In today's era of big data, three points of technology, seven points of data, whoever gets the data wins the world, and data is the new crude oil. Even if the same crude oil is obtained, the value that different companies can extract from crude oil is different due to differences in technology. Generally speaking, for the value of big data, everyone is familiar with data management, data-driven refined operations, etc.
These are mainly based on analysis and application scenarios. In addition, big data can also use the capabilities of AI. Make the most of the value. 1. The relationship between big data and AI We know that the most important way to implement AI is machine learning, and the essence of machine learning is to enable machines to have job title email list human learning ability or thinking ability after a large amount of data analysis, mining and model training. It can also be understood that data is the raw material, and AI is Production tools, AI and data combine to create new productivity. Of course, this process is also inseparable from the powerful computing resources provided by cloud computing. Many people also call this structural relationship
ABCI, namely: AI, BigData, Cloud, and IOT. 2. AI helps data analysis For students who have been working as data visualization product data product managers, have you ever encountered such confusion: "Abstract what you do is actually indicators + dimensions + visual charts, and what you do is just a change in business scenarios", if you want to Where should innovative breakthroughs go? Similarly, to do self-service BI products, is it data modeling, drag-and-drop analysis, configurable visual dashboards or the construction of large screens? The needs of the corners, this time, how to plan the next step of the product? In the article "How does data visualization have a soul", I have shared in detail that data visualization from the three levels of what data is, why, and how to do it, and what is the basic big data processing, calculation, query, display, As for why and how, more people's analysis ideas and processes need to be integrated into the product.