Five basic aspects of big data analytics

1 , visual analysis

Big Data analysis users have big data analysis experts, but also the average user, but they are the most basic requirements for big data analysis is visual analysis, because visual analysis can visualize big data features, and can be very easy to be accepted by the reader, as the picture to speak as simple as clear.

2 , data mining algorithms

The core of big data analysis is data mining algorithm, various data mining algorithms based on different data types and formats in order to more scientifically show the characteristics of the data itself, it is because these are recognized by the world statisticians of various statistical methods (can be called truth) in order to penetrate the data inside, Discover the value of recognition. Another aspect is because these algorithms for data mining can deal with big data more quickly, and if an algorithm takes years to come to the conclusion, the value of the big data will not be mentioned.

3 , predictive analytics capabilities

One of the final areas of application for big data analytics is predictive analytics, digging out features from big data, and creating models that can then be used to bring new data to the next generation and predict future data.

4 , semantic engine

Big data analysis is widely used in network data mining, from the user's search keywords, tag keywords, or other input semantics, analysis, to determine user needs, so as to achieve a better user experience and advertising matching.

5 , data quality, and data management

Big data analysis is inseparable from data quality and data management, high-quality data and effective data management, both in academic research and in commercial applications, to ensure that the results of the analysis are true and valuable. Big data analysis is based on the above five aspects, of course, more in-depth big data analysis, there are many more features, more in-depth, more professional big data analysis methods.

Five basic aspects of big data analytics