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Big data analysis refers to the methods, tools and applications used to collect, process and draw insights from different large and high-speed data centers. These data sets may come from a variety of sources, such as the Web, mobile applications, email, social media, and networked smart devices. They usually represent data generated at high speed and in various forms, ranging from structured (database tables, Excel tables) to semi-structured (XML files, web pages) to unstructured (image, audio files).
Traditional data analysis software cannot support this degree of complexity and scale, which is where systems, tools and applications designed for big data analysis play a role.

Now, you know what big data analysis is. But why is it important? Most importantly, how can understanding and using big data help us?

Data is closely related to our daily life. With the rise of mobile applications, social media and intelligent technologies associated with the Internet of Things (IoT), we now transmit more data than ever before, and the transmission speed is extremely fast. Thanks to big data analysis, organizations can now use this information to quickly improve the way they work and think, and provide value to their customers. With the help of tools and applications, big data can help you gain insights, optimize operations and predict future results.

Big data is important because it can provide insights to help make better decisions. In this way, retailers can polish their targeted advertising activities, and wholesalers can solve the bottleneck in the supply chain. It is also in this way that healthcare providers can discover new methods of clinical care based on patient data trends. Big data analysis provides a more comprehensive data-driven approach to decision-making, thus promoting growth, efficiency and innovation.
Now that you know the importance of big data and the importance of data analysis, let’s explore how big data analysis works.