But in every data-gathering project (IoT, Big Data, location-based data, etc.) the question arises: 'who actually owns the data?' And, accordingly, to what extent are the analysers allowed to handle the results of their own analyses?
In this session, we heard from experts in the field about the possibilities and the pitfalls. First, seasoned technology lawyers explained the different legal angles. Then, company TVH revealed how it handles data issues in its day-to-day operations. Presentations from the event are available, exclusively for Beltug members (after login):
Ownership of data is not a self-evident concept, Tom De Cordier, Partner – Data Privacy, Technology, Telecoms at CMS, began. The term ‘Big Data’ has only been used for about a decade, but the technique is much older. Tom took us through the ‘4 V's’ of Big Data:
His colleague, Julie Bossaert, Associate – Data privacy, Technology, Telecoms at CMS, then explained that many Big Data projects involve non-personal data (fully anonymised). In those cases, the data protection rules (such as GDPR) don't apply.
Content, purpose and impact are key words for determining whether data is personal or not. A common misunderstanding is that data is only ‘personal’ if it can be linked directly to a real person’s name. That doesn't always need to be the case.
Keep in mind though: 'fully anonymised' is an elastic concept: what is anonymised today might not be so in the future.
A pillar of any Big Data project is purpose limitation - what are the purposes of your project and what are the legal grounds? Another major question is the proportionality of the data you collect: including the requirement to limit the collection of personal data to what is directly relevant and necessary to accomplish the specified purposes.
Transparency is also an essential part of your Big Data road – but not always a smooth ride, Julie emphasised (slide 11).
Next, Kalman Tiboldi, Chief Business Innovation Officer at TVH Group took the floor, sharing how the world of forklifts and industrial spare parts is increasingly digitised. He mentioned, for example, the digital twin - the virtual mirror image of a product, which can be used in predictive maintenance.
TVH's IoT solutions are an extensive source of Big Data: Kalman explained how the machines produce raw data. But then decisions need to be taken regarding how to process and what to do with it. Digital strings are transformed into recognisable, tagged information - the enrichment of the data. It can then be used by several major participants (operators, makers, maintainers, etc.) to serve customers’ needs.
In regards to 'who owns the data?', Kalman highlighted two major points in the current legislation:
The discussion is now on the table at the European Commission. Currently, the tendency is for the entity that holds title to the device recording the data to be considered as the owner of the machine-generated data (MGD), (which covers virtually all IoT). E.g.: if the machine belongs to you, so does the data it generates.
But that doesn't mean you have the right to use that data!
On the other hand, can the manufacturer encrypt the data so that the subsequent owner cannot access it? At this point, the EU says no. But could we see an 'open data' situation in the future?
Kalman concluded by stating that the problem is not so much 'who owns the data?', but rather ‘how can it be accessed and used?’ And regulation is especially needed in regards to 'frand' (fair, reasonable and non-discriminatory) trading, he stated.
@BELTUG-sessie Big Data vs. GDPR. Anonieme data??? Praat enkel nog over probabiliteit van identificatie. Mooi Tom Decordier— alex vanzegbroek (@alexvzb) November 22, 2017
Access to more information about this topic and/or to download the paper is easy and fast, but exclusively for Beltug members (just login to get access).
Beltug gathers a lot of information. Here you find the advantages of Beltug membership
The Beltug Team
Click here to login