Tokenization of Data & Economies

As we move further into the Fourth Industrial Revolution, the role of data becomes increasingly important. Just as data is exploding in volume from the internet of things, mobile and other sources — so-called Big Data — so too is the pace of the technical transformation expanding at an exponential rate, making our interaction with machines and information more common, natural and powerful. Data is the basis for many revolutionary AI applications, from gene-sequencing to robotics, to modelling climate change, developing autonomous vehicles and improving agricultural yields.

Subsets of AI, machine-learning algorithms and especially deep learning neural networks require huge datasets for training. The more data, the more accurate their predictions. The more X-rays of tumors a neural network can analyse, the more likely it will accurately characterize the next one it is shown. Less data means less accuracy — which, realistically, means a disadvantage.

Digital information has become so entrenched in all aspects of our lives and society, that the recent growth in information production appears unstoppable. Each day on Earth we generate 500 million tweets, 294 billion emails, 4 million gigabytes of Facebook data, 65 billion WhatsApp messages and 720,000 hours of new content added daily on YouTube.

In 2018, the total amount of data created, captured, copied and consumed in the world was 33 zettabytes (ZB) — the equivalent of 33 trillion gigabytes. This grew to 59ZB in 2020 and is predicted to reach a mind-boggling 175ZB by 2025. One zettabyte is 8,000,000,000,000,000,000,000 bits.

Today a few large corporations are favored, as they are the best positioned to collect and process vast quantities of data from ecommerce, digital assistants and other sources. Several countries also have inherent advantages due to their sizeable populations, with vast pools of collectable data about many facets of life, from driving behaviors to cell phone use to internet browsing. This leads to an imbalance of power and wealth, caused by information being in the hands of the few, which gives them the opportunity to use these large volumes of data to draw meaningful inferences and achieve economies of scale.

What about countries with smaller populations, or without the resources to develop state-of-the-art technologies? Some of these countries may be small but are otherwise wealthy; they’re only disadvantaged by a lack of data. Other countries may be historically behind on common measures of development. All of them are at risk of falling from their current positions simply by lacking accessible and relevant data. Countries without this data have the risk of being left out. They would be relegated to providing the raw resources of production without any actual means of production.

In keeping with that, we find that the tokenization of data and hence economies can be factored within the following 6 key issues.

· Data Collection and Communication

· Assessing Impact and Implications

· Business of Data

· Data and Algorithm Ethics

· Data Governance and Sharing

· Blockchain and leveraging data

Data collection and communication: Technological advances are expanding the use of so-called big data, or complex digital information that can be quickly analysed in unprecedented quantities in order to discern patterns and trends. However, there are emerging issues that can make the collection and application of this data problematic — related to quality, security, storage, and privacy. The United Nations Global Working Group on Big Data for Official Statistics was established in 2014, to address these issues by creating standardized methods for capturing, managing, and processing data securely and ethically

Data governance and sharing: The sharing of data, the use of software necessary to generate and process it, and the models that are trained from it are becoming key elements of any research process. Data sharing enables the verification of published scientific results and the reuse of data — something that is ideally put into practice by governments, companies, and academic researchers in order to accelerate discovery and make timely, informed decisions. Ultimately, sharing data with an electorate, shareholders, and the scientific community provides greater accountability and transparency. It is already generally understood that data associated with publicly-funded research should be made available to the public, whenever possible. But there should also be incentives for private sector entities to share more of their data, in order to help advance related research and bolster accountability. Any form of data sharing should include guarantees for its owners to retain their rights to any that may be shared, and to ensure that the data are shared responsibly — with the aid of privacy-preserving methods or access controls when needed.

Data and Algorithm ethics: Predictive modelling is one of the most common, and promising, applications of modern data science. In many disciplines and endeavours we can apply models that support or automate decisions that have traditionally been performed by human experts — who are often expected to follow ethical principles. When it comes to medical diagnosis, for example, physicians are expected to follow the principles of beneficence (balancing benefits against risks) and non-maleficence (avoiding harm). These principles are enforced on multiple fronts, from professional training, to fiduciary duties, to regulation. Responsible data science involves developing and deploying predictive models that are subject to the same — or even greater — degree of ethical scrutiny as their human counterparts. On the research front, this requires articulating ethical principles tailored to each application, and developing tools to help facilitate and enforce them.

Business of Data: Innovative approaches to data stewardship manage trade-offs while creating inclusive value. Increasing digital connectivity has led to unprecedented volumes of online data. According to IDC, the “global datasphere” will grow from 33 zettabytes in 2018 to 175 zettabytes by 2025 — when three-quarters of the world’s population will interact with data every day, nearly half of all data will be available to the public via the cloud, and nearly a third of it will be provided in real-time to aid decision making. Companies and governments are increasingly using data to try to add value by delivering personalized healthcare, or by building smarter cities and public services. Data has been a particularly useful public health tool during the COVID-19 crisis; at least 25 countries have introduced contact-tracing applications meant to curb its spread. As data increasingly becomes a source of economic value, there is mounting pressure to share and use it in ways that benefit everyone. This means respecting personal freedoms like privacy and security, and actively preventing the use of data to perpetrate human rights abuses or to discriminate. Governments have introduced rules to enforce responsible data use, such as the European Union’s General Data Protection Regulation — which aims to give internet users more control over their personal data.

Expanded access to data could be ideal for all countries. However, the data originates with individual people. Their data individually and collectively is being used and will in the future be used in many instances for commercial purposes, and consequently has value. People should be able to share in the wealth that is created from their data. They should be compensated for this use.

Privacy laws today vary from country to country, and make the collection and dissemination of data across borders very difficult. Guarding privacy is clearly positive. However, if an individual chooses to provide their genetic data and health history for medical research, a mechanism to do so needs to be created for their benefit and for the benefit of larger populations. Of course, the purpose doesn’t always have to be as grand as medical research. The information could simply be our digital dust — including social media posts, the fitness analytics from our smart watch and the driving habits of everyday life.

One possibility to realize this vision is to create an exchange to determine the value of data, much like commodity exchanges. If someone — a country, an alliance of countries, a lab, an enterprise — wants to use data, they could offer tokens as a payment proxy. An exchange could establish the price of the tokens. As data exchanges publish value, people could see the value of their data and make an informed decision about whether to provide this to the exchange. This also needs to be complemented with “intended purpose”, as opposed to the historical trend of “source of origin” regulation of data, which may be enforceable through, for example, blockchain smart contracts.

Overall, we will see a rise in how individuals view and value their data. It will no longer be enough to have universal consent mechanisms, but instead a way to gauge and pay the residual value of the use of this data. We will also see trends towards economies who invest in extensive data capture and regulation mechanisms, being able to bypass others who have not yet made this shift. In either case, the individual response to data usage will by far supersede passing lifetime control over to larger organizations.

This article was produced using information and data from the World Economic Forum’s Strategic Intelligence platform.

Strategic Intelligence is an online digital platform that helps individuals and organizations decipher the potential impacts of accelerating global complexity, while counteracting the misleading and unreliable information that is circulating. The Strategic Intelligence tool helps you understand the global forces at play and make more informed decisions.

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