The definition of data might at first glance seem prosaic, but formulating a definitive and useful definition is surprisingly difficult. This question is important because of the protection given to data in law and ethics. Healthcare data are universally considered sensitive (and confidential), so it might seem that the categorisation of less sensitive data is relatively unimportant for medical data research. This paper will explore the arguments that this is not necessarily the case and the relevance of recognizing this.

The categorization of data and information requires re-evaluation in the age of Big Data in order to ensure that the appropriate protections are given to different types of data. The aggregation of large amounts of data requires an assessment of the harms and benefits that pertain to large datasets linked together, rather than simply assessing each datum or dataset in isolation. Big Data produce new data via inferences, and this must be recognized in ethical assessments. We propose a schema for a granular assessment of data categories. The use of schemata such as this will assist decision-making by providing research ethics committees and information governance bodies with guidance about the relative sensitivities of data. This will ensure that appropriate and proportionate safeguards are provided for data research subjects and reduce inconsistency in decision making.

乍一看，数据的定义似乎比较琐碎，但制定明确而有用的定义却非常困难。由于对法律和道德方面的数据给予了保护，因此该问题很重要。医疗保健数据被普遍认为是敏感的（并且是机密的），因此似乎不太敏感的数据的分类对于医学数据研究而言相对而言并不重要。本文将探讨并非一定如此的论点以及认识到这一点的相关性。

数据和信息的分类要求在大数据时代重新评估，以确保为不同类型的数据提供适当的保护。汇总大量数据需要评估与链接在一起的大型数据集有关的危害和利益，而不是简单地单独评估每个数据或数据集。大数据通过推断产生新数据，这必须在道德评估中得到认可。我们为数据类别的精细评估提出了一种方案。这样的图式的使用将通过为研究伦理委员会和信息治理机构提供有关数据相对敏感度的指导，来协助决策。