Life beyond big data: governing with little analytics
Life beyond big data: governing with little analytics
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DOI：
10.1080/03085147.2015.1043793
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发表时间：
2015-07
影响因子：
3.4
通讯作者：
Louise Amoore;V. Piotukh
中科院分区：
文献类型：
--
作者：
Louise Amoore;V. Piotukh
大数据在世纪的兴起标志着人们对什么是感兴趣或关注的统计概念的重大突破。数字数据的巨大扩展与高级分析算法的发展密切相关，这些算法可以理解数据。知识发现技术的出现为分析提供了从大量数据中的聚类和模式中导出感兴趣的对象或主题的能力，否则人类阅读是察觉不到的。因此，对于当代的认知和治理形式来说，大数据中的“大”的规模不如我们所说的“小分析”重要。根据亨利·柏格森（Henri Bergson）对感知形式的分析，这些感知形式“切割”出一系列与整体分离的数字，我们提出分析算法是感知的工具，没有它，大数据的广度将无法理解。分析技术将人类的注意力和决策集中在特定的人和感兴趣的事物上，同时取消或丢弃了从中提取的大部分物质背景。在摄取、分割和记忆的算法过程之后，我们阐明了分析引擎的使用如何改变了分析和知识的性质，从而改变了经济、社会和政治生活的治理性质。
Abstract The twenty-first-century rise of big data marks a significant break with statistical notions of what is of interest or concern. The vast expansion of digital data has been closely intertwined with the development of advanced analytical algorithms with which to make sense of the data. The advent of techniques of knowledge discovery affords some capacity for the analytics to derive the object or subject of interest from clusters and patterns in large volumes of data, otherwise imperceptible to human reading. Thus, the scale of the big in big data is of less significance to contemporary forms of knowing and governing than what we will call the little analytics. Following Henri Bergson's analysis of forms of perception which ‘cut out’ a series of figures detached from the whole, we propose that analytical algorithms are instruments of perception without which the extensity of big data would not be comprehensible. The technologies of analytics focus human attention and decision on particular persons and things of interest, whilst annulling or discarding much of the material context from which they are extracted. Following the algorithmic processes of ingestion, partitioning and memory, we illuminate how the use of analytics engines has transformed the nature of analysis and knowledge and, thus, the nature of the governing of economic, social and political life.