Framework for Open Data Maturity - Country Profiles and Clusters
Summary
The Open Data Maturity Assessment of the Publications Office of the EU
and European Commission is a highly recognised and widely known study
in the field of open data. It evaluates progress and effectiveness of
open data initiatives across four thematic dimensions intended to
capture the end-to-end value chain of open data: Policy, Portal,
Quality and Impact. The current open data maturity levels group
countries into four categories: 1) Beginners, 2) Followers, 3)
Fast-trackers, and 4) Trendsetters. In order to foster more effective
peer-to-peer learning, and better deliver tailored communication and
support, an explorative profiling and clustering analysis was
conducted. Beyond the existing absolute performance groups, profiles
help to better understand the open data approaches of countries and
how they go about improving open data practices. Macro country
clusters help to compare countries that are relatively similar, in
terms of economic, social, geographical, political, data and digital
background characteristics.
Body
EXECUTIVE SUMMARY

The OPEN DATA MATURITY ASSESSMENT of the Publications Office of the EU
and European Commission is a highly recognised and widely known study
in the field of open data. It evaluates progress and effectiveness of
open data initiatives across four thematic dimensions intended to
capture the end-to-end value chain of open data: Policy, Portal,
Quality and Impact. The current open data maturity levels group
countries into four categories: 1) Beginners, 2) Followers, 3)
Fast-trackers, and 4) Trendsetters.

In order to foster MORE EFFECTIVE PEER-TO-PEER LEARNING, and better
deliver tailored communication and support, an explorative profiling
and clustering analysis was conducted. Beyond the existing absolute
performance groups, profiles help to better understand the open data
approaches of countries and how they go about improving open data
practices. Macro country clusters help to compare countries that are
relatively similar, in terms of economic, social, geographical,
political, data and digital background characteristics.
[https://data.europa.eu/sites/default/files/img/media/Screenshot%202025-11-17%20at%2016.15.23.png]
FIGURE 1: Framework of existing open data maturity performance groups
and newly explored open data profiles and macro country clusters

 

By combining desk research, questionnaire data and statistical
analysis, open data profiles as well as macro clusters were
identified. Six parameters were considered to be most effectively
describing a COUNTRY’S OPEN DATA DESCRIPTIVE PROFILE: Governance
Approach, Open Data Quality Strategy, Domain of Impact, Portal
Foundations, Funding, and Transposition of the Open Data Directive.
Each country profile shows a unique mixture of open data approaches,
orientations and stances. How countries approach open data and put
policies into practice may explain performance differences. For
example, some countries may show signs of a top-down Governance
Approach, with an Open Data Quality Strategy focused on depth and
richness of data, all-round Impact, standardised Portal Technical
Foundations, high Funding levels and amended the Directive into
existing law. At the same time, other countries report a hybrid
Governance Approach, with an Open Data Quality Strategy focused on
diversity of open data types, more narrow and specialised Impact,
custom Portal Technical Foundations, low Funding and multi-instrument
approach of Open Data Directive Transpositions. Better understanding
these characteristics helps in knowing how results come about and
whether other countries should follow such examples.

In addition, MACRO COUNTRY CLUSTERS were identified in order for
countries to find most relevant peer countries. The five clusters of
countries with most commonalities are: Southern and Western European
Belt (Germany, France, Italy, Spain, Belgium, Portugal, Greece),
Central and Eastern Europe (Poland, Romania, Czechia, Hungary,
Austria, Bulgaria, Slovakia, Croatia, Slovenia), NorNeLux
(Netherlands, Sweden, Denmark, Finland, Luxembourg), Baltics
(Lithuania, Latvia, Estonia), and Island-based nations (Ireland,
Cyprus, Malta). These countries are most comparable when looking at
macro economic, social, geographical, political, data and digital
characteristics. Yet, the overall open data maturity levels of
countries within a cluster may differ. In order to bridge performance
gaps with peers, countries may examine the open data profiles of peer
countries closer, and exchange good practices, either in the context
of the EU or bilaterally.

Depending on the views of the Publications Office of the European
Union, European Commission and other relevant stakeholders, the 2026
EDITION OF THE OPEN DATA MATURITY ASSESSMENT may incorporate the
framework of open data descriptive profiles and macro country clusters
as presented in this report. For instance, by including the profiles
and clusters in the main report or in the foreseen mini-site that
accompanies the 2026 publication materials. Additions could be made
for both the EU27 Member States and the other participating countries
for the open data descriptive profiles, whereas specific data gaps
need to be addressed before the macro country clusters can be extended
to non-EU countries in future editions of the Open Data Maturity
Assessment too.

 

INTRODUCTION

 CONTEXT AND CURRENT OPEN DATA MATURITY COUNTRY PROFILES

The OPEN DATA MATURITY ASSESSMENT of the Publications Office of the EU
and European Commission is a highly recognised and widely known study
in the field of open data.[1]
[applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftn1] It
evaluates progress and effectiveness of open data initiatives across
four thematic dimensions intended to capture the end-to-end value
chain of open data: Policy, Portal, Quality and Impact. In particular,
the assessment measures the progress of European countries in making
public sector information available and stimulating its reuse, in line
with the Directive (EU) 2019/1024, also known as the Open Data
Directive.[2]
[applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftn2] In the
10th edition of the measurement in 2024, a total of 34 countries were
assessed: the 27 EU Member States, 3 European Free Trade Association
(EFTA) countries (Iceland, Norway and Switzerland) as well as 4 EU
candidate countries (Bosnia and Herzegovina, Albania, Serbia and
Ukraine).

Ever since the first Open Data Maturity Report in 2015, the EU27
Member States and other participating countries have been grouped
based on their overall open data performance. In general, such
typologies promote peer-learning and allow for taking more targeted
follow-up actions. The current open data maturity levels GROUP
COUNTRIES INTO FOUR CATEGORIES: 1) _Beginners_, 2) _Followers_, 3)
_Fast-trackers_, and 4) _Trendsetters_.
[https://data.europa.eu/sites/default/files/img/media/Screenshot%202025-11-19%20at%2010.54.27.png]
FIGURE 2: Four-category groups of participating countries based on
overall maturity score

 

These groups are based on the absolute overall open data maturity
score. To group the countries, overall open data maturity scores are
plotted from lowest to highest. Groups are demarcated where observable
gaps in the ordered scores are identified. The distribution of
composite maturity scores is skewed towards higher scores. The groups
in the 2024 report were as follows:

	* BEGINNERS (overall maturity score of 15–69 %). Bosnia and
Herzegovina (BA), Albania (AL), Malta (MT), Iceland (IS), Greece (EL),
Bulgaria (BG), Croatia (HR) and Romania (RO).
	* FOLLOWERS (overall maturity score of 74–80 %). Belgium (BE),
Germany (DE), Hungary (HU), Finland (FI), the Netherlands (NL), Sweden
(SE) and Switzerland (CH).
	* FAST-TRACKERS (overall maturity score of 83–90 %). Luxembourg
(LU), Serbia (RS), Austria (AT), Norway (NO), Portugal (PT), Slovenia
(SI), Latvia (LV) and Denmark (DK).
	* TRENDSETTERS (overall maturity score of 94–100 %). Cyprus (CY),
Estonia (EE), Italy (IT), Czechia (CZ), Lithuania (LT), Spain (ES),
Ireland (IE), Slovakia (SK), Ukraine (UA), Poland (PL) and France
(FR).

 

 THE VALUE OF OPEN DATA MATURITY CLUSTERS AND THE NEED TO EXPLORE
ALTERNATIVES

The four existing country groups in essence help countries understand
and communicate their absolute position in the European open data
maturity spectrum. Despite serving this purpose over the past years,
it is clear from conversations with Member States and reflections by
the Publications Office of the EU that these groupings are not always
appropriate for a contextualised COMPARISON AMONG COUNTRIES. For
instance, countries within the same performance group may highly
differ in terms of size, (data) infrastructure, re-user population,
etc, but they are nonetheless grouped together due their Open Data
Maturity scores. This presents two key challenges:

	* A de-contextualised interpretation of performance which may risk
undervaluing progress or challenges over time;
	* Reduced opportunity for meaningful peer-to-peer learning, as
countries may struggle to identify relevant peers to learn from.

Therefore, to more accurately reflect countries’ open data maturity,
foster more effective peer-to-peer learning, and better deliver
tailored communication and support, the Publications Office of the
European Union aims to DEVELOP A FRAMEWORK FOR ‘PROFILING AND
CLUSTERING’ COUNTRIES BASED ON INTRINSIC CHARACTERISTICS and
relevant descriptive parameters. This entails developing two metrics
which, when viewed with the Open Data Maturity scores, would enable a
layered approach to evaluating open data maturity. As displayed in
_Figure 3,_ the first new layer would be ‘OPEN DATA DESCRIPTIVE
PROFILES’, which is defined as a structured set of descriptive
parameters that characterise how countries implement open data in
practice, providing a contextual and practice-oriented view of their
behaviours and approaches. The second new layer would be ‘MACRO
COUNTRY CLUSTERS’, which is defined as groupings of countries based
on shared intrinsic characteristics—such as economic,
socio-cultural, geographical, political, and digital factors—that,
while external to open data, significantly influence its
implementation. Together, while the Open Data Maturity scores show
what countries achieve, the Descriptive Profiles explain how they
achieve it, and the Macro Country Clusters provide context for what
those achievements mean, given each country’s broader
characteristics.

Adding these profiles and clusters to the Open Data Maturity Report
could benefit several data.europa.eu stakeholders. For policymakers,
an exploration of meaningful profiles and clusters helps to tailor
future policy actions and learn more effectively from peer countries.
For media partners of data.europa.eu the presentation of findings into
profiles and clusters opens up new ways to write news articles about
the annual Open Data Maturity Assessment Report and progress made.
Citizens, educational stakeholders and private sector re-users are
expected to benefit from the publication materials too. By better
understanding open data approaches (open data descriptive profiles)
and relative performance (macro country clusters), may help them to
better spot new re-use opportunities within the more specific context
of their Member State.
[https://data.europa.eu/sites/default/files/img/media/Screenshot%202025-11-19%20at%2011.05.52.png]
FIGURE 3: Linkages between the open data profiles, open data maturity
performance and macro country clusters

 

The country profiles and clusters developed in this report can be
implemented at a later stage, for example in the 2026 EDITION OF THE
OPEN DATA MATURITY ASSESSMENT. Moreover, the Publications Office of
the EU plans to launch a data.europa.eu mini-site on which the Open
Data Maturity Report 2026 will be published. Country profiling is a
key differentiator of the Open Data Maturity mini-site, delivering
value far beyond the static report. By grouping countries based on
intrinsic characteristics, it transforms a complex, diverse dataset
into an intuitive experience. Instead of manually scanning dozens of
profiles, users can quickly identify relevant peers, benchmark
performance in context, and focus on targeted learning. Furthermore,
this approach turns raw data into actionable insight, fostering
smarter decision-making and more meaningful collaboration.

The following RESEARCH QUESTIONS have guided this exploratory
analysis:

	* _What parameters best reflect the open data performance and
characteristics of EU27 Member States?_
	* _How are intrinsic country characteristics associated with national
performance across the four dimensions of open data maturity?_
	* _Policy: What institutional or legal factors shape a country’s
approach to open data policy?_
	* _Portal: What role does user feedback or participatory design play
in shaping the evolution of open data portals, as well as how is the
management, maintenance, and strategic development of national open
data portals structured and organised, and how does this impact
usability and sustainability?_
	* _Quality: What infrastructure, technical capabilities or standards
(e.g., ICT infrastructure, interoperability frameworks) support the
publication of high-quality open data?_
	* _Impact: How do digital literacy levels influence the observed
impact of open data?_
	* _What is the feasibility of extending this profiling framework to
non-EU countries?_
	* _How can these parameters promote peer-to-peer learning and inform
actionable policy directions?_

Chapter 2 of this report details the methodological approach of the
analysis. Chapter 3 presents the findings from the desk research and
the insights about the identified open data descriptive profiles, as
well as the macro country clusters. In Chapter 4 the report concludes
with main observations and recommendations for future editions of the
Open Data Maturity Assessment.

 

METHODOLOGICAL APPROACH

In order to explore open data descriptive profiles and macro country
clusters, DESK RESEARCH WAS COMBINED WITH ANALYSING THE OPEN DATA
MATURITY QUESTIONNAIRE AND A SERIES OF STATISTICAL SOURCES.
Importantly, this pilot serves testing purposes. It should not be
interpreted as the beginning of a recurring data collection process
under the Open Data Maturity Assessment. In addition, this exercise
should lead to only minimal or no changes to the Open Data Maturity
questionnaire. Any future integration into the regular Open Data
Maturity Assessment cycle would require a separate decision and
planning process from the Publications Office of the EU. Yet, the
pilot study does provide the necessary information on measurement
frequency and periodicity in order to assess the feasibility of
regularly updating the country profile and cluster information and its
sustainability of maintaining it over time.

DESK RESEARCH was used to explore conceptual models and potential
profiling and clustering indicators. Other studies in the area of
data-driven and digital governments show different ways to profile and
cluster Member States based on performances and rankings. Next to
studies from various international and supranational organisations,
private sector publications were analysed.

Data from the OPEN DATA MATURITY QUESTIONNAIRE was used as the main
source for populating the open data maturity profile parameters for
the EU27 Member States. This includes values and properties related to
the four dimensions of the Open Data Maturity Assessment. The relevant
profiles were constructed looking at the combination of values and
properties. This allows for analysing how countries with similar
approaches and country profiles perform compared to each other in
terms of Open Data Maturity scores.

STATISTICAL SOURCES were used to develop the macro country clusters.
Factors include economic, social and cultural, geographical, political
and digital country characteristics. The more similar these contexts,
the more effective the comparison of countries is expected to be. This
helps to understand why some countries may underperform or overperform
relative to their peers. Given the scope and aim of the analysis,
existing and readily available data was taken into account. Specific
inputs from the EU27 Member States were not necessary, avoiding data
collection burden.

Government representatives from Greece (Beginner), Germany, (Follower)
and Slovakia (Trend-setter) were consulted to VALIDATE the framework
for open data maturity descriptive profiles and macro clusters. Openly
shared views and feedback on a draft framework helped to improve and
strengthen the results of the analysis. After standardising and
completing the open data profiles and macro country clusters for the
EU27 Member States, the feasibility of extending the framework to
non-EU countries was assessed. Relevant recommendations were listed,
such as the additional data collection that would be needed for
Iceland, Norway and Switzerland, as well as Albania, Bosnia and
Herzegovina, Serbia and Ukraine.

 

FINDINGS

 EXPLORATION OF CONCEPTUAL MODELS AND COUNTRY PROFILES FOR THE EU27

EU Member States have several characteristics in common. However,
one-on-one comparisons are oftentimes difficult to make. As a
consequence, there are many different ways to group EU Member States
and make meaningful comparisons. In order to find the most relevant
approaches to profiles and clustering, a series of OTHER STUDIES IN
THE FIELD OF (OPEN) DATA AND DIGITAL GOVERNMENT were analysed on the
use of country profiles and clusters.

Besides the Open Data Maturity Assessment, various international open
data measurements have been performed over the years. These
comparative studies also provide different ways to profile countries
and cluster them. For example, the OPEN DATA INVENTORY (ODIN) looks
into data coverage and openness, in order to rank countries both
globally and based on geographical regions.[3]
[applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftn3] The
country contextual factors included in the inventory for comparative
purposes are related to: legal frameworks, data commitments, global
indexes of statistical capacity, and global indexes of governance and
human development. The OPEN DATA BAROMETER is a global measure of how
governments are publishing and using open data for accountability,
innovation and social impact.[4]
[applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftn4] In terms
of country clustering, it distinguishes between governments that have
adopted the Open Data Charter and those that, as G20 members, have
committed to the G20 Anti-Corruption Open Data Principles. It compares
both clusters to analyse how such international commitments influence
open data performance. In a fairly similar way, the GLOBAL DATA
BAROMETER provides overall countries scores, based on performance in
data governance, data capabilities, and data availability. The
analysis takes into account key challenges and development areas, such
as governance foundations, critical competencies, public finance,
public procurement and political integrity.[5]
[applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftn5] The GLOBAL
OPEN DATA INDEX used to look into multiple types of open datasets and
their public availability and quality, without comparing countries on
the basis of other contextual factors.[6]
[applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftn6] Moreover,
the OECD OPEN, USEFUL AND RE-USABLE DATA (OURDATA) INDEX provides
insights into the data-related availability, accessibility and
government support.[7]
[applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftn7] In
addition, the OECD DIGITAL GOVERNMENT INDEX (DGI) assesses digital by
design, data-driven public sector, government as a platform, open by
default, user-driven approaches and proactiveness. Findings from both
OECD measurement frameworks are specifically analysed in terms of
performance gaps between the cluster of OECD member countries and
accession countries.
[https://data.europa.eu/sites/default/files/img/media/Screenshot%202025-11-19%20at%2010.57.23.png]
FIGURE 4: Illustration of Open Data Inventory (ODIN) country profile

 

Beyond the field of open data, one of the AI WATCH publications from
the Joint Research Centre of the European Commission illustrates how
countries may be grouped on the basis of descriptive orientations
rather than normative performance.[8]
[applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftn8] The study
explores conceptual ways to profile the national AI strategies based
on how much the strategy insists on three main focus areas concerning:
data, internal AI capacity, and the external AI network.
[https://data.europa.eu/sites/default/files/img/media/Screenshot%202025-11-19%20at%2010.57.52.png]
FIGURE 5: Clustering of national AI strategies

 

The United Nations E-GOVERNMENT DEVELOPMENT INDEX (EGDI) groups
countries on the basis of their overall score (low, medium, high, very
high), as well as based on their development and income levels.[9]
[applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftn9] It also
offers breakdowns by global regions and data comparisons on the levels
of countries and cities specifically. In a similar way, the ICT
DEVELOPMENT INDEX from the International Telecommunication Union
relates ICT performance to income groups, regions and gross national
income (GNI) per capita.[10]
[applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftn10] An
example of a Commission study that clusters countries based on both
absolute and relative performance is the EGOVERNMENT BENCHMARK.
Countries obtain a score for a series of indicators on eGovernment
provision and obtain a score for the extent to which the population
uses these eGovernment services. This results in a typology with five
eGovernment profiles: Neophytes (low digitalisation and low uptake),
High Potentials (low digitalisation, medium or high uptake),
Progressives (medium digitalisation, low uptake), Builders (high
digitalisation, low or medium uptake) and Matures (high
digitalisation, high uptake).[11]
[applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftn11] These
profiles increase the comparability of countries, as countries in the
same group are ought to be more similar with similar challenges moving
forward. The study also grouped EU countries into five clusters on the
basis of several homogeneous contextual background statistics, such as
population size, (digital) educational skills levels, urbanisation and
implementation maturity of digital infrastructure. More recent
editions of the study divided eGovernment provision and uptake into
four clusters: Non-Consolidated eGovernment (low digitalisation, low
uptake), Unexploited eGovernment (low digitalisation, high uptake),
Expandable eGovernment (high digitalisation, low uptake) and Fruitful
eGovernment (high digitalisation, high uptake).[12]
[applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftn12]
[https://data.europa.eu/sites/default/files/img/media/Screenshot%202025-11-19%20at%2010.58.04.png]
FIGURE 6: eGovernment Benchmark country clusters

 

Next to studies from various international and supranational
organisations, country profiling and clustering is also visible in
various private sector studies on data-driven and digital governments.
For example, the Huawei GLOBAL DIGITALISATION INDEX (GDI) clusters
countries according to their level of ICT maturity and economic
development: starters (low digitalisation levels, with relatively low
GDP per capita), adopters (average digitalisation levels, with average
GDP per capita), frontrunners (high digitalisation levels, with
relatively high GDP per capita).[13]
[applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftn13] The
DATA-POWERED ENTERPRISESsurvey from the Capgemini Research Institute
analyses two key pillars: data foundations and data behaviours. [14]
[applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftn14]Organisations
can be characterised based on all underlying indicators as: data
laggards (low data foundations, low data behaviours), data aware (low
data foundations, high data behaviours), data enabled (high data
foundations, low data behaviours) or data masters (high data
foundations, high data behaviours).
[https://data.europa.eu/sites/default/files/img/media/Screenshot%202025-11-19%20at%2010.58.41.png]
FIGURE 7: Global Digitalisation Index (GDI) country clusters

 

Similar quadrants are presented in several AI publications. For
example, the matrix from GARTNER’S AI OPPORTUNITY RADAR has two
axes, equally on the level of organisations. [15]
[applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftn15] It looks
at whether Artificial Intelligence augments everyday processes or
create something game-changing. Furthermore, it considers whether the
technology primarily adds value to internal audiences and operations
or external client-facing audiences. The BCG DISTRIBUTION OF AI
ECONOMIES covers six archetypes based on AI readiness and exposure: AI
emergents (bottom 10% readiness, low AI exposure), AI gradual
practitioners (average readiness, low AI exposure), AI exposed
practitioners (average readiness, high AI exposure), AI rising
contenders (high readiness, low AI exposure), AI steady contenders
(high readiness, high AI exposure).[16]
[applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftn16]

 
[https://data.europa.eu/sites/default/files/img/media/Screenshot%202025-11-19%20at%2010.58.57.png]
FIGURE 8: Distribution of economies across the archetypes of AI
adoption

 

The above studies show that profiling countries based on their
approaches as well as clustering countries based on macro context
variables can be done in various ways. In many studies the focus is on
using macro variables, such as economic statistics, to better
interpret findings and compare countries on their performance. Fewer
studies have been looking into what approach or country profile is
underlying performance. Hence, analysing the specific open data
descriptive profiles and macro country clusters are both considered to
be valuable in light of the Open Data Maturity Assessment and wider
studies on data-driven governments.

 

OPEN DATA DESCRIPTIVE PROFILES

APPROACH

While this study aims to go beyond the normative structure of the Open
Data Maturity Assessment by developing a more descriptive framework,
the data.europa.eu Open Data Maturity Assessment remains a valuable
starting point. This is because the Open Data Maturity Assessment
provides a comprehensive way to understanding the development of
countries in making public sector information available and
stimulating its reuse. In fact, since its inception in 2015, the
methodology and the concepts encompassed in the Open Data Maturity
Assessment have been regularly updated in order to reflect the most
prominent open data developments across Europe. Furthermore, given the
breadth and depth of its coverage, the contents of the Open Data
Maturity questionnaire serve as an effective starting point for
creating a descriptive framework for understanding countries’ open
data activities and behaviours.

This study follows a two-phase approach for developing Open Data
Descriptive Profiles: first, by conceptualising the key parameters,
and second, by operationalising them. Starting with the
conceptualising phase, due to the normative nature of the Open Data
Maturity questionnaire and its broad groupings of questions under each
dimension, the analysis deliberately moved away from its original
indicator-based format. Instead, this study utilises the Open Data
Maturity questionnaire not as a fixed evaluative tool, but as a
thematic foundation.[17]
[applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftn17] In this
sense, conceptualisation of the open data descriptive profiles took an
inductive approach: starting with questions under each dimension, and
ultimately arriving at a list of descriptive parameters that describe
_how_ countries engage with open data based on themes that appear
between questions. The process, as displayed in Figure 8, begins by
ungrouping the questions from their original indicator-based structure
within each dimension and examining them individually. The focus,
then, is shifted to identifying groups questions that represent shared
descriptive characteristics or behaviours. These groups then form the
basis for defining descriptive parameters through which countries’
open data practices can be profiled and better understood.

Once a set of descriptive parameters have been identified within each
dimension, they are then examined across dimensions to detect
overlapping themes. When overlaps are found, similar parameters are
merged, resulting in a more concise and integrated set of descriptive
parameters that span all four dimensions. It is important to note,
throughout both the initial identification and the cross-dimensional
comparison phases, the parameters are continuously revised, refined,
and tested for coherence. This ensures that each parameter is clearly
defined, avoids redundancy, and captures a distinct aspect of open
data behaviour. This will also entail rigorous evaluation of which
questions are most relevant to distinguishing a certain parameter, and
which questions should be omitted from the exercise due to a lack of
relevance. Nonetheless, the goal is to arrive at a focused and
manageable list of parameters that are analytically meaningful and
practical. By keeping the list as concise as possible, we aim to
ensure relevance, maintain analytical focus, and reduce unnecessary
complexity.

For example, several Open Data Maturity questionnaire items from the
Policy dimension were considered to describe the policy approach of a
country, rather than the evaluative results of such an approach. These
questions were grouped and then compared with approach-type of
questions from the dimensions Quality, Portal and Impact dimensions,
as to bundle related questions and shape a meaningful parameter.
[https://data.europa.eu/sites/default/files/img/media/Screenshot%202025-11-19%20at%2010.59.35.png]
FIGURE 9: Open data parameter development process

 

Once the final set of descriptive parameters are defined, the next
task is to concretise the values that can be used to measure them,
which we envision as the operationalising phase. This phase is driven
by the definitions of descriptive parameters that were defined in the
previous step. Moreover, each parameter can be measured in one of two
ways: either directly through the original questions that informed its
definition, or through custom-developed metrics inspired by those
questions. These values serve as operationalisations of the
descriptive parameters, allowing them to be meaningfully assessed. The
data sources for these values are either the direct responses provided
by countries to the questions that are maintained as values under each
parameter, or alternative sources where relevant. Furthermore,
Individual questions from the Open Data Maturity questionnaire serve
two key purposes. First, when grouped they can reveal shared
descriptive traits or behaviours. Second, they can act as direct
evaluative tools, i.e., a means of collecting specific data from
countries about particular open data characteristics. In this sense,
we utilise the questionnaire to conceptualise the country profiling
framework and its parameters, and these parameters are operationalised
(i.e., measured) through, but not limited to, the questions used to
conceptualise them, with additional sources as well.

The final step is to profile countries based on the results of each
associated value. These results provide insight into how a country
goes about or behaves with respect to a specific descriptive
parameter. It is important to note that this profiling framework can
be seen as a dynamic and evolving tool – to be continuously updated
in response to technological advancements, emerging practices, and
shifts in the open data landscape, just as it is in the overall Open
Data Maturity Assessment Methodology.

FINDINGS

CONCEPTUALISING PHASE

The process of conceptualising descriptive parameters from the Open
Data Maturity questionnaire involved two key steps. First, we examined
the wording and intent questions in order to identify commonalities in
what they aimed to assess. This thematic regrouping allowed us to
define a set of descriptive parameters that more accurately reflect
the underlying practices and behaviours of countries in relation to
open data. For example, several questions within the Policy dimension
focused on the presence and coherence of national strategies, legal
frameworks, and governance structures. These were collectively
interpreted as relating to the ‘Strategic and Legal Foundations’
of open data. Accordingly, we defined this parameter as: “the
existence, scope, and coherence of national and subnational open data
policies, strategies, and legal frameworks.” The full list of
preliminarily parameters can be found in Figures the Annex 5.2.

This process resulted in a total of 19 preliminary parameters: five
within the Policy dimension, four within the Quality dimension, four
within the Portal dimension, and six within the Impact dimension.
These parameters are displayed in the appendix, with asterisks beside
the questions that ultimately made it to the final set of parameters.

In the second step, these parameters were compared across dimensions
to identify overlapping themes and reduce redundancy. As previously
noted, this involved continuous revision and refinement to ensure each
parameter was clearly defined, analytically meaningful, and free of
redundancy. Building on this foundation, multiple factors were
considered when revising the original 19 parameters into a more
concise and integrated set. First, we assessed cross-dimensional
alignment to identify parameters that reflected similar behaviours
across different dimensions. One example is the Ecosystem Development
parameter under the Policy dimension, which focused on stakeholder
engagement in open data capabilities. This parameter was found to
closely relate to Community Engagement under the Portal dimension,
which examined stakeholder interaction with the national portal. Due
to their similarity, they were initially merged to form a unified
parameter capturing stakeholder collaboration.

The second factor considered was feasibility, specifically the
practicality of measuring each parameter using the available data.
Parameters were evaluated based on the clarity, consistency, and
relevance of the underlying questions. In the case of the merged
Ecosystem Development and Community Engagement parameter, it was
ultimately excluded from the final set due to feasibility concerns.
The questions and/or metrics associated lacked sufficient specificity
to support reliable cross-country comparison. Moreover, the behaviours
it aimed to capture were partially reflected in other, more robust
parameters such as the Governance parameter, which reduced its
distinctiveness and analytical value. Another example is the original
Data Accessibility and Reusability parameter, which sought to describe
how countries ensured that open data was both available and usable.
While conceptually rich, it drew on 19 underlying questions from both
the Portal and Quality dimensions, making it difficult to
operationalise in a focused and consistent way. To address this, the
parameter was revised and reframed into a more targeted open data
supply approach, which allowed us to isolate and measure specific
aspects of accessibility and reusability more effectively. This
transformation reflects a broader principle in our approach: while
conceptual breadth is valuable, it must be balanced with
methodological clarity and practical measurability to ensure the
framework remains both meaningful and usable.

This consolidation process led to a refined set of seven parameters.
Following a second round of evaluation, focused on the definitions and
underlying questions of each parameter, the final set was narrowed
down to six.

OPERATIONALISING PHASE

Each of the six parameters are operationalised by the most accurate
and relevant source of data that enables its measurement for
participating countries. In some instances, this meant keeping the
questions used to conceptualise the parameter as a metric for
measuring countries approaches. For others, additional data sources
(e.g., form the data.europa.eu portal, official documentation online)
were incorporated. The full list of data sources used to
operationalise each parameter is displayed in Figure 10.

 

		PARAMETER
		ID
		QUESTION

		Governance Approach
		P14
		How would you classify the model used for governing open data in
your country?

		 
		 
		 

		Open Data Quality Strategy
		PT39
		Does the national portal provide a way for non-official data (e.g.
community-sourced or citizen-generated data) to be published?

		PT7
		Does the national portal provide functionality for users to
contribute datasets they have produced or enriched?

		Q23
		Do you use a model (such as the 5-Star Open Data or FAIR) to assess
the quality of deployment of data in your country?

		Q24
		Do you conduct activities to promote and familiarise data providers
with ways to ensure higher quality data?

		Q15
		Does the national portal follow the DCAT-AP framework or, if not,
are standards in place to ensure interoperability with DCAT-AP?

		P6
		Does the national strategy/policy outline measures to incentivise
the publication of and access to citizen-generated data?

		 
		 
		 

		Domain of Impact
		I12
		Is any data on the impact created by open data on governmental
challenges (e.g. efficiency, effectiveness, transparency,
decision-making capacity) available in your country?

		I13
		Is the use of open data in your country having an impact on the
efficiency and effectiveness of the government (at any level) in
delivering public services?

		I14
		Is the use of open data in your country having an impact on
transparency and accountability of public administrations?

		I15
		Is the use of open data in your country having an impact on
policy-making processes?

		I16
		Is the use of open data in your country having an impact on
decision-making processes?

		I17
		Is any data on the impact created by open data on social challenges
(e.g. inequality, healthcare, education) available in your country?

		I18
		Is the use of open data in your country having an impact on
society’s ability to reduce inequality and better include
minorities, migrants, and/or refugees?

		I19
		Is the use of open data in your country having an impact on issues
about housing in urban areas?

		I20
		Is the use of open data in your country having an impact on the
issues of health and wellbeing?

		I21
		Is the use of open data in your country having an impact on the
society’s level of education and skills (e.g. data literacy)?

		I22
		Is any data on the impact created by open data on environmental
challenges (e.g. climate change and environmental degradation, as
highlighted in the European Green Deal) available in your country?

		I23
		Is the use of open data in your country having an impact on the
level of protection of biodiversity (e.g. maintaining a good air and
water quality)?

		I24
		Is the use of open data in your country having an impact on the
achievement of more environment-friendly cities (e.g.,
environment-friendly transport systems, waste management etc.)?

		I25
		Is the use of open data in your country having an impact on the
fight against climate change, for example by undertaking predictive
monitoring, preventive actions, or a differentiated response to
connected disasters?

		I26
		Is the use of open data in your country having an impact on the
consumption of energy based on fuel and the switch to renewables?

		I27
		Is any data on the economic impact (e.g. GDP, employment,
productivity, innovation, new businesses created etc.) of open data
available in your country?

		I28
		Is the use of open data in your country having an impact on the
level of employment?

		I29
		Is the use of open data in your country having an impact on the
level of innovation and the adoption of new technologies?

		I30
		Is the use of open data in your country having an impact on the
level of entrepreneurship (especially of women and minorities) and
business creation (especially with Small- and Medium-sized
Enterprises)?

		I31
		Is the use of open data in your country having an impact on the
level of productivity?

		 
		 
		 

		Portal Technical Foundations
		PT2
		What is the technology stack of your portal (e.g. based on uData,
CKAN, etc.)

		 
		 
		 

		Funding
		R6
		What is the annual budget of the national portal?

		 
		 
		 

		Transposition of the Open Data Directive
		P1
		Is there a national open data policy in your country and, if your
country is an EU Member State, does this include a national
legislation for the transposition of the Open Data Directive?

FIGURE 10: Specific questionnaire questions and data sources informing
each parameter

 

GOVERNANCE APPROACH

The GOVERNANCE APPROACH parameter is based on question P13, which
asks: “How would you classify the model used for governing open data
in your country?”. [18]
[applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftn18] Countries
can choose from three governance models—top-down, hybrid, or
bottom-up—which are used to profile their approach to open data
governance. 27 countries (_Albania, Austria, Bosnia and Herzegovina,
Belgium, Switzerland, Germany, Denmark, Spain, Finland, France,
Croatia, Hungary, Iceland, Italy, Lithuania, Luxembourg, Latvia,
Malta, Netherlands, Norway, Poland, Portugal, Romania, Serbia, Sweden,
Slovakia, Ukraine_) reported a hybrid approach, and 7 (_Bulgaria,
Cyprus, Czech Republic, Estonia, Greece, Ireland, Slovenia_) reported
a top-down model. Notably, no country identified with a bottom-up
model, suggesting that open data governance across Europe tends to
involve some level of central coordination or mixed responsibility.

However, insights gathered through consultations with Member States
reveal that the real-world governance of open data is often more
nuanced than the questionnaire suggests. Specifically, Member States
expressed that the concept of open data governance remains vague and
underdefined as it appears in the current question listed above. In
our discussions we explored the concept of open data governance with
Member States and their interpretation of the ‘hybrid’ category,
inquiring about whether it should be understood in terms of
coordination mechanisms, funding sources, or even technical
infrastructure. One country described a hybrid governance model is
both central government and external actors (such as NGOs) actively
contribute to the development and promotion of open data initiatives.
This interpretation reflects ‘hybrid’ as a collaborative
governance structure. Another country emphasised the importance of
understanding constitutional and organisational structures. They noted
that while their states and NGOs run their own programs, the
overarching governance is shaped by legal and institutional
frameworks. This highlights that governance can not only be about
coordination but also about legal authority and infrastructure.
Furthermore, these sentiments highlight the need for the Open Data
Maturity questionnaire to explicitly articulate the dimensions of
governance it seeks to capture.

OPEN DATA QUALITY STRATEGY

The OPEN DATA QUALITY STRATEGY parameter captures whether the
orientation of a country’s open data efforts is more tailored
towards diversity, depth, or both. When profiled, most countries (over
60%) adopt a depth-oriented approach, indicating that currently there
is a strong emphasis on data richness, technical standards, and
interoperability rather than expanding the diversity of data sources.

A depth-oriented approach implies a country places greater focus on
ensuring that the open data published is of high quality, maintaining
technical standards and interoperability. To assess this, three
questions from the Open Data Maturity questionnaire are considered,
specifically measuring whether the national portal complies with
interoperability standards (DCAT-AP) (Q15), whether a formal model
such as the 5-Star or FAIR framework is used to assess data quality
(Q23), and whether activities are undertaken to promote and
familiarise data providers with practices that ensure high-quality
data publication (Q24).[19]
[applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftn19]

A diversity-oriented approach implies a greater focus on publishing a
greater variety of data types. It reflects openness to community
participation and alternative data generation. For a
diversity-oriented approach, three questions from the questionnaire
are used, specifically measuring whether the national portal allows
user-contributed datasets (PT7) whether it supports the publication of
non-official data (PT39), alongside a third metric, whether the
national strategy/policy supports publication of and access to citizen
generated data (P6).

In order to distinguish a specific profile for countries under this
parameter, the responses to these two sets of questions were examined,
one for measuring data diversity and one for measuring data depth,
analysing the distribution of "yes" answers within each set. The
profiling logic is as follows: a country is considered more
‘depth-oriented’ if it has a greater number of ‘yes’ responses
to depth-related questions than to diversity-related ones, and vice
versa for a ‘diversity-oriented’ classification. If the number of
‘yes’ responses is equal across both sets of questions—or if all
responses are ‘yes’—the country is categorised as having a
‘balanced approach.’ Finally, if all responses are either ‘no’
or ‘I don’t know,’ the country is classified as
‘unclassified’.

Among the countries assessed, 21 (_Belgium, Switzerland, Czech
Republic, Germany, Denmark, Greece, Finland, Croatia, Hungary,
Iceland, Italy, Lithuania, Luxembourg, Latvia, Malta, Netherlands,
Norway, Romania, Serbia, Sweden and Slovenia_) were profiled as
demonstrating a depth-oriented approach. These countries focus on
improving the quality of open data rather than stimulating a wide
variety of open data. An additional 11 countries (_Albania, Austria,
Cyprus, Estonia, Spain, France, Ireland, Poland, Portugal, Slovakia
and Ukraine_) were found to exhibit a balanced approach, showing
quality approaches that cover both depth and diversity of open data.
_Bulgaria_ was the only country profiled with a diversity-oriented
approach, meaning it puts greater efforts on ensuring pluriform open
data compared to richness of datasets, while _Bosnia and Herzegovina_
was the sole country categorised as unclassified, as it demonstrates
neither depth nor diversity related orientation traits.

 

FIGURE 11: 
[https://data.europa.eu/sites/default/files/img/media/Screenshot%202025-11-19%20at%2011.07.16.png]
 

 

DOMAIN OF IMPACT

The DOMAIN OF IMPACT parameter describes the breadth of open data
impact across four domains: governmental, social, environmental, and
economic. This is based on five standardised questions per domain in
the Open Data Maturity questionnaire, focusing on governmental
(I12-I16), social (I17-I21), environmental (I22-I26) and economic
(I27-I31) open data impact.[20]
[applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftn20] These
questions assess the availability of impact data, contributions to
societal goals (e.g., biodiversity, public service delivery, social
inclusion), practical and strategic impacts (e.g., on cities, public
health, entrepreneurship), and influence on decision-making and
productivity. A country is considered "active" in a domain if it
answers "yes" to at least three out of five questions. Based on the
number of active domains, countries are categorised as follows:
"All-rounders" (active in all four domains), "Partial All-rounders"
(active in three), "Specialists" (active in one or two, with domains
specified), and "Non-engaged" (active in none). In total, 18 countries
(_Austria, Switzerland, Cyprus, Czech Republic, Denmark, Estonia,
Spain, France, Ireland, Italy, Lithuania, Netherlands, Poland,
Portugal, Serbia, Sweden, Slovakia, Ukraine_) demonstrated an
all-rounder approach, 9 (_Belgium, Bulgaria, Germany, Finland,
Hungary, Luxembourg, Latvia, Norway, Slovenia_) reported a partial
all-rounder approach, 2 (_Croatia, Iceland)_ demonstrated a
specialised approach, and 5 (_Albania, Bosnia and Herzegovina, Greece,
Malta, Romania_) demonstrated a non-engaged approach.

Consultations with Member States underscored the need for greater
clarity and specificity in defining what “impact” entails, as the
current formulation was perceived as too abstract. Several
participants emphasized that the definition should be more nuanced,
capturing dimensions such as the duration of impact (e.g., short-term
versus long-term effects). To address this, the definition of
“impact” in the questionnaire could be expanded to explicitly
include dimensions such as duration and scope.
[https://data.europa.eu/sites/default/files/img/media/Screenshot%202025-11-19%20at%2011.07.31.png]
FIGURE 12: Example of the Domain of Impact country profile and its
criteria 

 

PORTAL TECHNICAL FOUNDATIONS

The PORTAL TECHNICAL FOUNDATIONS parameter describes the technical
setup of a country’s national open data portal, with a specific
focus on the underlying technology stack. This parameter is derived
from question PT2[21]
[applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftn21] in the
Portal dimension of the Open Data Maturity questionnaire, which asks:
“What is the technology stack of your portal?”. Countries are
profiled based on whether their portal is built on a standard platform
(e.g., CKAN, uData, or Piveau), which are widely adopted open-source
solutions commonly used for open data portals, or on a custom
platform, which includes bespoke or proprietary systems tailored to
national or organisational needs. Although countries could be
categorised based on the specific platform they report, for the sake
of comparability, they are grouped into these two broader
categories. 22 countries (_Denmark, Serbia, Hungary, Bosnia and
Herzegovina, Germany, Croatia, Iceland, France, Austria, Switzerland,
Greece, Italy, Slovenia, Finland, Luxembourg, Portugal, Spain,
Ireland, Romania, Netherlands, Latvia, Ukraine_) reported utilising a
standard platform, while 12 (_Bulgaria, Estonia, Poland, Belgium,
Lithuania, Norway, Czech Republic, Slovakia, Malta, Sweden, Albania,
Cyprus_) reported a custom platform. It is important to note that the
platform technologies referenced may evolve or become obsolete over
time. Considering the ‘live’ nature of this framework, this
parameter will be updated routinely to reflect newer technologies,
ensuring its profiling is relevant and up-to-date.

FUNDING

The FUNDING parameter reflects the financial commitment and
sustainability of a country’s investment in open data, specifically
focusing on the annual budget allocated to the national portal. Due to
the limited availability of open and verifiable data on broader open
data investments, often considered back-end or confidential, this
parameter relies on question R6 from the Open Data Maturity
questionnaire, which asks: “What is the annual budget of the
national portal?”. To categorise countries, a percentile-based
method is used instead of averages, which can be skewed by extreme
values. This approach allows for a more accurate understanding of how
countries compare relative to one another. Based on this method,
countries are grouped into three categories: Low (budgets between €0
and €200,000), Medium (€200,001 to €600,000), and High (above
€600,001). 6 countries (_Germany, Denmark, Estonia, France, Norway,
Poland_) reported a relatively high funding, 7 (_Austria, Spain,
Ireland, Italy, Luxembourg, Netherlands, Portugal_) reported a
relatively medium budget, while 21 (_Albania, Bosnia and Herzegovina,
Belgium, Bulgaria, Switzerland, Cyprus, Czech Republic, Greece,
Finland, Croatia, Hungary, Iceland, Lithuania, Latvia, Malta, Romania,
Serbia, Sweden, Slovenia, Slovakia, Ukraine_) reported a relatively
low funding.

When consulting Member States regarding this funding parameter, the
need to broaden the scope of budget analysis beyond the national level
and the open data portal was highlighted. Namely it was expressed that
it would be more accurate to include regional and local funding, such
as municipal open data budgets, in order to capture the full picture
of financial commitment. Participants also underlined that examining
_how_ budgets are allocated—whether toward technical infrastructure,
capacity building, or promoting data reuse—can reveal important
insights into a country’s open data priorities. For example, one
country noted that its national portal budget is primarily directed
toward technical underpinnings, with less emphasis on reuse promotion.

These insights suggest that the funding parameter requires greater
nuance in both scope and structure. Rather than relying solely on a
single annual portal budget figure, it would be beneficial to change
the scope of question R6 to capture a broader set of variables.
Specifically, this may involve extending the temporal scope of budget
analysis. Since many Member States operate on multi-year financial
cycles, often undertaking major portal upgrades every three to five
years and allocating the remaining funds to routine maintenance, it is
more accurate to assess the average open data budget over the past
five years rather than relying on a single-year figure. In addition,
this may involve broadening the definition of budget to include both
national and regional allocations, disaggregated across key
expenditure categories such as technical infrastructure,
capacity-building, and promotional or engagement activities.

TRANSPOSITION OF THE OPEN DATA DIRECTIVE

 The TRANSPOSITION OF THE OPEN DATA DIRECTIVE parameter describes
_how_ countries have adopted or implemented the Open Data Directive
within their national legal frameworks. Unlike for the transposition
of the Public Sector Information Directive, detailed and publicly
available information on how countries have transposed the Open Data
Directive is more limited.[22]
[applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftn22]
Therefore, at the moment the most accurate and accessible source for
this parameter is question P1 from the Open Data Maturity
questionnaire, which asks: “Is there a national open data policy in
your country and, if your country is an EU Member State, does this
include a national legislation for the transposition of the Open Data
Directive?”.

The responses to this question provided valuable insights into the
legal instruments used for transposition, such as dedicated laws,
amendments to existing legislation, or broader digital laws, as well
as supporting strategies, roadmaps, and the institutional arrangements
for coordination and enforcement. This comparative analysis enabled to
group countries based on shared structural and procedural
characteristics in their approach to transposing and implementing the
Directive. Nearly all Member States have already implemented the
Directive or plan to do so within the next two years, so the emphasis
here is not on whether transposition has occurred, but rather, the way
in which it has been achieved. While non-EU countries are not formally
required to transpose EU legislation, they are included in this
analysis as active participants in the Open Data Maturity study.[23]
[applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftn23] In
addition, their inclusion remains valuable for peer-to-peer comparison
as many of these non-EU countries have voluntarily aligned with the
directive. In fact, only Norway and Bosnia and Herzegovina reported
that their transposition efforts are still in progress.

Countries are categorised into three distinct transposition types:
Amending existing laws, Standalone legislation, and multi-instrument
approach. 15 countries (_Belgium, Croatia, Czechia, Denmark, Estonia,
France, Greece, Iceland, Italy, Malta, Netherlands, Serbia, Slovakia,
Spain, Switzerland_), have amended the Directive into existing law,
meaning, they have integrated it into pre-existing frameworks without
introducing a separate dedicated act. Meanwhile, 10 countries
(_Albania, Austria, Cyprus, Germany, Ireland, Luxembourg, Poland,
Portugal, Romania, Sweden_), have standalone legislation which are
explicitly dedicated to open data and public sector information reuse.
Lastly, 7 countries (_Bulgaria, Finland, Hungary, Latvia, Lithuania,
Slovenia, and Ukraine_), report a multi-instrument approach, meaning
the Directive is transposed using a coordinated set of legal and
policy tools (e.g., amendments to several laws, additional decrees or
ordinances, strategic or policy documents that complement legal
measures).

OVERALL OPEN DATA DESCRIPTIVE PROFILES?

Following the classification of countries across each parameter, a
comprehensive overall profile was considered, an encompassing profile
that considers all parameters. It would be valuable to know whether
several countries have similar open data descriptive profiles, analyse
how these shared profiles relate to open data maturity scores, and
compare with countries having a different open data approach. However,
33 unique profiles appeared in this attempt among the 34 countries
under analysis. This high level of uniqueness posed a challenge:
nearly every country had a distinct profile, even when simplifying the
combinations of parameters. This made the peer-to-peer aim of this
study obsolete. Nevertheless, future iterations may revisit the
concept of overarching profiles to assess whether they can be refined
in ways that enhance their comparative utility and practical relevance
for participating countries. In any case, the open data maturity
scores and country-specific profiles can be related to countries
grouped in macro country clusters, discussed in the next section.

 

_MACRO COUNTRY CLUSTERS_

APPROACH

In order to facilitate peer-to-peer learning among countries, ideally
countries are compared that share as much similarities as possible.
The previous chapter analysed open data approaches and related
descriptive profiles. This showed how countries organise and improve
open data practices. By looking into the background characteristics of
countries and clustering them, it becomes clear whether similar
countries perform similarly, or whether some countries outperform
others, potentially because they use different open data approaches.

Taking inspiration from the studies discussed in chapter 3.1, a set of
macro context characteristics has been analysed to serve this purpose.
Factors include ECONOMIC, SOCIAL AND CULTURAL, GEOGRAPHICAL, POLITICAL
AND DIGITAL CHARACTERISTICS. The more similar these contexts, the more
effective the comparison of countries is expected to be.

FINDINGS

ECONOMIC CHARACTERISTICS

Economic characteristics are commonly used to find comparable
countries. The Eurostat country factsheets summarise key indicators
for all Member States, including indicators that relate to their
ECONOMY.[24]
[applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftn24] Key
examples of economic characteristics are: gross domestic product (GDP)
per capita, unemployment rates, inflation rates, and government debt
as percentage of the gross domestic product (GDP). Other economic
indicators were considered of less relevance in light of this open
data study, including country statistics on gender pay gap, minimum
wage, people at risk of poverty or social exclusion, etc.
[https://data.europa.eu/sites/default/files/img/media/Screenshot%202025-11-19%20at%2010.50.47.png]
FIGURE 13: Illustration of Eurostat country factsheets 

 

SOCIAL AND CULTURAL CHARACTERISTICS

Countries can also be clustered on the basis of SOCIAL AND CULTURAL
characteristics. The EU published official country profiles on its
website, listing several indicators.[25]
[applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftn25] A common
indicator is the population size of a country.[26]
[applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftn26]Demographics
are considered to be a defining element too (e.g. the ratio between
youth and elderly).[27]
[applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftn27] The same
goes for education levels.[28]
[applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftn28] The EU
has 24 official languages, besides minority languages, dialects, sign
languages, etc. Eight of these languages are official national
languages in multiple Member States.[29]
[applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftn29] These may
shape linguistic regions: Dutch (Belgium, Netherlands), English
(Ireland, Malta), Finnish (Finland, Sweden), French (Belgium, France,
Luxembourg), German (Austria, Belgium, Germany, Luxembourg), Greek
(Cyprus, Greece), Slovak (Slovakia, Czechia, Hungary), and Swedish
(Sweden, Finland). More specific and less relevant characteristics
include life expectancy, poverty rate, Gini coefficient and composites
like the Human Development Index (HDI) from the United Nations.[30]
[applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftn30]

GEOGRAPHICAL CHARACTERISTICS

Countries that are GEOGRAPHICALLY close to each other may be clustered
together. The EuroVoc Geographic Regions defines the EU27 countries
into four European regions: Central and Eastern Europe (Bulgaria,
Croatia, Czechia, Hungary, Poland, Romania, Slovakia, Slovenia),
Northern Europe (Denmark, Estonia, Finland, Latvia, Lithuania,
Sweden), Southern Europe (Cyprus, Greece, Italy, Malta, Portugal,
Spain) and Western Europe (Austria, Belgium, France, Germany, Ireland,
Luxembourg, Netherlands).[31]
[applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftn31] Although
the geographical size of countries is not included in this analysis,
urbanisation levels have been included as it often relates to other
key macro indicators.[32]
[applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftn32]
[https://data.europa.eu/sites/default/files/img/media/Screenshot%202025-11-19%20at%2010.52.42.png]
FIGURE 14: Example of political alliances in the EU

 

POLITICAL CHARACTERISTICS

The EU Member States have different types of POLITICAL SYSTEMS with
different types of government and different types of parliaments.
Government forms ranges from parliamentary, semi-presidential,
presidential, with federal, devolved and unitary structures, as well
as republic states and constitutional (popular) monarchies.[33]
[applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftn33] These
systems can play an important role in how democratic power and roles
are allocated. Accordingly, the (de)centralisation levels of Member
States differ. For instance, Ireland measures as most centralised for
fiscal, administrative and political decision making, while Germany
most decentralised.[34]
[applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftn34] Relevant
to open data and transparency, the World Bank good governance
indicators complement these macro characteristics, related to Voice
and Accountability, Political Stability and Absence of
Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule
of Law, and Control of Corruption.[35]
[applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftn35]Furthermore,
countries may have different political memberships. Apart from the
Euro and Schengen areas, the duration of the EU membership is
considered to play a role in the international cooperation among
different EU Member States. Other (in)formal regional groups are
considered to be less influential, such as the Benelux (Belgium,
Luxembourg, Netherlands), Iberian Summit (Portugal, Spain), Salzburg
Forum (Austria, Bulgaria, Croatia, Czechia, Hungary, Poland, Romania,
Slovakia, Slovenia) and New Hanseatic League (Denmark, Estonia,
Finland, Ireland, Latvia, Lithuania, Netherlands, Sweden).[36]
[applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftn36]

DATA AND DIGITAL CHARACTERISTICS

The European Commission’s Digital Decade Policy Programme sets the
digital priorities until 2030. It comes along a set of Digital Decade
Key Performance Indicators (KPIs) related to four cardinal points:
Digital Infrastructure, Digital Transformation of Business, Digital
Skills, and Digital Public Services.[37]
[applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftn37] Some of
these DATA AND DIGITAL CONTEXT CHARACTERISTICS have been included in
the clustering model, in light of countries’ open data maturity.
From the Digital Infrastructure cardinal point, overall 5G coverage
has been taken into account. From the Digital Transformation of
Business pillar Cloud take-up, Data analytics take-up and AI take-up
are factored in, which all can be considered enablers of open data or
technological developments that benefit from open data. From the
Digital Skills area Basic digital skills and the number of ICT
specialists may influence open data maturity scores.[38]
[applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftn38] Other
contextual factors deemed relevant, and for instance included in the
Interoperable Europe (IOPEU) and former National Interoperability
Framework Observatory (NIFO) are: Data and information skills that may
relate to data reuse opportunities, as well as R&D expenditures from a
wider innovation point of view.[39]
[applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftn39]

MAPPING OF THE EU27 MEMBER STATES ALONG MACRO COUNTRY CLUSTERS

The previous chapter analysed open data approaches and related
profiles. This showed how countries organise and improve open data
practices. By looking into the background characteristics of countries
it becomes clear whether similar countries part of cluster perform
similarly, or whether some countries outperform others, potentially
because they use different approaches.

To find groups of countries with similar macro characteristics, a
standard CLUSTER ANALYSIS was conducted, using K-means analysis in
KNIME, an open-source data science platform used in several EU
projects and initiatives.[40]
[applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftn40] A
deliberate split into five clusters was made to ensure each cluster is
both meaningful and includes multiple countries per cluster. After
collecting all data for all selected context variables, normalisation
was applied to synchronise variables using different scales (e.g. some
metrics run from 0% to 100%, while others from 0 to 1). Less relevant
nominal variables, such as linguistic regions, were left out of the
statistical analysis. However, these softer characteristics could be
included in country profile pages still, based on the individual
comparison preferences of policy makers, journalists, researchers,
citizens and others interested in open data maturity.

After running the analysis, Hungary, Romania and Croatia were grouped
to the closest statistical cluster, while these initially did not
belong in clusters with at least three countries. The analysis
resulted in the following clusters, based on set of 19 macro context
characteristics:

	* SOUTHERN AND WESTERN EUROPEAN BELT (Germany, France, Italy, Spain,
Belgium, Portugal, Greece): this group of countries are relatively
large in terms of population, with relatively many elderly compared to
youth, with average education levels and average shares of people
living in urban areas. These countries show average economic contexts
and welfare. This group contains countries that have been member of
the EU for a relatively long time. Political decision-making is
moderately decentralised, while governance standards are in line with
the EU average. Countries show average data and digital related
backgrounds and infrastructures.
	* CENTRAL AND EASTERN EUROPE (Poland, Romania, Czechia, Hungary,
Austria, Bulgaria, Slovakia, Croatia, Slovenia): this group of
countries has medium to large populations with demographical
compositions similar to the EU average. Relatively many citizens in
these countries attained educational primary and secondary education
and relatively many live in rural areas. The economic context in these
countries can be describes as below average. Various countries have
‘young’ EU memberships. Political decision-making is as
(de)central as the EU at large, while governance standards turn out
below average. The data and digital infrastructure of this cluster is
matured less than in other clusters.
	* NORNELUX (Netherlands, Sweden, Denmark, Finland, Luxembourg): this
group of countries are medium sized in terms of population showing
demographical compositions similar to the EU trend, yet with many
highly educated people, living in average urbanised environments. The
economic prosperity of the Nordic countries, Netherlands and
Luxembourg is above average. These countries have been relatively long
with the EU and share relatively decentral political decision-making,
with high governance standards. The wider data and digital capacities
of these countries are highly matured.
	* BALTICS (Lithuania, Latvia, Estonia): this group of countries have
relatively small populations with similar demographic structures and
educational levels as found elsewhere in the EU. Urbanisation levels
are above the EU tendency. The economic context and climate are
slightly less optimal than elsewhere in the EU. Furthermore, the
Baltic states have joined the EU more recently than countries in some
other clusters. Political decision-making processes are as (de)central
as in the EU at large, with average governance standards. The data and
digital infrastructures are on par with the rest of the EU too.
	* ISLAND-BASED NATIONS (Ireland, Cyprus, Malta): this group of
countries have fairly small and young populations, with a relatively
highly educated workforce. Comparatively, many people live in the
urban parts of the islands where these nations are situated. Economic
variables sit around the EU average. Besides medium to long EU
memberships, these countries share highly centralised levels of
political decision-making and average governance standards. The data
and digital environment of this cluster can be characterised as
(above) average.

In terms of open data maturity, some groups of countries perform
(slightly) better than others. However, these differences are not
substantial from a statistical point of view, based on one-way
Analysis of Variance (ANOVA) and Tukey Honestly Significant Difference
(HSD) post-hoc statistical test. In fact, the open data maturity
levels of the Clusters 1, 2 and 3 centre closely around the EU27
average of 84%, with respective scores of 83%, 84% and 82%. Even
though Cluster 4 averages 93% and Cluster 5 performs below average
with 79%, differences are not statistically significant compared to
each other. In other words, the FIVE COUNTRY CLUSTERS PERFORM
RELATIVELY ALIKE.

Moreover, different performances also occur within each of the
clusters. Some of the COUNTRY PERFORMANCES WITHIN A SPECIFIC CLUSTER
DO SHOW STATISTICAL GAPS, meaning a country outperforms or
underperforms one or several peer countries. For example, in the first
group of countries, France outperforms most of its cluster peers,
while Greece underperforms relative to countries like Germany, Italy,
Spain, Belgium and Portugal. In its country cluster, Poland
outperforms Croatia and Bulgaria, while the open data maturity of
countries in this cluster is fairly comparable, including those of
Romania, Czechia, Hungary, Austria, Slovakia and Slovenia.
Furthermore, Denmark outperforms most peers, while the Netherlands,
Sweden, Finland and Luxembourg perform much alike. In the last
cluster, it becomes visible that Malta lags behind its peers Cyprus
and Ireland, even though these countries have greater similarities
looking at their macro contextual characteristics.
[https://data.europa.eu/sites/default/files/img/media/Screenshot%202025-11-19%20at%2011.08.07.png]
FIGURE 15: Macro EU country clusters

 

OVERVIEW OF MACRO CONTEXT CHARACTERISTICS

The following statistical variables were used in the clustering
analysis (leaving out less relevant nominal variables*). The full
dataset is provided in a separate attachment.

 

		_COUNTRY CONTEXT CHARACTERISTICS_
		_DESCRIPTION_
		_MEASUREMENT FREQUENCY_
		_REFERENCE YEAR_
		_COVERAGE_
		_SOURCE_

		_ECONOMIC_
		 
		 
		 
		 
		 

		_GDP per capita (in PPS)_
		_The gross domestic product per capita in Purchasing Power Standards
shows the economic output per person adjusted for differences in price
levels._
		_Annually_
		_2024_
		_EU27+_
		_Eurostat_
[https://ec.europa.eu/eurostat/databrowser/view/sdg_08_10/default/table?lang=en]

		_Unemployment rate_
		_Share of the labour force without work._
		_Annually_
		_2024_
		_EU27+_
		_Eurostat_
[https://ec.europa.eu/eurostat/databrowser/view/tps00203/default/table?lang=en&category=t_labour.t_employ.t_lfsi.t_une]

		_Inflation rate_
		_The annual percentage change in the price index._
		_Annually_
		_2024_
		_EU27+_
		_Eurostat_
[https://ec.europa.eu/eurostat/databrowser/view/tec00118/default/table?lang=en&category=t_prc.t_prc_hicp]

		_Government debt as % of GDP_
		_Economic ratio between a country's total government debt and its
gross domestic product._
		_Annually_
		_2024_
		_EU27_
		_Eurostat_
[https://ec.europa.eu/eurostat/databrowser/view/sdg_17_40/default/table?lang=en&category=t_gov.t_gov_gfs10.t_gov_dd]

		_SOCIAL AND CULTURAL_
		 
		 
		 
		 
		 

		_Population size_
		_The total number of people residing in a country._
		_Annually_
		_2025_
		_EU27+_
		_Eurostat_
[https://ec.europa.eu/eurostat/databrowser/view/tps00001/default/table?lang=en&category=t_demo.t_demo_pop]

		_Demographic dependency ratio_
		_Ratio between the number of persons aged 65 and over and the number
of persons aged between 15 and 64._
		_Annually_
		_2024_
		_EU27+_
		_Eurostat_
[https://ec.europa.eu/eurostat/databrowser/view/tps00198/default/table?lang=en&category=t_demo.t_demo_ind]

		_Linguistic regions*_
		_Countries with the same language as official language._
		_Annually_
		_2024_
		_EU27_
		_Eurostat_ [https://europa.eu/eurobarometer/surveys/detail/2979]

		_Education level_
		_Share of people with tertiary educational attainment (ISCED levels
5-8)._
		_Annually_
		_2024_
		_EU27+_
		_Eurostat_
[https://ec.europa.eu/eurostat/databrowser/view/edat_lfs_9903__custom_17376930/default/table?lang=en]

		_GEOGRAPHICAL_
		 
		 
		 
		 
		 

		_Geographical regions*_
		_EuroVoc Geographic Regions: Northern Europe, Western Europe,
Central and Eastern Europe, and Southern Europe._
		_Annually_
		_2025_
		_EU27+_
		_Publications Office of the EU_
[https://eur-lex.europa.eu/browse/eurovoc.html?params=72#arrow_7206]

		_Neighbouring countries*_
		_EU countries that share a border._
		_Annually_
		_2025_
		_EU27+_
		_Publications Office of the EU_
[https://eur-lex.europa.eu/browse/eurovoc.html?params=72#arrow_7206]

		_Urbanisation level_
		_Share of the population living in cities._
		_Annually_
		_2024_
		_EU27_
		_Eurostat_
[https://ec.europa.eu/eurostat/databrowser/view/ilc_lvho01/default/table?lang=en&category=degurb.degurb_livcon]

		_POLITICAL_
		 
		 
		 
		 
		 

		_Political system*_
		_Form of government being a presidential republic, semi-presidential
republic, parliamentary republic or parliamentary constitutional
monarchy._
		_One-off_
		_2020_
		_EU27_
		_European Committee of the Regions_
[https://portal.cor.europa.eu/divisionpowers/Pages/default.aspx]

		_Degree of self-governance*_
		_Allocation of competence, varying from a unitary state, devolved
state, federacy or federation._
		_One-off_
		_2020_
		_EU27_
		_European Committee of the Regions_
[https://portal.cor.europa.eu/divisionpowers/Pages/default.aspx]

		_(De)centralisation_
		_Degree of political, administrative and fiscal decision-making
being central or decentral._
		_One-off_
		_2020_
		_EU27_
		_European Committee of the Regions_
[https://portal.cor.europa.eu/divisionpowers/Pages/default.aspx]

		_EU Membership (duration)_
		_Duration of the European Union memberships (since European Coal and
Steel Community)._
		_Annually_
		_2025_
		_EU27_
		_Commission Services_
[https://enlargement.ec.europa.eu/enlargement-policy/6-27-members_en]

		_Good Governance (composite)_
		_Levels of Voice and Accountability, Political Stability and Absence
of Violence/Terrorism, Government Effectiveness, Regulatory Quality,
Rule of Law, and Control of Corruption._
		_Annually_
		_2024_
		_EU27+_
		_World Bank_
[https://www.worldbank.org/en/publication/worldwide-governance-indicators]

		_DATA AND DIGITAL CHARACTERISTICS_
		 
		 
		 
		 
		 

		_Basic digital skills_
		_Individuals with basic or above basic overall digital skills._
		_Biennially_
		_2023_
		_EU27+_
		_Eurostat_
[https://ec.europa.eu/eurostat/databrowser/view/isoc_sk_dskl_i21/default/table?lang=en&category=isoc.isoc_sk.isoc_sku]

		_Data and information skills_
		_Individuals check the truthfulness of the information or content
found on the internet._
		_Biennially_
		_2023_
		_EU27+_
		_Eurostat_
[https://ec.europa.eu/eurostat/databrowser/view/isoc_sk_edic_i21/default/table?lang=en&category=isoc.isoc_sk.isoc_sku]

		_ICT specialists_
		_Employed ICT specialists as percentage of the total employment._
		_Annually_
		_2024_
		_EU27+_
		_Eurostat_
[https://ec.europa.eu/eurostat/databrowser/view/isoc_sks_itspt/default/table?lang=en&category=isoc.isoc_sk.isoc_sks.isoc_skslf]

		_Cloud uptake_
		_The share of enterprises that buy cloud computing services used
over the internet._
		_Annually_
		_2023_
		_EU27+_
		_Eurostat_
[https://ec.europa.eu/eurostat/databrowser/view/isoc_cicce_use/default/table?lang=en&category=isoc.isoc_e.isoc_eb]

		_AI uptake_
		_The share of enterprises that use at least one type of AI
technologies._
		_Annually_
		_2024_
		_EU27+_
		_Eurostat_
[https://ec.europa.eu/eurostat/databrowser/view/isoc_eb_ai/default/table?lang=en&category=isoc.isoc_e.isoc_eb]

		_Data analytics uptake_
		_The share of enterprises with data analytics performed by the
enterprise's own employees or by an external provider._
		_Annually_
		_2023_
		_EU27+_
		_Eurostat_
[https://ec.europa.eu/eurostat/databrowser/view/isoc_eb_das/default/table?lang=en&category=isoc.isoc_e.isoc_eb]

		_R&D expenditure_
		_Research and development expenditure as percentage of the gross
domestic product._
		_Annually_
		_2023_
		_EU27+_
		_Eurostat_
[https://ec.europa.eu/eurostat/databrowser/view/tsc00001/default/table?lang=en&category=t_scitech.t_rd]

		_Overall 5G coverage_
		_Broadband internet coverage with 5G technology._
		_Annually_
		_2024_
		_EU27_
		Eurostat
[https://ec.europa.eu/eurostat/databrowser/view/isoc_cbt__custom_11605508/bookmark/table?lang=en&bookmarkId=4ab4caba-1758-43f1-ba68-a339f9dac03d]

TABLE 1: Overview of relevant secondary statistical data sources for
the EU27

 

CONCLUSIONS AND NEXT STEPS

 IMPLEMENTATION CONSIDERATIONS FOR THE 2026 OPEN DATA MATURITY
ASSESSMENT

In essence, the COUNTRY PROFILING exercise repurposes the Open Data
Maturity Assessment from a normative evaluation tool into a
descriptive analytical framework. Using an inductive, two-phase
approach, questions from the Open Data Maturity questionnaire were
ungrouped and analysed to identify clusters of shared behaviours,
which were refined into six descriptive parameters: Governance
Approach, Open Data Quality Strategy, Domain of Impact, Funding,
Portal Technical Foundations, and Transposition of the Open Data
Directive. These parameters were then operationalised using a mix of
original questionnaire items and select external datasets, enabling
country profiles that reveal patterns in open data practices.

While this framework is designed to remain dynamic and adaptable,
evolving alongside technological and policy developments, insights
from country consultations and reflections by the research team
highlighted opportunities to refine these sources in future
iterations. Firstly, the existing inquiry about a country’s
governance model was seen as vague, with countries suggesting that the
question and the categories used (e.g., top-down, hybrid, bottom-up)
could be clarified further. This was also noted for the Domains of
Impact parameter. Member States highlighted the need for a clearer,
more specific definition of “impact”, as the current one in the
Impact dimension’s questions is seen as too abstract. Furthermore,
clear definitions are important, the more so as the Open Data Maturity
Assessment relies on self-declared survey data from government
representatives.

Regarding the Funding parameter, it was noted that the current focus
on national portal funding is too narrow; a broader question could be
added to capture all sources of funding supporting open data
activities across different levels of government, different types of
spendings and long-term budgeting. For the Transposition of the Open
Data Directive, a predefined categorisation, potentially based on the
typology developed in this study, could be introduced to allow
countries to self-identify their transposition approach. This would
enhance the accuracy and consistency of the data collected. Lastly,
for the Portal Technical Foundations parameter, Member States noted
that additional questions could help better capture the diversity and
complexity of national portal infrastructures. While the Portal
section of the Open Data Maturity questionnaire already contains a
wealth of relevant questions, there is a need to distinguish which of
these are most critical for profiling purposes. These insights point
to the potential for a more nuanced and comprehensive evidence base in
future iterations, improving both the accuracy and comparability of
country profiles.

These country profiles can be most effectively leveraged through the
mini-site, an interactive and accessible outlet designed to country
level exploration. The mini-site could feature the ability to view
each country’s individual classification under each of the six
descriptive parameters, and sort countries accordingly, as similar as
to sorting open data maturity scores by country name. Each profile
would be accompanied by clear labels, definitions, and the underlying
evidence base, including the specific Open Data Maturity questionnaire
questions and any supplementary data sources used. This transparency
would help countries better understand how their profile was
constructed and why others are in the same or a different profile. By
visualising this information interactively, the mini-site would enable
countries to identify relevant peers and simultaneously contextualise
their performance across the four dimensions of the Open Data Maturity
study.

 
[https://data.europa.eu/sites/default/files/img/media/Screenshot%202025-11-19%20at%2011.09.15.png]
FIGURE 16: Peer-to-peer learning in action

 

With regards to the MACRO COUNTRY CLUSTERING, it should be noted that
the identified peer groups could be included in the 2026 edition of
the Open Data Maturity Report and mini-site. For the mini-site
specifically, clusters could be used to group countries to present
their scores, as similar as to grouping scores by EU Member States and
non-EU countries. From a statistical point of view, countries in the
Southern and Western European Belt, Central and Eastern Europe,
NorNeLux, Baltics and Island-based nations are most similar. They
share most economic, social and cultural, geographical, political and
digital country characteristics. Comparing the open data maturity of
countries within those clusters offers most fair comparisons. At the
same time, initial responses from Member States indicate that the
statistical clusters are relevant for comparison, but cross-country
learning is much broader. Some countries may prefer to simply look at
the country size, neighbouring countries or government structure alone
to compare their results with other countries. Moreover, regardless of
the comparability there is a need to compare with top-performing
countries and learn from best practices. For the mini-website and
country pages this means that filtering and comparison options should
allow for a certain degree of flexibility.

Overall, both the open data profiles and macro country clusters can
help to promote peer-to-peer learning and inform actionable policy
directions, beyond countries’ open data maturity performance. It is
recommended to keep the existing performance-based groups of
Beginners, Followers, Fast-trackers, and Trendsetters, and to combine
with the specific open data profiles and macro country clusters to put
these score-based performances into perspective. This will help to set
MORE MEANINGFUL AND TAILORED POLICY RECOMMENDATIONS too, sparking
collaboration and accelerating open data maturity progress in Europe.

 

FEASIBILITY AND RECOMMENDATIONS FOR EXTENDING THE FRAMEWORK TO NON-EU
COUNTRIES

Considering the ambition to include all countries participating in the
Open Data Maturity benchmarking, this study extended the DESCRIPTIVE
PROFILING exercise beyond the EU27. This allowed us to explore how
non-EU27 countries align with the developed framework, offering early
insights into their potential classification. These findings
demonstrated that the profiling approach is flexible enough to
accommodate a broader set of national contexts, while also
highlighting areas where the framework could be refined to ensure
continued relevance and inclusivity. Looking ahead, expanding the
profiling to all participating countries presents an opportunity to
capture greater nuance in national approaches, while still enabling
meaningful clustering and comparison.

Extending the MACRO COUNTRY CLUSTERING framework to non-EU countries
is less realistic than for the open data profiles. More than half of
the 19 macro statistical sources cover the non-EU countries that
participate in the Open Data Maturity Assessment. However, it should
be noted that data is more widely available for the three European
Free Trade Association (EFTA) countries Iceland, Norway and
Switzerland, compared to the four EU candidate countries Albania,
Bosnia and Herzegovina, Serbia and Ukraine. Also, in several cases the
reference years for the data of the non-EU countries indicates less
timely and actual data. Although a partial country clustering is
expected to be possible, the missing values for some variables may
hinder a full clustering analysis, lowering the quality of the
clusters to which these non-EU countries would belong. Should the
clustering rely on country size, neighbouring countries or government
structure alone, the grouping is expected to be possible and stable
over time.

 

 

[1] [applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftnref1]
https://data.europa.eu/en/publications/open-data-maturity

[2] [applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftnref2]
https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex%3A32019L1024

[3] [applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftnref3]
https://odin.opendatawatch.com/

[4] [applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftnref4]
https://opendatabarometer.org/?_year=2017&indicator=ODB

[5] [applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftnref5]
https://globaldatabarometer.org/

[6] [applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftnref6]
http://index.okfn.org/

[7] [applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftnref7]
https://www.oecd.org/en/publications/2023-oecd-open-useful-and-re-usable-data-ourdata-index_a37f51c3-en.html;
https://www.oecd.org/en/topics/digital-government.html

[8] [applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftnref8]
https://ai-watch.ec.europa.eu/publications/ai-watch-european-landscape-use-artificial-intelligence-public-sector_en

[9] [applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftnref9]
https://publicadministration.un.org/egovkb/en-us/About/Overview/-E-Government-Development-Index

[10] [applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftnref10]
https://www.itu.int/dms_pub/itu-d/opb/ind/D-IND-ICT_MDD-2025-1-PDF-E.pdf

[11] [applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftnref11]
https://digital-strategy.ec.europa.eu/en/news/eu-egovernment-report-2015-shows-online-public-services-europe-are-smart-could-be-smarter

[12] [applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftnref12]
https://digital-strategy.ec.europa.eu/en/library/egovernment-benchmark-2022

[13] [applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftnref13]
https://www.huawei.com/en/gdi

[14] [applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftnref14]
https://www.capgemini.com/ar-es/wp-content/uploads/sites/28/2023/02/Understanding-Digital-Mastery-Report-2July18-17.pdf

[15] [applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftnref15]
https://www.gartner.com/en/information-technology/topics/ai-in-government

[16] [applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftnref16]
https://www.bcg.com/publications/2024/which-economies-are-ready-for-ai

[17] [applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftnref17]
See Annex 5.1 for the full list of questions and their relevant ID

[18] [applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftnref18]
See Annex for the full list of questions and their relevant ID.

[19] [applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftnref19]
See Annex for the full list of questions and their relevant ID.

[20] [applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftnref20]
See Annex for the full list of questions and their relevant ID.

[21] [applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftnref21]
See Annex for the full list of questions and their relevant ID.

[22] [applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftnref22]
https://digital-strategy.ec.europa.eu/en/policies/public-sector-information-directive

[23] [applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftnref23]
EEA members and EU candidate countries are generally expected to align
with the Open Data Directive, even though they face no legal
obligation or sanctions for non‑compliance.

[24] [applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftnref24]
https://ec.europa.eu/eurostat/cache/countryfacts/

[25] [applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftnref25]
https://european-union.europa.eu/principles-countries-history/eu-countries_en

[26] [applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftnref26]
https://ec.europa.eu/eurostat/databrowser/view/tps00001/default/table?lang=en&category=t_demo.t_demo_pop

[27] [applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftnref27]
https://ec.europa.eu/eurostat/databrowser/view/tps00198/default/table?lang=en&category=t_demo.t_demo_ind

[28] [applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftnref28]
https://ec.europa.eu/eurostat/databrowser/view/edat_lfs_9903__custom_17376930/default/table?lang=en

[29] [applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftnref29]
https://europa.eu/eurobarometer/surveys/detail/2979

[30] [applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftnref30]
https://hdr.undp.org/data-center/human-development-index#/indicies/HDI

[31] [applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftnref31]
https://eur-lex.europa.eu/browse/eurovoc.html?params=72#arrow_7206

[32] [applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftnref32]
https://ec.europa.eu/eurostat/databrowser/view/ilc_lvho01/default/table?lang=en&category=degurb.degurb_livcon

[33] [applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftnref33]
https://portal.cor.europa.eu/divisionpowers/Pages/default.aspx

[34] [applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftnref34]
https://portal.cor.europa.eu/divisionpowers/Pages/default.aspx

[35] [applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftnref35]
https://www.worldbank.org/en/publication/worldwide-governance-indicators

[36] [applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftnref36]
https://bridgenetwork.eu/rise-regional-groups-eu/

[37] [applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftnref37]
https://digital-strategy.ec.europa.eu/en/policies/2025-state-digital-decade-package

[38] [applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftnref38]
https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Skills_for_the_digital_age

[39] [applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftnref39]
https://interoperable-europe.ec.europa.eu/collection/portal/country-knowledge

[40] [applewebdata://1246B332-9556-4B12-A752-1CB5BB5C047B#_ftnref40]
https://ec-europa.github.io/bdti-infrastructure/blocks/bb_knime/

 
