This document presents how OECD countries are moving towards defining and implementing holistic public sector data governance practices at the national level. It discusses the main trends and challenges observed concerning data governance and proposes a public sector data governance framework drawing upon OECD best practices. The section then applies the model to briefly overview data governance practices across OECD member and partner countries.
1 Introduction
In the early 2000s, tech giants such as
Facebook realised how digital platforms and the 24/7 connected citizen provided
the ideal context to collect and re-use data for business purposes. The user
data opened a window of opportunity to start selling data-driven products and
services to any company and individual interested in designing ad hoc marketing
and communication strategies – from businesses to politicians.
Data collected through multiple sources
(from mobile phones to smart home devices) are now analysed to understand users
better and target potential customers or service users. These insights may help
organisations to drive citizens' choices, increase business revenues, influence
public votes, or design and deliver better services. There is a plethora of
technical solutions used for this purpose (e.g. artificial intelligence [AI],
big data, customer relationship ma
nagement), which places the access to and
sharing of data (EASD) as a precondition for data analysis techniques to help
increase the value for companies and shareholders.
Since The Economist published the article, "The
world's most valuable resource is no longer oil, but data" in 2017 (The
Economist, 2017[1]),
"data is the new oil" became the new buzzphrase and was sometimes
abused and misunderstood by data enthusiasts. While this data-oil analogy aimed
at increasing public awareness in response to raising data monopolies and
controlled data flows, it also helped to stress how new technologies and data
could help organisations make better decisions and increase business
intelligence.
Still, while the discourse on "data as
an asset" is well accepted nowadays, organisations, including the public
sector, often fail to govern, manage and value data like the other relevant
assets for their success. The neglect undermines the possibility of taking
advantage of the opportunities brought by the "datisation of a huge amount
of information that was previously intangible" (Chiesa, 2019[2]).
New challenges have also arisen from citizens'
data misuse and abuse, mainly by private sector organisations. The challenges
are paired with the inability of governments to take proactive action, keep up
with technological change, and understand the policy implications of data in
terms of trust and fundamental rights.
Responding to these challenges requires a
greater understanding of the structure and knowledge-sharing of how OECD
countries address data governance in the public sector. The need for governance
is well recognised by private sector actors but is only gaining traction in the
government sphere.
This white paper presents a brief overview
of how national governments across OECD member and partner countries increasingly
address data governance or have worked on developing specific elements. The paper
also presents a proposed public sector data governance model based on OECD good
practices on data management and sharing within the public sector, open
government data and digital government. While not exclusive, the elements
presented in the data governance model can be beneficial and act as a guide.
2 The Case for Good Data Governance in the Public Sector
Good data governance can contribute to
setting a shared vision, enhancing coherent implementation and coordination,
and strengthening the institutional, regulatory, capacity and technical
foundations to control better and manage the data value cycle, i.e. collect,
generate, store, secure, process, share and re-use data, as means to enhance
trust and deliver value.
Good data governance is imperative for
governments that aim to become more data-driven as part of their digital
strategy. It can help extract value from data assets, enabling greater data
access, sharing, and integration at the organisational level and beyond and
increasing overall efficiency and accountability. However, while the concept is
not new, most OECD governments struggle to implement it.
The OECD has observed the following trends
in the governance, management and sharing of public sector data:
a.
Data governance is
increasingly relevant to data protection practices at the global scale, both
exclusively and explicitly. Yet, a strong and unbalanced approach to data
overprotection can reduce the value of data sharing, such as in the delivery of
cross-border public services.
Recently, data misuse by private companies
and increasing concerns from citizens about data management in the public
sector have triggered government intervention to improve personal data
protection (OECD, 2019[3]). As a result, the ethical
and transparent use of data is now high on the political agenda.
Data flows have increased across
organisations, sectors (e.g. business-to-government) and borders, adding
another level of complexity to data governance in a globalised and
interconnected world. Data governance is no longer limited to organisational
boundaries but is a multinational concern resulting from cross-border data
sharing.
In this context, international instruments
such as the EU General Data Protection Regulation have sought to "give
back to citizens the control over their data" (OECD, 2019[4]) and take cross-national
action to prevent data misuse. The General Data Protection Regulation pushed
the data protection agenda forward, thus underlying the need for common
frameworks to ensure data protection across borders. Nevertheless, data
overprotection can result from the misunderstanding of national and
international regulations and drive change in policy approaches (e.g. from
openness by default to "open if possible, protected if needed").
The global challenge at this stage is thus
to ensure the right balance between free data flows and data protection, as
stated by Japan's Prime Minister Abe during his keynote speech at the World
Economic Forum in January 20192 (Japanese Government, 2019[5]).
b.
Data governance elements
are often part of broader digital transformation policies. However, these
components can be fragmented, thus reducing their whole-of-government value in
terms of public sector integration and cohesion. A holistic data governance can
help the government as a whole to join up.
While OECD countries have often defined
elements relevant to public sector data governance in the context of digital
government, open data, data management, and/or AI strategies and/or policies,
these elements are often fragmented. In some scenarios, this disconnection is
deeply rooted in the intricate governance arrangements supporting those
policies (e.g., different public sector organisations leading these policies or
lacking clarity in leadership and responsibilities), posing significant data
integration and sharing barriers.
Holistic data governance can also help enable
the Government as a Platform (one of the critical dimensions of a
digital government). For instance, the development of standard but flexible
data tools (e.g. data sharing platforms) provides solutions that can be re-used
across the broad public sector. At a more technical level, fragmentation also
results from legacy challenges regarding what the organisation generates. It
controls the data and the impossibility of sharing and accessing those data in
light of specific legal arrangements, leading to siloed policy and technical
solutions that add to the impossibility of building an integrated and connected
government. The lack of an overarching data governance model can lead to the
proliferation or duplication of data standards and technical solutions for data
sharing, thus hindering data interoperability across different organisations
and sectors and affecting the possibility of integrating data, processes and
organisations. It could also lead to multiple requests for citizens to provide
the same personal data numerous times to the public sector unnecessarily.
A data governance framework must ensure the
proper management of data through its entire life cycle (Ghavami, 2015[6]). For instance, in the
past years, the open government data movement allowed for a more in-depth
discussion of the need for strengthening data leadership and stewardship within
the public sector. The governance discussion also opened a more technical
discussion on improved data management practices, e.g. around the production,
storing, processing and sharing towards higher data openness. Nevertheless,
these elements were not understood as part of broader public sector data
efforts connecting all stages of the data value cycle. Countries suddenly
realised the value of cataloguing data for openness and discoverability
purposes but have failed to acknowledge how these initiatives also had relevant
policy benefits for productivity within the public sector.
On the other hand, in some OECD countries,
a well-established culture of public sector efficiency led to the development
of data registers to improve inter-institutional data sharing. Yet, this
mind-set overshadowed the growing value of opening up government data and
engaging and collaborating with external actors to solve policy challenges. As
a result, those countries that once led the former e-government movement (with
a strong focus on efficiency) lagged far behind those that doubled efforts to share
and open up data to users to promote business and social innovation.
OECD countries such as Canada, Ireland,
the Netherlands, the United Kingdom and the United States
have moved towards the definition of overarching data strategies to build
greater public sector cohesion and promote the integration of policies and
tools.
These strategies comprise most, if not all,
stages of the government data value cycle (from data production and its
protection to data openness and re-use). Still, each stage requires specific
arrangements, as they produce specific policy benefits (e.g., open data enables
the use of data as a platform for greater user engagement and collaboration,
and better data collection production practices can help reduce policy bias).
c.
Policy makers can
misunderstand data governance as the exclusive responsibility of IT
departments, but it also implies transformation and coherence of capacities,
policies, regulatory frameworks, leadership and organisational culture. Therefore,
more strategic public sector data governance approaches are needed.
The OECD has observed that a strong focus
on technical issues as the primary outcome of data governance can misguide
data-related policy decisions. For instance, by focusing primarily on the
adoption of technological solutions such as application programming interfaces
(APIs) and data standards (see the Overview of public sector data governance
practices later in this chapter), rather than also enabling the adequate
organisational, governance and cultural context to make those tools valuable to
address policy challenges. All of these are critical elements of good data
governance.
In some cases, OECD countries have invested
resources to define strategic roles (e.g. data stewards, chief data officers)
to support data governance by describing a more robust institutional fabric. Establishing
these strategic roles can help scale and sustain policy implementation and
build greater data maturity across the public sector (OECD, 2018[7]). The effort has occurred
either in the context of data strategies or open data policies [e.g. Korea
and the United States (see the Overview of public sector data governance
practices later in this paper)]. However, in most countries, data leadership
and/or stewardship are still misunderstood, thus confining data governance to
the activities of the IT department and not as a factor that can help achieve
policy goals through better data management and sharing practices.
d.
Public policies tend to
overlook the benefits of data governance. There is a need for promoting data
governance as a sublayer of policy arrangements. This can help extract value
from data for a successful policy.
Good data governance supports public sector
reform as a whole. In this light, its application is in line with core OECD
principles and guidelines in areas such as regulatory policy (OECD, 2012[8]), digital government
(OECD, 2014[9]),
public procurement (OECD, 2015[10]), budgetary governance
(OECD, 2015[11]),
open government (OECD, 2017[12]), public sector integrity
(OECD, 2017[13]),
and public service leadership and capability (OECD, 2018[14]).
In best-case scenarios, most or some of the
different elements of data governance (ranging from data strategies and
institutional and regulatory frameworks to infrastructure and architecture) are
nested within public sector digital transformation efforts and/or digital
government policies. However, while policy and decision makers within line and
coordination ministries (e.g. environment, transport, finance, public
administration) increasingly recognise the relevance of "data as an asset"
in their policy discourse, these policies often ignore the critical
contribution of data governance to policy success. This context is not endemic
to the public sector, for "today there is wide agreement that data is a
critical asset [among businesses], but that doesn't always translate into
taking the necessary actions to make that asset deliver real advantages"
(Algmin and Zaino, 2018[15]).
The issue is particularly relevant in the
context of cross-cutting public policies that require the sharing of, and
access to, data from multiple public sector organisations for policy
monitoring, compliance and evaluation purposes (e.g. public sector integrity, public
budgeting, regulatory policy), or in the context of cross-sectoral data-sharing
practices and governance arrangements (e.g. business-to-government data
sharing) (see flexibility and scalability later in this section).
Public policies other than digital
government can benefit from data governance as an underlying, yet
mission-critical, element for policy success. This could be achieved when
feasible by embedding different data governance elements in existing
organisational and policy structures. By doing so, policy makers can enable the
proper context and move from the overused discourse on data as an asset to the
definition of an environment where data serves specific needs across the policy
cycle.
e.
Good data governance
does not happen in isolation. It benefits from adopting open, inclusive,
iterative, collective and value-based approaches to its definition,
implementation, evaluation and change.
Good data governance is not the
responsibility of a small group of people. It should reflect the needs of a
globalised, fast-paced, diverse, digitalised and inter-connected world. Public
sectors must move away from closed and isolated ways of defining, implementing,
monitoring and evaluating their data governance frameworks and tools.
Governments can benefit from adopting open,
inclusive, iterative, collective and value-based data approaches when implementing
their data governance initiatives. For instance, stakeholder engagement can
help better identify data policy priorities and needs and assess the current
context regarding data capability within the public sector. Iterative
engagement can also help identify changing trends to take action and modify the
rules and tools supporting data governance.
In addition, establishing partnerships with
actors outside the public sector can help to:
· Take advantage of private sector digital solutions to improve, streamline and modernise the public sector data infrastructure (e.g. cloud or Software-as-a-Service solutions)
· Promote the publication of data produced by civil society organisations on government open data platforms or the publication of open government data on non-governmental data portals3
· Support data sharing among multiple stakeholders from different sectors and increase data owners' control and decision power over the sharing and using their data to address common policy challenges [16](see Figure 1. Deploying data trusts as tools to pursue common value).
Good data governance also benefits from
establishing a system of shared values and skills where all actors of the data
ecosystem support and are responsible for policy success (e.g. data stewardship
is shared among all relevant actors). At the same time, it implies defining and
deploying a set of open and shared tools (e.g. open standards, APIs and
algorithms) that can help promote integration within and outside the public
sector.
2.1 Deploying data trusts as tools in the pursuit of common value
To accelerate the collection and sharing of
data to harness artificial intelligence and other emerging technologies,
governments and other organisations face the increasing need to explore and
deploy tools for data management to protect data owners' rights while
addressing common goals.
Data trusts add to data governance tools
and build on long-standing legal trust frameworks applied to data management.
They can also promote data sharing in areas where it is not currently
happening. The Open Data Institute defines a data trust as a "legal
structure that provides independent stewardship of data". Independent
trustees are liable to make decisions about the data in the interests of the
trust's beneficiaries, which may be other organisations, citizens, end
consumers, or data users, by upholding laws and abiding by rules made when the
data trust was set up.
In 2018, the United Kingdom launched its AI
Sector Deal, a GBP 0.95 billion support package from the government and
industry to keep the United Kingdom at the forefront of the artificial
intelligence and data revolution. As part of the deal, the government committed
to exploring data-sharing frameworks such as data trusts together with the
artificial intelligence industry. The findings and recommendations of these
pilots were published in April 2019 (Office for Artificial Intelligence, 2019[17]).
3 Developing a Common Framework for Public Sector Data Governance
While some countries have made advancements
in clearly defining public sector data governance models, others have opted for
a less strict approach where data governance is not explicitly acknowledged but
implicitly takes place.
For instance, Luxembourg is working
towards developing a data governance framework in the context of the recently
adopted National Interoperability Framework. This work aims to take a more
progressive approach that adopts the three core principles of digital-first,
once-only, and transparency in the context of public sector data efforts.
Luxembourg's National Interoperability Framework also sets objectives to
promote open data, open standards and interoperability, machine-readable and
linked data, APIs and open-source software in the public sector.
Yet, approaches to public sector data
governance may vary in focus (e.g. a focus on technical governance aspects) or
reach (e.g. specific data governance elements are available but dispersed).
For this reason, the OECD proposes a
holistic model for data governance in the public sector to bring greater
clarity and structure to the definition and implementation of the concept
across countries. The model is based on the extensive OECD work on digital
government and government data and additional research by the OECD Secretariat.
Earlier versions of the model can be found in previous OECD digital government
reviews, namely the OECD Digital Government Review of Norway (OECD, 2017[18]), the OECD Digital
Government Review of Sweden (OECD, 2019[19]), the OECD Digital
Government Review of Peru (OECD, 2019[20]) and the OECD Digital
Government Review of Argentina (OECD, 2019[21]).
3.1 Data Governance Frameworks in the Public Sector - OECD Country Examples
3.1.1 New Zealand
The leading agency for government-held data
in New Zealand (Stats NZ) developed a new and improved data governance
framework for the New Zealand government. The framework is part of the agency's
numerous efforts to promote better data 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 "whole-of-data
life cycle approach", meaning public bodies and employees are encouraged
to think more strategically about the governance, management, quality and
accountability of their data over the whole data life cycle (i.e. from the
design and source of the data to its storing, publication and disposal).
Figure 1. New Zealand: Data governance
framework[22]
3.1.2 Norway
As part of its work in developing Norway's
national IT architecture, the Agency for Public Management and eGovernment
created an information governance model that positioned public sector data
management at the centre of the digital transformation of the Norwegian public
sector. By placing data at the heart of the information governance model and by
complementing it with strategic visions, policies, principles, standards and
guidelines for better use of public sector data, public bodies in Norway have
been given a rich set of tools to help leverage data as a strategic asset for
decision making and re-use.
Figure 2. Norway: Information governance
model[23]
3.1.3 Estonia
The data governance framework in Estonia is
built on three core components – data source, handling and storage, and purpose
– and stresses the importance of identifying and linking different data sources
(e.g. private sector data, administrative data and census data) to varying
types of data usages (e.g. policy analysis, research, operational), to ensure
the proper handling and storage of data strategically.
Four main challenges (gathering, guarding,
growing, and giving data) are crucial to creating a better data governance
framework. These challenges cover a large section of the data value chain, from
understanding data assets and establishing data governance principles to data
processing, sharing and disseminating meta information.
3.2 Integration and Coherence
Good data governance promotes integration
and systemic coherence and offers an everyday basis to use data to attain
shared policy goals and foster trust. Ergo, the model intends to highlight the
value of all organisational, policy and technical aspects for successful data
governance. It identifies a range of (non-exclusive) data governance elements
and tools and organises them into six groups. (see below and Figure 3. Data
governance in the public sector)
These six groups are then arranged under
three core layers of data governance (strategic, tactical and delivery) using
the three traditional data governance categories as guidance (strategic,
tactical and operational) as discussed and/or presented in Ghavami (2015[5]),
DAMA Internal (2017[26]) and the BARC's 9-Feld-Matrix [see Grosser (2013[27])
and BARC (2019[28])]. The model is also based on additional research, including
Ladley (2012[29]) and Sen (2019[30]):
3.2.1 Strategic Layer
See Figure 3
- (a) Leadership and vision
Some of the data governance elements in
this layer include national data strategies and leadership roles. It is worth
noting that the model considers data strategies as an element of good data
governance. This argument rests on the fact that data strategies enable
accountability and help define leadership, expectations, roles and goals. The
strategic layer also highlights how the formulation of data policies and/or
strategies can benefit from open and participatory processes, thus integrating actors'
inputs from within and outside the public sector towards greater policy
ownership.
3.2.2 Tactical Layer
See Figure 3
- (b) Capacities for coherent
implementation and (c) Legal and regulatory frameworks.
It enables the coherent implementation and
steering of data-driven policies, strategies and/or initiatives. It draws upon
the value of public sector skills and competences, job profiles, communication,
coordination, and collaboration as instruments to improve the capacity of the
public sector to extract value from data assets. It also highlights the value
of formal and informal institutional networks and communities of practice as
levers of public sector maturity and collective knowledge. This layer also
comprises data-related legislation and regulations as instruments that help
countries define, drive and ensure compliance with the rules and policies
guiding data management, including data openness, protection and sharing.
3.2.3 Delivery Layer
See Figure 3
- (d) Integration of the data value cycle, (e) Data infrastructure and (f) Data
architecture.
The delivery layer allows for the
day-to-day implementation (or deployment) of organisational, sectoral, national
or cross-border data strategies. It touches on different technical and policy
aspects of the data value cycle across its stages (from data production and
openness to re-use), the role and interaction of different actors in each stage
(e.g., data providers), and the inter-connection of data flows across stages.
Each stage is inter-connected but has specific policy implications with the
expected outcomes. For instance, data-sharing initiatives (e.g. the production
of good-quality, standardised and interoperable government data) can contribute
to data re-use by external actors in later stages (e.g. as open government
data). The adoption of technological solutions (e.g. cloud-based data-hosting
services, APIs, data lakes) takes place in this layer, for it supports those
policy goals defined in the strategic layer. It also relates, for instance, to
the need for re-engineering legacy data management practices and processes or
retrofitting and adapting legacy data infrastructures. Data interoperability
and standardisation also take place at this level.
Figure 3. Data governance in the public
sector[24]
The elements used to exemplify the plethora
of policy instruments, arrangements, initiatives and/or tools that countries
can use to deploy their data governance frameworks are not exhaustive. Thus,
countries might adopt different data governance elements and tools that better
fit their national context and public sector culture in line with the proposed
three layers and the six underlying categories presented in the model.
For the analysis presented in this section,
the data governance model explores practices at the national level (e.g.
national data strategies, central data standards and national data-sharing
platforms).
4 Flexibility and Scalability
The proliferation of data governance
frameworks and tools in the public sector can hinder data integration and
processes. Common policy goals (e.g. data protection) require coherent data
governance frameworks, meaningful instruments (e.g. policies, regulations) and
shared tools (e.g. data infrastructures, standards) that can help advance the
cohesive deployment of data efforts in the public sector. Yet, the definition
of a common data governance framework (from regulations and policy levers to
standards and data federation tools and standards) should also allow for
flexibility and scalability to avoid fragmentation, promote integration, and
increase the adoption of good governance practices across organisations, levels
of government, policy areas, sectors and borders.
This balance between adopting a structured
approach and allowing for flexibility and scalability can help foster a common
understanding, alignment and coherence of data efforts to support concerted
actions, address shared policy challenges, and deliver joint policy results.
Additionally, it can help to adjust the data governance model and its tools to
specific contexts and respond to changing needs (e.g. anticipatory regulation)
or ad hoc policy needs (e.g. different policy areas and stakeholders).
These arguments lay on the government as a
platform dimension of digital governments. Thus, developing a coherent data
governance framework enables public sector organisations to deploy and adopt
standard data solutions and tools.
The different elements presented in the
model and in this chapter address data governance from a national perspective
(see the Overview of public sector data governance practices later in this
chapter). However, the model is relevant in different contexts
(inter-institutional, cross-border) where public sector data governance plays a
crucial role in enabling data sharing and access.
The nature of the actors involved (the data
ecosystem) can add to the complexity of the data governance environment as
different actors have different needs and characteristics (e.g. sector, size) and
differing digital and data maturities. However, the need for more excellent
structure, flexibility, control, enforcement and compliance will also increase
as the complexity of the data governance environment evolves, its purpose
matures, and the needs of actors change, depending on whether it is implemented
in a decentralised, federated or multinational context.
4.1 Organisational
At this level, data are shared across units,
departments, and bodies within the same public sector organisation. Therefore,
data governance can improve the management, sharing, and access to data within
organisational boundaries. The need for a common data governance framework and
shared data governance tools increases once actors external to the organisation
join the data ecosystem.
4.2 Sector Specific or Policy Specific
Good data governance can also benefit a
pool of public sector organisations that share common goals and mandates and
produce and need to access share, or re-use standard datasets.
Earlier OECD efforts to promote good data
governance in specific policy areas include the OECD Recommendation of the
Council on Health Data Governance. It provides a set of principles to "encourage
greater availability and processing of health data within countries and across
borders for health-related public policy objectives while ensuring that risks
to privacy and security are minimised and appropriately managed" (OECD,
2017[25]).
Examples of data governance initiatives in
specific policy areas include the Geodata Strategy of the National Land Survey
Authority in Sweden. The Geodata Strategy brought greater coherence and
defined a set of common goals to foster the value of geodata for efficiency,
innovation, competitiveness and the achievement of Agenda 2030 (Lantmäteriet,
2016[26]). The four pillars of the
Swedish Geodata Strategy address different data governance elements, including
interoperability, standardisation, openness and user engagement (OECD, 2019[27]).
The United Kingdom's Ordnance Survey
provides another example of a maturing and more strategic sectoral data
governance environment. In 2017, the Ordnance Survey (the UK national mapping
authority) named its first chief data officer (Ordnance Survey, 2017[28]), and in 2019, it
released its data strategy to continue delivering the benefits of sharing and
opening accurate and quality mapping data for business impact (CIO UK, 2019[29]).
The Swedish and UK cases provide an
organised and solid approach to opening up government data and highlight how
the sharing of good-quality and trustworthy data requires taking action in the
earlier stages of the data value cycle (e.g. data production) (see the Overview
of public sector data governance practices later in this paper).
Another application case is the
evidenced-based policy-making work carried out by the Japanese government. Japan
has defined and implemented a solid, evidence-based and data-driven approach to
improve the impact of policies and public services since 2017. This work draws
upon data governance regulatory instruments published by the Japanese
government, namely the Basic Act on the Advancement of Public and Private
Sector Data Utilisation. For this purpose, the central government established a
governance structure to ensure the coherent implementation of evidenced-based
policy-making approaches across the broad public sector, including the
establishment of a cross-ministerial council (which also benefits from the
advice of external advisors) and the appointment of a director-general for
evidenced-based policy making across all ministries at the central level. This
case highlights the benefits of data governance and data for policy monitoring
and adequate decision-making in the public sector (Fukaya, 2019[30]).
In Argentina, the Ministry of
Justice developed a tool to improve the sharing of personal data in the context
of judicial investigations using the central standard interoperability platform
(INTEROPER.AR). The tool allows registered users (e.g. tribunals, prosecutors,
courtrooms) to request data from and between those data registers connected to
the interoperability platform (OECD, 2019[31]), speeding up data access
and reducing the time to respond to citizens.
While in Argentina, there is a need to
formalise data governance structures at the strategic layer, this case
illustrates the potential scalability of the interoperability tool. For
instance, its application can be expanded to other policy areas, including
public sector integrity, as recommended in the OECD Digital Government Review
of Argentina (OECD, 2019[32]) and the OECD Integrity
Review of Argentina (OECD, 2019[33]). However, such an
approach would require reinforcing the underlying data governance arrangements
for public sector integrity while developing, implementing and/or adapting the
specific rules and tools to respond to the ad hoc requirements of integrity
policies.
This is particularly relevant as public
sector integrity is a complex topic covering different areas with actors
sharing and requesting standard data taxonomies for monitoring, reporting
and/or auditing purposes (e.g. declarations of interest, gifts, open
contracting data, beneficial ownership, budget data). Therefore, the importance
of establishing a solid data architecture and infrastructure (technical layer) rests
not on its benefits to inter-institutional data sharing but on the value of
streamlined data-sharing practices to identify relationships between different
stakeholders and reduce, monitor, control or address integrity risks.
4.3 Multilevel
Another level of complexity is added when
data sharing occurs in a multilevel governance context. For instance, in
federal models of government, the balance between central and local power impacts
how the central government can access specific datasets owned and produced by
local authorities.
In Mexico, a federal country, the
central government developed the Open Mexico Network (Red Mexico Abierto,
2015-2017) to engage local governments in the central open data policy and
facilitate the publication of open government data produced by local
authorities on the central open data portal datos.gob.mx.
For this purpose, the central government created a network of institutional
contact points within public sector organisations at the state and municipality
levels. This institutional fabric improved communication and coordination, but
it also "ensured the efficient flow of tools and support provided by the
federal government for the standardisation and publication of open government
data" (OECD, 2018[34]).
Also, while central authorities can define
overarching data quality standards, in practice, the responsibility for data
quality falls on local governments, increasing the need to develop the proper
controls to ensure that data are produced in line with central standards for
policy monitoring purposes.
In Thailand, the former Ministry of
Information Communication Technologies (now the Ministry of Digital Economy)
designed a multilevel mechanism for reporting development data across all
levels of government. While this initiative did not move forward, its architecture
implied a complex data collection and sharing model, thus involving authorities
at the local, provincial, departmental and ministerial levels under the
leadership of the Office of the Prime Minister. This blend of actors, roles,
and responsibilities requires strict data quality controls to ensure the data's
quality, integrity, and trustworthiness across the entire data value cycle.
Indeed, most of these authorities still face legacy challenges resulting from
data fragmentation, duplicate standards, legal barriers and slow data-sharing
processes, thus hampering the timely access to data for policy and decision-making
(Wuttisorn, 2019[35])
and reinforcing the need for solid data governance.
4.4 Cross-sector
Common data governance frameworks
contribute to effectively implementing cross-sector data collection, sharing
and/or accessing initiatives. For instance, in regulatory compliance,
business-to-government reporting practices can benefit from implementing common
data governance structures and tools across all layers of the governance model.
In the Netherlands, the Standard
Business Reporting (SBR)5 reduced the burden imposed on businesses in the
provision of business information to local authorities and banks (SBR,
2019[40]). For this purpose, the SBR defined a shared public-private data
governance framework, creating, among others:
· A Steering Committee within the public sector defines the SBR's goals and programme of work, and a council decides the course of action, which benefits from insights from public and private sector actors. These elements reinforce the SBR's data governance strategic layer.
· At the tactical layer, the SBR created a co-ordinator role to ensure coherent implementation of the programme. The SBR also developed a devoted platform where public and private sector actors can monitor and advise on implementing the programme.
· At the delivery layer, the SBR standardised data definitions using a common data taxonomy defined by the Dutch government and streamlined and defined standard reporting processes. The digital government service (Logius)6 of the Dutch Ministry of the Interior and Kingdom Relations supports the technical aspects of the SBR.
Figure 4. Netherlands: Standard Business
Reporting[36]
4.5 Cross-border
Increased data flows across borders demand more
excellent government action to ensure data protection and ethical use,
particularly citizens' data, when those are collected, processed and used by
organisations from all sectors. The policy implications of cross-border data
flows, both in terms of positive and negative benefits, are thus vast, and
policy success requires the involvement of a plethora of actors at the global
scale, from international organisations to businesses, data protection
authorities and civil society organisations. OECD instruments such as the
Guidelines on the Protection of Privacy and Transborder Flows of Personal Data
(OECD, 2013[37])
have sought to bring greater coherence to cross-border data protection policies
and initiatives across OECD member and partner countries.
Transborder data flows have specific
implications for public governance and require more robust international data
governance arrangements and coherent multinational action.
Reinforcing cross-border data governance
can help monitor transnational infrastructure projects better and propel
greater regional integration (for example, the Australia & New Zealand
Infrastructure Pipeline, ANZIP). It also can support joint policy actions of
governments to prevent, combat and tackle corruption at the regional level
(e.g. by harmonising and enabling shared regulatory frameworks to allow
data-driven evidence to be used for auditing purposes within and across
governments, facilitating data access and sharing, etc.).
Shared data governance frameworks can also
help to improve cross-border public service delivery. For instance, in 2013, Estonia
and Finland agreed on a common agenda for developing a digital government
to support the implementation of cross-border digital services in areas such as
tax, health and education (OECD, 2015[38]). This enabled the
deployment of Estonia's X-Road data-sharing platform8 (see the Overview of
public sector data governance practices later in this chapter) in Finland. The
interconnection of Estonia's and Finland's X-Road platforms in 2018 (VRK, 2018[39]) has also led to more
significant, automated and secured cross-border data sharing, benefiting
service users and supporting the future development of additional cross-border
services in the region.
The success of the cross-border deployment
of the X-Road between Estonia and Finland not only relies on technical issues;
it also highlights the value of shared data governance policy structures at the
strategic level. Drawing on the bilateral agreement signed in 2013, in 2017,
Estonia and Finland agreed on the creation of the Nordic Institute for
Interoperability and Solutions, which "ensure(s) the development and
strategic management of the X-Road and other cross-border components for
eGovernment infrastructure" (NIIS, 2019[40]).
Overview of public sector data governance
practices at the national level across OECD member and partner countries
This section briefly overviews national
practices across OECD member and partner countries. It presents evidence and
data collected through different activities across the OECD under digital
government when feasible. These include national peer reviews, cross-national
reports, OECD surveys on digital government and open data, and work on the data-driven
public sector.
4.6 Strategic layer
4.6.1 National data strategies
The importance of better managing,
protecting and sharing data within the public sector is gaining traction across
the OECD. In front-runner countries, this has led to the development of
holistic national data strategies. These strategies are often nested within
public sector digitalisation efforts. Notable examples include the United
States' Federal Data Strategy, Canada's Data Strategy Roadmap for
the Federal Public Service, the Government Data Agenda in the Netherlands
and Ireland's Public Service Data Strategy.
For instance, the Dutch Government Data
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
responsible for implementing it.
The agenda also "pays specific
attention to the protection of public values and fundamental rights" (BZK,
2019[41]), thus including policy
issues related to data ethics and algorithm transparency. The agenda integrates
policy goals oriented to better data management in the public sector and the
publication and re-use of open government data. The relevance of the public
sector's organisational culture and knowledge-sharing for transformation change
are also underlined, which is in line with the OECD approach to the digital
transformation of the public sector [see, for instance, OECD (2019)[42]].
In Ireland, the central government
recently launched the Public Service Data Strategy for 2019-2023.10 The Irish
data strategy draws upon earlier data initiatives and policy instruments,
including the National Data Infrastructure and the Open Data Strategy. The Irish
data strategy clearly shows the need to bring a unified approach to public
sector data initiatives and define shared principles, goals, and actions to
support public sector cohesion (Office of the Government Chief Information
Officer, 2019[43]).
4.6.1.1
Example - The United States: Federal Data Strategy
In June 2019, the US government issued its
Federal Data Strategy, which presents a ten-year vision to unlock the full
potential of the country's federal data assets while safeguarding security,
privacy and confidentiality. The data strategy focuses on three core principles
(ethical governance, conscious design and a learning culture). It adds to
several existing initiatives, policies, executive orders and laws that have
helped make the United States a front-runner in terms of strategic management
and re-use of government data over the past few decades.
To capture the linkage between user needs
and appropriate management of data resources, the data strategy covers 40
practices that guide agencies throughout their adoption of the approach. To ensure
coherent implementation of the strategy in its early phase, federal agencies must
adhere to annual government action plans that include prioritised steps, time
frames and responsible entities. A draft version of the 2019-2020 Federal Data
Strategy Action Plan covers 16 steps seen as critical to launching the first
phase of the data strategy vision, including the development of data ethics
frameworks and data science training for federal employees.[44] [45] [46]
4.6.2 National Data Design Processes
The design process of national data
strategies is also relevant. The OECD has observed, for instance, that late
stakeholder engagement in the development of public sector digitalisation
strategies can decrease policy awareness, clarity, accountability and ownership
[see, for example, OECD (2019) [47]]. Early engagement can
help identify policy challenges that would otherwise be ignored and bring
relevant actors on board before implementing these strategies.
One relevant example in this respect is the
open consultation process launched by the Department for Digital, Culture,
Media and Sports in the United Kingdom to develop the UK National Data
Strategy. In June 2019, the Department for Digital, Culture, Media and Sports conducted
a public consultation to collect evidence and inform the development of its
National Data Strategy. The data strategy development will be followed by a
series of roundtables and testing exercises towards the publication of the
final document in 2020 (DCMS, 2019[48]).
It is also important to mention that while
countries are moving towards holistic policy approaches for public sector data
practices, a vast group of OECD member and partner countries have had more
focalised data policies for some time. Examples worth mentioning are the open
data policies in countries like France, Korea, and Mexico (OECD,
2018) and well-grounded data register policies in Denmark, Italy, Norway, and Sweden.
The Danish Basic Data Registers programme,
which dates back to 2013, has evolved from a strong focus on data-sharing
practices within the public sector to a hybrid approach where core public
sector data assets are shared for public access and re-use through a public
data distributor. In addition, the programme emphasises integration, for it
allows for public sector data access through web services and APIs (OECD, 2018[49]).
4.6.3 Leadership
The institutional governance model is also
a core element of good data governance, providing clarity regarding leadership
and accountability. However, it is essential to distinguish between political
and administrative leadership roles. On the one hand, political leadership
provides the high-level support needed to advance the policy agenda; however, political
administration changes can lead to vacant positions, resulting in reduced
political support for data policies. On the other hand, the leadership of top
management positions helps to implement and steer policy design and
implementation, thereby increasing the continuity and sustainability needed to
deliver results across political terms.
That said, some countries have formalised
leadership roles by attaching them to existent administrative structures.
Relevant examples include the Government's Chief Data Steward in New Zealand,
which the Chief Executive of Statistics New Zealand holds. The government's
chief data steward is in charge of leading the country's data policy. New
Zealand's case is also relevant in terms of policy accountability, as Stats NZ
releases a quarterly dashboard "highlighting key deliverables for their
data leadership role" under the Government's Chief Data Steward (Stats NZ,
2019[50]] ).
An earlier example is that of France's
Administrateur Général des Données, created in 2014 (French Government, 2014[51]) and attached to the
responsibilities of the head of the Etalab16 (the task force within the Office
of the Prime Minister in charge of coordinating the open data and artificial intelligence
policy in France). In Canada, the Data Strategy Roadmap for the Federal
Public Service recommends the creation of a Government Chief Data Steward to "clarify
roles and responsibilities around enterprise data leadership" (Government
of Canada, 2018[52]).
Others, however, have followed different
leadership models, which are less hierarchical and shared by various
individuals and respond more to the culture within their public sector. This
scenario is observed, for instance, in Nordic countries like Sweden,
where the central government has opted for a more consensus-based leadership
model in the form of a data taskforce composed of leading public sector
agencies (OECD, 2019[53]).
In either scenario, transparent leadership
is a precondition to help achieve policy goals (OECD, 2019[54]). It is also worth
mentioning that in some cases, open data leadership positions might act as
chief data officer (CDO) de facto, as in the case of Argentina (OECD,
2019[55]) and Mexico (OECD, 2016[56]).
4.7 Tactical Layer
Good data governance enables the coherent
implementation of data policies. Yet, successful policy implementation relies
on the intersection of different factors, ranging from establishing
inter-institutional coordination bodies grounded in adequate institutional
networks to capacity-building initiatives, collaboration and knowledge-sharing.
Also, while complex, the availability of the appropriate regulatory frameworks
(e.g. for data sharing, openness and protection) helps to create the right
environment for policy instrumentation (e.g. by reducing burdens and barriers
to data sharing) and in setting the rules for better-controlling data
management practices in the public sector.
4.7.1 Steering and Policy Coordination Bodies
Examples of policy steering or coordination
bodies include, for instance, Ireland's Data Governance Board, which was
created to formalise a sustainable "governance structure for the Public
Service, through which the development and implementation of data management
standards, guidelines and activities can be overseen" (Office of the
Government Chief Information Officer, 2019[57]).
In the United States, the draft
action plan of the Federal Data Strategy foresees the creation of a Data
Council within the White House Office of Management and Budget (OMB) by
November 2019 (Federal Data Strategy Development Team, 2019[58]). While the OMB Data
Council will help coordinate the Federal Data Strategy, it will also inform OMB's
"budget priorities for data management and use" (idem). These bodies
can also play an essential advisory role in ensuring that data strategies take
a risk-management approach and anticipate and respond to policy challenges as
they emerge. The Data Ethics Advisory Group in New Zealand provides an
example.
4.7.2 Chief Data Officers, Institutional Networks and Data Stewardship
The need for more robust institutional
networks and data stewardship in the public sector is also a growing priority
for countries. This draws upon the urgency to enact a paradigm shift from a
primarily technical perspective to one focused not only on compliance and
control over data management and sharing practices but also on strategic goals
and fostering a problem-solving approach centred on citizens.
As illustrated in previous OECD work on digital
government and open data (OECD, 2016[59]; 2018[60]; 2019[61]), some countries have
made a clear distinction between technical and strategic data roles in the
context of open data policies as a means to emphasise that digital and
data-driven transformation goes beyond mere technical aspects.
For instance, in Korea, the 2013 Act
on the Promotion, Provision and Use of Public Data established the roles of "officers
responsible for the provision of public data" and "data manager".
Officers responsible for providing public data coordinate the central open data
policy at the organisational level, translate its goals into clear actions, and
liaise with other organisations for this purpose. Data managers are in charge
of administrative and technical tasks, including compliance with data
standards, data quality and data publication.
In the context of national data strategies,
New Zealand's operational Data Governance Framework provides an exciting
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 "data
accountability and best practice data management across all data-handling
positions, to evolve beyond the need for traditional data governance roles
(e.g. data custodians, data stewards)" (Sweeney, 2019[62]).
Figure 5. New Zealand: Data stewardship in
the public sector (proposed model)[63]
In the United States, the 2018
Foundations for Evidence-Based Policy-making Act (signed into law on 14 January
2019) directs the head of each agency to "designate a non-political
appointee employee in the agency as the chief data officer of the agency"
(US Congress, 2019[64]). This is part of the
provisions of the Open, Public, Electronic, and Necessary Government Data Act
(OPEN Government Data Act), one of the Evidence-Based Policy-making Act (OECD,
2019[65]). These efforts
contribute to building a more mature data governance ecosystem within the
public sector, which can help to address potential sustainability risks across
political administrations.
4.7.2.1
United States: Chief Data Officers
The provisions of the Open, Public,
Electronic, and Necessary Government Data Act [66]describe the activities
and role of institutional chief data officers as follows:
The chief data officer of an agency shall:
1. 1. Be responsible for life cycle data management
2. coordinate with any official in the agency responsible for using, protecting, disseminating and generating data to ensure that the data needs of the agency are met
3. manage data assets of the agency, including the standardisation of data format, sharing of data assets and publication of data assets per applicable law
4. (…)
5. (…)
6. ensure that, to the extent practicable, agency data conform with data management best practices
7. engage agency employees, the public and contractors in using public data assets and encourage collaborative approaches to improving data use
8. support the performance improvement officer of the agency in identifying and using data to carry out the functions described in Section 1124(a)(2) of Title 31
9. support the evaluation officer of the agency in obtaining data to carry out the functions described in Section 313(d) of Title 5
10. review the impact of the infrastructure of the agency on data asset accessibility and coordinate with the chief information officer of the agency to improve such infrastructure to reduce barriers that inhibit data asset accessibility
11. ensure that, to the extent practicable, the agency maximises the use of data in the agency, including for the production of evidence (as defined in Section 3561), cybersecurity and the improvement of agency operations
12. identify points of contact for roles and responsibilities related to open data use and implementation (as required by the director)
13. serve as the agency liaison to other agencies and the Office of Management and Budget on the best way to use existing agency data for statistical purposes (as defined in Section 3561)
14. comply with any regulation and guidance issued under Subchapter III, including acquiring and maintaining any required certification and training.
4.7.3
Legal and Regulatory Frameworks
Regulation plays a vital role in data
governance; thus, its implications are vast. Regulation helps define the rules
to control the access to and sharing of data, promote openness, and ensure and
enforce the protection of sensitive data. These instruments also help define
and implement common data standards towards greater data interoperability and
streamlined data-sharing practices. However, regulation can also be an obstacle
to good data governance because the proliferation of fragmented instruments and
uncoordinated efforts can hinder cross-institutional data integration and
sharing. Taking an anticipatory approach can help identify risks and trends to
implement the needed regulatory action to foster public sector readiness to
change.
4.7.3.1
Anticipatory Innovation
Governance
As digital transformation is speeding up
and new and unforeseen risks emerge due to increased datafication, governments'
ability to anticipate and act upon uncertain futures becomes increasingly essential.
An important distinction between concepts has to be made:
· Anticipatory governance is acting on various inputs to manage emerging knowledge-based technologies and socio-economic developments while such management is still possible (Guston, 2014[59]). This may involve coordinated inputs from various governance functions (foresight, engagement, policy-making, funding, regulation, etc.).
· Anticipatory regulation is a function of anticipatory governance that uses regulatory means to create space for various technology options to emerge for sandboxes, demonstrators, testbeds, etc. This requires iterative regulation and standards development around an emerging field (Armstrong and Rae, 2017[60]).
· Anticipatory innovation governance is a broad-based capacity to actively explore options as part of broader anticipatory governance, with a particular aim of spurring on innovations (novel to the context, implemented and value-shifting products, services and processes) connected to uncertain futures in the hopes of shaping the former through the innovative practise (OECD Observatory of Public Sector Innovation (OPSI), 2019[61]).
Consequently, anticipation does not mean
predicting the future; instead, it is about asking questions about plausible
futures and then acting upon them by creating room for innovation (e.g.,
through regulation) or by creating mechanisms to explore different options in
government itself. Most governments today do not have a system for anticipatory
innovation governance (usually, mechanisms connected to the former are siloed
under specific policy fields or functions, e.g. foresight). In the face of
increased datafication, this is extremely important as choices made today
regarding the ownership, interoperability, privacy and control of data will
influence analytics and services built on the data that cannot be predicted or
foreseen today. For the latter, different mechanisms to explore possible
futures are needed. To this end, the Observatory of Public Sector Innovation
has launched an Anticipatory Innovation Governance Project in which the OECD
and leading countries will test different mechanisms for anticipation in
practice.
4.7.3.2
Soft Law Instruments for Data
Interoperability and Quality
Across OECD member and partner countries,
examples of regulatory instruments related to data governance are vast. These
instruments cover different policy issues, from sharing and interoperability to
open government data.
In Brazil, the central government is
advancing on the development of a new data-sharing decree which will help to
improve clarity about the different levels of permitted access to government
data [including full access, partial (restricted to only a few public sector
organisations and bodies), protected data (the custodian defines data access
rules)]. Data sharing is identified as one of the foundational principles of
Brazil's Digital Governance Strategy towards more integrated public services,
data openness and the creation of value for citizens (OECD, 2018[67]).
In the United Kingdom, the 2017
Digital Economy Act helped to bring further coherence and streamline
data-sharing practices in the public sector with a resulting positive impact on
citizens, including, for instance, by eliminating the vast range of previous
legal gateways blocking data sharing among public sector organisations in the
context of fuel poverty payment requests and payments (Roberts, 2019[68]).
Also, in 2017, Italy developed a set
of technical regulations on the territorial data of public administrations in
adherence with the EU INSPIRE Directive. Italy also created a national metadata
catalogue to guarantee the discoverability and clarity of spatial data and
related services. Italy has also implemented a more stringent regulatory
framework to safeguard personal data and protect the public administration's
data. These regulations, framed in the context of the Digital Administration
Code and the Three Annual Plan for ICT in the Public Sector, define a set of
security measures issued by the Agency for Digital Italy to evaluate and
improve the digital security of the public sector.
Softer legal and regulatory instruments,
such as codes of practice, recommendations or guidelines, often follow these
instruments.
As described in the OECD Open Government
Data Report (OECD, 2018[6]), countries have also made advancements in
establishing suitable legal and regulatory environments for open government
data. Recent examples include the 2016 Digital Republic Law (Loi pour une
Republique Numerique) in France, the 2016 Basic Act on the Advancement of
Public and Private Sector Data Utilisation in Japan and the 2017 Law for the
Promotion of E-government in Germany (OECD, 2018[6]). Executive decrees on open
government data are also available in Argentina, Brazil, Mexico and Peru.
4.7.3.2.1
Argentina: Guide for the
Identification and Use of Interoperable (data) entities
As part of several efforts to bring order
to data management and sharing practices within the Argentinian public sector,
the National Direction of Public Data and Public Information published the
Guide for the Identification and Use of Interoperable (data) entities[69]. The guide is an ongoing
effort to ensure that public and private sector organisations can follow simple
methods to generate, share and/or consume good-quality government data,
therefore putting the data as a service vision in practice.
It guides how to produce simple identifiers
for data that different public sector organisations produce but that at the
same time are regularly shared among organisations (e.g. country >
country_id). Consistent and increasing efforts have been underway since 2017 to
ensure this core reference framework for government data is available through
APIs.
4.7.3.2.2
France: The General Reference
Framework for Interoperability
In France, the General Reference Framework
for Interoperability [70]offers recommendations to
promote interoperability across information systems within the public sector.
Following the rationale of the European
Interoperability Framework, the French framework focuses on different levels of
interoperability, setting standards for each level that are to be implemented
by public sector organisations. Standards are therefore established for
technical, semantic or syntactic interoperability to guarantee that public
sector organisations, their dispositions and systems are as interoperable as
possible:
· Semantic interoperability refers to the meaning of different words, which often varies among public sector organisations. This interoperability aims to streamline the definition of words across public sector organisations to ensure agreement regarding the meaning of data exchanged and the context of the exchange.
· Technical interoperability refers to data formats, exchange protocols, and the conditions and formats of storing these data. This interoperability ensures that data can be exchanged adequately among public sector organisations and in the correct format.
· Syntactic interoperability is a subset of the technical interoperability as it focuses on the technical format data should have to be appropriately exchanged among public sector organisations.
4.7.3.2.3
Italy: White Paper on
Artificial Intelligence
In March 2018, Italy published the White
Paper on Artificial Intelligence[71]. The white paper
recommends that all administrations ensure the quality and usability of the
data they provide to ensure these data are used to test and refine artificial
intelligence systems. Additional tools, modelled to fit the needs of the public
administration concerning the use, interpretation and release of data, are
available on the national data catalogue dati.gov.it and the National
Guidelines for the Valorisation of Public Information Assets.
4.7.4 Skills: Capacity building, collaboration and knowledge-sharing
Public sector capacity, talent, and
collective knowledge are core elements of good data governance in the public
sector and broader public sector reforms, including digitalisation and
innovation efforts. For this reason, OECD instruments such as the OECD
Recommendations of the Council on Digital Government Strategies (OECD, 2014[72]) and on Public Service
Leadership and Capability (OECD, 2018[73]), as well as the OECD
Declaration on Public Sector Innovation, acknowledge their value as pillars of
transformational and cultural change.
Building greater and systemic public sector
capacity has different implications from a public sector data governance
perspective, including:
· Purpose (outcome): What is the purpose of (the policy issue)? Data governance must support the business strategy and achievement of the goals. This translates into the need for clarity regarding expected outcomes when implementing data governance initiatives. For instance, while closely related, a capacity-building programme specifically deployed to improve data sharing for public service delivery might differ from one that focuses on promoting ethics and values in the design of public sector algorithms.
· Provider: Who provides support? In earlier stages of data-related initiatives, the support provided to public sector organisations will play a key role in increasing policy take up and awareness. In addition, this support can help build the right set of skills by providing training that will improve the capacity for implementation. For instance, in Mexico, the central government (2012-18) created the Open Data Squad as the government task force guiding public sector organisations in publishing open government data (OECD, 2018[74]).
· Receiver: Who is the target of capacity-building activities? Good data governance in the public sector is translated into a different set of skills. It is needed for various groups of public officials, from political appointees or public managers to technicians. In Argentina, the Secretariat of Public Employment developed a series of skill development programmes that target different groups of public sector employees, for example, the Lideres en Acción programme for young officials and the Construyendo Nuestro Futuro programme for high-level public managers (OECD, 2019[75]). These initiatives complement those in place in the context of the activities of Argentinia's government innovation lab, LABgobar, which focuses on building more technical data skills.
· Assessment: Which skills are needed to achieve the purpose? Better targeting capacity-building activities demands an evaluation of the current data capacity gaps. An example is the National Digital Skills survey conducted in New Zealand in 2017 to assess digital skills in the tech sector and across the government. The survey results informed the report Digital Skills for a Digital Nation and helped target capacity-building activities in the country (New Zealand Digital Skills Forum, 2018[76]).
· Coherence: How can public sector organisations standardise the data skill needs? Using common job descriptions and frameworks improves coherence when attracting talent to the public sector and promotes inter-institutional mobility and career development. As referenced in earlier OECD work (OECD, 2019[77]), one of the most well-known frameworks for job descriptions in the digital and data domain is the United Kingdom's Digital, Data and Technology Profession Capability Framework.18
· Mainstreaming: How to move from learning silos to collective knowledge? Digital and physical platforms and learning environments can help promote peer learning and knowledge sharing. They can also help identify, share and promote the mobility of existent talent within and across the public sector. Canada's cloud-based platform GCcollab is an example of a collaborative digital space that allows public servants, citizens, students and academics to exchange knowledge. The Canadian government has also created an agile model for public workforce mobilisation called Free Agents, which allows public servants to switch job positions across the government for short periods, depending on their skill set.
· Openness and engagement: How can external talent and knowledge be leveraged? Good public sector data governance benefits from acknowledging that public sector organisations are not siloed entities in the data ecosystem. Open knowledge practices and partnerships with actors of the data ecosystem beyond the public sector, such as universities and entrepreneurs, can help build capacity within the public sector and attract talent when needed.
4.8 Delivery layer
The delivery layer integrates the set of
processes, mechanisms and tools that allow for the operational implementation
of data governance at a more granular level.
4.8.1 The data value cycle
The data value cycle is complex, for it is
the crossroad of the most strategic and tactical aspects of the data governance
(regulations, policies) with those that are more technical (e.g. the
architecture and infrastructure supporting data management, sharing, access,
control and re-use). For instance:
· Different stages of the data value cycle call for various technical skills and roles (e.g. data custodians, data architects, data scientists). This draws on the different outputs resulting from data processing at each stage. Implementing training and capacity-building programmes at the tactical level supports the growing availability of these skills (see previous section).
· Each stage of the data value cycle faces specific challenges that may require policy actions. For instance, bias can occur in the data collection stage, negatively affecting how policies are informed and the resulting interventions designed using those data as input. In the United Kingdom, the Department for Digital, Culture, Media and Sports has hosted events focusing on addressing the gender data gap (Roberts, 2019[63]), recognising that data on issues disproportionately affecting women are either never collected or of poor quality. To reduce gender bias in data collection, the UK government has developed a government portal devoted to gender data.21
· The data value cycle is a continuum of inter-related, not siloed, stages, where different actors add value and contribute to data re-use. For instance, government initiatives focusing on producing good-quality data can contribute to greater interoperability, sharing and openness in later stages. In Argentina, the data-as-a-service approach aims at securing the production of good-quality and interoperable public sector data (OECD, 2019[3]). Using this tactic, the government facilitates publishing, sharing and re-using public sector data (including open data) by public entities and external consumers.
· The data value cycle may reflect organisational processes resulting from legacy systems. Reassessing or re-engineering these processes is crucial to ensure that digitalisation and data-driven efforts contribute to transformation and avoid perpetuating inefficient processes in the digital world.
· Data protection takes place (or should take place) across all different stages, with data custodians playing a pivotal role in ensuring the trustworthy and protected processing of the data. These officials should also manage risks of data corruption or data leaks (intentional or not) across the whole value chain, which can also have undesired effects on public trust.22
· The data value creation process is not linear but cyclical (value cycle).23 The idea of a value cycle implies a shift in thinking from the value chain as a linear process to an iterative cycle that benefits from evolution and learning (Cordery, Woods and Collier, 2010[65]). When this rationale is applied to the data value chain, it reflects the whole policy-making process (from definition to implementation, evaluation and revision). It increases the impact of investments on sound data management practices, for data are continuously produced, analysed, shared, used and re-used to inform and evaluate policy.
4.8.2 National Data Infrastructures and Architectures
Some of the most technical aspects of data
governance take place in the context of data infrastructure and architecture.
These two elements can help advance data-sharing and management practices
across institutions, sectors and borders and build the foundations for
delivering public value (e.g. through better public service delivery).
Estonia's
X-tee platform (X-Road until 2018) is one of the most well-known examples of a
sound data-sharing infrastructure in the public sector. The development and
deployment of the X-tee platform set the foundations for real-time data sharing
between Estonian public sector organisations. Created in 2001, X-tee implies implementing
a data federation model that helped build more effective, seamless and
streamlined public services.
The value of the X-tee relies on its
integrating role. Thus, it aims to provide a whole-of-government solution
(government as a platform in practice) to enable the secure and authenticated
sharing of data across previously siloed data sources. The use of the X-tee in
Estonia is regulated by law, and public sector organisations willing to access
or share data from or with other public sector organisations are obliged to use
the X-tee tool. This helps avoid the proliferation of other data-sharing
solutions in the public sector and promotes cohesion in Estonia. These efforts
provided a cornerstone crucial to building a digital government, enabling
integrated services and platforms within and outside the public sector, and
increasing the benefits for citizens and businesses in the country. Also, the
cross-border Estonian-Finnish X-Road platform model has been implemented in
other countries, such as the Faroe Islands, Iceland, Japan and Kyrgyzstan (E-Estonia,
2018[78]).
Another example of the willingness of OECD
countries to improve their national data infrastructure is the Data Federation
Project in the United States.
4.8.2.1
United States: Data Federation
Project
The US Data Federation Project aims to
bring greater coherence to data federation practices in the US public sector to
support policy decisions better, increase operational efficiencies, enable the
diffusion of shared processes and infrastructures, foster an integrated
government, and combat silos.
The proliferation of the different data
federated models using various tools, processes, and infrastructure could,
therefore, be prevented and gradually replaced with a single and scalable data
federation model developed by the central government. This would follow a "government
as a platform" approach. Thus, the overall goal is to build a shared tool
for data federation that can be adopted across the public sector.
The project will draw on the collection of
best practices regarding efforts to collect, combine and exchange data from
disparate sources and across different public sector organisations and levels
of government. In addition, it aims to establish data standards, offer
guidelines, and deliver reusable tools such as automated aggregation to foster
knowledge sharing across public sector organisations and effectively re-use
government data from different sources.
4.8.2.2
Other Notable National Data
Infrastructures and Architectures
Italy developed the National Digital Data
Platform to improve its national data architecture and infrastructure. This
platform offers big data solutions, including data lakes, to facilitate easy
access to, sharing, and analysis of large volumes of raw and unstructured data
from the public administration. It demonstrates an increasing understanding
among governments of the need to design data infrastructures and architectures
adapted to emerging new technologies, including artificial intelligence and
machine learning. In the context of open data, the Italian data portal
dati.gov.it also responds to the need for more robust collaborative data
sharing within the public sector. It is based on the principle of a "federation
of catalogues", which allows any public sector organisation to "feed"
the data catalogue with periodic updates. The catalogue, therefore, also helps
measure the outputs of the open data policy in terms of data availability.
Opportunities for greater openness and
collaboration with external actors have emerged due to governments' demand for
better and more efficient data-sharing infrastructures. For instance, in the United
Kingdom, the Digital Marketplace project has brought external providers of
digital solutions closer to the public sector by providing resources such as
the G-cloud framework, which guides external suppliers of cloud-based services
when delivering services to public bodies. Inspired by the UK model, Norway
has launched a project to create a similar procurement platform for cloud-based
services following its 2016 cloud-computing strategy.
Also, APIs are growing fast across OECD
member and partner countries to integrate data, processes and organisations
(including those outside the public sector) in real time. In Brazil, the
central government's integration platform and API catalogue Conecta.gov29
allows public sector organisations to more efficiently and effectively share
data between themselves, facilitating the implementation of the once-only
principle (as defined by Brazilian law30 in 2017).
APIs are also being provided for public
access in the context of open government data policies across different OECD
countries, including Australia, Canada, Colombia, Denmark,
France, Mexico, Portugal, Switzerland and the United
Kingdom (OECD, 2018[79]).
As mentioned earlier in this chapter, the
Nordic countries of Denmark, Norway and Sweden have all
secured more robust policies for base data registers, enabling real-time
sharing of public information within (and sometimes outside) the public sector.
Realising the benefits of effective sharing of base registers, several other
countries are starting to look at similar solutions. In Brazil, a new
Data Sharing Decree [80] will include the creation
of a citizen base register to improve the quality of citizen identification and
biographical information and facilitate an end-to-end digital public service.
The need for greater data standardisation has also gained traction across OECD countries within the public sector and in cross-sectorial and international efforts to foster regulatory compliance, public sector accountability, integrity and citizen engagement. For instance:
· As part of its quest to protect citizens' digital rights and personal information, the French National Commission on Information Technology and Civil Liberties (CNIL) created a standard on data protection governance[81], which comprises 25 technical requirements for private and public organisations managing personal data, to comply with the EU's General Data Protection Regulation. Singapore also provides technical guidelines for ethical data sharing between organisations, with its Trusted Data Sharing Framework [82] released in June 2019.
· The XBRL [83] digital business reporting standard is an example of a data standard adopted by governments worldwide. It allows financial statements and reporting information to move rapidly, accurately and digitally between private and public sector organisations using a common reporting term language, simplifying regulatory compliance and business reporting. The XBRL standard is today used by governments in OECD countries such as Germany, Japan and the United States. The SBR project in the Netherlands (see flexibility and scalability earlier in this paper) is another excellent example of a country applying business reporting standards to cut red tape and improve regulatory compliance through digital solutions.
· Partnerships such as the C5 (which groups Argentina, Colombia, France, Mexico, the United Kingdom and Ukraine) reflect cross-national efforts to spur the definition and implementation of coherent open contracting data practices. This includes adopting common international data standards such as the Open Contracting Data Standard[84], which offers a series of guidelines regarding the release of standardised, high-quality and reusable data and associated documents for each phase of a public contracting process. The recent partnership between the Open Contracting Partnership (leading the Open Contracting Data Standard) and the Infrastructure Transparency Initiative will help to pave further the way for the increased adoption of better data management and open data practices in the context of public infrastructure and enhance the quality of the Infrastructure Transparency Initiative's Infrastructure Data Standard.
____________________________
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