Big data does not only mean big data, but more importantly, big data should be analyzed, and only through analysis can we get a lot of intelligent, in-depth and valuable information.

The following Hefei IT training/introduces five basic aspects of big data analysis.

1. Visualization and analysis of data analysis experts or ordinary users, data visualization is the most basic requirements of data analysis tools.

Visualization can intuitively show the data, let the data speak for themselves, so that the audience to hear the results.

2. Data mining algorithm visualization is for people to see, data mining is for the machine to see.

Clustering, segmentation, isolated point analysis and other algorithms allow us to go deeper into the data and mine the value.

These algorithms have to handle not only the volume of big data, but also the speed of big data.

3. Predictive Analytics CapabilitiesData mining allows analysts to understand data better, while predictive analytics allows analysts to make some predictive judgments based on the results of visual analytics and data mining.

4. Semantic engineSince the diversity of unstructured data brings new challenges in data analysis, a series of tools are needed to parse, extract, and analyze the data.

Semantic engines need to be designed to intelligently extract information from "documents".

5. Data quality and data managementData quality and data management are some of the best practices in management.

Handling data through standardized processes and tools ensures a pre-defined, high-quality analysis.