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The important influence of big data on thinking mode
2. Efficiency rather than accuracy: In the past, the sampling analysis method was used in scientific analysis, which required higher accuracy, because the sampling method only aimed at the analysis of some samples, and the analysis results would amplify the error after being applied to the complete set of data. In other words, many small mistakes will cause big mistakes. This leads the traditional algorithm to pay more attention to improving accuracy rather than efficiency. In the era of big data, the full sample analysis method is adopted instead of sampling method, so there is no error amplification. Therefore, big data has the characteristic of "second-level correspondence", which requires massive data to be analyzed in a few seconds to get real-time results, otherwise it will lose data value.
3. Related rather than causal past. The purpose of data analysis, on the one hand, is to explain the development mechanism behind things; On the other hand, it is used to predict what may happen in the future. Now in the era of big data, causality is no longer important, and people turn to "correlation" instead of causality.
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