Towards a taxonomy for Business-to-Government data sharing

1.Introduction

Business-to-Government (B2G) data sharing is commonly defined as a collaboration between a private company or organisation and the public sector (conceived at different levels: local, regional, national or supra-national), where the former makes available its data to the latter.11 The aim of the sharing of data should be a public interest purpose, like, for example, the protection of the environment or the response to a public emergency such.

The last few years have seen this phenomenon growing, as research has shown how privately held data of different kinds could have a huge potential when used to tackle societal policy issues. Many examples of B2G data sharing initiatives can be found in literature, especially in the domain of production of official statistics, and they’re often based on one-off voluntary kind of cooperation agreements, more or less explicit, between the actors (private and public sector) (as an example, see [1]).

B2G data sharing is a concept that presents implications in legal terms and will have to be considered by legislators in future proposals that involve the sharing of data. Actually, as of the third decade of the 21st century, B2G data sharing started appearing in the legislative process of the European Union: one of the aims of the European Strategy for Data22 is the adoption of “legislative measures on data governance, access and reuse”. Along this line, two Regulations have been adopted: the Data Governance Act,33 already applicable in the EU, and the Data Act,44 that will be applicable starting from September 2025. In particular, the Data Act contains specific provisions concerning mandatory B2G data sharing in exceptional circumstances (for example in case of a public emergency) or when needed to implement a legal mandate, if the data availability is not guaranteed through other means. In those specific cases, private companies shall be asked to share data with the public sector to allow a quick and secure response to the public threat, but at the same time minimizing the burden on businesses.55

There exist different situations where B2G data sharing can give a valuable contribution in addressing public purposes: from emergencies (among the ones ruled by the Data Act), to the construction of official statistics indicators and to the use in research, just to name a few. Some examples have already been implemented; on the topic of emergency situations, the use of mobile network operators’ data by the European Commission’s Joint Research Centre to help fighting COVID-19 [2]. In the area of official statistics, the agreement between Eurostat and some collaborative economy platforms (Booking, Airbnb, Expedia and Tripadvisor) allows the statistical office of the European Union to produce some figures and data on short-stay accommodations across the EU.66 In all these circumstances, the agreements under which the data are shared may be of completely different kinds (going from one-off collaborations that give access only to a current view on the data, to periodic high frequency updates), depending on the willingness and openness of private companies to cooperate with the public sector. But different situations require different levels of the quality of data, depending on several factors; for example, in the case of an emergency situation, timeliness represents a key principle to guarantee data quality, while if the same dataset is used for research activities, the level of importance of the timeliness principle is not excessively prominent.

Figure 1.

Elements of the EU taxonomy of public services compared to the elements of the proposed taxonomy. Source: own elaboration.

There are many elements that motivate our work; first, the heterogeneity concerning the quality principles that characterise all possible B2G data sharing situations. Secondly, as such kind of partnerships for the sharing of data is increasing (not only in the EU context), there exists a scientific interest in understanding better their features. Finally, in light of the provisions of the European Strategy for Data77 (and in particular of the Data Act88) B2G data sharing initiatives will become more and more common, and we clearly see the need of an instrument that goes towards this direction. Our objective is to understand and classify the different circumstances in which B2G data sharing may occur and the characteristics that each of them should have. In practice, we aim at creating a taxonomy of B2G data sharing agreements (in all possible forms they may be established), in which we first identify all the different situations where B2G data sharing may occur, and then we add attributes (in the form of identified quality principles) that characterise the different B2G data sharing situations. The last step in this activity is the identification of B2G data sharing situations where the levels of the identified quality principles are the same, in order to reduce the cardinality of the taxonomy and to group together similar situations. As an supplementary step, we propose some additional elements in the form of an information set that we think is needed as a complement to every B2G data sharing situation. This work aims to provide further information that can help clarify specificities and requirements of B2G data sharing in order to ultimately stimulate and enable more dynamic and relevant data flows. The authors would like to stress that the taxonomy aims at being descriptive, trying to map all the possible kinds of B2G data sharing situations that are being implemented, and not at being seen as a prescriptive tool, nor at planning its operationalisation (that indeed remains an interesting topic to focus on, but out of the scope of the present work). Moreover, the authors would like to specify that they do not have a specific mandate to start the development of the taxonomy, but they act independently for scientific purposes.

The methodology that we followed to develop the taxonomy is the one shown in [3]: we started from the identification of the meta-characteristics (the classification of B2G data sharing situations and the characteristics of each of them), and then we identified the meta-dimensions of the taxonomy through a ‘conceptual-to-empirical’ approach. These meta-dimensions are structured on two levels (or layers): the first layer contains themes, patterns and attributes, while the latter dimension is in turn characterised by three meta-dimensions (the second layer), namely spatial and temporal, methodological, and legal and governance. Section 2 contains a detailed explanation of all these elements. In Section 3 the three identified meta-dimensions are put together to concretely build the taxonomy, while Section 4 contains the proposal of an information set that should ideally accompany every B2G data sharing initiative.

2.Elements of the proposed taxonomy

As mentioned in Section 1, our proposed taxonomy first aims at identifying all the situations when B2G data sharing can occur, and, as a second step, defining the quality attributes that are needed in each situation with the final goal to cluster different instances of B2G data sharing into subgroups.

The identification of all possible B2G data sharing situations builds upon the building blocks of a taxonomy [4] developed by the European Commission’s Directorate General for Informatics (DIGIT) and that aims at mapping all European public services through two elements: themes (also referred to as thematic areas, like ‘Defence’, ‘Education’, ‘Health care’, just to name a few) and patterns (that are public service types, or the core services of governments, like for example ‘Control and monitoring’). As visualised in Fig. 1, these two elements are adapted to our work’s specific needs and become the first two building blocks (meta-dimensions) of our proposed taxonomy. A third building block is then added – the so called attributes –, composed of a selected subset of quality principles that characterise the different B2G data sharing situations.

2.1The European taxonomy of public services

The European taxonomy of public services [4] was published in 2019 by the European Commission’s Directorate General for Informatics (DIGIT) to help public administrations in harmonising their catalogue of services. In particular, one of its goals is to make it easy for users to find public services, as well as to compare them across different Member States.

The EU taxonomy of public services is built as the combination of two elements, themes and patterns, that together create a ‘high-level generic public service’ [4]. Public services can be allocated to one theme and one pattern only. The following two subsections deep dive into the details of those two elements and the choices we made for the proposed taxonomy.

2.1.1Themes

Themes are the first element of the EU taxonomy of public services [4] and can also be referred to as thematic areas. To understand the meaning of themes of the EU taxonomy of public services, DIGIT created a visual explanation available in Fig. 2.

The list of themes from the EU taxonomy of public services is composed by 31 elements; examples of themes are ‘Defence’, ‘Education’, and ‘Environmental’, just to name a few.

For our work, we decided to consider the full list of themes as they appear in the EU taxonomy of public services because they cover all thematic situations in which B2G data sharing could occur (even if some cases could appear more theoretical than others – see for example ‘Religious’). The complete list of themes is available in Table 1.

Table 1

List of themes from the EU taxonomy of public services

Themes
Agriculture and food	Animal	Border control
Culture, sport and leisure	Defence	Digital
Education	Emergency	Environmental
Family	General business	General government
Health care	Housing and building	Legal
Life event and identity	Manufacturing	Media
Monetary policy	Money and debt	Natural resources
Public space management and heritage	Religious	Retail
Stock market	Tourism and travelling	Transportation and transportation infrastructure
Utilities	Voluntary organisation and charity	Welfare and social care
Work

Source: [4].

Figure 2.

Explanation of a theme. Source: [4].

The EU taxonomy of public services does not foresee any sub-theme, as its authors state that “Some sub-themes were identified, but this was done on an ad hoc basis and should be further refined.”[4, Page 42]. For the construction of our taxonomy, this first level of themes appears to be enough, but it could be worth considering possible future extensions of them. For example, a proposal of a possible sub-theme for ‘Manufacturing’ is the sub-classification of industrial ecosystems foreseen in the European Industrial Strategy,99 that contains 14 industrial ecosystems that span from ‘Aerospace and defence’ to ‘Agri-food’ and ‘Construction’, just to name a few.

Table 2

List of patterns from the EU taxonomy of public services

Patterns	Sub-patterns
Framework	Procedures	Measures	Law definition	Management (of a bureaucratic structure)	Asset management	Collective infrastructure	Schemes and plans
Information	Information	Advice
Registration	Registration
Certification	Certification	Licensing	Permission	Authorisation
Financing	Financial support	Material support	Provision of free services	Provision of discounted services
Production	Governmental service at normal price	Maintenance of property	Maintenance of infrastructure	Provision of infrastructure or a charge	Waste management
Feedback	General complaints	Injury/damage complaints	Feedback	Appeals against decisions	Mediation
Control and monitoring	Control	Monitoring	Testing	Assessment	Law enforcement
Taxation	Taxation

Source: [4].

Figure 3.

Explanation of a pattern. Source: [4].

2.1.2Patterns

The second element of the EU taxonomy of public services [4] is composed by patterns, that are public service types, or the core services of governments. When public services are broken down to their core by removing any context, the detail of a core service remains. The overarching concept of all these details is defined as a pattern.

As done for themes, to understand the meaning of patterns in the EU taxonomy of public services, DIGIT created a visual explanation available in Fig. 3.

The EU taxonomy of public services is conceived with the following nine patterns: ‘Framework’, ‘Information’, ‘Registration’, ‘Certification’, ‘Financing’, ‘Production’, ‘Feedback’, ‘Control & monitoring’, ‘Taxation’, and each of them is proposed with a list of sub-patterns [4] that can help understanding better the various cases (see Table 2 for the complete list).

Starting from our own considerations and reflections on B2G data sharing, we imagined the following four patterns as fit for our purpose: ‘Control & monitoring’, ‘Official statistics’, ‘Planning & management’, ‘Research’.

The reasoning behind these choices relies on the answer to the question ‘for what public purpose aims could a public entity use privately held data?’. We think that a public entity may need some data to plan in advance some interventions (like the impact of a new policy) but also to check their impact afterwards; additionally, a public entity may need data to be able to produce official statistics at different levels of detail, or to carry out some research activities (that may afterwards lead to the other three patterns). The EU taxonomy of public services does not supply a definition for each pattern, but it rather explains them through a series of sub-patterns. The analysis of those sub-patterns helped us find a correspondence between our own ideas of patterns and those already present in DIGIT’s taxonomy.

‘Control & monitoring’ is a pattern already defined in the EU taxonomy of public services, and the sub-patterns that explain its meaning are control, monitoring, testing, assessment and law enforcement; it refers to all those activities that a public entity may carry out to perform an ex-post monitoring check.

For the other three patterns that we envisioned we need a deeper investigation on the EU taxonomy of public services’ sub-patterns to check whether some of the patterns have already been identified and included in DIGIT’s taxonomy. First, ‘Planning & management’ could be considered as part of the ‘Framework’ pattern, as the sub-patterns identified in DIGIT’s taxonomy are procedures, measures, law definition, management (of a bureaucratical structure), asset management, collective infrastructure and schemes and plans. This pattern relates to a public service type which activities are carried out ex-ante (in a planning phase). Second, ‘Official statistics’ may be included into the pattern ‘Information’, as its sub-patterns are information and advice. It can be seen as a public service type that supports the other ones, both ex-post and ex-ante.

Finally, ‘Research’ is the only pattern that is completely brand new and that we have decided to add, as a match in DIGIT’s taxonomy could not be found. It relates to experimental activities that could be carried out in support of the other three patterns, but as a preliminary step that could afterwards lead to the establishment of a consolidated public service.

The final list of patterns of our taxonomy is then the following:

• 1. ‘Control & monitoring’, that includes all possible monitoring activities to be carried out ex-post.

• 2. ‘Information’, that includes official statistics and, more in general, all those activities aimed to provide information and advice to public entities (but not under an experimental framework, that is indeed covered under ‘Research’).

• 3. ‘Framework’, that includes all activities that may help public entities in the planning phase of their day-by-day work (ex-ante).

• 4. ‘Research’, where the experimental activities (before becoming structural) are included.

To exemplify he identified patterns, we may take one specific source of data to showcase the different situations: mobile network operators data. In fact, they may be used to plan the introduction of limitations on the number of tourists in cities (ex-ante, the pattern in this case is ‘Framework’), or after the introduction of these measures to monitor how effective they may be (ex-post, pattern ‘Control & Monitoring’). But the same source of data may also be used by National Statistical Institutes as an alternative to census data to identify the population on a territory in a faster and less costly way (pattern ‘Information’), but also as an attempt to study a phenomenon during an emergency situation (as it was done by the Joint Research Centre of the European Commission during the COVID-19 pandemic [2] – pattern ‘Research’).

For each situation, in principle only one pattern applies (the same should be valid also for themes), but we acknowledge the fact that some B2G data sharing agreements may cover more than one theme and pattern. In such cases, the agreement should be split into multiple parts, in such a way that each of the single parts can be categorised under one theme and one pattern only.

3.The structure of the taxonomy

After an overview of the elements that are the core parts of the taxonomy of B2G data sharing, it is time to merge them. The taxonomy is a combination of the identified themes and patterns, and users can classify B2G data sharing agreements combining one theme with one pattern (for example, ‘Control & monitoring’ – pattern – and ‘Health care’ – theme). This way, the first layer of the taxonomy is composed by 124 different situations, that is the result of the four patterns multiplied by the 31 themes.

Each single B2G data sharing initiative has to be characterised by the 12 identified attributes – the second layer of the taxonomy – by assigning different levels of importance to each of them. As an example, in an emergency situation (pattern ‘Control & monitoring’, theme ‘Emergency’) timeliness would constitute one of the most important features for the data, while continuity over time could be considered marginal. The latter, by contrast, could result in being fundamental in other situations (like in all those that fall under the ‘Information’ pattern – to be understood as official statistics). Figure 4 shows how the two layers are combined in a specific B2G data sharing situation, namely research (pattern) in education (theme).

Figure 4.

Combination of the first and second layer of our proposed taxonomy. Source: own elaboration.

The next step of this work would be to assign a level of importance (high, medium, low) to each of the attributes in the complete set of identified B2G data sharing situations. After this activity is completed, some similarities will appear in different combinations of patterns and themes; this will allow to group some of the B2G data sharing situations and will constitute the final step of development of the taxonomy.

4.Information set for B2G data sharing

While fine-tuning the list of attributes, we identified some essential information that accurately characterises the specific B2G data sharing situations. In the context of B2G data sharing initiatives, we recognized the critical need for accompanying the agreements with some details. As a result, we propose an information set to enhance B2G data sharing; it aims to provide comprehensive context information and to facilitate effective communication between private companies and public entities.

To the best of our knowledge, at the moment there exists no specific archive or inventory of available B2G data sharing initiatives; a couple of tools may be considered as a sort of inventory/repository for B2G data sharing initiatives to some extent, but their primary aim is broader than this. The first one is the Tenders Electronic Daily (TED),1212 the online version of the ‘Supplement to the Official Journal of the EU’ that contains data about public procurement in the EU (above a certain threshold). This database does not specifically address B2G data sharing initiatives, as it is established to increase transparency in public procurement, but it may contain examples of data acquisition procedures from private companies to public entities. The second example is the Data Collaboratives Explorer1313 created and maintained by The GovLab;1414 data collaboratives are defined as a new form of collaboration that goes beyond the public-private partnership model, and the explorer contains a set of examples where public value is generated by exchanging data in different sectors, categorising initiatives as data cooperatives or pools, prizes and challenges, research partnerships, intelligence products, Application Programming Interfaces (APIs) and trusted intermediaries.

As these examples are not specifically targeted at B2G data sharing initiatives and due to the limited amount of information contained (as those tools were created with different purposes in mind), we suggest the following information set to be provided for B2G data sharing initiatives in possible future inventories/repositories. We identified the following list of features to be part of this basic information set:

The spatial and temporal coverage information to be provided depends on the type of analysis carried out and on the public entity that performs it.

Indeed, if for example a regional tourism office would like to analyse the flows of foreign tourists within its borders over a summer period through mobile phone data, the spatial coverage would be limited to its territory, and the temporal coverage to some months over the year, possibly comparing the same time frame with previous years. In case the same analysis would be carried out by a National Statistical Institute, while the temporal coverage could remain the same, the spatial coverage of the dataset should refer to the whole country, not only to one specific region.

Another information to be specified is the statistical unit that is subject to analysis (namely, the population coverage). Considering the above-mentioned example, if the analysis of tourism flows is limited to foreign tourists, the statistical units considered would only be foreign SIM cards whose presence was registered in the region/country during an identified time frame. At the same time, in case the analysis should cover all tourism flows (national and foreign), all SIM cards registered at a given moment in the region/country should be included, without discriminating on their country of release.

A fundamental feature that should be part of the information set are some details on the type of data; without delving into the specific details that a proper codebook should contain, for the purpose of the information set it would be sufficient to indicate the broad typology of the data part of the B2G data sharing agreement (for example, mobility data, consumer prices and behaviours data, energy consumption data, etc.) and whether the B2G data sharing involves the actual data, or only an anonymised subset or a synthetic version of the full dataset.

A detail that is specifically targeted at B2G data sharing situations is the type of partnership scheme that has been agreed between the private company and the public entity; on this specific issue, different kind of collaborations have been identified in literature. For example, the Publications Office of the European Union [14] identifies five different types of collaboration: multi-party data sharing agreement, data donorship, data partnerships, data intermediaries and data sharing by regulation. Another proposal of models for B2G data sharing comes from the European Commission [15]: data donorship, prizes, B2G data partnerships, intermediaries and ‘civic data sharing’. Finally, Micheli [16] identifies a series of models for B2G data sharing specifically targeted at cities: data donorship, public procurement of data, data partnerships and pools, and data sharing obligations. These examples show how literature still not completely agrees on well defined types of collaborations that may be established, but even if the topic is evolving, it would be important for B2G data sharing situations to be accompanied by this kind of information.

Concerning the control over the use of data, it is essential to specify in the information set whether the data provider has retained some control over the data that are part of the B2G data sharing agreement or not; this aspect is important to assess the independence over the use of the data.

Moreover, the information set should contain details about the IT infrastructure used; in fact, the data may be processed at the provider’s premises (without physically moving the data) or the datasets may be shared with the public entity and processed on its own infrastructure. Depending on the choices made on whether the data are moved or not, some implications may arise in order to ensure reproducibility and auditability of the analytical work, and also in terms of security and privacy.

Last but not least, concerning the attributes of the taxonomy that present ‘high’ importance rankings, the information set should include the metadata referring to those specific attributes. For example, a B2G data sharing agreement that presents a high importance level of the degree of data reusability attribute should already include its contractual terms in the information set, in a way to reiterate the importance of the attribute.

5.Conclusions

This paper presents a first proposal of a taxonomy for B2G data sharing, therefore it is expected to be further consolidated and integrated with new insights, especially in the ‘attributes’ part. Moreover, after the structure of the taxonomy will be consolidated, the following step of the work will be to identify the levels of importance of each attribute in the different B2G data sharing situations, in order to group cases where situations are different, but the quality principles required are similar.

The paper aims at providing a basic information set that should accompany any B2G data sharing situation, to start setting the scene for possible future inventories or archives of this type of initiatives (something that at the moment is not included in the two broad inventories mentioned in Section 4). This work aims to provide insights on the differences of B2G data sharing settings, commonly grouped as a single instance of data flows whereas they exhibit a broad and relatively diverse range of goals, contexts and therefore requirements.

Before proceeding with the next steps, the authors would like to get feedback from the scientific community concerning the validity of the approach and the proposed structure of the taxonomy, in order to refine and readjust some elements that may appear weak. Moreover, specific quantitative techniques could be applied to determine the best way to rank the identified attributes, like the Analytical Hierarchy Process (AHP) [17, 18]. This ranking process can also be extended to involve a panel of experts, using, for example, DELPHI surveys [19]. Next steps would also include the testing of the validity of the taxonomy on existing B2G data sharing inventories/repositories (see Section 4). A possible solution for this could be the extraction of a list of data acquisitions from the Supplement to the Official Journal of the European Union (TED)1515 database, that includes all EU public tenders above specific contract values and it is aimed at increasing transparency in public procurement.

Finally, as the paper presents a proposal of a taxonomy for B2G data sharing, the next activity could also step towards the proposal of a taxonomy for Government-to-Government (G2G) and Government-to-Business (G2B) data sharing that could be interoperable with the one proposed here – once finalised. While the interoperability of the two taxonomies would bring great value, a G2G data sharing taxonomy may be something difficult to create, as public entities may exchange data between them without a notification to the outside world. Additionally, in Europe the open data directive1616 sets the obligation for governments to publish some specific datasets as open data, with no specific agreement behind the actors (as it is foreseen by law), and no specific purpose for the use of this kind of data; this way, the introduction of a G2G / G2B data sharing taxonomy may be less useful than a B2G data sharing one.