An alternative recovery plan

See better

Competing priorities and a siloed approach to economic recovery challenge our ability to tackle the long-term issues facing society. An approach employing ‘big data’ and data science could transform our ability to see where public spending will achieve the greatest good.

The effects of COVID-19 have brought into focus acute social challenges – gaping disparities in wealth, fitness, health and access to employment and services, across racial, regional and postcode lines. At the same time, the economy is contracting and public finances are under exceptional pressure.

Decisions made now have consequences that will stretch out far into the future. For society, as well as the economy, it matters what money is, or is not, spent on. Investment should be targeted where it will have the biggest impact in terms of both social outcomes and economic stimulation.

However, maximising value for money is hampered by poor visibility of the challenges faced, difficulty in seeing and understanding the correlations between those challenges, institutionally siloed thinking, and a propensity to spend in areas that will deliver visible near-term results, or where there is strong precedence – for example transport projects or repairs to leaking school roofs. Although better roads, railways and school buildings can be expected to bring economic and social benefits, are those benefits the greatest possible. Are the right priorities being set?

Focus effort for best effect

Worldwide, long-term economic and societal strength – and in the UK, achievement of the government’s levelling-up agenda – require attention to some fundamental societal challenges, including educational disadvantage, mental illness and gender inequality.

High value for money can be achieved through interventions to make a positive difference to the life chances of children and adults in our most disadvantaged communities.

Digitalisation provides the means to overcome the historic difficulty in seeing, understanding and addressing complex social challenges, so as to identify where to focus effort for greatest effect. For example, there are diverse symptoms of adverse childhood experiences – various forms of abuse, neglect and household dysfunction – that are recorded by schools, doctors, hospitals, dentists and social services.

Break down silos

If this data was combined into a digital model, it would become possible to identify patterns, trends and correlations.

Cause and effect relationships could be seen – the effects on different segments of society of a pandemic, a recession, closure of a major employer, or positive government interventions, for example.

While data remains siloed it is impossible to view the big picture of societal need and understand the detail within it. Bringing it together in an integrated digital model would provide new ways of seeing and producing new and improved information. This in turn would provide greater insight into challenges and opportunities, and empower better-targeted action, to achieve a greater impact.

Our digital platform, Moata, is designed to support the kind of data modelling required to prioritise interventions for greatest societal benefit. Its core functionality includes artificial intelligence, machine learning, data orchestration, data analytics, visualisation, application programming interface integration, data storage and security (including GDPR compliance), user management and user experience.

Build social resilience

Enabling children to attend school and learn well, and providing their parents with the opportunity to learn and develop skills, should improve communities’ ability to work and provide them with long-lasting resilience to future pandemics.