E-commerce and retail
Retailers accumulate pricing, catalogue and demand histories that are difficult to reconstruct from outside.
- Examples: historical product prices, catalogue changes, inventory levels, aggregated purchase patterns
- Possible users: pricing-intelligence firms, consumer-trend researchers, demand-forecasting developers
- Watch for: customer-level purchase data is personal data; marketplace terms may restrict reuse
Financial services
Lenders and payment businesses hold performance histories that support risk modelling.
- Examples: aggregated loan performance, repayment trends, portfolio statistics, risk indicators
- Possible users: credit-risk modellers, investors, economic researchers
- Watch for: banking secrecy, regulatory obligations and personal data mean aggregation is usually essential
Logistics and transportation
Carriers and operators record how goods actually move, which is valuable for planning and benchmarking.
- Examples: delivery performance, route and transit-time histories, fleet utilisation, aggregated shipment volumes
- Possible users: supply-chain analytics providers, route-optimisation developers, infrastructure planners
- Watch for: client contracts may treat shipment details as confidential; driver location data is personal data
Forestry and geospatial
Field measurements collected over many seasons are often unique and expensive to replicate.
- Examples: forest inventory, land-use observations, timber and growth measurements, monitoring records
- Possible users: environmental and climate researchers, remote-sensing model developers, land and carbon analysts
- Watch for: landowner agreements and public-funding conditions may affect rights
Healthcare and life sciences
Health data can be highly valuable for research, and it is also the most tightly regulated.
- Examples: eligible research datasets, properly licensed clinical information, aggregated operational metrics
- Possible users: medical researchers, life-sciences companies, health-technology developers
- Watch for: health data is special-category data under the GDPR; ethics approvals and consent terms apply
Gaming and digital platforms
Platforms generate detailed records of engagement and system behaviour at scale.
- Examples: aggregated engagement patterns, game performance statistics, platform operations, fraud trend indicators
- Possible users: behavioural researchers, anti-fraud developers, AI developers studying interactive systems
- Watch for: player data is personal data; platform and store terms may restrict use
Related guides
General information only, not legal advice. Last updated 13 October 2026.