2. Data governance in the public sector
This chapter presents how OECD countries are moving towards the definition and implementation of holistic public sector data governance practices at the national level. It discusses the main trends and challenges observed in relation to data governance and proposes a public sector data governance framework drawing upon OECD best practices. The chapter then applies the model to provide a brief overview of data governance practices across OECD member and partner countries.
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 reuse data for business purposes. This opened a window of opportunity to start selling data-driven products and services to any company and individual with an interest 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 better understand users and target potential customers, or service users. These insights are used to drive citizens’ choices, increase business revenues, influence public vote, 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 management), which places the access to and sharing of data (EASD) as a precondition for data analysis techniques to help increase the value that is created 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 buzz phrase, 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 to take better decisions and increase business intelligence.
Still, while the discourse on “data as an asset” is well accepted nowadays, organisations, including from the public sector, often fail to govern, manage and value data in the same way as the other assets that are relevant for their success. This 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]).
Enabling the right cultural, policy, legal, regulatory, institutional, organisational, and technical environment is necessary to control, manage, share, protect and extract value from data. Yet, organisations from the public and private sector often face legacy challenges inherited from analogue business models, ranging from outdated data infrastructures and data silos to skill gaps, regulatory barriers, the lack of leadership and accountability, and an organisational culture which is not prone to digital innovation and change.
New challenges have also arisen resulting from citizens’ data misuse and abuse cases, mainly by private sector organisations. This is 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 basic rights (see Chapter 4).
Responding to these challenges requires greater understanding, structure and knowledge-sharing in relation to how OECD countries address data governance in the public sector. This is well recognised by private sector actors, but is only gaining traction in the government sphere.
This chapter presents a brief overview of how national governments across OECD member and partner countries are increasingly addressing data governance as a whole, or have worked on developing specific elements of it. The chapter also presents a proposed model for data governance in the public sector, 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 guide the analytical work of this chapter.
Good data governance can contribute to setting a common vision; enhancing coherent implementation and co-ordination; and strengthening the institutional, regulatory, capacity and technical foundations to better control and manage the data value cycle, i.e. collect, generate, store, secure, process, share and reuse data, as means to enhance trust and deliver value (see Chapter 3).
Good data governance is imperative for governments that aim to become more data driven as part of their digital strategy. It can help to 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 are struggling to put it into practice.
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 in a more exclusive and explicit fashion. 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 has triggered government intervention to improve the protection of personal data (OECD, 2019[3]). As a result, the ethical and transparent use of data is now high on the political agenda (see Chapter 4).
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 a matter limited to organisational boundaries, but 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 own data” (OECD, 2019[3]), 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 the protection of data across borders. Nevertheless, data overprotection can result from the misunderstanding of national and international regulations and drive change in terms of policy approaches (e.g. from openness by default to “open if possible, protected if needed”1).
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[4]).
b) Data governance elements are often in place as 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 to join up government as a whole.
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 lack of clarity in terms of leadership and responsibilities), therefore posing important barriers for data integration and sharing.
A holistic data governance can also help in enabling Government as Platform (one of the key dimensions of a digital government) (see Chapter 1). For instance, the development of common but flexible data tools (e.g. data sharing platforms) provide solutions that can be re-used across the broad public sector. At a more technical level, fragmentation also results from legacy challenges in terms of what organisation generates and 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 multiple times to the public sector unnecessarily.
A data governance framework must ensure the proper management of data through its entire life cycle (Ghavami, 2015[5]). 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. This 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 (see Chapter 3). 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 as a means 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 find solutions to 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 make share and open up data to users as means to promote business and social innovation.
OECD countries such as Canada, Ireland, the Netherlands, United Kingdom and the United States have moved or are moving towards the definition of overarching data strategies as means 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 reuse) (see Chapter 3). 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 in reducing 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. There is therefore a need for more strategic approaches to data governance in the public sector.
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 key 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 through the definition of a stronger institutional fabric. The establishment of these strategic roles can help in scaling and sustaining policy implementation and building greater data maturity across the public sector (OECD, 2018[6]). This has taken place 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 chapter)]. 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 to extract value from data for 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 digital government (OECD, 2014[7]), open government (OECD, 2017[8]), public service leadership and capability (OECD, 2018[9]), public sector integrity (OECD, 2017[10]), public procurement (OECD, 2015[11]), regulatory policy (OECD, 2012[12]), and budgetary governance (OECD, 2015[13]).
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 co-ordination ministries (e.g. environment, transport, finance, public administration) increasingly recognise the relevance of “data as an asset” in their policy discourse (see Chapter 3), these policies often ignore the key 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[14]).
This 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 chapter).
Public policies other than digital government can benefit from data governance as an underlying, yet mission-critical, element for policy success. When feasible, this could be achieved by embedding different data governance elements in existent organisational and policy structures. By doing so, policy makers can enable the right context and move from the overused discourse on data as an asset to the definition of an environment where data serve specific needs across the whole policy cycle.
e) Good data governance does not happen in isolation. It benefits from the adoption of 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 need to 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 putting in place their data governance initiatives. For instance, stakeholder engagement can help to better identify data policy priorities and data needs, and to assess the current context in terms of data capability within the public sector. Iterative engagement can also help to identify changing trends in order 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 use of their data to address common policy challenges4 (e.g. see Box 2.1. Deploying data trusts as tools in the pursuit of 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 in promoting integration within and outside the public sector.
In the process of accelerating the collection and sharing of data to harness artificial intelligence and other emerging technologies, governments, businesses and other organisations face the increasing need of exploring and deploying sound tools for the management of data to protect the rights of data owners while addressing common goals. Governments are therefore starting to explore new instruments that can facilitate ethical and fair data sharing between different actors of the data ecosystem.
For instance, as part of the OECD project on enhancing access to and sharing of data, partnerships such as “community-based data-sharing agreements” have been discussed as ways of increasing access to data while ensuring it is done safely and ethically (OECD, 2017[15]). These types of partnerships or frameworks highlight the flexible and forward-looking approach of data governance in managing potential risks from data sharing.
Data trusts add to the above-mentioned proposed data governance tools. They build on long-standing legal trust frameworks applied to the management of data, and can be used to promote data sharing in areas where it is not currently happening. As defined by the (Open Data Institute, 2018[16]), a data trust is a “legal structure that provides independent stewardship of data”. Independent trustees are liable to take decisions about the data in accordance with the interests of the trust’s beneficiaries, who 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. As described by (Wylie and McDonald, 2018[17]), it helps to view data trusts as containers that holds assets, define governance and manage liabilities. The terms ruling data trusts can be adjusted depending on the data type or actors involved. Thus, this flexibility can support the adoption of “anticipatory regulation”, a new regulatory framework developed by Nesta,1 in the context of data governance (Element AI and Nesta, 2019[18]).
In 2018, the United Kingdom launched its AI Sector Deal, a GBP 0.95 billion support package from government and industry to keep the United Kingdom at the forefront of the artificial intelligence and data revolution (BEIS and DCMS, 2018[19]). As part of the deal, the government committed to explore data-sharing frameworks such as data trusts together with the artificial intelligence industry. The UK government partnered with the Open Data Institute (ODI) to explore how a data trust, as defined by the ODI, could increase access to data while retaining trust. As part of this work, the ODI worked with three pilot projects focused on diverse challenges: tackling illegal wildlife trade, reducing food waste and improving public services in Greenwich. The findings and recommendations of these pilots were published in April 2019 (Office for Artificial Intelligence, 2019[20]).
Note: For more information see: https://www.nesta.org.uk/report/renewing-regulation-anticipatory-regulation-in-an-age-of-disruption.
Sources: OECD with contributions from the UK Office for Artificial Intelligence and NESTA; Element AI and Nesta (2019[18]), Data Trusts: A New Tool for Data Governance, https://hello.elementai.com/rs/024-OAQ-547/images/Data_Trusts_EN_201914.pdf; BEIS and DCMS (2018[19]), AI Sector Deal, https://www.gov.uk/government/publications/artificial-intelligence-sector-deal/ai-sector-deal; Wylie, B. and S. McDonald (2018[17]), What Is a Data Trust?, https://www.cigionline.org/articles/what-data-trust; OECD (2017[15]), Programme for OECD Expert Workshop – Enhanced Access to Data: Reconsiling Risks And Benefits of Data Reuse, https://www.oecd.org/internet/ieconomy/oecd-expert-workshop-enhanced-access-to-data-copenhagen-programme.pdf; Open Data Institute (2018[16]), “Defining a ‘data trust’”, https://theodi.org/article/defining-a-data-trust; Office for Artificial Intelligence (2019[20]), AI Sector Deal One Year On, https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/819331/AI_Sector_Deal_One_Year_On__Web_.pdf.
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 takes place in an implicit fashion.
For instance, Luxembourg is working towards the development of a data governance framework in the context of the recently adopted National Interoperability Framework. This work aims at taking 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 terms of 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 as an effort 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 carried out 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[21]), the OECD Digital Government Review of Sweden (OECD, 2019[22]), the OECD Digital Government Review of Peru (OECD, 2019[23]) and the OECD Digital Government Review of Argentina (OECD, 2019[3]).
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).
Source: Sweeney, K. (2019[24]), “An operational data governance framework for New Zealand government”,
https://statsnz.contentdm.oclc.org/digital/collection/p20045coll1/id/2657.
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 the management of public sector data 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 reuse.
Source: OECD (2017[21]), Digital Government Review of Norway: Boosting the Digital Transformation of the Public Sector, https://doi.org/10.1787/9789264279742-en.
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 different types of data usages (e.g. policy analysis, research, operational), in order to strategically ensure the proper handling and storage of data.
Four main challenges (the gathering, guarding, growing and giving of data) are identified as crucial to face in order to create 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, data sharing and dissemination of meta information.
Source: Sweeney, K. (2019[24]), “An operational data governance framework for New Zealand government”, https://statsnz.contentdm.oclc.org/digital/collection/p20045coll1/id/2657; (2017[21]), Digital Government Review of Norway: Boosting the Digital Transformation of the Public Sector, https://doi.org/10.1787/9789264279742-en; Mägi, M. (2019[25]), “Data for law making”.
Good data governance promotes integration and systemic coherence, and offers a common basis to use data in order to attain shared policy goals and promote 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 different groups. (a-f; see Figure 2.3. 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]):
Strategic layer [including (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 can 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 the inputs of actors from within and outside the public sector towards greater policy ownership.
Tactical layer [including (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, co-ordination, 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.
Delivery layer [including (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 different stages (from data production and openness to reuse), the role and interaction of different actors in each stage (e.g. as data providers), and the inter-connection of data flows across stages. Each stage is inter-connected but has specific policy implications in relation to the expected outcomes. For instance, data-sharing initiatives (e.g. the production of good-quality, standardised and interoperable government data) can contribute to data reuse 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.
Source: OECD (2019[3]), Digital Government Review of Argentina: Accelerating the Digitalisation of the Public Sector, https://doi.org/10.1787/354732cc-en.
The elements used to exemplify the plethora of policy instruments, arrangements, initiatives and/or tools that can be used by countries to deploy their data governance frameworks is not exhaustive. Thus, countries might opt for adopting 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 purpose of the analysis presented in this chapter, the data governance model explores practices at the national level (e.g. national data strategies, central data standards and national data-sharing platforms).
The proliferation of data governance frameworks and tools in the public sector can hinder the integration of data 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 in order 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 and 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 (see Chapter 1). Thus, the development of a coherent data governance framework enables the deployment and adoption of common data solutions and tools among public sector organisations.
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 key role in terms of enabling the sharing of and access to data.
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) as well as differing digital and data maturities. However, the need for greater structure, flexibility, control, enforcement and compliance will also increase as the complexity of the data governance environment evolves; its purpose matures; the needs of actors change; and depending on whether it is implemented in a decentralised, federated or multinational context.
Organisational
At this level, data are shared across units or departments and bodies within the same public sector organisation. Thereby, data governance can improve the management, sharing of and access to data only 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.
Sector- or policy-specific
Good data governance can also benefit a pool of public sector organisations that share common goals and mandate, and produce, need to access, share or reuse common 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[31]).
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[32]). The four pillars of the Swedish Geodata Strategy address different data governance elements, including interoperability, standardisation, openness and user engagement (OECD, 2019[22]).
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[33]) and in 2019 it released its data strategy in order to continue delivering the benefits of sharing and opening accurate and quality mapping data for business impact (CIO UK, 2019[34]).
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 chapter).
Another application case is that of the evidenced-based policy-making work carried out by the Japanese government. Japan has defined and implemented a strong evidenced-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 itself for policy monitoring and effective decision making in the public sector (Fukaya, 2019[35]).
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 common 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[3]), therefore speeding up data access and reducing the time to respond to citizens.
While in Argentina there is a need for formalising 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[3]) and the OECD Integrity Review of Argentina (OECD, 2019[36]). 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 in order 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 common 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) does not rest exclusively on its benefits to inter-institutional data sharing, but on the value of streamlined data-sharing practices as a means to identify relationships between different stakeholders and reduce, monitor, control or address integrity risks.
Multilevel
Another level of complexity is added when data sharing takes place in a multilevel governance context. For instance, in federal models of government, the balance between central and local power has an impact on 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 co-ordination, 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[37]).
Also, while central authorities can define overarching data quality standards, in practice the responsibility for data quality falls on local governments, increasing the need for developing the right 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 (OECD, forthcoming[38]). This blend of actors, roles and responsibilities requires strict data quality controls to ensure the quality, integrity and trustworthiness of the data across the whole 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[39]) and reinforcing the need for a solid data governance.
Cross-sector
Common data governance frameworks contribute to the effective implementation of cross-sector data collection, sharing and/or accessing initiatives. For instance, in the context of regulatory compliance, business-to-government reporting practices can benefit from the implementation of 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 in charge of defining the SBR’s goals and programme of work, and a council in charge of deciding 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 created a devoted platform where public and private sector actors can monitor and provide advice on the implementation of 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 common reporting processes. The digital government service (Logius)6 of the Dutch Ministry of the Interior and Kingdom Relations provides support on the technical aspects of the SBR.
Source: Groenveld, B. (2019[41]), Standard Business Reporting (SBR).
Cross-border
Increased data flows across borders demand greater government action to ensure the protection and ethical use of data, 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[42]) 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 call for stronger international data governance arrangements and coherent multinational action.
Reinforcing cross-border data governance can help to better monitor transnational infrastructure projects and propel greater regional integration (for example the Australia & New Zealand Infrastructure Pipeline, ANZIP)7. 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 the development of digital government as means to support the implementation cross-border digital services in areas such as tax, health and education (OECD, 2015[43]). 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 inter-connection of both Estonia’s and Finland’s X-Road platforms in 2018 (VRK, 2018[44]) has also led to greater, 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[45]).
This section presents a brief overview of national practices across OECD member and partner countries. When feasible, it presents evidence and data collected through different activities across the OECD under digital government. These include national peer reviews, cross-national reports, OECD surveys on digital government and open data, the work on data-driven public sector.9
Strategic layer
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 or is leading 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[46]), thus including policy issues related to data ethics and 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 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 for the digital transformation of the public sector [see, for instance, OECD (2019[22])].
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 is clear on the need for bringing a unified approach to public sector data initiatives and defining shared principles, goals and actions in order to support public sector cohesion (Office of the Government Chief Information Officer, 2019[47]).
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 centres on three core principles (ethical governance, conscious design and a learning culture). It adds to several existing initiatives, policies, executive orders and laws that over the past few decades have helped make the United States a front-runner in terms of strategic management and reuse of government data.
In order 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 strategy. To further ensure coherent implementation of the strategy in its early phase, federal agencies are required to 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 launch the first phase of the data strategy vision, including the development of data ethics frameworks and data science training for federal employees.
Sources: Executive Office of the President (2019[48], Federal Data Strategy: A Framework for Consistency, https://www.whitehouse.gov/wp-content/uploads/2019/06/M-19-18.pdf; Federal Data Strategy Development Team (2019[48])), 2019-2020 Draft Federal Data Strategy Action Plan, https://strategy.data.gov/action-plan.
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 instance, OECD (2019[22])]. Early engagement can help identify policy challenges that would otherwise be ignored, and bring relevant actors on board prior to the implementation of 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 for the development of the UK National Data Strategy. In June 2019, the Department for Digital, Culture, Media and Sports carried out a public consultation to collect evidence and inform the development of its National Data Strategy.11 The development of the data strategy will be followed by a series of roundtables and testing exercises towards the publication of the final document in 2020 (DCMS, 2019[49]).
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[6]), and well-grounded data register policies in Denmark, Italy, Norway and Sweden.
The Danish Basic Data Registers programme,12 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 reuse through a public data distributor.13 In addition, the programme puts an emphasis on integration, for it allows for public sector data access through web services and APIs (OECD, 2018[50]).
Leadership
The institutional governance model is also a core element of good data governance, as it provides clarity in terms of leadership and accountability. It is, however, important to make a distinction between political and administrative leadership roles. On the one hand, political leadership provides the high-level support needed to advance the policy agenda; however, changes of political administration can lead to vacant positions, resulting in reduced political support for data policies.14 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 Chief Data Steward in New Zealand, which is held by the Chief Executive of Statistics New Zealand. The Government Chief Data Steward is in charge of leading the data policy in the country.15 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 Chief Data Steward (Stats NZ, 2019[51]).
An earlier example is that of France’s Administrateur Général des Données, created in 2014 (French Government, 2014[52]) and attached to the responsibilities of the head of the Etalab16 (the task force within the Office of the Prime Minister in charge of co-ordinating the open data and artificial intellignece policy in France). In Canada, the Data Strategy Roadmap for the Federal Public Service recommends the creation of a Government Chief Data Steward as a means to “clarify roles and responsibilities around enterprise data leadership” (Government of Canada, 2018[53]).
Others, however, have followed different leadership models, which are less hierarchical and shared by different 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[22]).
In either scenario, the need for a clear leadership is a precondition to help to achieve policy goals (OECD, 2019[3]). 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[3]) and Mexico (OECD, 2016[54]).
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 the establishment of inter-institutional co-ordination 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.
Steering and policy co-ordination bodies
Examples of policy steering or co-ordination 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[47]).
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[48]). While the OMB Data Council will help in co-ordinating the Federal Data Strategy, it will also have the role of informing OMB’s “budget priorities for data management and use” (idem). These bodies can also play an important 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 (see Chapter 4) provides an example in this regard.
Chief data officers, institutional networks and data stewardship
The need for stronger 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 [see (OECD, 2016[55]; 2018[6]; 2019[56])], 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 the provision of public data are in charge of co-ordinating the central open data policy at the organisational level, translating its goals into clear actions and liaising 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 Framework17 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 “data accountability and best practice data management across all data-handling positions, with the goal of evolving beyond the need for traditional data governance roles (e.g. data custodians, data stewards)” (Sweeney, 2019[24]).
Source: Sweeney, K. (2019[24]), “An operational data governance framework for New Zealand government”, https://statsnz.contentdm.oclc.org/digital/collection/p20045coll1/id/2657.
In the United States, the 2018 Foundations for Evidence Based Policymaking 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[57]). This is part of the provisions of the Open, Public, Electronic, and Necessary Government Data Act (OPEN Government Data Act), which is one component of the Evidence-Based Policymaking Act (OECD, 2019[3]). 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.
The provisions of the Open, Public, Electronic, and Necessary Government Data Act describe the activities and role of institutional chief data officers as follows:
The chief data officer of an agency shall:
1. be responsible for life cycle data management
2. co-ordinate 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 standardization of data format, sharing of data assets and publication of data assets in accordance with 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 on 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 co-ordinate 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 maximizes 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 the acquisition and maintenance of any required certification and training.
Source: US Congress s (2019[57]), H.R.4174: Foundations for Evidence-Based Policymaking Act of 2018, https://www.congress.gov/bill/115th-congress/house-bill/4174/text.
Legal and regulatory frameworks
Regulation plays a key role in the context of data governance, thus its implications in this respect are vast. Regulation helps in defining the set of rules to control the access to and sharing of data, promote openness, and ensure and enforce the protection of sensitive data. These instruments help also in the definition and enforcement of common data standards towards greater data interoperability and streamlined data-sharing practices. However, regulation can also be an obstacle for good data governance for the proliferation of fragmented instruments and uncoordinated efforts can hinder cross-institutional and data integration and sharing. Taking an anticipatory approach can help to identify risks and trends in order to implement the needed regulatory action to foster public sector readiness to change.
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 important. An important distinction between concepts has to be made:
Anticipation is the process of creating knowledge – no matter how tentative or qualified – about the different possible futures. This may include, but is not limited to, developing not only scenarios of technological alternatives, but also techno-moral (value-based) scenarios of the future (Nordmann, 2014[58]).
Anticipatory governance is the process of acting on a variety of inputs to manage emerging knowledge-based technologies and socio-economic developments while such management is still possible (Guston, 2014[59]). This may involve inputs from a variety of governance functions (foresight, engagement, policy making, funding, regulation, etc.) in a co-ordinated manner.
Anticipatory regulation is a function of anticipatory governance which uses regulatory means to create space for sandboxes, demonstrators, testbeds, etc. for various technology options to emerge. This requires an iterative development of regulation and standards 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, but is rather about asking questions about plausible futures, then acting upon it by creating room for innovation (e.g. through regulation) or through creating the mechanisms to explore different options in government itself. Most governments today do not have a system in place for anticipatory innovation governance (usually mechanisms connected to the former are siloed under specific policy fields or functions, e.g. foresight). This, in face of increased datafication, is, however, extremely important as choices made today regarding the ownership, interoperability, privacy and control regarding data will influence analytics and services that will be 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, together with leading countries, the OECD will test out in practice different mechanisms for anticipation.
Source: Information provided by the OECD Observatory of Public Sector Innovation.
Across OECD member and partner countries, examples of regulatory instruments related to data governance are vast. These instruments cover different policy issues from data sharing and interoperability to open government data. Examples of regulation related to data protection are provided in Chapter 4.
In Brazil, the central government is advancing on the development of a new data-sharing decree which will help to improve clarity in relation to 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 (data access rules are defined by the custodian)]. Data sharing is clearly 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[62]).
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[63]).
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 developed a national metadata catalogue as a fundamental tool for guaranteeing 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.
Often softer legal and regulatory instruments, such as codes of practice, recommendations or guidelines, follow these instruments.
As described in the OECD Open Government Data Report (OECD, 2018[6]), countries have also made advancements in establishing the right 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.
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. The guide is an ongoing effort to ensure that both 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 provides guidance on how to produce simple identifiers for data that are produced by different public sector organisations, 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 make sure this core reference framework for government data is available through APIs.
France: The General Reference Framework for Interoperability
In France, the General Reference Framework for Interoperability offers a series of 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:
The 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 there is agreement regarding the meaning of data that are exchanged and on the context of the exchange.
The technical interoperability refers to data formats and data exchange protocols as well as the conditions and formats of storage of these data. This interoperability ensures that data can be properly exchanged among public sector organisations and in the right format.
The syntactic interoperability stands as a subset of the technical interoperability as it focuses on the technical format data should have in order to be properly exchanged among public sector organisations.
Italy: White Paper on Artificial Intelligence
In March 2018 Italy published the White Paper on Artificial Intelligence. The white paper recommends that all administrations ensure the quality and usability of the data they provide in order to ensure these data are used to test and refine artificial intelligence systems. Additional tools, modelled to fit the needs of the public administration in relation to 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.
Sources: Argentina and France : OECD (2019[3]), Digital Government Review of Argentina: Accelerating the Digitalisation of the Public Sector, https://doi.org/10.1787/354732cc-en. with information from Direction Interministérielle du Numérique et du Système d’Information et de Communication de l’État (2015), Référentiel Général d’Interopérabilité: Standardiser, s’aligner et se focaliser pour échanger efficacement, http://references.modernisation.gouv.fr/sites/default/files/Referentiel_General_Interoperabilite_ V2.pdf) ; Italy: AGID (2018), White Paper on Artificial Intelligence at the service of citizens, Available at: https://ia.italia.it/assets/whitepaper.pdf.
Skills: Capacity building, collaboration and knowledge-sharing
Public sector capacity, talent and collective knowledge are core elements not only of good data governance in the public sector, but also of 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[7]) and on Public Service Leadership and Capability (OECD, 2018[9]), 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 for (the policy issue)? Data governance must support the business strategy and achievement of the goals (DAMA International, 2017[26]). This translates into the need for clarity in terms of 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 to build the right set of skills by providing training towards greater capacity for implementation. For instance, in Mexico, the central government (2012-18) created the Open Data Squad as the government task force in charge of guiding public sector organisations in the process of publishing open government data (OECD, 2018[6]).
Receiver: Who is the target of capacity-building activities? Good data governance in the public sector is translated into a different set of skills and needs for different 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[3]). 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 assessment of the current data capacity gaps. An example of this is the National Digital Skills survey conducted in New Zealand in 2017 to assess digital skills in the tech sector and across government. The results of the survey informed the report Digital Skills for a Digital Nation and helped to target capacity-building activities in the country (New Zealand Digital Skills Forum, 2018[64]).
Coherence: How can public sector organisations standardise the data skill needs? The use of 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[3]), 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 GCcollab19 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,20 which allows public servants to switch job positions across the government for short periods of time, depending on their skill set.
Openness and engagement: How to leverage the value of external talent and knowledge? 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.
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.
The data value cycle
The data value cycle (see Chapter 3) is in itself 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 reuse). For instance:
Different stages of the data value cycle call for different technical skills and roles (e.g. data custodians, data architects, data scientists). This draws on the different outputs that result from data processing at each different stage. The implementation of training and capacity-building programmes at the tactical level support 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 take place in the data collection stage, thereby having negative consequences on how policies are informed and on the resulting interventions designed using those data as an 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 that disproportionately affect women are either never collected or of poor quality. In an attempt 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 reuse. For instance, government initiatives that focus on the production of 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]). By using this tactic, the government facilitates the publication, sharing and reuse of public sector data (including open data) by public entities and external consumers.
The data value cycle may reflect organisational processes that might be the result of legacy systems. Reassessing or re-engineering these processes is crucial to ensure that digitalisation and data-driven efforts contribute to transformation and avoid the perpetuation of inefficient processes in the digital world.
Data protection takes place (or should take place) across all different stages with data custodians having a key 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 its implementation, evaluation and revision); and increases the impact of investments on sound data management practices, for data are continuously produced, analysed, shared, used and reused to inform and evaluate policy.
The relevance of the data value cycle and its implications in the context of governments and public sectors is discussed more in depth in Chapter 3.
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 (known as X-Road until 2018)24 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 the implementation of 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 public sector cohesion in Estonia. These efforts provided a cornerstone that has been 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[66]).
Another example of the willingness of OECD countries to improve their national data infrastructure is the Data Federation Project in the United States.
The US Data Federation Project aims to bring greater coherence to data federation practices in the US public sector in order to better support policy decisions, 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 different 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 could 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 for automated aggregation in order to foster knowledge sharing across public sector organisations and effective reuse of government data coming from different sources.
Source: Originally published in OECD (2019[3]), Digital Government Review of Argentina: Accelerating the Digitalisation of the Public Sector with information from Lindpainter, J. (2019[67]), “The US Data Federation wants to make it easier to collect, combine, and exchange data across government”, https://18f.gsa.gov/2019/03/05/the-us-data-federation.
In an effort to improve its national data architecture and infrastructure, Italy developed the National Digital Data Platform. This platform offers big data solutions, including data lakes,25 to facilitate easy access to, sharing of 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 stronger collaborative data sharing within in the public sector. It is based on a principle of a “federation of catalogues”, which allows for 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 as a result of governments’ demand for better and more efficient data-sharing infrastructures. For instance, in the United Kingdom, the Digital Marketplace26 project has brought external providers of digital solutions closer to the public sector, by providing resources such as the G-cloud framework,27 which guides external suppliers of cloud-based services when delivering services to public bodies. Inspired by the UK model, Norway has launched a project aiming at creating a similar procurement platform for cloud-based services following its 2016 cloud-computing strategy.28
Also, the use of APIs is growing fast across OECD member and partner countries as an effort 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 easily 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[6]).
As mentioned in the earlier in this chapter, the Nordic countries of Denmark, Norway and Sweden have all secured stronger policies for base data registers, enabling real-time sharing of public information within (and in some cases 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 Decree31will 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 not only within the public sector, but also in the context of 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,32 which comprises 25 technical requirements for private and public organisations managing personal data, in order 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 Framework33 released in June 2019. See Chapter 4 for a more in-depth discussion on data ethics in the public sector.
The XBRL34 digital business reporting standard is an example of a data standard adopted by governments across the world. 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, and therefore simplifies regulatory compliance and business reporting. The XBRL standard is today used by governments in OECD countries such as Germany, Japan and the United States.35 The SBR project in the Netherlands (see Flexibility and scalability earlier in this chapter) is another good example of a country that is 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 the adoption of common international data standards such as the Open Contracting Data Standard, 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 further pave 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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Notes
← 1. See, for instance: https://www.cessda.eu/News-Events/News/CESSDA/Open-if-possible-protected-if-needed-Research-data-via-DANS.
← 2. “We must, on one hand, be able to put our personal data and data embodying intellectual property, national security intelligence, and so on, under careful protection, while on the other hand, we must enable the free flow of medical, industrial, traffic and other most useful, non-personal, anonymous data to see no borders, repeat, no borders”. Extract from Prime Minister Abe’s speech at the World Economic Forum Annual Meeting. For the complete speech, see: https://japan.kantei.go.jp/98_abe/statement/201901/_00003.html.
← 3. See, for instance, the case of the French open data portal in OECD (2018[6]) and the case of Mexico’s central government partnerships with civil society organisations in OECD (2018[37]).
← 4. See, for instance, the work on data collaboratives led by Govlab in the United States: https://datacollaboratives.org.
← 5. For more information see: https://www.sbr-nl.nl/sbr-international.
← 6. For more information see: https://www.logius.nl/english.
← 7. For more information see: https://infrastructurepipeline.org.
← 8. For more information see: https://e-estonia.com/solutions/interoperability-services/x-road.
← 9. The Overview of public sector data governance practices section provides definitions that explain specific data governance aspects, but it does not intend to provide the reader with a comprehensive set of descriptions and concepts. For this purpose, the author recommends referencing the available literature on data governance, such as DAMA International (2017[26]).
← 10. For more information see: https://www.osi.ie/wp-content/uploads/2018/12/Public-Service-Data-Strategy-2019-2023.pdf.
← 11. For more information see: https://www.gov.uk/government/publications/national-data-strategy-open-call-for-evidence.
← 12. For more information see: http://grunddata.dk.
← 13. For more information see: https://datafordeler.dk.
← 14. See, for instance, UK National Audit Office (2019[70]).
← 15. For more information see: https://www.data.govt.nz/about/government-chief-data-steward-gcds.
← 16. For more information see: https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000029470857.
← 17. For more information see: https://statsnz.contentdm.oclc.org/digital/collection/p20045coll1/id/2657.
← 18. For more information see: https://www.gov.uk/government/collections/digital-data-and-technology-profession-capability-framework.
← 19. For more information see: https://gccollab.ca/about.
← 20. For more information see: https://apolitical.co/solution_article/how-can-government-get-top-talent-canadas-free-agents-work-where-they-want.
← 21. For more information see: https://www.gov.uk/government/publications/gender-database/gender-data.
← 22. See, for instance, the case of the National Statistics Office in Argentina in OECD (2019), Digital Government Review of Argentina.
← 23. For more information see OECD (2015[68]), Van Ooijen et al. (2019[71]) and Open Data Watch (n.d.[72]).
← 24. “X-tee is a data exchange layer used in Estonia. Until 2018, it was named X-Road in English. Since 2018, however, X-Road is only used to refer to the technology developed together by Estonia and Finland through MTÜ Nordic Institute for Interoperability Solutions. The Estonian X-tee is now also called X-tee in English.” Source: Republic of Estonia Information System Authority: https://www.ria.ee/en/state-information-system/x-tee.html.
← 25. DAMA International’s Guide to the Data Management Body of Knowledge defines a data lake as “an environment where a vast amount of data of various types and structures can be ingested, stored, assessed, and analysed”. For more information see: https://technicspub.com/dmbok.
← 26. For more information see: https://www.digitalmarketplace.service.gov.uk.
← 27. For more information see: https://www.gov.uk/guidance/g-cloud-suppliers-guide.
← 28. For more information see: https://www.difi.no/rapport/2018/08/innkjopsordningmarkedsplass-skytjenester.
← 29. For more information see: https://catalogo.conecta.gov.br/store.
← 30. Law No. 13,460 of 26 June 2017, available at: www.planalto.gov.br/ccivil_03/_ato2015-2018/2017/Lei/L13460.html.
← 31. Information received from the Brazilian government (Secretaria de Governo Digital). The new data-sharing decree is expected to be released in 2019.
← 32. For more information see: https://www.cnil.fr/en/what-you-should-know-about-our-standard-data-protection-governance.
← 33. For more information see: https://www.pdpc.gov.sg/news/latest-updates/2019/06/first-comprehensive-trusted-data-sharing-framework-now-available.
← 34. For more information see: https://www.xbrl.org/the-standard/what/an-introduction-to-xbrl.
← 35. For more information see: https://www.datatracks.co.uk/ixbrl-blog/xbrl-around-the-world.
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