Victor Mayer-schoenberg cited various examples in the book "The Age of Big Data" to illustrate a truth: when the era of big data has arrived, we should use big data thinking to explore the potential value of big data.
what is big data thinking? Victor Mayer-schoenberg thinks: 1) All data samples are needed instead of sampling; 2) Focus on efficiency rather than accuracy; 3) Focus on correlation rather than causality.
we believe that big data is not "big" but "useful". Big data thinking is to be able to fully understand the value of data and know how to use big data to provide a basis for business decisions, that is, to create business value through data processing.
the core of big data thinking is to understand the value of data and create business value through data processing
Harvard Business Weekly pointed out that data scientists are the sexiest profession in the 21st century. After obtaining massive data, we should consider how to use the data. Data scientists are engineers who use scientific methods and data mining tools to find new data insights. The era of big data highlights the importance of data scientists and the necessity of combining data analysis with business. When the hardware and infrastructure are available to generate massive data, someone needs to turn a large number of scattered data into structured data for analysis, and integrate and clean it up to form a result data set.
talent radar is a typical example. Based on the network data left by each person on the Internet, which contains personal information such as his life track, social words and deeds, and relying on the analysis of these data, his interest map, personality portrait and ability evaluation are stripped from his online behavior. Talent Radar, a talent recommendation platform based on data mining, helps enterprises to achieve more efficient job matching and provide headhunting service. In order to evaluate a technician's professional skills, Talent Radar will use the data such as the number of posts posted on professional forums (such as Github, CSDN, Zhihu, Lilac Garden, etc.), the number of content cited, the influence of the cited people, etc., and build a model through this information to complete the judgment of its professional influence. At the same time, Weibo's data has been fully utilized. The social relationship reflected in it is also one of the factors to judge a person's professional ability. Therefore, judging the professional influence of users' friends on social networks is also a key point in talent radar recommendation system. At the same time, even if the recommended person's personal ability is difficult to meet the professional needs, if he has a good friend relationship, he can also serve as a suitable "recommender" to spread the task to the next level. Different users have different behavior habits on social networks, such as the time rule of sending Weibo and the length of time in professional forums. These behavior patterns can be used to judge their working hours and see if they meet the corresponding job requirements. Through the integration and analysis of various data sources, talent radar can not only help enterprises improve the efficiency of talent recruitment on the premise of saving costs. Compared with the traditional headhunting business, it can screen talents more widely and objectively by using group intelligence, and it can also avoid the false performance of some job seekers in direct interview to some extent because of its passive measurement. Its current customers include Taobao, Microsoft, Baidu and other well-known enterprises.
in December 213, Amazon obtained a new patent for "anticipatory shipping", which enabled the company to start delivering goods even before customers clicked "Buy". This technology can reduce delivery time and reduce the number of times consumers visit physical stores. In the patent document, Amazon said that the time delay between ordering and receiving goods "may weaken the enthusiasm of customers to buy goods from e-commerce." Amazon pointed out that it will predict the products that customers in a specific area may buy but have not ordered according to earlier orders and other factors, and package and send these products. According to the patent, these pre-delivered goods are stored in the delivery center or truck of the courier company before the customer places an order. When predicting the "expected delivery" products, Amazon may consider customers' past orders, product searches, wish lists, contents of shopping carts, returns, and even the length of time the customer's mouse cursor stays on a certain product. This patent shows that Amazon hopes to make full use of the massive customer information it has to form a competitive advantage.
the most essential application of big data lies in prediction, that is, analyzing certain features from massive data, and then predicting what may happen in the future. When different data streams are integrated into a large database, the breadth and accuracy of prediction will be greatly improved.