Conclusions

The limited availability of skilled IT professionals is a bottleneck to the exploitation of big data technologies and applications. This calls for better and broader training and education. Only through education, research and outreach activities can Switzerland draw national and international talent to the field, further strengthening its already strong position in big data.

Universities, including universities of applied sciences, should enrol more IT students and offer tailored programmes in big data at the bachelor, master, and doctoral levels. Courses on pertinent aspects of big data should be made available in disciplines that make frequent use of big data. This may increase gender diversity in IT since big data is broader than core computer science, requiring interdisciplinary skills that also encompass societal, commercial, and legal aspects.

Continued educational offerings on big data should be expanded for IT professionals and apprentices, covering all aspects of the big data pipeline. Expertise in data collection and preparation is needed, including skills in integrating, cleaning, and linking data. Further, there is a need for skilled use of infrastructures for storing, managing, and extracting value from data, which calls for training on the rapidly evolving open-source libraries available for these purposes. It is important to master tools that visualise the outcomes of data analyses. And finally, professionals must understand the legal, regulatory, and ethical issues surrounding big data. This can be achieved by offering interdisciplinary programmes.

Since everyone in society is affected by the rapid developments in big data, education in big data must also be intensified and continuously modernised in schools, high schools, and professional apprenticeships. It would cover data science and its various facets, including law, ethics, and societal issues.

Investigators in research and development projects will increasingly need legal or ethical insight to decide which data can be used or shared and to design appropriate services. If neither the project participants, their immediate peers nor their organisation have the necessary legal and ethical expertise, the cautious decision is to not use or share available data – even if the use or sharing would be valuable and perfectly legal and ethical. As a result, a gap opens between real-world settings and the investigated experimental settings, with negative consequences for example in areas such as personalised medicine, public health, or sustainability.

This type of problem is particularly acute at universities, public research institutions, and applied science institutes, as research opportunities are lost, and education programmes become less attractive. The situation is further exacerbated by the risk of negative media coverage and limited protection of university employees. Project managers need a service, either in-house or within the public administration, that provides them with competent, trustworthy, dependable, and quotable advice on legal and ethical issues as well as on communication.

How should activities be designed to be legally compliant and ethically sound? What are the reasons for possible limitations? And how can activities be communicated transparently, particularly in case of contentious public debate? A service could provide advice on such questions, easy-to-follow guidelines for practical implementation, and easy-to-understand explanations of legal and ethical considerations. In addition, it should offer audits of data usage and sharing that address the specific challenges of a project, carried out by qualified, possibly specifically certified, auditors.

The functioning and outcomes of big data applications often lack transparency, which can generate mistrust. This can be countered on the one hand by defining pertinent properties of big data applications such as fairness, bias, discrimination, explainability, transparency, and accountability. And on the other hand by establishing means that enable application providers to show that their applications possess such properties. To build reasonable trust in big data applications, and thus to enable more widespread use and increase their benefits, it is recommended to establish means to certify relevant application properties.

Numerous applications of big data are already found in non-sensitive domains, often resulting in improved performance and efficiency and in cost savings. However, they also hold potential to enable considerable benefits in sensitive settings, for example in law enforcement or social and health services. Processes for regulatory compliance and certification of compliance already exist in important areas, such as energy, construction, or trade. It is recommended to extend them to also encompass properties specific to big data applications.

The collection and use of high-quality health data is a key enabler of evidence-based and personalised medicine. It improves diagnoses, allow early detection of high-risk patients, enables the discovery of interactions between diseases or drugs and the identification of risk factors of disease progression, and enhances patient compliance with treatments. However, to enable the use of big data analytics in healthcare, the legal, ethical, administrative, financial or IT barriers must be overcome. The Swiss health system is relatively decentralised and largely organised at the local level, which creates challenges for legislation and administration seeking to enable nationwide data collection and use. From the perspective of data science, this organisation can result in fragmented, local IT solutions with reduced security and quality. The access to data in the right place at the right time and the interfaces among the actors must be improved considerably.

Switzerland has established electronic patient dossiers (EPDs) and quality measures in the health sector that demonstrate the possibility for federal initiatives in this domain. In its current form, the EPD amounts to an unstructured collection of often scanned documents that lacks structured summaries and indices, harmonisation of data formats, semantic interoperability and standardised terminology. Furthermore, it lacks compatibility with automated data analysis requirements and standardisation guidelines. Still, the EPD has enormous potential if an anonymisation layer is introduced to enable collective patient data analyses. There are privacy-preserving technologies that can help in this regard, such as data enclaves, where data is processed without being directly accessible from the outside, or federated analytics that enable local processing of distributed data to avoid compromising security. However, such technologies need further development and require committees with the relevant ethical, legal, scientific, and statistical knowledge to supervise use of the data.

There is enormous potential for the deployment of big data technologies in many sectors of industry and business as well as in public administration. Specifically, deployment of big data technologies coupled with data collection can be applied to public policy making and the monitoring of policy effectiveness, in sectors such as health, energy, finance, transport, spatial planning or sports. Among other beneficial effects, data collection and evaluation enable effectiveness comparisons with policies in other countries as well as identifying improved policies and best practices. This necessitates careful consideration of privacy and security aspects and their proper handling.

In return, data used for the design and evaluation of policies should be publicly available, which again calls for privacy and anonymisation considerations. Altogether, the relevant offices and entities must be strengthened and expanded to handle the increasing workload and necessary communication. Procedures and mechanisms for implementing this cooperation need to be established and developed.

The development of big data applications is accelerated by the availability of relevant data. However, the collection of data is generally associated with substantial costs. To guarantee continued collection of valuable data by researchers, it is important to ensure a good cost-benefit ratio. Thus, data collection should be incentivised when it comes with substantial costs, for example by allowing delayed publication to enable initial value creation by data collectors before having to make the data available (akin to the role of patenting as a means of encouraging investment in making inventions), by ensuring that funding agencies cover the costs of complying with the FAIR principles for scientific data management that may well extend beyond the end of a project, and by requiring data publication as a prerequisite for publishing associated research results. A nuanced approach to data sharing is needed that recognises that not all data is equally valuable so that resources are spent wisely.

Applications development can also be accelerated by having benchmarks that encompass anonymised datasets and use cases representing common scenarios in the targeted application domains. Such benchmarks can serve as references for application development and testing and for improving the accuracy and predictive capabilities of algorithms. They can enable more effective large-scale deployment and validation of big data applications.

The increased availability of open-source software can accelerate value creation from data. Thus, the creation of incentives for the sharing of tools as open-source software is recommended. For example, impact on society through the development of open-source software with wide-scale adoption should be rated on par with citation counts in the evaluation of scientific careers. This would not only promote an important public service, but might also attract international talent to Switzerland and become an overall important element in the digitalisation of Switzerland.

Applications of big data have a profound and broad impact on society. Regulation can accelerate responsible value creation while limiting adverse effects. A more proactive approach to regulation therefore promotes responsible value creation, facilitates competition and innovation, and serves democracy better.

The big data divide – the asymmetric relationship between those who collect, store, and analyse big data and those subjected to data collection – is an inevitable consequence of a society that values freedom and diversity. Instead of trying to eliminate it, it is recommended that legislation identifies realistic harms that could result from the big data divide and develops legal safeguards for those who are disadvantaged.

Successful big data applications call for trust and acceptance. When putting in place frameworks under which data can be collected, analysed and used, one should not only insist on the creation of (self-regulatory) standards that balance the interests of companies and customers, but should also empower customers to make informed decisions.

Overall, it is of high importance to develop legal safeguards to compensate harm caused by the big data divide by setting standards for data collection, sharing, and analysis, enabling the protection of groups rendered vulnerable due to the deployment of big data technologies.

Regulation based on the sovereignty of states over a finite physical space falls short when it comes to big data. Instead, international coordination and cooperation is needed to safeguard the security of digital infrastructures and the privacy and other data-related rights of citizens. Nonetheless, efforts can and should also be made at a national level to ensure data privacy. National and cantonal policy makers and administrations as well as universities and scientists are required to strengthen and complement the national legal framework.

A national data protection and data-related rights agenda includes numerous actors and topics. Therefore, it is important to encourage the creation of strong ties among the many stakeholders from the wide range of disciplines involved in the creation of big data applications. In addition, a methodology should be developed for establishing best practices for gathering and anonymising data, providing secure storage data, and ensuring privacy-preserving value extraction. For example, the development of data enclaves for highly sensitive data appears to be a valuable option to ensure privacy. Furthermore, the existing concept of informed consent should be complemented by specific protection mechanisms.

Various privacy-preserving techniques are being researched, but their real-world deployment will take time. Some of them could contribute to a national data privacy and data-related rights strategy, such as for example the appointment of data trustees protecting personal data of individuals, or the implementation of fairness criteria related to big data analytics to avoid discrimination. To facilitate the daily handling of privacy-related issues, further data protection guidelines should be developed. An expert competence centre such as, or within, the National Cyber Security Centre, offering a public service for addressing legal questions around privacy issues in big data deployment could be also established.

The globalisation of data flows and the increased deployment of big data-based applications make it necessary to establish harmonised cross-border regulatory frameworks that cover international trade. While negotiations in the World Trade Organization (WTO) are still ongoing, bilateral and regional (preferential) trade agreements increasingly regulate trade in digital trade and services, as well as data flows. The new rules often encompass aspects of data protection, cybersecurity, and commercial confidentiality.

While Switzerland engages actively in negotiations, further support is recommended. Similarly, it is highly recommended that Switzerland provides its input in the ongoing development of the OECD Guidelines on Responsible Business Conduct (RBC). Finally, Switzerland played an important role as promoter of the UN Internet Governance Forum in Geneva; in view of the increased tensions in the digital world, it is recommended that Switzerland makes efforts to help avoid fragmentation in the regulation of the data-driven economy.