European Commission: “The guidance fulfils an obligation in the Regulation on the free flow of non-personal data (FFD Regulation), which requires the Commission to publish a guidance on the interaction between this Regulation and the General Data Protection Regulation (GDPR), especially as regards datasets composed of both personal and non-personal data. It aims to help users – in particular small and medium-sized enterprises – understand the interaction between the two regulations.
In line with the existing GDPR documents, prepared by the European Data Protection Board, this guidance document aims to clarify which rules apply when processing personal and non-personal data. It gives a useful overview of the central concepts of the free flow of personal and non-personal data within the EU, while explaining the relation between the two Regulations in practical terms and with concrete examples….
Non-personal data are distinct from personal data, as laid down in the GDPR Regulation. The non-personal data can be categorised in terms of origin, namely:
- data which originally did not relate to an identified or identifiable natural person, such as data on weather conditions generated by sensors installed on wind turbines, or data on maintenance needs for industrial machines; or
- data which was initially personal data, but later made anonymous.
While the guidance refers to more examples of non-personal data, it also explains the concept of personal data, anonymised and pseudonymised, to provide a better understanding as well describes the limitations between personal and non-personal data.
What are mixed datasets?
In most real-life situations, a dataset is very likely to be composed of both personal and non-personal data. This is often referred to as a “mixed dataset”. Mixed datasets represent the majority of datasets used in the data economy and commonly gathered thanks to technological developments such as the Internet of Things (i.e. digitally connecting objects), artificial intelligence and technologies enabling big data analytics.
Examples of mixed datasets include a company’s tax records, mentioning the name and telephone number of the managing director of the company. This can also include a company’s knowledge of IT problems and solutions based on individual incident reports, or a research institution’s anonymised statistical data and the raw data initially collected, such as the replies of individual respondents to statistical survey questions….(More)”.