=Paper=
{{Paper
|id=Vol-2797/paper1
|storemode=property
|title=Data Strategies, Policies and Agenda
|pdfUrl=https://ceur-ws.org/Vol-2797/paper1.pdf
|volume=Vol-2797
|authors=David Osimo,Francesco Mureddu,Vassilios Peristeras,Anita Cioffi,Cristina Moise,Charlotte van Ooijen
|dblpUrl=https://dblp.org/rec/conf/egov/OsimoMPCMO20
}}
==Data Strategies, Policies and Agenda==
https://ceur-ws.org/Vol-2797/paper1.pdf
Data Strategies, Policies and Agenda
David Osimo*, Francesco Mureddu**, Vassilios Peristeras***,
Anita Cioffi****, Cristina Moise*****, Charlotte van
Ooijen******
*Lisbon Council for Economic Competitiveness and Social Renewal, david.osimo@lisboncouncil.net
**Lisbon Council for Economic Competitiveness and Social Renewal, francesco.mureddu@lisboncouncil.net
***Lisbon Council for Economic Competitiveness and Social Renewal, vassilios.peristeras@ec.europa.eu
****Deloitte, acioffi@deloitte.it
*****Lisbon Council for Economic Competitiveness and Social Renewal, cristina.moise@lisboncouncil.net
******Lisbon Council for Economic Competitiveness and Social Renewal, charlotte.van-
ooijen@lisboncouncil.net
Abstract: Every sector of the economy is affected by the data revolution and so is government.
Data is becoming one of the top strategic priorities of digital government. Many local, national
and supra-
strategies can vary substantially. In most cases, there is an overarching digital agenda include a
digital government policy. Other data strategies are developed in the context of industrial policy,
typically covering the overall economy, beyond government, and are designed to grasp the
opportunities of artificial intelligence. This paper looks at dedicated self-standing data strategies
aiming to maximize data driven innovation in public administration: the Data Agenda Government
for the Netherlands, at the New Zealand Data Strategy and Roadmap, and at the initiatives
Barcelona Data Commons and Findata. These strategies are typically implemented by those
countries or agencies that are at the forefront of data driven public services. Each case includes
a high profile, strategic policy initiative. The cases can be considered pioneers in ensuring a
strategic approach to data governance in public administration.
Keywords: Data Agenda, Building Blocks, Data Analytics
1. Introduction
1.1. Background of the Paper
The data explosion is affecting all aspects of the society and the economy and public administration
is no exception. Data is a fundamental resource for carrying out all government activities, from
regulation to service provision. And governments everywhere and at all levels are looking into the
opportunities of data driven innovation, and in many cases experimenting with it. IDC estimates
that central government is the fifth largest industry of the of the big data analytics market, covering
about 7% of the expenditure, and growing fast. A recent study by Deloitte (2016) identified 103 cases
Copyright ©2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
12 Ongoing Research
of big data analytics in government. In that regard, the Communication on "Data, Information and
context, a data strategy (DataStrategy@EC) and a related Action Plan have been set-up in 2018, with
the objective of transforming the EC in a data-driven organisation. The eight actions of the Action
Plan are centred around 5 different dimensions: data, people, technology, organisation, policy. The
data strategy highlights indeed that these dimensions need to mature and evolve harmonically to
deliver a real transformation on how data is used in the decision-making processes. In 2019, an
operational governance framework has been set up to closely follow-up the implementation and the
evolution of the Action Plan. The 2016-2020 ISA² (Interoperability solutions for public
administrations, citizens and businesses) programme funded with a budget of 131 million euro, aims
to support the development of digital solutions that enable public administrations, businesses and
citizens in Europe to benefit from interoperable cross-border and cross-sector public services. All
these initiatives foster data-centric public administration. But where do we stand? To understand
that the European Commission has commissioned the study Data Analytics for Member States and
Citizens, which provides policy Directorate Generals of the European Commission and Member
States public administrations with a knowledge base and guidance on the adoption of public sector
data strategies, policy modelling and simulation tools and methodologies, and data technologies
fostering a data-centric public administration. Specifically, the study covers three domains in
relation to data analytics in government:
1) Data strategies, policies and governance: initiatives in the public sector both at the strategic
level, such as data strategies, data strategies, data governances and data, management plans;
and at organisational level, aimed to create units or departments, and to elaborate new
processes and role;
2) Policy modelling and simulation: initiatives to improve policy analysis through new data
-
hypothesis testing, and tools allowing policy makers to carry out scenario analysis through
intuitive interfaces;
3) Data technologies: new architectures, frameworks, tools and technologies to be used by
public administrations to gather, store, manage, process, get insights and share data. This
domain includes the study of how data are governed as well as data collaboratives, and in
particular stresses the joint analysis of governance and technologies.
The paper presents the result for domain 1 building on five case studies selected for the in depth
analysis, based on the level of ambition and maturity: Barcelona Data Commons, Data Agenda
Government in the Netherlands, New Zealand Data Strategy and Roadmap, Secondary use of health
and social data (Finland), and Udbetaling Denmark. The cases can be considered pioneers in
ensuring a strategic approach to data governance in public administration. These are not economy-
wide data strategies like the digital agendas or data economy strategies, but initiatives focusing on
greater adoption of data driven solutions in the public sector. The pape starts off by providing a
basic description of the strategies, their objectives and structure. The third section deals with the
fundamental components of its governance, in terms of mechanisms in place. The fourth section
looks at three necessary enablers of all strategies: the safeguards, the skills and the monitoring
mechanisms. The final section looks at the results, the lessons learnt and the policy recommendations
and is built on the insight presented in the analysis.
Ongoing Research 13
2. What are the Data Strategies For?
A data strategy can be defined as a set of actions aimed to fully leverage on the potential of available
data for evidence based policy making. There is an abundance of data strategies nowadays, but they
can mean very different things in different contexts. This section describes what are data strategies
for, in terms of objectives and key components (building blocks).
2.1. Cases Under Analysis
The paper stems from an analysis on how Member States approach the concept of defining a data
strategy, plus the following cases:
Barcelona Data Commons. Barcelona has been one of the first cities in Europe to establish a
Chief Data Officer. the City Council thought it was a priority to open the Oficina Municipal de
Dades (OMD - Municipal Data Office), which is now responsible for the management, quality,
governance and use of data controlled and/or stored by Barcelona City Council and all of its
associated bodies (both public and private). The current volume of information generated by
citizens and the City Council itself through its activities, combined with the existing
technology and the science of data, allows to make a leap forward and become more efficient
at capturing, storing and analysing data, as well as drawing conclusions that help with
decision-making for new public measures. This relates also to the creation of data commons
and greater control of citizens over their data, as piloted by the Decode project;
NL DIGITAAL - Data Agenda Government. The Agenda centres on the value of data as a tool
to address policy and social challenges. The Dutch Ministry of the Interior and Kingdom
Relations leads the implementation of the agenda, but both central and local governments are
s specific attention to the protection of
the algorithm transparency. The agenda integrates policy goals oriented to better data
management in the public sector and the publication and reuse of open government data. The
transformation change are also underlined, which is in line with the OECD approach for the
digital transformation of the public sector;
Framework provides an interesting example where data stewardship is seen more as a skill to
be built up among public officials rather than a formal role. This approach aims to embed
-handling positions,
with the goal of evolving beyond the need for traditional data governance roles (e.g. data
y for government-held data in New Zealand
(Stats NZ) developed a new and improved data governance framework for the New Zealand
management practices across the public sector, and to leverage data as a strategic asset for
decision making. One of the central pillars of the framework is the adoption of a so-called
-of-
think more strategically about the governance, management, quality and accountability of
14 Ongoing Research
their data, over the whole data life cycle (i.e. from the design and source of the data to its
storing, publication and disposal);
FINDATA. Finland has a long history of collecting extensive data in registers but making use
of the data has been difficult and inefficient. In 2019 a new Act on Secondary Use of Health
and Social Data entered in force in Finland. With the new enabling legislation, Finland has
become the first country in the world to successfully enact a law on the secondary use of well-
being data that meets the requirements of the European General Data Protection Regulation
(GDPR). The new legislation enables and expands the use of social and healthcare data from
the traditional areas of research and statistics to those of management, development,
innovation, education, planning, and steering and supervision work. The new act facilitates
the establishment of a new central data permit authority in Finland, known as Findata. Findata
has been established and operates within the National Institute for Health and Welfare, but as
a separate entity. Findata is the one-stop-shop responsible for streamlining and securing the
secondary use of social and health data. It guarantees a flourishing ecosystem around the
secondary use of social and health data streamlining the processes for the issuing of research
permits and data collection and ensuring that data is being used in secure environments,
thereby maintaining the trust that the general public have in authorities and the public sector.
This case study reports how the new Act on Secondary Use of Health and Social Data has been
developed, the main themes of it and provides an overview of the Finland legal framework
around the use of the health and social data. It also described in detail the Findata one-stop-
shop, how it works, the governance, and how it guarantees access to the social and health data.
A descrption of the users, data sources and data lakes of Findata it is also provided.
2.2. Objectives
The strategies generally pursue the combined goals of fostering data analytics for public value
creation and ensuring trust, accountability and citizens agency over how data are used. Both
dimensions are important, although arguably with different emphasis.
The Danish, Dutch, New Zealand and Finnish cases are driven by the primary goal to increase
data analytics and reuse, and balance this with a strong emphasis on safeguards, ethical aspects and
consent as pre-conditions or enablers of data reuse. For instance, in the case of New Zealand the
rationale for the data strategy is to address the disconnect between the rhetoric (which focusses on
opportunities) and the reality of data-driven policies (which focus on minimizing risks of data
misuse). The data strategy is part of the overarching government goal to get more value from data.
-
aims to make Finland an internationally renowned pioneer in health business and in well-being. In
the Danish case, the strategy addresses a clear concrete need: to fight fraud and detect errors at an
early stage.
dimension related to data and technological sovereignty for instance with data provision
requirements in the context of public private partnerships.
Ongoing Research 15
This difference is related to the specific policy context. In Barcelona, the data strategy is part of
the wider ci
the narrower strategic priorities related to digital government, social affairs or data reuse.
The role of the private sector is also different. In the Finnish case, the private sector is clearly
identified as a data reuser, while in the Barcelona and Danish case it is identified mainly as a data
provider. In the Dutch case, companies are considered as both data providers and reusers. What is
clear across all strategies is that the private sector and citizens are part of the stakeholders that need
to be involved in building a data ecosystem for public value creation.
The very existence of a strategy reveals the long-term importance of the topic. In all cases,
governments sought via the strategy to ensure a structural commitment to the data priority (at least
three years).
2.3. Building Blocks
The analysis of the different priorities of the strategies reveals many commonalities. All strategies
include both the element of data exploitation, and the ethical and data protection aspect. Another
common aspect is the recognized importance of data sharing as a fundamental pillar of data driven
innovation. The New Zealand and Dutch cases also include a strong emphasis on knowledge sharing
and skills.
Table 1: Cases and Related Building Blocks
Case Building Blocks
BCN 1) Understanding data as an urban infrastructure, just as the provision of water
and energy are. Data is seen as a meta-utility that will enable the city of
Barcelona to support more effective delivery of public services to Barcelona
citizens for greater equity, safety and quality of life;
2) Integrating the use of Big Data & Data Analytics to improve public
decision-making (Data-driven projects).
3) Treating data as a common asset, and making it available for social and
that the immense economic value that citizen-produced data represents
should be returned back to those that generate that value in the first place:
the citizens.
4) Enforcing data & algorithmic transparency (Data Ethics). This not only
requires opening up data, but also encouraging the reuse, providing
citizens with the tools and knowledge to be able to verify these, and to be
informed about automated decisions and their underlying algorithms.
5)
shifting agency and control to citizens themselves that have the right to
decide what data they want to share, with whom and on what terms.
16 Ongoing Research
NL 1) Problem-solving with a data-driven approach: five social challenges have
been selected: energy transition, manure issue, infrastructure and spatial
bottlenecks, poverty and the issue of debt, and subversive crime.
2) Focusing on legislation and public values to develop new general
principles on a responsible way of dealing with data taking into account
legal and ethical frameworks.
3) Improving the quality of government data and using it more efficiently to
ensure government has the right data and is able to share (open) data at the
right time and in the right way in order to foster a service-oriented and
transparent public sector.
4) Collecting and sharing knowledge about a data-driven approach (sharing
of best practices.)
5) Investing in people, organisations and changes in corporate culture (to
address skills needs and cultural change).
NZ 1) Invest in making the right data available at the right time
a) To provide visibility of key data sets and proactively address gaps
b) To improve accessibility of government held data
c) To open up more non-sensitive, non-confidential data to the public
2) Grow data capability and supporting good practice
a) To take a strategic and coordinated approach to uplifting capability across
the public sector.
b) To make better use of existing data capability.
3) Build partnerships within and outside government
a) To co-design the future data system and work together to maximise use
and impact of data.
b) To co-
4) Implement open and transparent practices.
a) To establish appropriate accountabilities and protection mechanisms
b) To build public knowledge and understanding of how they can benefit
from data use.
FI 1) To enable efficient and secure processing of personal data collected during
the provision of social and health care as well as personal data collected for
the purpose of steering, supervision, researching and collecting statistics
on the social and health care sector;
2) to allow the collected personal data to be combined with the personal data
held by Social Insurance Institution of Finland, Population Register Centre,
Statistics Finland and Finnish Centre for Pensions;
3) to secure the legitimate expectations, rights and freedoms of individuals
when processing personal data.
Ongoing Research 17
DK 1) High quality data analysis across registry
2) Trusted collaboration with sectoral department (national and local)
3) Collaboration with foreign authoriy
4) Special initiatives
When looking at the actual implementation of the strategies, other aspects emerge. First, there is
limited technological investment. The two larger scale, more ambitious whole of government
strategies of the Netherlands and New Zealand do not include the creation of any horizontal
g services. It is worth mentioning
that the initial case selection included one of the most high-profile national data infrastructural
service, the Italian Data Analytics Framework: however, in the course of the study the platform has
been quietly scaled down. The Dutch strategy instead includes projects on five specific challenges:
energy transition, manure issue, infrastructure and spatial bottlenecks, poverty and the issue of debt,
and subversive crime. Barcelona, which does have a centralised data and analytics infrastructure
also advances its data analytics activities in an incremental way, based on priority policy challenges.
Second, all strategies include data mapping. In the case of Barcelona, it is the Data Office itself which
carries out the mapping exercise, while in the others it is part of the distributed data stewardship
and process tasks.
3. The Governance of Data Strategies
3.1. Governance Mechanisms
Governments adopt very different approaches to the governance of data strategies. The political
positioning of the data strategy and related implementation agencies is varied. First and foremost,
the strategies do not entail the promulgation of new legal provisions, but remain at the level of
strategic document. Only in the case of Finland, because of the sensitive nature of the data a
dedicated law has been approved to clarify the scope of reuse of health data. Only in the case of
fundamental factor in ensuring stability and compliance. In the other cases, the data strategy sits
under specific ministries: in NL and NZ, under the department in charge of digital government,
while in Finland it is the ministry of social affairs, in Denmark it is the social payments agency
(Udbetaling Danmark) under the ministry of employment. On the same line, Barcelona is the only
have a major role as responsible for the management, quality, governance and use of data controlled
and/or stored by Barcelona City Council and all of its associated bodies (both public and private).
And the Data Office is not only the coordinator but also the implementer of the Data Commons
Strategy. In the other cases, the roles are softer. There is no Chief Data Officer, but the effort is
government. Data stewardship
In particular,
the goal is to spread the role of data stewardship across each agency, as illustrated in the figure
below.
18 Ongoing Research
Figure 1: Role of Data Stewardship Across Each Agency
The case of Findata is also different, as its role is not to ensure data quality or sharing but to
manage the data and consent flows. As such, it is a dedicated agency with strong enforcement roles
but limited to the data and consent flows. This different degree of centralization is related with the
broadness of the strategy: the broader the scope, the more decentralized the approach. The Dutch
and New Zealand initiatives have a very broad mandate cutting across all departments and levels
of government, while the Barcelona and Finland are more focused (respectively on one institutional
level and on a specific data type). In any case, whether more or less centralized, all initiatives have
an extensive set of boards and steering groups including a wider variety of participants, as outlined
in the table below.
Table 2: Sets of Boards and Steering Groups
Case Building blocks
Steering with institutional representatives from national govt agencies and
NL
local government organisations
Sounding board with technical staff
Executive data committee with the central political and executive
BCN
management
Transversal data coordination board includes representatives of 21 city
departments
Data protection board with 14 departments dealing with sensitive data
Ongoing Research 19
The Digital government partnership includes the public sector chief
NZ
executives and four cross government working groups
Steering group includes social welfare ministries, social insurance, central
FI
statistical office, representatives of social and health care providers
Cooperation forum with municipalities and their national association, more
DK
operational
The coordination group with the national association of municipalities , more
strategic
-creation
with all relevant agencies. The extensive range of consultation and collaboration activities in place
is repeatedly mentioned as leading edge and unique with respect to traditional processes. As
-operation between public authorities, companies and
as an outcome, but in doing so it requires the adoption of a systematic silos breaking approach as
gaining independent technical guidance and expertise on what was important to stakeholders and
place also online through a Sharepoint platform.
This co-creation activity can also extend beyond public administration, towards external
stakeholders such as business and civil society. New Zealand and Finland extensively involved these
players in the shaping of the strategy, in order to make sure that it includes the perspective of
external users. This activity went beyond traditional consultation, as made clear in the case of
and public sectors worked together to prepare the implementation simultaneously with the
legislation process. It was a unique way of working and something carried out for the first time at
-creation aspect often is too focussed on the process, rather than
on the final output: interviewee mention the lack of user orientation of some services as a clear
The budget assignment for the strategies is typically moderate. In the Dutch case, it includes 10
mil
is nearly four millions euros for 2018/19. In Denmark, 3,4 million euros per year, and in New
Zealand there is no dedicated funding for cross agency work. The budget for Barcelona, in this case,
stands out taking into account that it refers to a single city, and it reflects the strong role of the
municipal data office in implementing the strategy. The Danish case actually generates revenues far
in excess of its costs (62 million euros in 2019).
3.2. Data Sharing and Quality
Data sharing and improved data quality is a general priority across all strategies. Greater sharing of
high quality of data can be considered one of the main goals across the board: for Denmark, New
Zealand and the Netherlands, across ministries and levels of government; for Barcelona, across
20 Ongoing Research
different municipal agencies; and for all the different player in the value chain. Data sharing has
different levels of compliance. In the Netherlands, it is compulsory for public administration to share
and reuse data from the base registries, and some of the base registries are open to the public. Six
base registries, the BAG, BRK, BRT, BGT, and BRV are in part or completely available as open data.
But beside these base registries, there are no strong compliance mechanisms. In the words of one
-
policy levers in both the Dutch and Barcelona cases to encourage public organisations to share data
is communication on two aspects: why and how. Why data sharing should happen focuses on
demonstrating data-driven value creation in best practices. How data sharing can happen focuses
on knowledge exchange between organisations on topics such as quality and standards. Similar soft
arrangements with regards to data standards are in place in other strategies, as it remains a challenge
to ensure compliance. In New Zealand, a Cabinet mandate empowers the GCDS to direct agencies
to adopt common capabilities and data standards. Agencies are enabled and supported by the GCDS
to voluntarily adopt those standards. Data standards can be of different sort. Dutch base registries
ters, as well as x/y coordinates. Yet the
adoption of standards is also mentioned as a clear critical aspect looking ahead it is not a result
fully achieved. All the strategies clearly adopt open standards as a priority.
4. Key Enablers
4.1. Safeguards
All strategies have strong emphasis on safeguards, not just in terms of mere compliance, but to create
a shared data culture that maximizes analytical power with ethical values. Typically, the data
protection competence is separated from the data stewardship or responsibility competence. The
notion of safeguards, accordingly, spans well beyond compliance with GDPR, to encompass a full
ethical framework. Notably, the concept includes not only data processing, but the ultimate purpose
of what is done with the data, with the goal of keeping the interests of citizens first, rather than those
of government. This is why in Barcelona the ultimate goal is to empower citizens with data, citizens
are involved through experimentation and consultation, and the activities of the strategy include
algorithmic accountability and how public decisions are influenced by data. Wherever possible,
data-driven projects will be able to check the algorithms using simulations based on city data.
Likewise, using open source code or other means, third-party technology suppliers must reveal the
underlying logic behind any IT process for (automated) decisions pertaining to any of their systems
used by the City Council. By the same token, the Dutch strategy has developed general principles
for the responsible use of data, after several municipalities had indicated running into difficulties
regarding data sharing with companies. The Dutch case also shows the importance of the purpose
of analysis, namely to avoid that data analytics is carried out with punitive purposes. Similarly, in
it comes to the investigation, including the need for notification and the impossibility to access
sensitive data in other registries (e.g. criminal records). Citizens control over their data is also an
important issue. It is one of the leading principles of the Barcelona data sovereignty scheme, defined
evant systems, over the
Ongoing Research 21
collection, storage, use, transfer and publication of their data, whether it be of a technical, scientific,
Control over data (Regie op gegevens), which aims to give citizens and businesses more control on
being developed to support citizens and businesses in managing their data. Ultimately, this should
result in a generic cross-sectoral framework that enables secure, reliable and user-friendly digital
exchange of data between governments, private and social organizations But this broad perspective
s a done deal.
In the Netherlands, stakeholders find the application of GDPR quite complicated, especially in
the social domain. In the social challenge on poverty and debts, for instance, it is not clear which
data are allowed to be linked, whether analysis can be done or not, and whether subsequent policy
o. On
a similar note, On 5 February 2020, a Dutch court has ruled the way in which the government uses
the fraud detection system SyRI, which links data from different sources, such as the Tax Agency,
The Unemployment Agency and municipalities, as an infringement of Article 8 of the European
balance between preventing and combating fraud in the interest of economic well-being on the one
hand and the violation of privacy on the other. This ruling urges the Dutch government to go back
to the drawing board.
4.2. Skills
The lack of adequate skill is a major issue across all strategies, and all strategies include actions to
address it.
These actions are basically:
Training of civil servants. In the Netherlands, data is part of the training provided by the
National academy for digitisation (RADIO). In Barcelona, the services provided by the data
office are accompanied by training in order to ensure sustainability. In Nez Zealand, GCDS
has developed e-learning modules for R coding capabilities.
Profiles: in Barcelona, a new job profile for data scientists has been created. In New Zealand,
GCDS create a data and statistical capability framework to support training and recruitment
Recruitment: in the Netherlands, a trainee programme was created in the public sector. At the
central level, the ministry and the Statistics department have internal expertise and offer
support services to other government department. In Finland, Findata has 20 data scientists,
while the Danish data mining unit has 25. The issue is then not only recruitment but retaining
talent by setting up stimulating working environment that combines IT excellence with public
purpose.
Communities of practice: in Barcelona, an inventory of data enthusiasts has been created to
provide training and support, and communities of practice are encouraged in all cases.
Centre of competence: To deliver support, the strategies create centre of competence that offer
services to another department. This is prominent in the Netherlands, with the creation of LED
22 Ongoing Research
experts centre and a network of specialised data labs. In New Zealand the data capability hub
offers similar services. The municipal data office in Barcelona is also a centre of expertise for
other departments
4.3. Monitoring
With the exception of the Danish case, monitoring remains very lightweight across all strategies.
There are no KPI in place and no systematic monitoring. In the Netherlands there is a reporting
system in place for projects launched under the strategies, which ultimately is presented to
parliament. In Denmark, there is strong accountability mechanisms due to market-like relation
between the national agency Udbetaling Danmark and the municipalities, with clear Key
Performance Indicator, Service Level Agreements and financial accountability.
5. Conclusions and Lessons Learned
5.1. Success, Challenges and Lessons Learned
The first and foremost result of the strategies is putting the topic on the agenda and raising
widespread awareness in public administration about the strategic value of data. As the Dutch case
ation with external stakeholders, for
instance in the case of Barcelona it visibly changed the importance of data in the relations with
private providers and civil society.
This increased awareness has been reflected in greater access and reuse of data. In the case of the
Netherlands and New Zealand, it has enabled greater data sharing between different departments
and levels of government. In the case of Barcelona, this has resulted in data clauses in public
contracts that allow the municipality to access and use data generated through third party service
provision. In the case of Findata, it has cut by an order of magnitude the time needed to access
sensitive health data for research purposes from three years to three months.
It is clear that these strategies led to greater policy awareness and propensity to data sharing. Yet
when it comes to visible results of these data sharing, the picture is uneven. Pilots, such as in the
Dutch and Barcelona cases, show the potential to use data to improve policy and services, and enable
quick delivery on different areas such as renewable energy, public order, housing and poverty. But
while pilots are a good start, all strategies also point to the limited uptake and implementation of
data analytics projects as a major challenge compared to expectations.
The limited visibility of the results goes hand in hand with the reluctance to share data, in a
classical chicken and egg problem. As the New Zealand case shows, there is still a problem in gaining
shared ownership of the strategies as many agencies deprioritise work that is very beneficial for the
system as a whole, but is not overtly beneficial for their own agency. Despite data sharing being the
declared goal of the strategy, it remains one of the most important challenges: this is not only due to
technical barriers related to legacy and interoperability, but also to the resistance and lack of know-
how to share data as well as the low quality of the data. In fact, one of the issues is that secondary
Ongoing Research 23
use of data requires greater data quality and reveals the limitations of data quality, as shown by the
Dutch case.
In other words, policy prioritisation is necessary but not sufficient. Just as in other domains such
as research data, or open government data, or business data, top down mandates have to be
combined with clear benefits and opportunities for reuse: they should be demand driven whether
internal (municipalties in the Danish case) or external (reusers of health data). This is why focussed,
thematic intervention such as on health and social data, where the business cases is clear, have more
opportunity to grow.
Obviously, one of the expected barriers to data sharing is data protection. Because of GDPR is
still relatively new and there is limited knowledge about its practical implementation, several
organisations exhibit cautiousness on going ahead with a data project. This continues to be true, but
what is clear is that this barriers can be overcome when there is a clear case for it. Again, data
protection is a much more powerful barrier when it goes hand in hand with lack of a business case.
The lack of data skills is obviously a problem. But this is not limited to the mere absence of a
sufficient number of data scientists in public administration. It refers to the more general problem
of data literacy among civil servants, and notably decision makers, which leads to a dangerous
cultural gap between the data scientists and policy makers. But this also includes the lack of a policy
knowledge by data scientists (policy literacy). This is particularly risky because the ethical
implications of data do not concern only the compliance with data protection, but the overall
approach to data for policy: for instance, the purpose of data driven policies could spectacularly
backfire if it is used with a punitive approach in particular with regard to social issues. The required
cultural change concerns therefore all players in the data value chain.
The reason is that data is not a commodity. Extracting value from it requires not only cleaning
and preparation, but plenty of assumption and choices in building analytical models. This
processing implies that the results of the analysis are to be carefully considered in light of the
limitations and assumptions. Data analytics should not be treated as a black box managed by data
scientists where data are the input and decisions are the outputs. The recent example of Covid-19,
where modelling tools have been at the root of potentially disastrous decisions by policy makers, is
a clear demonstration of the need for distributed data competences in every policy domain and of
the need of close collaboration between data scientists, data analysts and decision-makers. And the
Danish case shows how important the collaboration between data analysts and frontcase workers
is.
5.2. Policy Recommendations
The in-depth analysis suggests a set of recommendations for policy makers at EU and national
level:
Start with the problem, not with the technology. Building a data strategy does not necessarily
entail an investment in a technological data analytics platform, and certainly it does not start
with it. Very few strategies include such investment, and those who do are typically vertically
focussed on specific sectors or organisations. On the other hand, there are not many examples
24 Ongoing Research
of successful whole of government data analytics platform, but there is room for focussed
centralised technological components, as shown by the Reproducible Analytical Pipeline case
analysed in task three. A common trait of most advanced horizontal and vertical strategies is
a demand driven approach: providing a variety of support mechanisms, from governance to
skills to support services, to address real problems, such as health, poverty, urban issues. Focus
on the key questions to be answered and the policy problems to be solved. This is important
in order to deliver tangible results.
Analyse permanently user needs. Users include both data holders and data reusers, both
internal and external. Too often user needs remain assumed or based on anecdotical evidence.
Not only it is necessary to formally analyse them in the first place, but perhaps more
importantly to constantly monitor them over time to adapt to how solutions are used. The
constant collaboration between the Danish Data Mining Unit and the municipalities frontline
case workers is a clear example of this. Iteration of delivery is therefore crucial no service is
designed perfectly the first time.
Co-creation is a fundamental component of the strategy. Bringing internal and external
stakeholders onboard is a necessary (not sufficient) condition of success. But it is equally
important to keep stakeholders onboard after the strategy is launched, during the
implementation. Other government agencies need to see the benefit to share data and to
conform to the required standard and processes, because there are costs in doing so. Of course,
there is a shared perception among decision-makers that data is a strategic resource and that
investment is needed, but this is only sufficient for kickstarting the process: the difficult part
lies ahead.
It is not sufficient to consult and co-create with stakeholders: what matters is delivering results.
There is a lack of business case for data innovation. Existing strategies should focus, as in the
case of the Netherlands and New Zealand, on delivering short term results via small scale
pilots on topical issues. But pilots should be the beginning of service delivery, as shown by the
Findata case, and their results should be well documented and shared. The problem is not only
the difficulty in demonstrating impact the ultimate benefits in terms of quality of public
service. It is the actual difficulty to demonstrate deployment and adoption simple projects
that work and deliver. Data strategies should balance long term perspectives to data
stewardship with short term delivery of pilots.
In order to ensure delivery, it is crucial to take a practitioner led approach. The most successful
strategies are those were data experts in public administrations are brought together and given
a visible role in the process, as in the Netherlands with the creation of a cross department
sounding board with data analysts and policy experts. There is a permanent gap between data
experts and decision makers, and for data strategies to work, data experts should be
empowered. And communities of practices are the fundamental tool to enable mutual learning
and empowerment of practitioners.
Create a data culture across department and institutional level. Data-driven innovation
requires cultural change, training and bringing in new resources from the outside. New centre
of competences (such as the Dutch labs) have to be created. Data training should be provided
to all civil servants, and in particular to decision makers. But it also requires the reinforcement
of internal capacity and the creation of effective communities of practice that cut across
Ongoing Research 25
government silos, and the creation of knowledge and expertise centre to facilitate knowledge
exchange between data champions and novices.
-term process, expectations need to be managed correctly and hype should
be avoided. Delivering data driven innovation is no
not a commodity. It requires extensive work for access, preparation and cleaning, but also for
processing and reprocessing. There is a constant risk of disappointment that backfires. It is
important for data leaders to raise realistic expectations from other stakeholders and to start
by focussing on data availability. Pilots should be selected based on two criteria: a genuine
need and access to available data. Luckily, the evolution towards a data culture is visible across
society and the economy, and it is here to stay particularly so following the ongoing
pandemic crisis. There is no need to overhype the opportunities.
A robust ethical framework is crucial and can be instrumental to innovation. The results are
The safeguards can work hand in hand with more data reuse, by creating a shared data
stewardship culture. Actions for data protection compliance should be integrated with those
on increased data literacy: in fact, the lack of a data culture is damaging for both data
protection and data innovation. But an ethical approach goes beyond compliance with data
protection and includes also what is done with the data, for instance to avoid any punitive
spirit in the services being put in place to fight poverty based on the data gathered.
Monitoring should be present and structured but not drive the process. Milestones and KPI
should be core part of any strategy and it is currently very rarely the case. KPI should not
concern only outputs, but also the inputs and the process, such as the percentage of datasets
in line with the required standards, the access to base registries, and the number of
departments taking part in the different activities. In fact, the main compliance mechanism in
the case of such soft strategies is monitoring and reporting, as shown by the Dutch case where
the most important control mechanism is reporting to Parliament. And they become
fundamental in ensuring the long term collaboration of different stakeholders, as in the Danish
case. Further, they allow comparison and benchmarking of the strategies for data management
developed and adopted in different contexts.
References
Deloitte (2016). Big data analytics for policy making Article title. Report prepared for DG DIGIT.
About the Authors
David Osimo
David Osimo is director of research with 20 years of experience as an advisor on information society and
innovation policies, inside and outside government, at local and international level (Open Evidence, Tech4i2
Ltd, European Commission Joint research centre, IPTS, Regione Emilia-Romagna). David is able to combine
research and practitioners skills: as a researcher, he is mostly known for his pioneering work on web 2.0 in
government and science, on which he advised the European Commission and the United Nations
Development Programme. He also published articles on e-government, future science, research and
innovation policy and ICT statistics and he is an experienced keynote speaker at high-level events such as
Ministerial Conferences and the Digital Agenda Assembly. As a practitioner, he created web- platforms for
26 Ongoing Research
policy-making (CommentNeelie.eu, Daeimplementation.eu, and the Grand Coalition Pledge Tracker) and
inducement prizes for innovation (iMinds INCA awards and Ideamocracy.it). David is a native Italian speaker
and is able to work and interact in English, Spanish and French.
Francesco Mureddu
Francesco Mureddu is an innovation and technology policies analyst and strategist. Francesco has been
primarily involved in designing and delivering consultancy and research projects for the European
Institutions in a wide variety of topics including Information Society and ICT, e-government, Smart Cities,
ICT-enabled social innovation, future science, research and innovation policies. His specific fields of
expertise include evaluation of policy initiatives and programmes, impact assessments, policy monitoring
and benchmarking, policy development, counterfactual impact evaluation, technology road mapping and
scenario development, economic and econometric modelling, cost/benefit and sensitivity analysis,
Council, Associated Researcher at the Centre for North South Economic Research (CRENoS) of the University
of Cagliari, as well as co-founder of the cyber security startup Intelligence Framework Inc. He also has
professional experience in international institutions such as the European Commission and the European
Central Bank. Francesco holds an MA in Economics from the Catholic University of Louvain and a PhD in
Economics from the University of Cagliari and is able to work and interact in English and Spanish.
Vassilios Peristeras
Vassilios is team leader at the unit data operations, technology and research, directorate-general
informatics, European Commission. From 2010 2016, he has worked as Programme Manager in the European
Commission, Interoperability Solutions for European Public Administration (ISA) Programme being in charge
for semantic interoperability and open data. He has also been the coordinator of European Interoperability
Framework revision. Vassilios is also Assistant Professor at the International Hellenic University, School for
Science and Technology in Thessaloniki, Greece, and he is also Research Fellow at KU Leuven, Public
Governance Institute. His teaching and research focus on the areas of eGovernment, eParticipation,
interoperability, open and linked data and semantic web technologies
Anita Cioffi
Graduated in Economic in the 2014, Anita has developed consulting experience in leader advisory firms. She
is involved in multiple international assignments mainly for European Commission (DG DIGIT, CEF, JRC) and
Institutions (EP, EMA) focusing on the development of data strategy, big data and analytics solutions (among
others the European Commission data platform, and the CEF Big Data Test Infrastructure building block),
design of business models, processes and services. She has collaborated also in several projects and studies
within the ISA² programme (Programme on Interoperability Solutions for European Public Administrations)
and in particular on the EIF (European Interoperability Framework) and NIFO (National Interoperability
Framework).
Cristina Moise
Cristina has an extensive expertise in statistical analysis, having worked well at the European Commission
on complex DG RTD statistical modeling and representation, including the flagship Innovation Performance
Indicators project. In this respect, she provided statistical analyses for the support documents used for
various policy analyses and discussions. Cristina has experience in working with data from different sources
(EU Industrial R&D investment scoreboard, COMTRADE / COMEXP, Eurostat, OECD, World Bank), as well as
with data from patents and bibliometrics, IUC database, Data mining, RIO-PSF website. She has an
undergraduate degree in computer science and a master in Economic IT.
Ongoing Research 27
Charlotte van Ooijen
Charlotte is a researcher and policy analyst in digital government with a specialisation in data governance
and public trust. In January 2020, she joined the Lisbon Council as a Research Fellow Digital Government.
a policy analyst at the Organisation for Economic Cooperation and
Development (OECD, 2015-2019), leading the analytical work on the Data-Driven Public Sector and
contributing to several country reviews on digital government and (open) data, and to the broader OECD
work on the digital transformation of the public sector, digital government indicators, open government
data, digital welfare, innovation policies and technology trends. Charlotte completed her PhD thesis at the
Tilburg Institute for Law, Technology and Society (TILT) at Tilburg University in the Netherlands (2014). Her
government-citizen relationship. She also worked as a lecturer in Public Administration at Tilburg University
(2008-2011) and Erasmus University Rotterdam (2013-2014) and was an associate researcher at the Centre
of Administrative and Political Sciences (CERSA) of the CNRS and Panthéon-Assas | Paris II University (2014-
2015). Besides her mother tongue Dutch, Charlotte speaks English, French, Spanish and German.
Additionally, she has taken up the study of Italian.