Five Basic Aspects of Big Data Analytics

1. Analytic Visualizations Whether it's for data analytics experts or regular users, data visualization is the most basic requirement for data analytics tools. Visualization can intuitively show the data, let the data speak for itself, and let the audience hear the results.

2. Data Mining Algorithms Visualization is for people to see, data mining is for machines. Clustering, segmentation, isolated point analysis and other algorithms allow us to go deep inside the data and mine the value. These algorithms not only deal with the volume of big data, but also the speed at which it can be processed.

3. Predictive Analytic Capabilities Data mining allows analysts to understand the data better, and predictive analytics allows analysts to make some predictive judgments based on the results of visual analytics and data mining.

4. Semantic Engines We know that the diversity of unstructured data brings new challenges in data analysis, and we need a series of tools to parse, extract, and analyze data. Semantic Engines need to be designed to intelligently extract information from "documents".

5. Data Quality and Master Data Management

Data quality and master data management are management best practices. Working with data through standardized processes and tools ensures a pre-defined, high-quality analysis.

If Big Data is indeed the next big technological innovation, we would do well to focus on the benefits it can bring us, not just the challenges.