By Fatima García-Elena (The Data City) and Raquel Ortega-Argilés (The Productivity Institute)
In October 2024, the UK government introduced “Invest 2035: The UK’s Modern Industrial Strategy,” a comprehensive plan to ensure economic resilience and growth over the next decade. This strategy focuses on eight critical sectors expected to drive innovation and productivity: advanced manufacturing, clean energy industries, creative industries, defence, digital and technologies, financial services, life sciences, and professional and business services. However, while these sectors represent the backbone of the UK’s economic ambitions, a significant challenge remains: how can we translate these high-level priorities into actionable, data-driven insights for policymakers, investors, and researchers?
Bridging the Gap Between Strategy and Data
The solution to the problem lies in industrial classification frameworks— systems that categorise businesses into sectors for analysis. Traditional tools like Standard Industrial Classification (SIC) codes, while foundational, often fall short when it comes to emerging industries. For example, how can we measure the economic impact of “clean energy industries” using a system designed decades ago? On the other hand, Real-Time Industrial Classifications (RTICs), developed by platforms such as The Data City, capture the dynamic and real-time changes in industry landscapes, including emerging sectors like quantum technologies and advanced materials. The existing disconnect between these frameworks has hindered cohesive data collection, limiting stakeholders’ ability to measure progress, allocate resources, and identify growth opportunities.
To address this issue, The TPI Productivity Laboratory at The Productivity Institute and The Data City have developed an innovative methodology, including a lookup table mapping the government’s eight priority sectors to SIC codes and RTICs. This resource bridges the gap between historical and modern classification systems and unlocks a new era of precision in economic analysis.
A Three-Step Methodology: From Theory to Action
The success of the project relies on a robust three-step approach that combines qualitative expertise with advanced statistical analysis:
- Qualitative Mapping: Experts first aligned each of the eight sectors with relevant Real-Time Industry Codes (RTICs) and Standard Industrial Codes (SICs). For example, the “Advanced Manufacturing” sector was mapped against RTICs such as Robotics and Autonomous Systems and Electronics Manufacturing, which reflect cutting-edge activities absent in older SIC codes. In contrast, established sectors like “Financial Services” were linked to traditional SIC codes (e.g., Security dealing on own account) while also integrating RTICs like Fintech to capture for innovation.
- Real-Time SIC Analysis: Using The Data City’s Real-Time SIC (RSIC) data—a dynamic update to static SIC codes—the team analysed company-level data within each RTIC grouping. This analysis revealed the most prevalent RSICs for each sector, such as 71129 (Other engineering activities) dominating Advanced Manufacturing.
- Statistical Significance Testing: To avoid arbitrary thresholds, the team normalised RSIC distributions using location quotients (which measure sector concentration) and combined these with raw prevalence percentages. By converting these scores into z-scores, they were able to identify statistically significant SICs, ensuring only the most relevant codes influenced the final lookup table.
Empowering Stakeholders with Precision
The outcome results in a transformative tool that converts strategic vision into detailed insights. For example, policymakers can now monitor the growth of “Clean Energy Industries” using SIC codes like 35110 (Production of electricity) and RTICs such as Net Zero. Meanwhile, investors can utilise RSIC data to identify high-potential startups in Quantum Technologies. Researchers also benefit from a unified framework to analyse trends across both traditional and emerging sectors, potentially helpful for robustness and sensitivity analysis.
This methodology also highlights the evolving nature of industries. By incorporating RTICs—validated by organisations such as the Department for Science, Innovation and Technology (DSIT)—the lookup table encompasses sectors that do not fit conventional classifications. This approach ensures that the UK’s industrial strategy remains adaptable in a rapidly evolving global economy.
Conclusion: Data as the Catalyst for Growth
The collaboration between The TPI Productivity Data Lab and The Data City demonstrates how innovation in data science can enhance policy impact. By harmonising historical frameworks with real-time insights, this partnership enables stakeholders to navigate complexity, allocate resources strategically, and promote growth across the UK’s priority sectors. As the Invest 2035 initiative unfolds, these tools will be indispensable for transforming ambition into measurable progress—showing that the future of economic strategy lies not just in vision but also in the power of data.
For those shaping tomorrow’s economy, this lookup table is more than just a resource; it serves as a compass. With it, the path to sustainable growth has never been more straightforward.
If you want to learn more about the TPI Productivity Lab, visit https://lab.productivity.ac.uk/
