spenders and high influencers. Want to get word-of-mouth promotion from users by receiving free makeup services. This is the perfect combination of transaction data and interaction data to provide a solution to the business challenge. "Informatica's technology helps this. The home retailer enriches its customer master data with data from social platforms to make its business services more targeted.
Retailers also monitor customers as they move around the store and interact with merchandise. They combine this data with transaction records to perform analysis to provide advice on which items to sell, how to place them, and when to adjust selling prices. This approach has helped a leading retail company reduce its inventory by 17%. At the same time, while maintaining market share, the proportion of high-margin private-label products has been increased.