Blog Data Economy

Data Economy

Because of its role as a driver and enabler of new data-driven innovations, data can be understood as an economic good in its own right, shared and traded much like any other commodity to create economic, environmental and social value. However, the bold prediction that data would become a ‘new asset class’ has yet to materialise. This raises two important questions: Firstly, why has the data economy not taken off? Secondly, what are the obstacles and how can they be overcome?

How to set the data economy in motion?

Although the amount of data being generated today is increasing rapidly, the vision of a data economy has yet to be realised. To a large extent data stays very much within the control of the respective data collecting or generating entity. Because of its role as a driver and enabler of new data-driven innovations, data can be understood as an economic good in its own right, shared and traded much like any other commodity to create economic, environmental and social value. However, the bold prediction made in 2011 by the World Economic Forum that data would become a ‘new asset class’ has yet to materialise. This raises two important questions: Firstly, why has the data economy not taken off? Secondly, what are the obstacles and how can they be overcome?

A piece of metadata such as the response mode may seem less informative than direct questions, as asked in the surveys (for example, on the available ICT capital or particular digital technologies used by a firm). On an individual level, it is likely that there is significant “noise”: e.g. a firm may be lagging behind in terms of digitalisation, but the person responding to the survey happens to do so online, or vice versa. However, such cases are likely to cancel out when information is aggregated across a large number of firms. The share of firms adopting online response mode in a region or an industry can thus be informative in regard to digitalisation trends. Furthermore, because this response mode is not usually analysed by researchers and hence not considered by firms as relevant information, our metric is less biased by desirability. Demand effects may play a role even in anonymous surveys, for instance because a firm would like to see their industry do particularly well on a certain indicator, such as technology adoption. Another advantage is that this measure captures the actual usage of a technology (computer, Internet) by a firm’s employee, rather than mere technology availability within a firm.

Why has the data economy not taken off?

Special characteristics of data

Let us first take a look at the asset itself. Data is unlike other factors of production such as labour or capital for a number of reasons, being used by many entities synchronously without diminishing its value. Hence, there is no rivalry in usage. However, the economic and informative value of data tends to increase nonlinearly. This means that to a large extent huge amounts of data are needed to generate value insights, which has also meant that fragmented data markets providing individual user data have not been successful.

Although data is often seen as a commodity, in general terms it is not. The value of data differs for each application, to the extent that waste for one organisation might be gold for another. Hence, data is mostly non-fungible as it cannot be interchangeably used and its value depends on how and why it was collected as well as its level of reliability and validity. This complicates data pricing as buyers and sellers may have very different perceptions of any data’s inherent value, which changes over time as data can quickly become obsolete.

Unequal access to data

Data is also extremely unevenly distributed among organisations, often being locked away in proprietary data silos, which gives the firms that are owners a competitive edge in increasingly data-driven innovations, especially regarding artificial intelligence. Thus, for competitors or new market entrants these large, proprietary data silos owned by dominant online gatekeepers have erected high barriers which are thwarting innovation and creating a very unlevel playing field.

Intra-organisational barriers

For many organisations the barriers which block engagement with the data economy are created in-house. From a technology perspective, data is often locked up in many internal data silos, information on the data (metadata) is unavailable and a coherent data strategy is missing. Moreover, organisational and cultural issues are frequent impediments. In many organisations responsibilities and decision-making authorities are not clearly defined and bureaucracy together with a lack of easy-to-use tools stifles and frustrates entrepreneurial employees. All these internal problems impede data’s use as a strategic asset to create value within and beyond organisational boundaries.

Regulatory uncertainty and concerns

In a recent study among German firms, participants stated that legal and regulatory issues are among the key barriers to participation in the data economy. Especially with regard to personal data, companies fear that they will be blamed for using data more broadly, albeit within legal limits. In the marketplace great uncertainty exists about what is deemed appropriate by consumers and legal authorities. Hence, these perceived regulatory risks cause not just companies, but also governmental institutions to refrain from active participation in a data economy. Thus, scepticism prevails as risks appear greater and more tangible than any potential rewards.

What needs to be done?

Turning the idea of a data economy that leverages into reality the inherent economic, environmental and social value of the huge amounts of data generated is not necessarily easy. However, there is progress on many fronts that address technical, organisational, market and regulatory issues. I will now highlight some of the most promising approaches.

Privacy Preserving Data Usage

The data economy faces a paradox. All want to benefit from data’s increased availability and use, but at the same time concerns regarding privacy, trade secrets and confidentiality impede individual and corporate participation. Hence, techniques to protect privacy, trade secrets and confidentiality by design are crucial enablers of the data economy. Fortunately, privacy-enhancing techniques such as differential privacy, zero knowledge proofs, or synthetic data generation are increasing in number. While these techniques differ from each other, they all perform a masking function such that information about individuals remains private and cannot be reverse engineered. While all the approaches are fighting with their own particular challenges and do not solve all trade-offs between privacy and data accuracy, they do provide promising ways to address the paradoxical demands of stakeholders.

Data Governance and Strategy

Participation in the data economy requires that organisational data is properly managed. An organisation-wide data strategy is required that defines goals, measures progress, aligns stakeholders’ demands and prioritises investment. Data governance guidelines need to specify clearly responsibilities, ownership and decision-making rights. For organisations to treat data as a strategic resource, it is important that respective strategies and governance are not just implemented on paper, but that there is a clear commitment and roadmap of the entire organisation to use data more openly and comprehensively.

Data Portability and Ownership

There is a stark imbalance regarding the amount of data controlled by companies. While they cannot of course be blamed for their success, we need measures that effectively level the playing field and make data more available to enhance competition and innovation. Recent research has shown that it is socially and economically more favourable if users control their own data and can decide how, who and why their data should be used compared to providers’ data ownership. Thus, giving users more control could lead to a social welfare-maximising allocation of data and unlock proprietary data silos.

Providing such control is technically feasible and could be achieved, for example, via personal online data stores (PODS) or data co-operatives (e.g., MIDATA). Regulation in the EU also mandates that users can transfer their data from one service provider to another. But research has revealed low awareness and motivation to make use of data portability regulations. Moreover, technical obstacles hinder the smooth transition of data. Hence, the EU plans to reinforce data portability mandates especially for gatekeepers.

Data Access

Regulators and governments will need to do more to improve data access. Firstly, they need to be role models themselves in providing open access to data and make this a priority. Secondly, regulators have to play a more active role in incentivising or mandating that gatekeepers grant improved access to their data. The EU is currently working on a number of regulations that inter alia aim at better access and freer flow of data that benefits a larger set of stakeholders in the data economy. To achieve these goals, regulators need to balance between company versus public interests very carefully.

Federated Data Spaces

Some of the approaches outlined can be facilitated by federated data spaces (FDS). FDS such as Gaia-X or X-Road are emerging concepts that enable (unknown) parties to share data in a reliable and safe environment by providing an interoperable and standardised infrastructure. Put simply, in an FDS a potential buyer of third-party data can check if required data is available and on what terms. Buyers and sellers can connect their existing data infrastructures via application programming interfaces (APIs). The common rules of FDS are defined jointly by participants. Hence, the FDS concept facilitates the safe connection of heterogeneous data infrastructures, restricting lock-in effects and the exploitation of market power by design.


There is one—maybe the most crucial—lever that I have yet to mention and this affects us all. If we want to use data to increase economic prosperity, conserve resources and improve the quality of life, we need to rethink how we deal with our data and about the role that our organisations can play to turn the vision of a data economy into reality. Too often, I observe high interest, but low commitment to action. Rather, the common mindset—especially in Germany—is to focus on potential threats instead of opportunities, excessive perfection instead of healthy pragmatism and risk avoidance instead of risk mitigation.

To be clear, I am not arguing that individuals and organisations should not critically reflect upon the consequences of participating in a data economy. But to find out what the data economy could bring about we first need to try it out. A survey among German companies illustrates this paradox. While only 1% perceive their companies as leaders in data-driven innovation, the majority of respondents (63%) state that their companies are not sharing any kind of data with others. However, to use data more effectively for economic, environmental and social value creation we must be bolder than that.

Beyond the applications of this particular digitalisation measure, the above analyses provide an example for the potential usefulness of “coincidental” data such as survey metadata, which was not deliberately collected, but nevertheless can still generate insights.

The blogs published by the bidt represent the views of the authors; they do not reflect the position of the Institute as a whole.