The data economy is the economy of tomorrow being built today. It is one piece in the big puzzle of how we ensure that our future is fair, sustainable and prosperous. It is relevant to companies, individuals and societies in all regions, industries and roles. For businesses, new ecosystem business models and networked data collaborations will distribute shares in benefits and opportunities more fairly.
The data economy is where the collection and use of data are a key part of economic activity. It is the economy of tomorrow being built today. Just as the economy as we understand it today affects us all, so will the data economy, writes data strategy and policy specialist and researcher Viivi Lähteenoja in this article (originally published on the Finnish Innovation Fund Sitra’s website in April 2023).
It is not an ICT company niche but concerns companies, individuals and societies in all regions, industries, and roles.It is therefore crucial for everyone to understand its basics. In the same way as we all learn about the basics of government, biology and history at school as subjects of universal relevance, so it is becoming more and more urgent for more and more people – citizens, consumers, professionals and individuals generally – to learn about the data economy.
The data economy signifies the greatest transformation of the century: it will transform every domain and even the way we understand the world.Laura Halenius Project Director, Sitra
We need fairness
Because we are all affected by the data economy, it is also important to ensure that its impacts are fair. This means that people, companies and entire societies worldwide each have their fair share of the benefits and burdens of the data economy. In the EU, the notion of a fair data economy has seen significant focus and investment this decade. “The data economy that we build must be aligned with our values,” is the rallying call from Brussels, and the EU is putting both its legislative muscle and its money where its mouth is.
The benefits and possibilities of fair data economies are being continuously discovered and realised. This article describes four key aspects of the benefits and potential of fair data economies:
- the possibilities arising from crossing the delta between the current shape of the data economy and the way fair data economies work;
- the opportunities offered by ecosystemic and networked approaches to data;
- the potential for trustworthy data practices that characterise fair data economies; and
- the economic benefits of a fair data economy.
Making data serve European people and businesses
Putting people in control of their data and using data to empower European businesses are among the key goals of the European Commission.
The European strategy for data (2020) stresses the principle of digital sovereignty and contains a vision for achieving it through human-centric initiatives, such as MyData and the establishment of common European data spaces, for making data serve people and businesses.
Nearly 2 billion euros is being invested in data spaces, cloud, and AI through various funding instruments.
The data strategy has also prompted the development and publication of a number of new and updated regulations, including
- fair competition on digital markets (DMA),
- effective content moderation by digital services (DSA),
- safe and ethical AI (AIA),
- better use of IoT data and cloud computing services (DA),
- better use of public sector held data and new kinds of data collaboration infrastructures (DGA),
- provision of digital identities for people and businesses (eIDAS2),
- improved cybersecurity (NIS2), and
- increased public sector data interoperability (IEA).
To guide and harmonise European efforts, a Digital Compass has been published with targets for developing skills, government, business and infrastructure for the “digital decade” until 2030. Each of these pieces of the policy puzzle will affect European individuals and businesses as the single digital market – common European data spaces – is developed. See Sitra’s study on EU data regulation.
1. How does the fair data economy differ from the existing data economy?
Fair data economies are ones that apply principles of fairness – defined in further detail by the societies in which they function – to correct for the current imbalances of power and unfairness that we encounter today. The term fair data economy implies the conviction that things can and should be better for people, companies and societies.
Under a fair data economy:
- markets are fair to all sizes of enterprise and to companies, individuals, and societies in all regions worldwide;
- Individuals are not forced to defend themselves against companies who break the law;
- consumers have a real choice between good alternatives when it comes to the technology and digital services they buy;
- interference in democratic, political processes is banned, including manipulation using misinformation and disinformation micro-targeted both at susceptible groups of voters and political decision-makers.
When we contrast these with the current state of global and local data economies, it is immediately clear why a model that has fairness at its core is desperately needed. We can point to at least three kinds of unfairness that we currently encounter.
- Within local data economies, calls for a more level playing field are increasing as Big Tech and other large enterprises are seen to have an unfair advantage over startups and SMEs. For example, promising startups are bought up and absorbed by larger companies to eliminate competition and to boost R&D efforts with the result that market power is concentrated in the hands of the already powerful actors with superior resources, including data. Europe is a global loser when it comes to profits generated in the global data economy. US and China-based companies dominate the markets in all corners of the world. If Europe is losing out on its fair share of the economic pie, the situation for the Global South is far worse.
- On the level of individuals and communities, the current state of the data economy, with its characteristic logic of data hoarding, is doubly harmful for people. People suffer due to the loss of privacy from excessive data collection. This in turn leads to abuses due to that loss of privacy (what Shoshana Zuboff has dubbed “surveillance capitalism”) as is well documented, for example concerning Instagram, where the data-driven operating model was known to cause significant harm especially among teenage but the company did not react. This harm is further compounded by the too often excessive demands placed on individuals to guard against violations of their rights to data protection as granted by the GDPR and other regulations.People also face limited choice as consumers. Have you ever tried to get through a single day without Google? It’s nearly if not entirely impossible. Could you switch to a computer operating system not provided by Apple or Microsoft? Very few people, companies or public administrations could.
- On the societal level, the current model is producing immeasurable harm and threats to how democracy functions. The most notorious case is the Facebook-Cambridge Analytica scandal, where personal data was used for psychological targeting in political campaigning, most famously in the 2016 presidential campaign of Donald Trump in the US and the ‘Vote Leave’ Brexit campaign in the UK.
2. Deepening collaborations: opportunities from networks and ecosystems
So what is the way forward towards fair(er) data economies?
While effective regulation is obviously essential, one of the critical business obstacles to overcome is the siloed nature of data use, because only by doing so can the full potential and benefits of data use be realised fairly for all.
Key principles for fair data economies are ecosystems thinking and networked data collaborations. Data collaborations within a single company involves reusing data across different systems in different parts of the company and are associated mainly with cost savings resulting from optimisation and gains in efficiency and productivity. Such collaborations are the first step on the road to realising the full potential benefits from data use.
Data collaborations among multiple companies, multiple groups of organisations, collaborations between domains and collaborations involving multiple sectors of society, promise exponentially more for all participants and their beneficiaries. This added value can be realised in terms of distributed investment and other costs, other burden sharing, improved innovation potential and other direct and indirect benefits in the form of company profits and improved services and products for the end user.
Emerging models of ecosystems thinking and networked data collaborations that are taking hold in Europe today are based on ideas of
- human-centric (as opposed to organisation-centric) data use, such as the MyData model,
- life events of people (and organisations), such as the service ecosystems when there is a death in the family member, or when registering a limited liability company,
- and phenomenon-based learning contributing to the development of learning and innovation skills.
Consider a company that will have individual systems using data, such as a CRM. They will have departments that use multiple individual systems, such as a marketing department. And the company itself will be made up of these multiple departments and more.
This company will in turn be part of an interdependent group of many organisations, such as a supply chain. That group will further be part of an industry involving many such groups, for instance the retail industry. That industry will be a part of the private sector that reaches across all industries, and the private sector will always be interconnected with public, research, civil society and media sectors.
At each level, there are silos marked by boundaries that prevent or at least hinder data use outside that specific silo. These boundaries may be
- technical (such as data model incompatibility or lack of APIs),
- based on business considerations (such as business secrets, strategic insights),
- legal (imposed by such things as contractual terms, requirements of purpose limitation, or prohibition on unfair market practices),
- or cultural (such as a lack of understanding of the potential benefits or the fear and overestimation of the risks involved with using data outside its specific silo).
Mobility ecosystem creates value through sharing data
In the Finnish ecosystem for mobility – traffic, transport, and logistics – steps are being taken to further generate value from data through collaboration. Rules and principles for effectively sharing data between operators, coordinating different services, and seamlessly connecting the devices used by vehicles, infrastructure, service providers and customers have been formalised in the shape of an ecosystem rulebook.
About 140 organisations have already joined Fintraffic – a Finnish company that controls and manages national traffic on land, air and sea – in their mission to make the most of a fair data economy. The policies codified in the rulebook will enable Finnish traffic sector operators to provide the best possible traffic and logistics services for their customers and to create new business opportunities for companies.
Data improves circulation and re-use of demolition waste in Vattuniemi
Efforts are being made to boost the circulation and re-use of construction and demolition waste material in a development project in Vattuniemi, Helsinki. The plan is to demolish 16 properties and to use as much of the demolition material for new construction as possible. According to estimates there will be 170,000 tonnes of concrete alone, which equals roughly 3,400 lorries worth of material that could be repurposed. The
Vattuniemi project involves the entire value network: demolition, logistics, design, construction, and relevant authorities. Data use is critical for the project because in the past the re-use of demolition materials has been hindered by lack of visibility concerning supply and demand. Data about the demolished materials will be compiled on a shared digital platform for the entire value network to access. When all the data is made available, well before demolition takes place, the materials can be taken into account when planning new construction or passed on to other construction industry operators.
3. We need to talk about trust
Discussion about the fair data economy often emphasises the need for trust. According to a survey report published by Sitra, lack of trust in the service provider prevents 37% of survey respondents from using digital services at least to some extent. When asked about the importance of the features of different digital applications and services, the most important were information security (63% consider it very important), the trustworthiness of the service provider (57%) and the clear and transparent indication of the purpose of user information collection (51% ).
According to the same study, the most important features of a trusted service provider are:
- the possibility for the customer to delete all the information they have given to the provider;
- the possibility of granting or refusing permission for the provider to sell one’s information to a third party, and
- whether the service clearly states how the customer’s information will be used.
For companies, being trusted as a brand is a critical requirement. If your customers do not trust you, they will vote with their feet and take their business elsewhere. (Naturally, this does not apply to companies with monopolistic market positions such as highlighted by the example above of Google.)
Encouraging brand loyalty – a manifestation of customer trust – is therefore a key strategic goal for both consumer and B2B companies. The flip side of this goal is to avoid the kind of damage to one’s brand that adversely affects customer loyalty. A visible breach of trust – being seen as untrustworthy – is one of the most effective ways to destroy established brand loyalty.
As the easiest way to seem trustworthy is to be trustworthy, responsible and fair data practices within the company are business critical.
Can we eliminate trust?
Collaborations of all kinds require trust, and data collaborations are no exception. However, it is becoming something of a mantra in such contexts to call for eliminating or at least minimising (the need for) trust entirely by technological means, usually involving distributed ledger technologies like blockchains. A key promise of these types of technologies is to provide guarantees in the form of unalterable records of transactions and irrefutable verification for different kinds of claims.
The phenomena involved in the Web 3.0 concept (broadly constructed as inclusive of collective governance models like DAOs , applications in decentralised finance like bitcoin, digital copyright models like NFTs , metaverses, transaction protocols such as smart contracts, and identity management paradigms like SSI) are an example of a specific approach to trust along these lines.
When there are calls for “eliminating trust” in the context of the data economy, it is important to put these in the technological context to which they properly belong and to recognise that some kinds of trust will remain inevitable and desirable in human collaborations, including with data.
Sharing skills data
The Finnish company Vastuu Group’s Luotettava Työntekijä (Trustworthy Employee) service helps students and job seekers to recognise and describe their skills and competences and to create a digital resume. These in turn help find employers and support services in your region and industry.
Vastuu Group collaborates with employment services ecosystems and networks to ensure that personal skills and competencies data moves between different databases and employment services – always with the person’s consent.
The service provides a meeting place for employers and employment service providers.
4. Where’s the money in a fair data economy?
A basic tenet of the fair data economy is that it benefits all and not only the incumbent “winners” in the data economy. At the macro level, ecosystemic business models and networked data collaborations enlarge the economic pie for all while distributing shares more fairly. They stand as an alternative to the current business models of surveillance capitalist and “winner-takes-all” market logic. These are based on network effects that incentivise data hoarding and the platformisation of digital services and that only serve the global oligopoly of Big Tech.
At the micro level, it can be harder to see the benefits of data collaborations, especially since they are often framed as exercises in data sharing. “Sharing is caring, sure, but I have a business to run here!” Metaphors for data that highlight its value–data as the new gold, oil, fuel for AI, and so on–create the sense that it shouldn’t be shared if you care about profitable business. This mode of thinking contributes to some of the cultural barriers to data collaborations described above. Companies and their leaders are sceptical about data collaborations, especially “data sharing”, often prompting questions like “Why should I share data if I don’t benefit from it?” “What if my competitor benefits from the data I share?” “Why would someone else even want this data?”
It may be helpful to make use of the concept of “data mesh”, which is gaining traction in enterprise data governance circles, with its principle of “data as a product”. Data sharing, better described as data collaborations, can be framed in terms of any reuse, exchange, and/or monetisation of products (meaning anything designed from a product perspective, with a clear definition of user problem or needs, functionalities and qualities that address those needs, and so on). Applying “product thinking” to data can support legal certainty and business confidence in a company’s ability better to understand and manage the potential risks often associated with “data sharing”.
With product thinking, the questions around data collaborations suddenly change. “Why should I share data?” becomes “Could I monetise my existing assets by exchanging or selling them as data products?” “What if my competitor benefits?” becomes “What are the things I need to include in my data product’s terms and conditions?” And “Why would anyone want this data?” becomes “How can I prepare this data to turn it into a useful product for someone?”
The metaphor of “data as a product” is not without its drawbacks, however. In the context of personal data, with its special legal, ethical, and societal features, the language and thinking in terms of productisation and monetisation can lead to highly problematic and potentially harmful outcomes for both the individuals whose data is used and the companies using this data. As with any metaphor: use this one with care!
Towards a fair, sustainable and prosperous future
Fair data economy is an avenue towards a brighter future overall.
For companies, a fair data economy offers opportunities for innovation as well as a fair chance of success and a level playing field in a balanced market.
For individuals, a fair data economy means the ability to trust that organisations of all kinds are behaving responsibly with regard to personal data about people as well as better consumer choices between good, easy, and trustworthy services and products.
For societies, a fair data economy means increased prosperity and societal functioning, generating overall well-being.
The fair data economy is not a goal pursued for its own sake, nor is it some magical techno-solutionist cure to all the world’s ills. It is one piece in the big puzzle of how we ensure that our future – and the future of our children and our planet – is fair, sustainable, and prosperous. A future that we can be proud of leaving for those who come after us, a future worth wanting.
In this vein, key Finnish organisations involved in the development of the data economy have announced their “Will to act”: Finland urgently needs to create fair data economy structures and solutions to use data to renew business, strengthen productivity and prosperity, and achieve positive environmental impacts. Doing so will also boost the resilience of society and the economy.
Join us in building a fair data economy!
Critical perspectives on the data economy
How sociotechnical imaginaries shape consumers’ experiences of and responses to commercial data collection practices
Can data ever know who we really are?
How do data come to matter? Living and becoming with personal data
When data is capital: Datafication, accumulation, and extraction
Data, Human Rights & Human Security
Algorithms and their others: Algorithmic culture in context
This article is written by Viivi Lähteenoja, chair of the board of MyData Global. Viivi is a data strategy, policy, and ethics professional, researcher and advocate. She works towards human-centric and ethically sustainable personal data to benefit people and societies.
This article was originally published on the Finnish Innovation Fund Sitra’s website in April 2023.