Exploring business models for public open data resources : insights and recommendations from the data.europa.eu campaign : 2024 summary paper.
Date: 2024-11-11
Institutions: Publications Office of the European Union
Subjects: artificial intelligence, business model, data governance, dissemination of information, open data, public institution, public sector, public-private partnership, report, right to reuse information

Exploring Business 
Models for Public Open 
Data Resources 

Insights and recommendations from the data.europa.eu 
campaign 
2024 Summary paper 

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This study has been prepared as part of data.europa.eu, an initiative of the European Commission. The Publications 
Office of the European Union is responsible for the management of data.europa.eu contracts. 
For more information about this paper, please contact the following. 

European Commission 
Directorate-General for Communications Networks, Content and Technology 
Unit G.1 Data Policy and Innovation 
Email: CNECT-G1@ec.europa.eu 

data.europa.eu 
Email: info@data.europa.eu 

Author: 
Anna Pizzamiglio 

Last update: 30 September 2024 
https://data.europa.eu/ 

DISCLAIMER 
The information and views set out in this publication are those of the author(s) and do not necessarily reflect the 
official opinion of the Commission. The Commission does not guarantee the accuracy of the data included in this study. 
Neither the Commission nor any person acting on the Commission’s behalf may be held responsible for the use that 
may be made of the information contained herein. 

Luxembourg: Publications Office of the European Union, 2024 
© European Union, 2024 

The Commission’s reuse policy is implemented by Commission Decision 2011/833/EU of 12 December 2011 on the 
reuse of Commission documents (OJ L 330, 14.12.2011, p. 39, ELI: http://data.europa.eu/eli/dec/2011/833/oj). Unless 
otherwise noted, the reuse of this document is authorised under the Creative Commons Attribution 4.0 International 
(CC  BY  4.0)  licence  (https://creativecommons.org/licences/by/4.0/).  This  means  that  reuse  is  allowed,  provided 
appropriate credit is given and any changes are indicated. 

ISBN: 978-92-78-44500-3 

doi: 10.2830/6425248 

Catalogue number: OA-01-24-170-EN-N 

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Contents 
Executive summary ........................................................................................................................................................... 4 

1. Introduction: business models for services based on open data ................................................................................. 5 

1.1. 

The open data opportunity ............................................................................................................................. 6 

Open data and artificial intelligence ............................................................................................................................. 8 

Challenges in integrating open data into AI systems .............................................................................................. 10 

1.2. 

Open data economics: insights from the discussion paper ........................................................................... 11 

Understanding data-driven business models ......................................................................................................... 11 

Business models for public services ........................................................................................................................ 14 

2. Barriers to the establishment of sustainable business models .................................................................................. 16 

2.1. 

Overview of current state .............................................................................................................................. 16 

3. Policy recommendations: how to foster the development and application of business models for open data public 
resources ......................................................................................................................................................................... 22 

3.1. Promoting an ecosystem approach ..................................................................................................................... 23 

3.2. Implementing support mechanisms for public institutions ................................................................................. 23 

3.3. Encouraging user-centric data publication strategies ......................................................................................... 23 

4. Conclusion ................................................................................................................................................................... 24 

5. References and further reading .................................................................................................................................. 25 

List of figures 
Figure 1: 2024 data.europa.eu campaign .................................................................................. 5 
Figure 2: AI business models .................................................................................................... 10 
Figure 3: Open data value chain .............................................................................................. 12 
Figure 4: Open data business model options .......................................................................... 13 
Figure 5: Respondents to the survey ....................................................................................... 17 
Figure 6: Visualisation of survey answers ................................................................................ 17 
Figure 7: Barriers to data-driven value creation ...................................................................... 18 
Figure 8: Visualisation of survey answers ................................................................................ 20 
Figure 9: Visualisation of survey answers ................................................................................ 22 

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Exploring  Business  Models  for  Public  Open  Data 
Resources 

Executive summary 
This summary paper, Exploring Business Models for Public Open Data Resources, is the second of two papers aimed at 
fostering the engagement of the public sector as reusers of open data. It sheds light on the results from the 1-year 
campaign featuring a first scoping paper on new business models for open data, a data.europa.eu academy webinar 
and a focus group on how business models are applied to the public sector and open data specifically. It incorporates 
the insights from the campaign, draws conclusions and provides policy recommendations on how data.europa.eu can 
enable and stimulate the application of business models for open data by public institutions across Europe. 

It  explores  the  transformative  potential of open  data  in  fostering  innovation, economic  growth  and  a  competitive 
advantage in the modern economy. While the general value of open data – publicly available datasets that can be 
freely used, modified and shared – is recognised, the specific mechanisms for generating significant economic value 
remain largely unexplored. The European Union has promoted open data accessibility and reuse through initiatives 
like the open data directive and the digital Europe programme. However, technical, legal and cultural barriers need to 
be addressed to fully realise the benefits of open data. 

The paper addresses the critical need for business models that transform open data from a resource into a catalyst for 
economic growth. The research is guided by the following question. 

How  can  sustainable  business  models  for  open  public  sector  data  resources  be  established  and  supported  to 
maximise value creation and reuse across national and local levels in Europe? 

To foster the development and application of business models for open data, the following policy recommendations 
are proposed. 

1.  Promoting  an  ecosystem  approach.  Encourage  public–private  partnerships  and  support  intermediary 

organisations to bridge gaps between data providers and users. 

2. 

Implementing support mechanisms for public institutions. Invest in data literacy and analytical skills, provide 
robust technological infrastructure and establish clear data governance frameworks. 

3.  Encouraging  user-centric  data  publication  strategies.  Engage  with  data  users  to  understand  their  needs, 

prioritise high-value datasets and ensure data quality and usability. 

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1. Introduction: business models for services based on open data 
In the modern economy, data has undeniably become a key driver for innovation, growth and competitive advantage. 
Governments and businesses alike have recognised this transformative potential. However, while the general value of 
data is well understood, the specific mechanisms through which open data – publicly available datasets that can be 
freely  used,  modified  and  shared –  can  generate  significant  economic  value  remain  largely  unexplored.  The 
transformation of open data into tangible economic benefits is not as straightforward as anticipated, leaving a gap 
that this paper seeks to address. 

Despite the widespread availability of open data, many public and private sector entities struggle to harness its full 
potential. The challenge lies not in the data itself but in the lack of structured approaches to effectively use it. Open 
data  is  non-rivalrous  and  non-excludable,  meaning  it  can  be  used  by  multiple  entities  simultaneously  without 
depletion. However, this unique characteristic also presents challenges in creating sustainable business models that 
can  capture  and  generate  economic  value.  The  European  Union,  through  initiatives  like  the  open  data  directive 
(Directive (EU) 2019/1024) and the digital Europe programme, has made significant efforts to promote the accessibility 
and reuse of high-value datasets. Yet, the path to fully realising the benefits of open data is fraught with technical, 
legal and cultural barriers that need to be addressed. 

This paper delves into the critical need for business models that can transform open data from a mere resource into a 
powerful catalyst for economic growth. By examining the insights from the data.europa.eu campaign and leveraging 
frameworks such as the service-dominant business model design, we propose strategic recommendations to foster a 
collaborative open data ecosystem. The data.europa.eu campaign consisted of a discussion paper, a webinar, one mini 
survey and a focus group, which provided valuable insights into the challenges and opportunities in the creation of 
sustainable business models for services based on open public data. The campaign targeted a wide audience of open 
data professionals and enthusiasts, and the survey received more than 200 answers, while the webinar was attended 
by a similar number of people – a testament to the relevance of and interest in the topic. 

Figure 1: 2024 data.europa.eu campaign 

More  specifically,  this  summary  paper  aims  at  providing  an  overview  of  existing  methods  and  good  practices  in 
establishing a sustainable public data resources ecosystem, identifying the key drivers that promote the adoption of 
business models for open data and the barriers that hinder their implementation. 

To guide the research activities carried out during the campaign that led to the publication of this summary paper, the 
following research question was formulated. 

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How  can  sustainable  business  models  for  open  public  sector  data  resources  be  established  and  supported  to 
maximise value creation and reuse across national and local levels in Europe? 

To answer this main question, the following sub-questions were formulated. 

•  What  are  the  existing  methods  and  good  practices  in  establishing  a  sustainable  public  data  resources 

ecosystem? 

•  What barriers exist to data-driven value creation in the public sector, and how can they be overcome? 
•  What  are  the  key  political,  economic,  social,  technological,  legal  and  environmental  factors  impacting  the 

sustainability of open data business models? 

•  What  support  mechanisms  do  public  institutions  need  to  successfully  create  value  from  open  data 

ecosystems? 

These  questions  are  considered  from  a  practice-oriented  angle,  supported  by  the  data.europa.eu  community 
engagement activities that have taken place throughout the campaign since the publication of the discussion paper 
New Business Models for Data-driven Services in November 2023. 

The introduction begins by outlining the transformative potential of open data for society and the economy, drawing 
insights from the initial discussion paper. It highlights the significant economic value that open data can unlock. The 
various business models that leverage open data identified in the discussion paper are then summarised. In addition 
to summarising the insights on open data economics from the discussion paper, this section also outlines the benefits 
of applying business models to the delivery of public services. 

The second section, barriers to the establishment of sustainable business models, provides an overview of the current 
state of the use and application of business models for open data, as revealed by the survey and webinar. It discusses 
the practices and experiences of different public institutions engaging with open data. This section also delves into the 
aspirations  and  concerns  regarding  sustainable  business  models  for  open  data,  capturing  the  perspectives  shared 
during the focus group discussions. 

The  final  section,  policy  recommendations:  how  to  create  value  from  open  data  public  resources,  offers  policy 
recommendations on establishing sustainable business models for open data. It suggests ways to overcome barriers 
and provides actionable insights for public institutions and stakeholders to maximise the creation of value for and 
reuse of open data across Europe. 

By following this structure, the summary paper aims to present an analysis of the challenges and opportunities that 
arise  when  creating  sustainable  business  models  for  open  data,  supported  by  data  and  expert  insights.  The 
recommendations provided will serve as a roadmap for fostering open data reuse and driving innovation in public 
services. 

The open data opportunity 

1.1. 
The transformative potential of open data for society and the economy is immense. Open data has the potential to 
unlock  substantial  economic  value,  estimated  to  reach  between  EUR 199.51 billion  and  EUR 334.2 billion  by  2025 
(Capgemini Invent et al., 2020). 

With the advent of new data-hungry technologies, such as artificial intelligence (AI) and blockchain, open data  can 
create substantial value by making more data available. Increased access to data can foster the creation of start-ups 
and enable them to scale up, driving significant economic and societal benefits. Large amounts of quality data are 

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crucial not only as input during the operation of AI systems but also to train them initially. Without access to high-
quality data, algorithms cannot learn effectively. Even the most powerful AI techniques with cutting-edge hardware 
are significantly less  useful without  high-quality data. In this respect,  global data oligopolies  are likely to turn into 
global AI oligopolies if access to data is not managed strategically from  both technological and policy perspectives 
(European Commission et al., 2023). Open data fosters innovation by serving as the raw material in the development 
of  new  applications  and  services.  Using  open  data,  start-ups  and  tech  companies  can  create  solutions  addressing 
societal  challenges,  from  smart  city  applications  to  personalised  healthcare.  The  open  data  canvas  framework 
illustrates how data elaboration and customisation can transform raw open data into enterprise-specific assets, driving 
value creation (Digital Government: Research and Practice, 2022). 

Additionally,  open  data  is  potentially  a  key  factor  in  developing  and  scaling  up  a  European  GovTech  ecosystem. 
GovTech, or government technology, refers to the deployment of digital solutions to improve public service delivery, 
enhance administrative efficiency and foster greater citizen engagement. The importance of GovTech lies in its ability 
to transform how governments operate, making them more agile, responsive and capable of meeting the evolving 
needs of their citizens. By integrating technology into  their core functions, governments can streamline processes, 
reduce costs and deliver higher-quality services. As for AI, the effective use of open data is crucial for the growth and 
scalability of  GovTech  in Europe. Open data  provides the essential information required for developing innovative 
digital  solutions.  By  making  large  datasets  accessible  and  reusable,  open  data  initiatives  enable  the  creation  of 
applications  that  analyse  and  interpret  this  data  to  offer  actionable  insights,  driving  smarter  decision-making  and 
enhancing service delivery. For instance, open data can be employed to create tools that manage urban infrastructure, 
optimise traffic or improve public health responses. Furthermore, the availability of open data attracts tech start-ups 
and small and medium-sized enterprises, fostering an environment of innovation and competition. This leads to the 
development of advanced solutions that address public sector challenges, amplifying the impact of GovTech initiatives 
across Europe. By enabling public sector innovation and collaboration with technology start-ups and adopting service-
dominant business models, public institutions can leverage open data to co-create value with private sector partners, 
facilitating the development of innovative digital solutions tailored to public needs. The resulting ecosystem not only 
stimulates economic growth and competitiveness but also drives the delivery of more responsive and effective public 
services, ultimately creating a more dynamic and collaborative landscape for digital governance in Europe. 

Despite this immense potential, both research and practice on the creation of business models for services based on 
open data are still in their infancy, with many public institutions and private enterprises yet to fully embrace open data 
initiatives (Capgemini Invent et al., 2020). This lag represents an untapped opportunity for Europe to leverage open 
data to drive innovation, enhance public services and stimulate economic growth. 

Scattered around Europe, some national and local governments, together with businesses and often with the support 
of the European Union, have developed very effective programmes to stimulate open data use and value creation. 
However, such programmes are relatively not well known and seldom scale up. Oftentimes, open data (re)use in the 
public sector results in the creation of services for citizens and businesses. As Turetken et al. (2019) note, when what 
is being sold is not a good but a service, a shift in mindset and culture is needed. This shift entails moving away from 
focusing solely on individual products or services towards providing integrated solutions that deliver value-in-use to 
customers within a collaborative ecosystem involving multiple stakeholders. Adopting an ecosystem approach in the 
design of business models for digital public services is key. 

As will be explored in more detail in the following sections, open data is a force for good but requires a collaborative 
effort  between  the  public  and  private  sectors  to  realise  its  potential.  A  concerted  effort  should  address  existing 
barriers, promote data literacy and implement supportive policies and frameworks. 

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Open data and artificial intelligence 
AI’s transformative impact on various sectors, including the public sector and the data ecosystem, is profound. For 
instance, AI mobility services can improve the passenger experience through automated vehicles and advanced traffic 
management. In healthcare, AI can identify new drug solutions, enhance clinical trials and improve patient care. 

A  concrete  example  can  be  found  in  the  United  Kingdom,  where  the  National  Health  Service  has  successfully 
implemented AI software that has had a tangible impact on reducing missed appointments. This AI system has reduced 
missed appointments by 30 %, and significantly, it has helped reallocate the remaining 70 % of missed slots to other 
patients, thus improving the utilisation of healthcare resources. This not only enhances the efficiency of healthcare 
delivery but also ensures that more patients receive timely care (1). 

The relationship between open data and AI is mutually reinforcing. Open data can enhance AI systems by exposing 
them to larger and more diverse datasets, increasing the likelihood of accurate and useful predictions. Conversely, AI 
can extract additional value from open data by analysing large datasets and identifying trends and patterns that might 
not be apparent through traditional analysis techniques. For instance, an AI system trained to predict forest fires can 
analyse weather data, satellite images and historical trends to identify patterns that standard statistical methods might 
miss. 

The  broad  range  of  subjects  covered  by  open  data  expands  the  potential  use  cases  for  AI  systems,  enabling  the 
development of new AI-driven products and services. These innovations require relevant data that is accessible and 
easy to use. The availability of diverse datasets through open data is essential for driving innovation and creating new 
economic  opportunities.  AI  systems  can  leverage  this  variety  to  tackle  societal  challenges,  in  turn  generating 
socioeconomic value. 

For example, in applications such as urban planning and smart city development, open data on traffic patterns, public 
transportation usage, energy consumption and population density can be integrated and analysed by AI systems to 
improve urban infrastructure and services. An AI system can analyse this diverse dataset to identify bottlenecks in 
traffic  flow,  optimise  public  transportation  routes  and  recommend  energy-efficient  building  designs.  This 
comprehensive analysis can help city planners make data-driven decisions to enhance urban mobility, reduce energy 
consumption and improve the overall quality of life for residents. By leveraging open data, AI systems can provide 
actionable insights that lead to smarter, more sustainable cities, demonstrating the powerful synergy between open 
data and AI. 

Additionally, integrating open data into AI development can lead to more transparent and accountable AI systems. By 
using open data, AI developers can ensure their systems are built on diverse and representative datasets, reducing the 
risk of bias and increasing the reliability of AI predictions. This transparency is crucial for building public trust in AI 
systems and ensuring they are used ethically and responsibly. 

In a nutshell, open data provides the necessary foundation for training and refining AI systems, while AI can unlock 
additional  value  from  open  data  by  identifying  insights  and  patterns  not  apparent  through  traditional  analysis 
techniques. This synergistic relationship underscores the importance of open data in the continued development and 
application of AI, highlighting its role in shaping a more innovative and equitable future. 

Because of the immense potential of AI, AI-driven business models, such as AI application-as-a-service (AIaaS) and AI 
infrastructure-as-a-service (AI-IaaS), are already emerging. The former involves developing AI applications for specific 

(1) 

For more details, see https://www.england.nhs.uk/2024/03/nhs-ai-expansion-to-help-tackle-missed-appointments-and-improve-waiting-
times/. 

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use cases defined by customers, while the latter provides computational services and pre-trained algorithms. These 
models highlight  the  importance of data  collaboration, with AI companies often partnering with data providers to 
enhance their solutions. AI systems rely heavily on large amounts of quality data for training and operation (Open Data 
Institute et al., 2018). The emergence of AI-AaaS and AI-IaaS models is driven by several key factors, reflecting broader 
trends in technology and business needs. 

A primary driver is the increasing demand for AI capabilities across various industries. Companies today are eager to 
integrate  AI  into  their  operations  to  improve  efficiency,  decision-making  and  the  customer  experience.  However, 
developing in-house AI solutions requires significant resources – both financial and human. Building AI systems from 
scratch requires substantial investment in data infrastructure, computing power and highly specialised talent – making 
it  prohibitive  for  many  businesses,  especially  small  and  medium-sized  enterprises.  AIaaS  and  AI-IaaS  models 
democratise access to advanced AI technologies by providing scalable AI tools and services on a subscription basis, 
making them accessible even to companies with limited resources. 

Another key factor is the complexity of AI development. AI systems are inherently complex, involving sophisticated 
algorithms, large datasets and ongoing maintenance to ensure models remain effective over time. By leveraging AIaaS, 
businesses can bypass much of this complexity. These  services offer pre-built AI models and tools that have  been 
refined by experts, allowing businesses to focus on applying AI to their specific needs rather than the intricacies of 
developing AI from the ground up. This not only speeds up the implementation of AI solutions but also reduces the 
risks associated with AI development. 

Scalability and flexibility are also crucial factors contributing to the rise of AIaaS and AI-IaaS. As businesses scale up, 
their AI needs often expand in complexity and scope. AI-IaaS platforms provide the ability to scale resources up or 
down  based  on  demand,  without  the  need  for  significant  capital  investment  in  infrastructure.  This  flexibility  is 
particularly beneficial for industries that experience fluctuating workloads or those that need to manage large volumes 
of data. Cloud computing advancements have further facilitated this trend by providing the necessary infrastructure 
to support large-scale AI operations across multiple geographies, with minimal latency. 

Furthermore, cloud computing has been instrumental in the proliferation of AIaaS and AI-IaaS models. Cloud platforms 
now  offer  robust  and  secure  environments  where  AI  services  can  be  deployed,  managed  and  scaled  globally.  This 
integration of AI with cloud services has not only reduced the costs associated with AI adoption but has also enabled 
hybrid models through which businesses can combine on-premises systems with cloud-based AI solutions. 

Lastly, the growing ecosystem of AI tools and platforms offered by major technology companies is reinforcing the shift 
towards AIaaS and AI-IaaS. Companies like Google, Microsoft and Amazon are heavily investing in their AI platforms, 
providing a wide range of AI services – from machine learning and natural language processing to predictive analytics. 
These  platforms  come  with  extensive  support  and  integration  options,  which  simplify  the  process  of  adopting  AI 
technologies for businesses, further accelerating the trend towards AIaaS and AI-IaaS. 

The latest surge in AI interest and investment highlights a trend towards business models that focus on collecting and 
isolating data into separate silos. Companies adopting this strategy view data access as crucial for gaining a competitive 
edge in developing, deploying and running AI systems. As a result, these companies often limit access to this type of 
resource. This set-up is defined as ‘open algorithm + closed data’, and it has led to disparities in data access, creating 
a  gap  between  data-rich  corporations  and  data-poor  start-ups. Open  data  can help  solve  this  issue  as opening  up 
datasets has the effect of democratising access to data, in turn enabling the development of many other data-fuelled 
AI services (Open Data Institute et al., 2018). 

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Figure 2: AI business models (Open Data Institute et al., 2018) 

Challenges in integrating open data into AI systems 
Integrating  open  data  into  AI  systems  presents  several  complex  challenges,  which  can  be  broadly  classified  into 
technical, legal and ethical domains. One of the primary technical challenges is related to the quality and consistency 
of the data. AI systems rely on high-quality, structured data to function effectively, yet open data often originates from 
a  variety  of  sources  with  differing  standards  and  practices.  This  variability  can  lead  to  issues  such  as  incomplete 
datasets, inconsistent formats and potential biases. When data quality is compromised, the AI models trained on such 
data are likely to produce inaccurate predictions and unreliable outcomes, which can have serious implications for 
decision-making processes. The importance of addressing these issues is highlighted in research by Zhang and Dafoe 
(2019), who discuss the risks posed by low-quality data in AI applications. 

Another significant challenge is data interoperability. Open data is typically generated by different organisations, often 
using  a  wide  range  of  platforms,  formats  and  standards.  This  diversity  can  create  substantial  difficulties  when 
attempting  to  integrate  these  datasets  into  a  cohesive  AI  system.  The  lack  of  standardisation  across  data  sources 
complicates the data preprocessing phase, as AI systems may struggle to reconcile discrepancies in data structures, 
terminologies or models. This lack of interoperability can limit the ability of AI systems to effectively analyse and learn 
from  open  data,  thereby  reducing  the  potential  benefits  of  combining  diverse  datasets.  The  need  for  improved 
interoperability standards in AI and data integration has been emphasised in the work of Janssen et al. (2012). 

Legal and ethical challenges further complicate the integration of open data into AI systems. Open data is often subject 
to  varying  degrees  of  regulation  concerning  privacy,  security  and  intellectual  property  rights.  Organisations  must 
navigate  these  legal  frameworks  carefully  to  ensure  compliance  while  integrating  open  data  into  AI  systems. 
Moreover, ethical considerations, such as the potential for biased AI outcomes or the misuse of data, require careful 
management. Organisations must establish robust governance frameworks that address these concerns, ensuring that 
the integration of open data  into AI systems is conducted in a manner that is both legally compliant  and ethically 
sound. This aspect of the challenge is discussed by Floridi et al. (2018), who argue for a more ethical approach to data-
driven AI, emphasising the need for transparency, accountability and fairness in AI development and deployment. 

In summary, the integration of open data  into  AI systems is fraught with challenges  that span technical, legal and 
ethical  dimensions.  Addressing  these  challenges  requires  a  concerted  effort  to  improve  data  quality  and 
interoperability, alongside developing governance frameworks that can manage the complex legal and ethical issues 
involved. 

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1.2.  Open data economics: insights from the discussion paper  
Business models are essential for transforming the potential of open data into tangible economic and societal benefits. 
The  unique  characteristics  of  open  data,  such  as  its  non-rivalrous  and  non-excludable  nature,  present  both 
opportunities and challenges for creating sustainable business models. The discussion paper New Business Models for 
Data-driven Services (European Commission et al., 2023) identifies several archetypes of business models that can be 
employed to generate revenue and deliver value from open data. 

Understanding data-driven business models 
The concept of a business model, central to economic and trading activities, has evolved significantly with the advent 
of the internet and the rise of digital economies. A business model defines how an organisation creates, delivers and 
captures value. For open data, the challenge lies in leveraging a public resource to generate financial sustainability. 
The business model canvas proposed by Osterwalder and Pigneur (2005) is widely used to represent the architecture 
of value generation and capture. While multiple versions of the canvas exist, the model remains a standard tool for 
designing open data business models. 

Before  discussing  business models, it  is important to  examine  the economic aspects of information goods and the 
value chain of open data. Shapiro and Varian’s work in Information Rules: A strategic guide to the network economy 
(1998)  offers  a  comprehensive  account  of  the  cost  structure  of  information  goods.  The  fundamental  features  of 
information  goods  include  high  first-copy  costs,  low  reproduction  costs  and  economies  of  scale.  Applying  this 
conceptualisation to take into account the specificities of open data, the following elements can be identified. 

1.  The cost structure of open data, much like information goods, is primarily dominated by initial production 
costs. Once public sector data is generated and made available in an open format, the costs associated with 
reproducing  and  distributing  additional  copies  become  minimal.  This  characteristic  leads  to  significant 
economies of scale, where the average cost per unit decreases as the volume of data copies increases. Such 
economies  of  scale  enable  the  broad  dissemination  and  reuse  of  data  at  relatively  low  costs,  thereby 
enhancing accessibility and use across various sectors. 

2.  The reuse of open data is often a second-order effect rather than the main goal behind the generation of 
data. Public sector organisations primarily collect data for specific operational purposes such as regulatory 
compliance, service provision or internal reporting. Since the publication of data is not the primary goal of the 
data  generation  process,  this  leads  to  datasets  not  being  curated  or  user-friendly,  often  resulting  in  non-
negligible maintenance and upgrading costs. This adds layers of complexity to the cost dynamics of open data 
initiatives. Continuous data generation, storage and curation processes incur ongoing expenses, which must 
be managed effectively to sustain the value of open data. Further, while economies of scale allow for data to 
be  widely  disseminated  at  minimal  additional  costs,  the  lack  of  direct,  clear  and  quantifiable  returns  on 
investment can be a disincentive to information sharing. Public institutions may be hesitant to invest in open 
data  initiatives  without  clear  evidence  of  the  benefits  and  returns.  This  hesitation  highlights  the  need  for 
structured frameworks and business models that can quantify the impact of open data reuse, providing the 
necessary incentives for sustained information sharing. It is crucial to consider these economic specificities of 
open data when looking into business models that integrate open data into the value proposition. 

3.  The process that spans from the generation of a data asset to its consumption is far from being linear and 
is subject to diverse interpretations. The discussion paper identifies three relevant aspects pertaining to the 
open data value chain: the activities conducted, the relevant stakeholders and the outputs generated in each 
step  of  the  value  chain.  The  main  added-value  activities  carried  out  along  the  chain  are  data  generation, 
dissemination, retrieval, storage, categorisation, exposure, reuse and consumption, while the outputs of the 
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different  steps  are  raw  data,  refined  data  and  fit-for-purpose  products  and  services.  Finally,  a  number  of 
archetypal  stakeholders  operating  along  the  value  chain  have  been  presented  to  highlight  the  concurrent 
presence of government agencies and for-profit and not-for-profit entities with no expectation of exhaustivity 
(Figure 3). 

Figure 3: Open data value chain 

In the context of open data, three strategic decisions are pivotal when determining which business model to choose: 
the  type  of  data  elaboration,  the  role  of  open  data  in  the  value  proposition  and  the  pricing  mechanism.  Data 
elaboration may include aggregation, structuring, classification, geo-referencing, validation, data mash-ups and visual 
analytics.  The  role  of  open  data  can  range  from  being  the  final  product  itself  to  a  key  ingredient  or  a  marginal 
component of the value proposition. Pricing mechanisms  vary  from premium models  (paid services)  to freemium 
(free basic services with paid premium features) and free models (supported by advertising or other means). 

12 

 
 
 
 
 
(*) PSI = public sector information. 
Figure 4: Open data business model options (Ferro and Osella, 2012) 

The discussion paper and the review of the literature identify 14 different types of business models  for integrating 
open data into one’s value proposition (European Commission et al., 2023). These models have a number of common 
traits and, based on the categorisation by Zeleti et al. (2014), can be condensed into five main categories: freemium, 
premium, open source, infrastructure and demand/supply-oriented platforms. 

•  The freemium model offers a basic product or service for free while charging for premium features. In the 
context of open data, this might involve providing access to basic datasets for free while offering advanced 
analytics, customisation or additional data services for a fee. 

•  The premium model involves providing high-end services and products at a premium price, targeting a niche 
market with a high profit margin. For open data, this could mean offering highly specialised datasets or data 
services that cater to specific industries or research needs. 

•  The open-source model involves making source code or data publicly accessible, with revenue generated from 
added-value services and dual licensing. Organisations can monetise their code or data by offering professional 
support, bespoke services or enhanced versions under a different licence. 

•  The infrastructure model provides an initial product at a low price or for free, with subsequent products or 
services generating revenue, akin to razors and blades. For open data, this could involve offering free access 
to basic infrastructure, such as data storage or processing platforms, while charging for advanced features, 
additional storage or premium processing capabilities. 

•  The  demand/supply-oriented  platform  model  customises  datasets  based  on  client  needs,  with  revenue 

derived from the added value of data aggregation, curation and enrichment. 

When discussing business models and open data, it is important to stress that the benefits of open data extend far 
beyond ‘open data start-ups’. While some businesses are specifically established to exploit open data opportunities, 
many others are leveraging open data to enhance their existing products and services or to develop new ones. This 
demonstrates  that  a  wide range  of  businesses  can  benefit  from  open  data,  highlighting  its  broad applicability  and 
potential.  Also,  open  data  is  not  only  about  consumption –  its  publication  also  creates  substantial  value  for  small 

13 

 
 
 
 
businesses. By accessing open data, businesses can innovate, attract new customers, improve customer experiences 
and capture new markets. And lastly, but perhaps most importantly, the most significant value of open data often 
emerges when it is combined with other data sources. Businesses can address complex problems by integrating diverse 
data types from open, shared and closed sources, resulting in stronger value propositions and more effective solutions. 
This approach enhances the overall utility and impact of open data (European Commission et al., 2023). 

Business models for public services 
Open data represents an opportunity for businesses and public  institutions alike. While  businesses are acquainted 
with  the  concept  of  a  business  model,  the  same  cannot  always  be  said  of  public  institutions.  However,  adopting 
business models can help public institutions understand the broader ecosystem in which they operate. They provide 
a  structured  framework  for  identifying  the  various  stakeholders,  resources  and  processes  involved  in  service 
delivery, enabling a more holistic approach to managing public services. 

Innovative  business  models  for  public  services  can  enable  public  institutions  to  leverage  open  data  effectively, 
fostering  the  development  of  new  services  and  improving  existing  ones.  Further,  incorporating  a  business  model 
perspective  shifts  the  discussion  from  mere  cost  efficiency  to  a  more  comprehensive  evaluation  of  organisational 
productivity  and  effectiveness.  Traditionally,  public  sector  productivity  has  been  viewed  through  the  lens  of  cost 
efficiency, focusing on reducing expenditure and optimising resource use (Jörden et al., 2024). However, this narrow 
focus  can obscure  the  broader  benefits  that  structured  business  models  bring.  Business  models  facilitate  a  better 
understanding of value creation, delivery and capture, ensuring that public services are not  only efficient  but  also 
impactful and sustainable (Wirtz et al., 2021). This shift is essential for recognising the full spectrum of public value 
generated through open data  initiatives, including enhanced service  quality, increased transparency and improved 
citizen engagement. 

The concept of public service provision models, adapted from traditional business models, emphasises the need to 
orchestrate and finance public services effectively. This perspective allows public entities to manage how they create, 
deliver and capture value, ensuring that public services are both sustainable and responsive to citizens’ needs (Lorenz 
et al., 2024). Public service provision models differ from conventional business models primarily in their focus on 
public  value  rather  than  profit.  These  models  centre  around  addressing  public  needs  and  delivering  value  that 
enhances  the  quality  of  life  for  citizens.  This  often  involves  intangible  benefits  such  as  increased  trust  in  public 
institutions and greater transparency, alongside tangible outcomes like improved service efficiency. For instance, the 
use of AI and data analytics in public services can streamline processes and provide more personalised services to 
citizens.  Such  technological  advancements  require  robust  business  models  that  support  ongoing  innovation  and 
adaptability within public institutions. 

To  effectively  implement  and  scale  public  services, public  institutions must  adopt  a  comprehensive  approach  that 
includes  understanding  their  value  propositions,  value  creation  and  delivery  mechanisms,  and  value  capture 
strategies. This involves recognising the unique challenges of public service provision, such as regulatory constraints, 
the need for inclusivity and the importance of public accountability. By integrating business model thinking into their 
strategic planning, public institutions can develop frameworks that not only meet immediate service delivery needs 
but also anticipate future demands and opportunities for innovation. 

For instance, Finland has embraced the circular economy model in its national strategy, aiming to transition from a 
linear economy to one that is regenerative by design. The Finnish Innovation Fund Sitra has been instrumental in this 
shift,  using  business  models  to  analyse  how  different  sectors  of  the  economy  can  operate  more  sustainably.  By 

14 

 
 
 
 
 
 
adopting  the  circular  economy  model,  Finland  has  been  able  to  reduce  waste,  promote  the  sustainable  use  of 
resources and create new business opportunities in areas such as recycling and renewable energy (2). 

Another  great  example  of  how  public  institutions  can  use  the  ecosystem  business  model  to  foster  innovation  is 
Germany’s  ‘industry  4.0’  initiative  in the  manufacturing  sector.  Industry 4.0  integrates  cyber-physical  systems, the 
internet of things and cloud computing to create smart factories. The German government has supported this initiative 
by bringing together various stakeholders, including manufacturers, technology providers and research institutions, to 
create  a  robust  innovation  ecosystem.  This  model  has  allowed  Germany  to  maintain  its  competitive  edge  in 
manufacturing by leveraging the strengths of different stakeholders within the ecosystem (3). 

And  last  but  certainly  not  least,  Barcelona  has  implemented  a  smart  city  strategy  that  applies  a  business  model 
approach to urban planning and public services. By leveraging data and technology, the city has created an ecosystem 
that connects various public services, such as transportation, energy and waste management, to improve efficiency 
and sustainability. The smart city model allows Barcelona to address urban challenges by integrating technology into 
the fabric of the city, leading to more responsive and efficient public services (4). 

Public  sector  reuse  of  open  data  frequently  leads  to  the  creation  of  services  aimed  at  citizens  and  businesses. 
According  to  Turetken  et  al.  (2019),  when  the  focus  shifts  from  offering  tangible  goods  to  providing  services,  it 
necessitates a substantial change in both mindset and organisational culture. This change involves transitioning from 
an emphasis on stand-alone products or services to offering integrated solutions that generate value for customers 
within a collaborative network of multiple stakeholders. This approach is defined as the service-dominant business 
model. This relatively new conceptualisation offers a transformative approach to business model design, particularly 
relevant  for  digital  services.  Unlike  the  traditional  goods-dominant  logic,  which  focuses  on  the  production  and 
exchange of tangible products, the service-dominant logic emphasises the co-creation of value through interactions 
and relationships between service providers and consumers. This approach is particularly suited to the digital age, 
where services are often dynamic, customisable and highly dependent on user engagement and feedback. 

In  the  context  of  public  services  and  the  reuse  of  open  data,  adopting  a  service-dominant  business  model  can 
significantly enhance the way public institutions deliver value. This model encourages public entities to view citizens 
not just as passive recipients of services but as active participants in the value-creation process. For example, digital 
platforms that allow citizens to contribute data, provide feedback or co-design services can lead to more effective and 
user-centric public services. The focus on co-creation also helps public institutions build stronger relationships with 
their stakeholders, fostering a sense of ownership and engagement among citizens. 

Implementing a service-dominant business model requires public institutions to rethink their operational strategies 
and  service  delivery  mechanisms.  It  involves  integrating  digital  tools  and  technologies  that  facilitate  real-time 
interaction and collaboration with citizens. Additionally, it necessitates a cultural shift towards viewing public services 
as dynamic and evolving processes, rather than static offerings. By embracing the principles of the service-dominant 
business model, public institutions can create more responsive, efficient and innovative services that better meet the 
needs of their communities. 

Lastly, business models can provide a structured framework to address the lack of impact measurement for open 
data. By adopting business models, public institutions can better replicate successful projects across different regions 
or  services,  thereby  increasing  the  impact  of  positive  innovations  and  enabling  widespread  benefits.  Lorenz  et  al. 

(2) 
(3) 
(4) 

For more information, see https://www.sitra.fi/en/topics/a-circular-economy/. 
For more information, see https://www.gtai.de/en/invest/industries/industrial-production/industrie-4-0. 
For more details, see https://www.beesmart.city/en/smart-city-blog/smart-city-portrait-barcelona. 

15 

 
 
 
 
 
 
 
 
(2024)  argue  that  adopting  business  models  can  help  public  institutions  scale  up  their  initiatives  more  effectively, 
ensuring that successful projects have a broader reach and impact. But, concretely, in what ways can the public sector 
create value from open data? The campaign following the discussion identified five adaptations of the business model 
categories listed in the previous section. 

•  Through data brokerage. Public sector organisations can act as intermediaries between data providers and 
data users, facilitating the exchange of data and generating revenue through transaction fees or commissions. 
•  Through  data  analytics.  Public  sector  organisations  can  use  open  data  to  develop  data  analytics  services, 

providing insights and recommendations to other organisations or to the public. 

•  Through  data-driven  products  and  services.  Public  sector  organisations  can  develop  new  products  and 

services based on open data, such as mobile apps, dashboards or data visualisations. 

•  Through  data  licensing.  Public  sector  organisations  can  license  their  data  to  other  organisations  for 

commercial or non-commercial use, generating revenue through licensing fees. 

•  Through data sharing partnerships. Public sector organisations can partner with other organisations to share 

data and jointly develop new products and services. 

2. Barriers to the establishment of sustainable business models 
This section aims at providing an overview of the current state of the adoption of business models for public services 
based on open data, as revealed by the survey, focus group and webinar. It goes over the practices and experiences 
of different public institutions in engaging with open data business models,  analysing  the barriers  that  arise when 
establishing processes to generate value through a purposeful and effective use of data released in an open format. 

2.1. 

Current state of open data in Europe’s public institutions 
The limited existing literature explores different business models and canvases for open data, providing templates to 
apply to business processes and service development. But what are public institutions doing in practice? 

The survey carried out as part of the present campaign provides new data points that provide a useful snapshot of the 
perception of the community. The purpose of the survey was to better understand the level of interest in and degree 
of knowledge about business models for open data among public administrations in Europe, and which approaches 
have already been explored. We surveyed a diverse range of public administrations across Europe, including national, 
regional and local governments, along with public institutions and agencies, for a total of 209 respondents (Figure 3) 
that, due to multiple-choice questions, translated into a total of 335 answers. 

16 

 
 
 
 
 
Figure 5: Respondents to the survey 
Figure 6 illustrates whether organisations have employed business models that effectively integrate open data into 
their  value  propositions.  A  large  majority,  72 %  of  respondents,  indicated  that  their  organisations  have  not  yet 
integrated open data into their business models. Only 28 % of respondents reported successful integration of open 
data. 

Figure 6: Visualisation of survey answers 

Zooming in on the results, the difference between sectors is striking, with a significant difference between the public 
and private sectors. Put simply, business models integrating open data are largely unknown in the public sector, with 
only 22.3 % adopting them versus 42.9 % in the private sector. 

Barriers to data-driven value creation in the public sector have been extensively identified in the literature and usually 
fall  into  two  categories:  (1)  technical  or  (2)  institutional.  Last  year’s  summary  paper,  Creating  public  sector  value 
through the use of open data (European Commission et al., 2023), provides a clear and data-informed overview. 

17 

 
 
 
 
 
 
 
The most common technical barriers are: 

●  data silos, and the related lack of data harmonisation and interoperability; 
●  poor data quality (and management); 
● 

licensing issues. 

Meanwhile, the most common institutional barriers to the reuse of open data by public institutions are namely: 
the need to consider public institutions as data reusers from the outset of the data publication process; 
the lack of ‘self-awareness’ of public institutions that they are open data reusers; 
the lack of contact between the person or body responsible for the open data portal(s) and the ecosystem of 
users. 

● 
● 
● 

Figure 7: Barriers to data-driven value creation 

Integrating findings from last year’s campaign with insights from this year’s campaign offers further food for thought 
and analysis. In addition to the usual challenges of technological, policy and legal barriers, the main issue with creating 
business models for public services based on open data remains cultural. 

The low level of experimentation with business models in the public sector can indeed be traced back to the prevailing 
view that public services should primarily create value for the public and do not require a business model in the same 
way private sector services do. Throughout the campaign, this issue arose multiple times, with participants from the 

18 

 
 
 
 
 
 
 
 
 
webinar often underlining that for public bodies, the reason behind the publication and opening of data is making it 
available to citizens and third parties for them to use and develop innovative solutions and services. 

However, this is a flawed perception for two main reasons. 

(1)  It is important for the public sector to be able to develop their own digital solutions. Long-term trends such 
as  ageing  populations,  increasing  inequality  and  climate  change,  along  with  unpredictable  challenges  like 
migration  and  security,  impact  citizens’  well-being  and  resilience.  To  be  prepared  for  these  changes  and 
challenges,  long-term visions,  evidence-based  investments  and  coordinated  actions  from  governments  are 
needed. To realise this, a data-driven public sector is needed, as the effective integration of data revolutionises 
the design, delivery and monitoring of public policies and  services through data management, sharing and 
usage. Leveraging data as a strategic asset is essential for governments to enhance public sector intelligence, 
thereby improving their capacity to develop policies and services that are sustainable, inclusive and trusted in 
the long run (van Ooijen et al., 2019). 

(2)  Public  services,  despite their  focus  on  public value, still  need  to  be  orchestrated  efficiently to  maximise 
public  value.  To  put  it  simply,  concept  of  public  value  may  be  described  as  the  analogue  of  the  desire  to 
maximize shareholder value in the private sector (Coats and Passmore, 2018). 

A significant part of the barrier is the difference in terminology between the public and private sectors. Stemming from 
a flawed interpretation of ‘value’, public sector entities often do not see themselves as needing ‘business models’, a 
term that connotes profit-driven motives are inconsistent with their mission of public service (Timeus et al., 2017). 
This terminological difference contributes to a misunderstanding of how business model frameworks can be adapted 
to enhance public value creation without compromising their core objectives. 

Additionally, public services need to be orchestrated and financed efficiently. Without a business model perspective, 
public institutions may struggle to manage resources effectively, leading to inefficiencies and wasted opportunities. 
The structured approach provided by business  models helps in understanding the costs associated with delivering 
services  and  identifying  potential  revenue  streams,  even  if  not  profit-oriented.  This  is  crucial  for  ensuring  the 
sustainability of public services, especially in times of budget constraints (Wirtz et al., 2021). The disparity between 
the public and private sectors in adopting business models suggests that public institutions may lag behind in terms of 
service quality and efficiency. The private sector’s higher degree of experimentation with business models allows for 
continuous improvement and adaptation to changing needs and technologies. Public institutions, on the other hand, 
risk becoming stagnant and less responsive to the needs of their citizens if they do not adopt similar approaches. This 
gap has significant implications, as it can lead to public dissatisfaction and reduced trust in public services (Timeus et 
al., 2017). 

A significant barrier to the uptake of open data in the public sector is the lack of clear ways to measure the use, impact 
and success of such data. This issue was analysed in the discussion paper Measuring Data Demand Within the Public 
Sector (van Ooijen et al., 2022), also produced within the data.europa.eu context. The paper highlights that without 
robust metrics and evaluation frameworks, it is difficult for public institutions to demonstrate the value generated 
from open data initiatives. This measurement challenge further hinders scaling up successful initiatives and ensuring 
their sustainability.  While society  has developed markets to facilitate  the buying and selling of goods and services 
through the exchange of currency, we have not yet cultivated a comprehensive method to quantify the value that is 
generated through the open sharing of information products. For example, new information may have a significant 
19 

 
 
 
 
 
 
 
effect on  people’s health, their ability to choose  a suitable education  and  the way they interact with their natural 
environment and with other people. Whereas such effects have inevitable personal and social value, they are unlikely 
to  be  reflected  in  any  performance  indicator  that  is designed  to measure  the  contribution  of  the  public  sector  to 
economic value or public value (Jetzek et al., 2019). The non-application of business models to public services based 
on open data further exacerbates this issue. 

By addressing this cultural resistance and promoting the adoption of business models, public institutions can unlock 
the full potential of open data, driving innovation, efficiency and improved public services. This shift requires a change 
in mindset, recognising that business models are not just tools for profit-making but essential frameworks for creating 
sustainable and impactful public services. 

Similar  conclusions  can  be  drawn  from  the  analysis  of  the  answers  from  another  survey  question.  To  assess  the 
relevance of the different approaches, the survey circulated among the data.europa.eu open data community included 
a question on what type of approaches have been explored by the surveyed institutions. And, as shown in Figure 8, 
most  organisations  at  the time  of  the  survey  (February  2024)  had  not  yet  engaged  in  activities  to  assess  business 
models.  This  confirms  that  the  integration  of  business  models  for  open  data  remains  in  its  infancy  for  many 
organisations. 

Figure 8: Visualisation of survey answers (multiple choices allowed) 

Moreover, the visualisation of the  survey answers provided in Figure 8 also shows that, when organisations assess 
possible business models that integrate open data in their value proposition, the preferred options are data analytics 
or data-driven products and services. What does this tell us? 

One of the key reasons data analytics is so highly preferred could be that the reuse of open data is often a second-
order effect rather than the primary goal behind the generation of data. Many public sector organisations collect data 
primarily for specific operational purposes, such as regulatory compliance, service provision or internal reporting. The 
potential  for  reuse,  while  recognised,  is  not  the  driving  factor  in  the  data  generation  process.  Consequently,  data 
analytics becomes a natural choice for these organisations, as it allows them to derive additional value from data that 
was  initially  collected  for  other  purposes.  This  approach  enables  organisations  to  uncover  new  insights  and 
applications from existing datasets, making data analytics a practical and attractive option. 

20 

 
 
 
 
 
 
 
Moreover,  the  high  preference  for  data-driven  products  and  services,  such  as  mobile  apps,  dashboards  and 
visualisations, suggests a strong demand for tools that make data accessible and actionable for users. These products 
can  enhance  user  engagement,  provide  real-time  information  and  support  interactive  data  exploration,  making  it 
easier for organisations to demonstrate the value of open data to stakeholders. Despite being highly beneficial, the 
preference for this type of reuse also creates key challenges, mainly of a cultural nature. These challenges are faced 
not only by public sector institutions but also by businesses. 

The focus group discussion provided valuable insights into why adopting an ecosystem perspective in service design 
and delivery can be challenging for both the public sector and businesses. 

Firstly, organisational silos and resistance to change pose significant barriers. Public sector institutions and businesses 
are often structured in a way that  promotes siloed thinking, where  departments  and units operate independently 
rather  than  collaboratively.  This  structure  can  hinder  the  flow  of  information  and  resources  needed  to  develop 
integrated solutions. Additionally, resistance to change is common, as shifting to an ecosystem perspective requires 
altering established processes and mindsets, which can be met with reluctance from employees and management 
alike. 

Secondly, a lack of interoperability and standardisation can impede the development of collaborative ecosystems. 
For an ecosystem to function effectively, the various stakeholders involved must be able to seamlessly share data and 
resources. This requires interoperable systems and standardised protocols, which are often lacking, especially in the 
public sector. Without these, it becomes challenging to integrate services and deliver cohesive solutions that span 
multiple organisations or departments. 

Thirdly,  differing  goals  and  priorities  among  stakeholders  can  complicate  collaboration.  Public  sector  entities  and 
businesses often have different objectives,  performance  metrics and regulatory constraints. Aligning these  diverse 
goals  to  work  towards  a  common  purpose  within  an  ecosystem  requires  significant  effort  and  negotiation.  Lastly, 
establishing  trust  among  stakeholders,  which  is  crucial  for  effective  collaboration,  can  be  difficult  when  there  are 
varying interests and expectations. 

However,  not all is negative. While highlighting a lack  of adoption and exploration of business models, the  survey 
results at the same time show a relatively high level of awareness of their importance. The data reveals that 55 % of 
respondents had given prior thought to such business models, while 45 % had not. This indicates a moderate level of 
awareness  and  consideration  of  data-driven  business  models  across  the  surveyed  organisations.  Sector-specific 
insights  reveal  unsurprisingly  that  the  private  sector  has  a  higher  level  of  engagement,  with  more  respondents 
indicating  prior  consideration  of  business  models.  In  contrast,  European  Union  institutions,  national  and  regional 
public sector organisations and non-governmental organisations display mixed levels of prior consideration, suggesting 
varying degrees of readiness and awareness. 

21 

 
 
 
Figure 9: Visualisation of survey answers 

The implications here are twofold. First, there is a growing awareness of the importance of business models for data-
driven services, although this awareness is not yet universal. Second, the data underscores the need for continued 
education and support to help organisations across all sectors understand and develop viable business models for 
open data. 

3.  Policy  recommendations:  how  to  foster  the  development  and  application  of  business 
models for open data public resources 
The European Union has long recognised the transformative potential of open data in driving innovation, transparency 
and economic growth. Over the past two decades, a series of legislative measures have been introduced to foster an 
open  data  ecosystem.  The  open  data  directive  (Directive  (EU)  2019/1024),  which  replaced  the  public  sector 
information directive, marks a significant milestone in this journey. By mandating the accessibility and reuse of high-
value datasets, the open data directive aims to unleash the power of data for the benefit of citizens, businesses and 
public  administrations  across  Europe.  The  European  Commission’s  commitment  to  this  vision  is  confirmed  by  the 
digital Europe programme, which supports projects that enable the widespread use and reuse of public sector data. 
However, the path to fully realising the benefits of open data is fraught with challenges. These include technical, legal 
and cultural barriers that must be overcome to create a thriving data economy. 

In this context, it is essential to adopt a holistic approach that not only ensures the availability of open data but also 
promotes  its  effective  use  and  reuse.  This  involves  fostering  an  ecosystem  where  public  institutions,  private 
enterprises and civil society collaborate to co-create value from open data. 

Furthermore, the Interoperable Europe Act and other legislative initiatives highlight the need for a cohesive framework 
that  supports  data  interoperability  and  standardisation  across  EU  Member  States.  By  addressing  these  and  other 
challenges, we can build a robust infrastructure that supports the seamless flow of data, encourages innovation and 
drives economic growth. 

The following policy recommendations are designed to support the establishment of sustainable business models for 
open  data,  foster  a  collaborative  ecosystem  and  unlock  the  full  potential  of  data-driven  innovation  in  Europe.  By 
implementing  these  recommendations,  we  can  ensure  that  open  data  becomes  a  cornerstone  of  Europe’s  digital 
transformation, driving progress and prosperity for all. 

22 

 
 
 
 
The insights from the data.europa.eu campaign highlight the transformative potential of open data and underscore 
the necessity of robust business models to unlock this potential. The policy recommendations proposed are of two 
types: (1) recommendations to foster the (re)use of open public data (despite the focus of this policy paper being on 
business models, it is impossible to talk about business models harnessing open public data’s potential without its 
widespread use) and (2) recommendations on how to use business models to unlock the power of open data. 

3.1. Promoting an ecosystem approach 
Discussions about the importance and potential of open data have been going on for decades, and despite that, we 
haven’t  seen  much  progress.  It  is  important  to  recognise  that  top-down  pressure  from  policy  alone  is  unlikely  to 
improve the state of ecological data availability and accessibility. A collaborative effort is thus required to harness the 
entrepreneurial spirit of new generations of innovators and the strength of established corporations. 

A  successful  open  data  ecosystem  indeed  requires  a  collaborative  approach  that  involves  multiple  stakeholders, 
including  public  institutions,  private  enterprises,  academia  and  civil  society.  The  service-dominant  business  model 
design framework provides a useful lens for this approach, emphasising the creation of value through collaborative 
and integrated processes. Key recommendations include the following. 

Encouraging  public–private  partnerships.  Facilitate  partnerships  between  public  and  private  sectors  to  co-create 
value from open data. Public–private partnerships can leverage the strengths of both sectors to develop innovative 
solutions and services. 

Fostering a culture of collaboration. Promote a mindset shift from a goods-centred logic to a service-dominant one. 
This involves viewing open data as a resource for co-creating value with stakeholders rather than merely a product to 
be consumed. 

Supporting  intermediary  organisations.  Establish  and  support  intermediary  organisations  that  can  bridge  gaps 
between data providers and users, fostering collaboration and facilitating data sharing. 

3.2. Implementing support mechanisms for public institutions 
To  maximise  the  value  creation  from  open  data,  public  institutions  require  specific  support  mechanisms.  These 
mechanisms  should  address  both  the  technical  and  organisational  challenges  identified  in  the  campaign. 
Recommendations include the following. 

Building data literacy and skills. Invest in training programmes to enhance data literacy and analytical skills within 
public institutions. This will enable public servants to better understand and utilise open data. 

Providing technological infrastructure. Develop and maintain robust technological infrastructure that supports data 
sharing, processing and reuse. This includes cloud services, application programming interfaces and platforms for data 
integration. 

Establishing clear data governance frameworks. Implement comprehensive data governance frameworks that define 
roles, responsibilities and processes for data management and reuse. These frameworks should ensure data quality, 
security and compliance with legal standards. 

3.3. Encouraging user-centric data publication strategies 
Effective reuse of open data requires that data be published in ways that meet user needs. This involves tailoring data 
publication strategies to ensure data is accessible, relevant and usable. Recommendations include the following. 

23 

 
 
 
 
Engaging with data users. Regularly engage with data users to understand their needs and preferences. This can be 
done through surveys, focus groups and user feedback mechanisms. 

Prioritising  high-value  datasets.  Focus  on  publishing  high-value  datasets  identified  by  the  Commission,  such  as 
geospatial,  environmental  and  mobility  data.  These  datasets  have  significant  potential  for  economic  and  societal 
impact. 

Ensuring data quality and usability. Adopt standards and best practices for data quality and usability. This includes 
providing metadata, ensuring data is machine-readable and using common formats and standards. 

4. Conclusion 
The exploration of open data as a driver of innovation and economic growth underscores the transformative potential 
embedded in publicly available datasets. However, realising this potential requires more than just data accessibility; it 
demands robust business models and supportive ecosystems that can translate data into tangible value. The European 
Union’s initiatives, including the open data directive and the digital Europe programme, provide a solid foundation, 
yet significant barriers remain – technical, legal and cultural – that must be addressed. 

This paper has highlighted the critical need for sustainable business models that can catalyse the economic value of 
open  data  across  various  sectors.  By  adopting  an  ecosystem  approach,  fostering  public–private  partnerships  and 
emphasising user-centric data publication strategies, public institutions can better harness the power of open data. 
Furthermore, the integration of AI and open data represents a significant opportunity for innovation, particularly in 
creating smarter, more efficient public services. However, the path forward requires overcoming challenges related to 
data quality, interoperability and legal frameworks. 

The recommendations outlined in this paper provide a roadmap for policymakers and public institutions to maximise 
the value of open data. By addressing the identified barriers and embracing collaborative, service-dominant business 
models, Europe can foster a vibrant data economy that not only drives innovation but also delivers substantial societal 
benefits. 

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5. References and further reading 
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Coats, D. and Passmore, E., (2018), Public value: the next steps in public service reform, Work Foundation, The. 
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Jetzek, T., Avital, M. and Bjorn-Andersen, N. (2019), ‘The sustainable value of open government data’, Journal of the 
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ISBN 978-92-78-44500-3 

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