大数据分析中的误区——基于纽约市的成功实践的案例分析

发布时间：2018-05-21 06:52

本文选题：大数据分析 + 数据的背景　；参考：《情报杂志》2017年04期

【摘要】：[目的/意义]我国有关大数据分析的研究中流行一些说法:数据能讲述故事;要全体不要抽样;要相关不要因果。这些说法可能具有误导性。[方法/过程]通过分析纽约市"市长数据分析办公室"利用大数据分析帮助改善城市部门绩效方面的成功案例,检验上述说法的正确性。[结果/结论]大数据分析不等于让数据自己说话,数据自己是不会说话的。成功的大数据分析首先需要分析人员确定方向、目标,然后根据它们确定分析的对象,有针对性的收集和分析数据。数据够了就好,"要全体不要抽样"不是必要的。大数据分析首先需要了解数据的背景,离开了其所处的背景的数据是没有价值的。大数据分析除了关注数据,还需要关注数据之外的很多因素,包括政治因素。
[Abstract]:Objective / meaning: data can tell stories, do not sample all, and do not correlate with causality in the study of big data analysis in China. These statements may be misleading. [method / process] the validity of the above statement is verified by analyzing the success stories of the New York City Mayor's Office of data Analysis using big data analysis to help improve the performance of the urban sector. Big data analysis is not the same as letting the data speak for itself, the data itself does not speak. Successful big data analysis first requires the analyst to determine the direction, target, and then determine the object of the analysis according to them, collect and analyze the data pertinently. As long as the data is enough, it is not necessary to ask for all to be sampled. Big data analysis first needs to understand the background of the data, the data without its background is of no value. In addition to focusing on data, big data analysis needs to focus on a number of factors, including political ones.
【作者单位】：澳门理工学院;
【分类号】：G201

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