Turning Data into Productivity: Insights from Scotland Productivity Week 2026

_{Dr Gian Luca Vriz (Research Fellow, The University of Edinburgh); Professor Bridgette Wessels (The Productivity Institute/University of Glasgow); Eleonora Vanello (Head of Peer Works, Prosper); Magdalena Getler (Head of Research Growth) and Rui Cardoso (Head of Public Sector Engagement) both Smart Data Foundry}

Overview and Purpose

This briefing presents key insights from the workshop “Turning data into action: a practical workshop on productivity”, held at the Edinburgh Futures Institute as part of Scotland Productivity Week 2026. The session brought together researchers and data specialists to examine how emerging forms of “smart data” can strengthen the evidence base for productivity analysis and improve the design and responsiveness of economic policy.

The workshop was grounded in a widely recognised challenge: while productivity remains central to economic wellbeing—shaping wages, job quality, public services and living standards—our ability to measure and understand productivity is increasingly constrained. Traditional, survey-based data sources are under pressure, with declining participation rates, uneven representativeness across regions and population groups, and significant time lags between data collection and policy use.

At the same time, there has been a rapid expansion in the availability of alternative forms of data, particularly financial and administrative data. These sources, when used responsibly, offer new opportunities to generate more timely, granular and policy-relevant insights into economic activity. The workshop explored how these data sources can complement official statistics and support more agile, evidence-informed decision-making.

Reframing Productivity Through Data

A central theme emerging from the workshop is that data is not neutral: it plays a constitutive role in shaping how productivity problems are defined, whose experiences are visible, and which policy solutions are considered viable. Understanding productivity therefore requires attention not only to measurement techniques, but to the underlying data infrastructures that enable (or constrain) analysis.

Smart data, particularly large-scale financial and business datasets, provides a markedly different lens on economic activity. Consumer finance data, derived from millions of anonymised bank accounts, allows longitudinal analysis of income, expenditure, and financial resilience. At the same time, SME financial datasets reveal patterns of firm performance, employment, revenue and productivity across sectors and regions.

Together, these sources offer a more dynamic and fine-grained understanding of economic behaviour. Rather than relying solely on aggregated national indicators, they enable analysis of how productivity is experienced across places, sectors, and social groups. This is particularly important in the UK context, where regional disparities in productivity remain persistent and pronounced.

From Measurement to Insight: Complementing Official Statistics

A second key insight is that smart data should not be viewed as a replacement for official statistics, but as a complementary resource that enhances existing data systems. Work presented in the workshop demonstrates that combining financial transaction data with traditional survey data can significantly improve the measurement of living standards and consumption.

For instance, research conducted in collaboration with the Institute for Fiscal Studies shows that integrating banking data into conventional statistical models produces more accurate estimates of local consumption patterns. This is significant because consumption is often a more robust indicator of lifetime wellbeing than income alone, particularly in contexts of income volatility. The ability to generate more precise, geographically detailed measures of consumption strengthens the evidence base for addressing regional inequalities and targeting policy interventions.

Linking Data Domains: New Insights into Productivity and Inequality

The workshop also highlighted the transformative potential of linking datasets across domains, particularly in understanding the complex relationships between health, financial wellbeing, and labour market participation.

Projects presented during the session illustrated how linking health and financial data can reveal the economic impacts of chronic conditions such as long COVID, including reduced income, increased benefit reliance, and withdrawal from the labour market. Similarly, linking housing, energy and health data provides new insight into how living conditions—such as cold or underheated homes—contribute to adverse health outcomes, with long-term implications for productivity and wellbeing.

These examples demonstrate a shift towards more integrated and causally informed analysis. Rather than examining economic outcomes in isolation, smart data allows researchers and policymakers to explore how overlapping systems—health, housing, finance—interact to shape economic participation and productivity.

From Data to Action: Enabling Place-Based Policy

A critical issue for policy is not only the availability of data, but its usability. The workshop emphasised the importance of translating complex datasets into tools that can inform practical decision-making.

The Economic Wellbeing Explorer provides a clear illustration of this approach. By combining financial data with administrative and open datasets, it offers a multi-level view of economic wellbeing across national, regional and local contexts. The platform enables policymakers to track indicators of financial stress—such as persistent overdrafts, low financial resilience, or high expenditure relative to income—in near real time.

This type of tool supports a shift towards more preventative, place-based policy. By identifying emerging risks early, it enables targeted interventions tailored to specific communities, rather than relying on reactive or one-size-fits-all approaches. In this sense, data becomes not only a descriptive resource, but an operational one that directly informs service design and resource allocation.

Governance, Ethics, and Public Trust

The expanded use of smart data raises important questions about ethics, governance, and public trust. The workshop highlighted that the value of such data depends fundamentally on robust systems of data protection and accountability.

Secure infrastructures, such as Trusted Research Environments, play a central role in enabling access to sensitive data while ensuring privacy and security. These environments operate under established governance principles, including the “Five Safes” framework, which ensures that data is used only by appropriate users, for legitimate purposes, in secure settings, with outputs that do not compromise confidentiality.

Importantly, the discussion underscored that public trust is not simply a technical issue, but a social one. Demonstrating clear public value, maintaining transparency about data use, and engaging citizens in governance processes are essential to sustaining support for data-driven approaches to policy.

Implications for Research and Policy

The workshop points towards several interconnected priorities for future research and policy development. There is a clear need to advance the measurement of financial vulnerability, moving beyond income-based indicators to capture more dynamic dimensions such as financial resilience, expenditure patterns, and exposure to shocks.

At the same time, there is a growing opportunity to strengthen the link between micro-level data and macroeconomic outcomes. Understanding how individual and firm-level behaviours aggregate into regional and national productivity trends remains an important analytical challenge.

The integration of health and economic data also represents a significant research frontier. As demonstrated in the workshop, health conditions have profound implications for labour market participation and productivity, yet these relationships are often poorly captured in traditional datasets.

For policymakers, the key implication is the need to invest not only in data access, but in data capability. This includes building capacity within local and national institutions to interpret and use data effectively, as well as embedding data-driven approaches within policy design processes. Equally important is the development of governance frameworks that maintain high standards of ethics and public accountability.

Conclusion

The workshop makes clear that improving productivity is not simply a matter of economic policy, but of data innovation. By integrating traditional statistical approaches with new forms of smart data, it is possible to develop a more timely, granular and inclusive understanding of economic activity.

This shift has important implications for how productivity is conceptualised and addressed. It enables a move away from aggregate, nationally focused models towards approaches that are more sensitive to place, context, and lived experience. In doing so, it opens up new possibilities for designing policies that are both more effective and more equitable.