Big data applications and which industries?

Big data is applied to various industries, including finance, automotive, food and beverage, telecommunications, energy, entertainment and other sectors of society have been integrated into the traces of big data.

1, manufacturing: the use of industrial big data to improve the level of manufacturing, including product failure diagnosis and prediction, analysis of process flow, improve the production process, optimize the production process energy consumption, industrial supply chain analysis and optimization, production planning and scheduling.

2, the financial industry: big data in high-frequency trading, social sentiment analysis and credit risk analysis of the three major areas of financial innovation play a significant role.

3, the automotive industry: the use of big data and the Internet of Things technology driverless cars, in the near future will come into our daily lives.

4, the Internet industry: the use of big data technology to analyze user behavior, product recommendations and targeted advertising.

5, catering industry: the use of big data to realize the catering O2O model, completely change the traditional catering business.

6, the telecommunications industry: the use of big data technology to achieve customer offline analysis, timely grasp of the tendency of customers to leave the network, the introduction of customer retention measures.

7, the energy industry: with the development of smart grid, electric power companies can grasp the massive amount of user power information, the use of big data technology to analyze the user's power consumption patterns, can improve the operation of the power grid, the rational design of the power demand response system, to ensure the safety of power grid operation.

8, logistics industry: the use of big data to optimize the logistics network, improve logistics efficiency and reduce logistics costs.

9, city management: the use of big data to achieve intelligent transportation, environmental monitoring, urban planning and intelligent security.

10, biomedicine: big data can help us realize epidemic prediction, intelligent medical care, health management, but also can help us decipher the DNA, to understand more of the mystery of life.

11, public **** security field: the government to use big data technology to build a strong national security system, public **** the application of big data analysis in the field of security, counter-terrorism stabilization and all kinds of cases analysis of information technology means, with the help of big data crime prevention.

12, personal life: big data can also be applied to personal life, the use of personal big data associated with each person, analysis of personal life behavior trajectory, to provide more thoughtful personalized service.

The value of big data goes far beyond this. The penetration of big data into every industry is the core element that drives social production and life.

Expanded Information

Seven Typical Big Data Application Cases

1, Macy's real-time pricing mechanism. Based on demand and inventory, the company's SAS-based system adjusts prices for as many as 73 million items in real time.

2. Tipp24?AG's betting and forecasting platform built for the European gaming industry. The company uses KXEN software to analyze billions of transactions and the characteristics of its customers, and then uses predictive modeling to deliver dynamic marketing campaigns to specific users. The initiative reduces predictive model building time by 90 percent. SAP is trying to acquire KXEN.

3. Wal-Mart's search. The retail oligopoly designed its latest search engine, Polaris, in-house for its Web site, Walmart.com, utilizing semantic data for text analysis, machine learning and synonym mining. According to Walmart, the use of semantic search technology has resulted in a 10 to 15 percent increase in online shopping completion rates. "For Walmart, that translates into billions of dollars." Laney said.

4. Video analytics in fast food. The company uses video to analyze the length of waiting queues and then automatically varies what the electronic menu displays. If the queue is long, it displays foods that can be served quickly; if the queue is short, it displays foods that are more profitable but have relatively long preparation times.

5. Morton's Steakhouse brand recognition. When a customer jokingly tweeted the Chicago-based steakhouse chain to order food to be delivered to Newark Airport in New York (where he would be arriving after a long day of work), Morton put on its own social show. First, Twitter data was analyzed and it was discovered that the customer was a regular customer of the restaurant and a frequent tweeter. Based on the customer's previous orders, the flight he or she was traveling on was surmised, and then a tuxedo-clad waiter was dispatched to serve the customer dinner.

6. PredPol?Inc. PredPol, through a partnership with police in Los Angeles and Santa Cruz and a group of researchers, predicts the chances of a crime occurring based on variations of a seismic-prediction algorithm and crime data, which can be accurate to within 500 square feet. In the areas of Los Angeles where the algorithm was applied, the distribution of burglary and violent crime dropped by 33 percent and 21 percent.

7.?Tesco?PLC (Tesco) and operational efficiency. The supermarket chain collects data on 7 million refrigerators in its data warehouse. This data is analyzed for more comprehensive monitoring and proactive maintenance to reduce overall energy consumption.