It is now nearly a decade since the terms “big data” (Manyika et al., 2011) and “AI” (Levy, 2010) hit the mainstream of business thought, promising a combination of new types of insight and greater efficiency. In this editorial, I reflect on the impact of these trends and outline two competing narratives around the direction of the research and insight sector for the next decade. When considering the impact of big data upon the research and insight sector it was common to suggest that, by 2020, major research firms would be replaced by data platform companies such as Google, Facebook, and Twitter (Lewis, 2012). In an environment where these powerful firms controlled the majority of behavioral data on consumers, and where data analysis is increasingly automated, conventional wisdom was that these firms would be winners. Although this prediction has not fully come to pass, the current rapid digitization of society and the subsequent growth in digital data sources have done nothing to reduce the power of this narrative. In its purest form this is a narrative of decline for the “traditional” research sector with change driven by technological and commercial disruption. This would lead to a slow devaluation of professional research with trends such as self-service research, better analysis tools, easier access to lower cost samples, and the general discounting of the value of stand-alone qualitative research. Methods that are not efficient—or “scientific”—are relegated. And so on. For research “disruption is coming from every angle” (Rademaker & Joosen, 2018). I counter this with a different narrative, one that reflects the messy reality of our new digital world and the emerging social and political complexity that surrounds the collection and use of personal data. In turn, this narrative offers an opportunity to conceptualize a different future for research practice. To support this narrative, I offer four examples of how this complexity challenges the concept of unfettered growth of big data as an insight tool, and the dominance of “big tech.” First, given the understandable prominence of COVID-related news it would be easy to miss that 2020 has been a busy year around the world for legislation seeking to protect consumer data. Many research practitioners have articulated the ways in which General Data Protection Regulation (GDPR) is problematic. For example, a research note by Richard Webber (2020) in this issue highlights the challenges created by data protection regulations when collecting ethnicity data required for analyzing COVID mortality rates. However, it is increasingly clear that despite perceived limitations, the demand for greater protection over the use of personal data has resulted in GDPR being

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2020 年代的研究：从大数据到更大的监管

“大数据”（Manyika 等人，2011 年）和“人工智能”（Levy，2010 年）这两个术语成为商业思想的主流，承诺将新的洞察力和更高的效率结合起来，现在已经近十年了。在这篇社论中，我反思了这些趋势的影响，并围绕未来十年的研究和洞察力领域的方向概述了两种相互竞争的叙述。在考虑大数据对研究和洞察领域的影响时，人们普遍认为，到 2020 年，主要的研究公司将被谷歌、Facebook 和 Twitter 等数据平台公司所取代（Lewis，2012）。在这些强大的公司控制着消费者的大部分行为数据并且数据分析越来越自动化的环境中，传统观点认为这些公司将成为赢家。尽管这一预测尚未完全实现，但当前社会的快速数字化和随后数字数据源的增长并没有削弱这种叙述的力量。以最纯粹的形式，这是“传统”研究部门衰落的叙述，技术和商业中断推动了变革。这将导致专业研究的贬值缓慢，其趋势包括自助研究、更好的分析工具、更容易获得低成本样本以及对独立定性研究价值的普遍折扣。效率不高或“不科学”的方法被降级。等等。对于研究，“颠覆来自各个角度”（Rademaker 和 Joosen，2018 年）。我用不同的叙述来反驳这一点，一个反映了我们新数字世界的混乱现实以及围绕个人数据收集和使用的新兴社会和政治复杂性。反过来，这种叙述提供了一个机会来概念化研究实践的不同未来。为了支持这种说法，我提供了四个例子，说明这种复杂性如何挑战大数据作为洞察工具不受限制地增长的概念，以及“大技术”的主导地位。首先，鉴于与 COVID 相关的新闻的重要性是可以理解的，因此很容易错过 2020 年在全球范围内对于寻求保护消费者数据的立法来说是忙碌的一年。许多研究从业者已经阐明了通用数据保护条例 (GDPR) 存在问题的方式。例如，理查德·韦伯 (Richard Webber)（2020 年）在本期的研究报告强调了在收集分析 COVID 死亡率所需的种族数据时数据保护法规所带来的挑战。然而，越来越清楚的是，尽管存在局限性，但对个人数据使用的更大保护的需求导致 GDPR 被