Big data requires special techniques to efficiently process large amounts of data over a tolerable amount of time. Technologies applicable to big data include massively parallel processing (MPP) databases, data mining, distributed file systems, distributed databases, cloud computing platforms, the Internet, and scalable storage systems.
5 Essential Aspects of Big Data Analytics
1. Visual analysis
Big data analysis users include big data analysis experts and ordinary users, but the most basic requirement for big data analysis for both of them is visual analysis, because visual analysis can intuitively present the characteristics of big data and can be easily analyzed. Readers' acceptance is as simple and clear as looking at the pictures and talking.
2. Data mining algorithm
The theoretical core of big data analysis is data mining algorithms. Various data mining algorithms are based on different data types and formats to more scientifically present the characteristics of the data itself. It is precisely because of these that they are recognized by statisticians around the world. Various statistical methods (which can be called truth) can go deep into the data and dig out recognized values. Another aspect is that these data mining algorithms can process big data more quickly. If an algorithm takes several years to reach a conclusion, then the value of big data will be impossible to say.
3. Data quality and data management
Big data analysis is inseparable from data quality and data management. High-quality data and effective data management can ensure the authenticity and value of analysis results, whether in academic research or commercial applications. The basis of big data analysis is the above five aspects. Of course, if you go deeper into big data analysis, there are many more distinctive, in-depth, and professional big data analysis methods.
4. Predictive analysis capabilities
One of the final application fields of big data analysis is predictive analysis, which involves mining characteristics from big data and scientifically establishing models. Then new data can be brought in through the models to predict future data.
5. Semantic engine
Big data analysis is widely used in network data mining. It can analyze and judge user needs from users' search keywords, tag keywords, or other input semantics, thereby achieving better user experience and advertising matching.
Looking back at the history of data analysis, data science and technology have developed rapidly, with various new tools and new languages emerging one after another. People's ability to process data and obtain information can be said to have exploded.