Where are you on the data journey?
January 2025
Data: The key to smarter public services
Data has the potential to reshape public services, driving efficiency, innovation, and economic growth. According to McKinsey & Company, smarter use of data could increase operational efficiency in government by up to 20%, freeing up valuable resources to address pressing social challenges and improve frontline services.
Across the public sector, there are already examples of data-driven success. Optimising ambulance response times, improving traffic flow through open road data, and predictive modelling for healthcare planning are just a few areas where data is making a real impact.
But this is just the tip of the iceberg. For the public sector to truly capitalise on this wealth of information, more work is needed to overcome entrenched barriers and harness the potential of emerging technologies. Through collaboration and innovation, the government can create lasting improvements that benefit everyone.
The idea that government departments operate in silos is a familiar one – and with good reason. Because of this, opportunities to share data and collaborate across departments are often lost, limiting the ability to tackle complex, cross-cutting challenges. Sir Robert Chote, Chair of the UK Statistics Authority, highlighted the “unrealised potential” that connected data holds for improving policies and public services.
Chris Whitty, giving evidence to the Covid enquiry, also said recently: “I regret to say I think we have slipped backwards since our time in the pandemic in terms of bringing data together.”
Efforts to address this, such as the Digital Information and Smart Data Bill, mark an important step forward. However, legislation alone won’t create the cultural change required. The real challenge lies in fostering an environment where data is treated as a shared asset rather than a departmental resource.
Departments need to recognise the value of smarter data collection and ensure that the necessary information flows seamlessly across the public sector. This must happen without compromising data protection or weakening oversight. The barriers to data sharing are often organisational rather than technical – a shift in mindset is needed to prioritise collaboration and transparency.
A key part of this shift involves creating clear incentives to manage data in a way that benefits cross-organisation working. It’s natural that when resources are stretched, departments will focus on their own priorities and the fuzzier benefits of collaboration can feel secondary. But implementing the government’s challenging long-term missions will need a broad team effort across the whole of the public sector. Guiding that work with the right data will be an important contribution – the focus and importance of those missions is an opportunity for government to think differently about data and to foster a more joined-up approach.
Artificial intelligence (AI) and machine learning are opening up new possibilities – from predicting patient needs to giving us new ways to enhance the productivity of knowledge workers. The government has recognised this and recently launched its AI Opportunities Action Plan, which aims to use AI to boost growth and deliver more efficient public services. However, the success of AI is only as good as the data that feeds it. As the saying goes: “garbage in, garbage out.”
AI-driven decisions depend on high-quality, relevant, and accurate data. Yet, too often, the data that informs these models is incomplete, outdated, or unfit for purpose. To harness the full potential of AI, public sector organisations need robust systems for managing data quality and ensuring that the right datasets are applied to the right problems.
AI outputs can be flawed or biased without careful oversight, leading to real-world consequences. A case in point was the automated analytics in the benefits system, which mistakenly flagged Bulgarian nationals as high-risk, resulting in unjustified suspensions. This illustrates the difficulties of training and applying complex models to give consistently reliable unbiased results.
That doesn’t mean we shouldn’t be trialling innovative approaches, but to mitigate these risks, public sector organisations must embed data quality controls at every stage – from data collection to model deployment. Clear frameworks for auditing data, ensuring accuracy, and addressing biases are essential to maximising the value of AI and safeguarding public trust.
The benefits of data-driven services are undeniable, but public trust remains fragile. Access to health records, for example, could accelerate life-saving research, yet previous attempts to expand data sharing in healthcare have been met with resistance over privacy concerns.
People are right to question how their data is used, and governments must address these concerns head-on. Transparency is critical – individuals need to understand why their data is collected, how it will be used, and what safeguards are in place to protect their privacy.
Building trust means giving people greater control over their personal information and ensuring that robust privacy protections are baked into every data initiative. When handled responsibly, public data can be a force for good – but only if citizens feel confident that their rights and interests are being respected.
Data is one of the most valuable assets the public sector holds, with the power to drive more responsive, efficient, and innovative services. Across different sectors, the potential for data to improve lives is immense – but unlocking this potential requires leadership, collaboration, and a shared commitment to responsible data use.
By fostering a culture of openness, investing in AI-ready data systems, and prioritising public trust, the government can ensure that data becomes a catalyst for positive change. The time to embrace this opportunity is now – and the benefits could reshape public services for generations to come.
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January 2025
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