Big Data Quantity vs. Quality

Increasing adoption of big data technologies brings about the big data dilemmas:


• Quality vs. quantity
• 质量 vs. 数量
• Truth vs. trust
• 事实 vs. 信心
• Correction vs. curation
• 修改 vs. 策展
• Ontology vs. anthology
• 本体论 vs. 选集

Big data technologies are developing fast. Silicon Valley is excited about new capabilities (which very few are using). In my mind, the best thing to do right now is to enable vast and vague data sources that are commingling in the new and immature data stores, and are confined in mature data stores. Companies store more data than they can process or even fathom.

My imagination fails at quintillion rows.  Instead, it paints a continuous loop: data enables analysis, analytics boosts the value of data. How to do this? It starts dawning on market — through information quality and information governance!

大数据科技迅猛发展,硅谷对该项新技术也倍感兴奋(虽使用频率较低)。在我看来,目前我们最好将粗略的大数据资源大量融入尚未发展完善的新型数据存储,使它们一同存在于趋向成熟的数据存储中。通常,公司会储存大量数据,数量之多令人咂舌,已然超过自身的处理能力。而我的幻想,也在那串长长的 1018 显示结果中幻灭。其实,这是一个循环效应:数据帮助人们进行分析,而分析则增加了数据的价值。这一切是如何做到的呢?第一步便是通过信息质量和信息管理在市场上崭露头角!

From: The Gartner

Translated by Sarah, CCJK Technologies Co.,Ltd.

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