What are the differences between big data news and traditional data news?

What is the difference between big data analysis and traditional data analysis? Many people may think that big data analysis is mysterious, but in fact big data analysis is not so mysterious.

What is the difference between big data analysis and traditional data analysis? Many people may think that big data analysis is mysterious, but in fact big data analysis is not so mysterious.

First, there is an essential difference between big data analysis and traditional analysis, that is, traditional analysis is based on structured and relational data. And often take a small data set to predict and judge the whole data. But now is the era of big data, and the concept has completely changed. Today's big data analysis is to directly store and manage the entire data set.

Secondly, the previous analysis is small sample analysis, so it is often necessary to predict the characteristics of the whole data set with small sample books, which determines that the collected small samples must be of high quality, otherwise the prediction results will be greatly biased. The current big data analysis is the analysis of complete sets of data, so we should tolerate some noise of the data.

Thirdly, the original traditional data analysis is to analyze and predict the global data on the basis of small sample data analysis. Therefore, the reasoning process of causality is often used in the whole process of prediction and analysis. In the current big data analysis, causality is not the focus, but the analysis based on the whole data set. What enterprises need to understand is that customs clearance

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What is the difference between data analysis in the era of big data and the past?

China | 2014 February 2 1

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What is the difference between big data analysis and traditional data analysis? Many people may think that big data analysis is mysterious, but in fact big data analysis is not so mysterious.

What is the difference between big data analysis and traditional data analysis? Many people may think that big data analysis is mysterious, but in fact big data analysis is not so mysterious.

First, there is an essential difference between big data analysis and traditional analysis, that is, traditional analysis is based on structured and relational data. And often take a small data set to predict and judge the whole data. But now is the era of big data, and the concept has completely changed. Today's big data analysis is to directly store and manage the entire data set.

Secondly, the previous analysis is small sample analysis, so it is often necessary to predict the characteristics of the whole data set with small sample books, which determines that the collected small samples must be of high quality, otherwise the prediction results will be greatly biased. The current big data analysis is the analysis of complete sets of data, so we should tolerate some noise of the data.

Thirdly, the original traditional data analysis is to analyze and predict the global data on the basis of small sample data analysis. Therefore, the reasoning process of causality is often used in the whole process of prediction and analysis. In the current big data analysis, causality is not the focus, but the analysis based on the whole data set. What enterprises need to know is the analysis of relevance and the characteristics of regularity. For example, beer tends to rise with the sales of diapers, so under the analysis of big data, we don't need to know why the sales of beer and diapers are growing simultaneously, just need to know that diapers and beer are growing simultaneously. Based on this result, we can formulate many business strategies and marketing tools.

Fourth, the current data are often massive, especially many emerging data, which are very time-sensitive, breaking the lagging means of collecting, cleaning, storing and analyzing the original data. Many analysis requirements are often real-time and need to be collected and analyzed at the same time, which is another major feature of big data analysis.