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Highlights
- Data is at the core of public policy frameworks, but its true potential can only be harnessed when it is shared freely across systems.
- Government agencies across the globe are introducing regulations to drive sharing of citizen data across public policy systems, in keeping with privacy norms.
- Public trust is of prime importance when building data sharing models. To ensure that citizens willingly share their personal data, government agencies need to implement watertight mechanisms for overall management of data.
In this article
In this article
The need for data sharing
The capability to derive actionable insights from data is critical for smooth governance.
Data plays a key role in formulating public policy and designing intuitive public services. The true potential of data, however, can only be unlocked when it is shared freely and not bound by traditional silos. Data sharing can help government agencies improve citizens’ lives, optimize resources, and drive the sustainability agenda across areas. Even as government agencies have started to invest in data analytics capabilities to achieve the aforementioned, the full benefits of data can only be realized by sharing this data and collaborating with other government and public sector organizations.
A recent example of why data sharing is important has been the COVID-19 pandemic. It underlined the need for cross-sectoral reuse of data, which calls for coherence across sector-specific data governance frameworks. For example, Canadian company BlueDot used artificial intelligence to predict the spread of a certain corona virus much before the World Health Organization (WHO) released a statement alerting people to its emergence. HealthMap, at Boston Children's Hospital, and San Francisco based Metabiota also identified the initial signs of COVID-19. Anonymized mobile call data records obtained from telecommunications services providers have been reused to monitor and control the spread of COVID-19. Perhaps, we could have saved more lives if data sharing would have happened faster and across a wide range of datasets and data elements.
The current state of data sharing – the UK, Australia, and the European Union (EU) in focus
The UK, Australia, and the EU are aggressively pursuing data sharing models.
UK’s Biobank is a large-scale biomedical database and research resource that contains in-depth genetic and health information from half a million UK participants. The database is periodically updated and is globally accessible to approved entities for undertaking research into life-threatening diseases. It is a key contributor to the advancement of personalized medicine and has enabled several scientific discoveries that improve human health.
The Intergovernmental Agreement on Data Sharing which came into effect in July 2021 in Australia mandates government agencies to share public sector data by default. A step in the right direction, it highlights the need for public sector agencies to share data for more effective and fruitful collaborate. While the principle has been established, it remains to be seen how the policy is implemented. Government agencies will have to periodically assess the effectiveness and efficiency of the national data sharing program by measuring the outcomes against objectives and ascertaining the improvement in government services and citizen experience. This will help determine the amendments to such agreements.
The EU’s aim is to become a role model for a data-driven society, to create a single market for data and to facilitate common European data spaces. However, looking at the global market for data sharing and applications, Europe is yet to position itself as a global player in the field.
Which datasets need to be shared?
Sharing data isn’t enough; sharing the right data makes all the difference.
The key areas where data sharing comes into focus include family, domestic and sexual violence, child abuse, health, natural hazards and emergency management, road safety, and so on. With the rapid adoption of artificial intelligence (AI) and machine learning(ML), the importance of data sharing is underscored, even in traditionally less data-intensive fields such as energy resources consumption, agriculture, manufacturing, and construction. Government agencies can determine local needs and updated these datasets. Some of the data elements from health and crime are already being recorded and shared globally, however the overall usage is still low.
The latest annual report from the Australian Digital Health Agency shows that just 2.69 million of the 23 million people registered for a My Health Record accessed it in 2020-21. And this was largely driven by people accessing their COVID-19 vaccination records and test results. Governments must increase public awareness, highlighting the importance of data sharing, to increase this number so that clinicians can devise quality care strategies and researchers can expedite the discovery of new drugs and therapies.
It is only obvious that securing consent from citizens and instituting strong data protection mechanisms are key to sharing health related data. As per the Roy Morgan Research poll conducted on behalf of Research Australia, approximately 91% Australians would be willing to share de-identified medical data if it went toward research purposes. Australia Privacy Act in Australia protects the data privacy for its citizens. The EU’s General Data Protection Regulation (GDPR) and New Zealand’s Privacy Act aim to ensure data privacy for their citizens and residents.
Data from multiple data sources will help governments, researchers, scientists, and policymakers to make correlations, identify patterns of unusual activity, and identify anomalies to drive informed decision-making. Some potential use cases are listed below:
Sharing of identified health data of citizens with health providers for clinicians to provide high-quality care
Sharing of de-identified health data for researchers to find new drugs and therapies
Sharing of data from restaurants, travel data, and so on, with public health departments to arrive at eating preferences and their health impacts
Sharing of transport data from flights, trains, metros, and buses to identify the possibility of transmission of viruses
Sharing of Google search results to identify the spread of diseases
Sharing of non-intrusive test results from the sewage systems to identify the spread of diseases (this will help governments minimize tests on individual citizens, reducing government expenditure)
Sharing of inflation data, citizens spend data, and so on, to predict employment or unemployment status as well as preempting economic downturn and recession.
Sharing of property registrations data can help with smart city planning.
Public trust is non-negotiable
While sharing of data has improved globally, primarily due to the COVID-19 pandemic, challenges remain.
Not everyone is okay with sharing personal information, not all of it at least. Some datasets carry highly sensitive information, which is why the willingness to share them is relatively low. The absence of specific security and privacy standards, lack of transparency, inaccuracy of data, frequent cases of data theft and data breaches (especially, in the media) only compound the challenge.
In addition to fragile public trust, there are issues such as the lack of coordination across public departments, poor interoperability, as well as poor data quality and analysis capacity that need attention. A survey revealed that one in 10 Australians have opted out of the My Health Record (MHR) system, leaving the participation rate at 90.1%.
The key to data sharing lies in how data is handled. Many data sharing programs and initiatives are cancelled due to the lack of public trust and concerns around data utilization. By handling data safely and securely, data users can increase the trust of the data subjects and increase citizens’ inclination to share data. The steep rise of social media shows that people are willing to share their data if they see a clear value in doing that. This means the purpose of data collection needs to be clearly articulated to the data subjects, right at the start. Government agencies need to build the confidence in people and should campaign the benefits of data sharing. For example, how medical research and drug discovery become a lot more focused and faster, if the needed health data is shared.
From theory to action
Government agencies need to embrace a culture that encourages data sharing.
The Organization for Economic Co-operation and Development (OECD) has laid out a set of privacy guidelines –to promotes transparency, ensure accountability, and enable value creation by making government data available to all. Government agencies shall share public sector data by default, where it can be done securely, safely, lawfully, and ethically. To develop citizen-centric public services, data sharing among councils, data sharing councils with a state, a state sharing data with other states in the country and state(s) sharing data with the central or federal government is extremely important. We recommend the following to establish a robust data sharing mechanism:
- Define standard operating procedures (SOPs) for data sharing activities
- Develop nationwide data network
- Improve data discoverability
- Share-once use-often model for aggregate de-identified administrative data
- Design robust governance frameworks
- Modernize the outdated data architecture
- Bridge the skills gap to onboard the right set of data scientists
Robust data governance frameworks and modern data architectures are essential for data sharing. A cloud-based data sharing platforms can help public sector departments to share data. While data sharing is important, the ethical aspect of maintaining public trust in data sharing is of paramount importance. Trust, public engagement, transparency, user control, the flexibility to opt out, ad important aspects to consider.
The public sector is facing a perception challenge when it comes to privacy, consent, and use of citizen data. There is a need for stronger regulatory frameworks around owning, storing, and sharing data across different levels of government. The public sector collects a high volume of data every day. At present, across most government agencies, this data is stored in legacy systems such as data warehouses, resulting in silos of data distributed across government departments and agencies. The absence of a standardized data architecture also makes it difficult to share data between departments rapidly and precisely. Establishing a modern data foundation from the start can eliminate many challenges down the line, including data quality problems where rigid systems can lead to duplicated and obsolete datasets. The COVID-19 pandemic has highlighted the need for governments to put in place robust data sharing mechanisms in order for data to be available with full transparency and on time.
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