Turning EO Data into Decision-Grade Workflows
A SpaceX satellite over a coastal area
Credit: SpaceX on Unsplash
From pixels to P&L in agriculture, energy and insurance
Earth observation (EO) creates enterprise value when it changes a decision. The winning model moves up the stack: fuse multi-sensor data, run domain-specific models, and deliver outputs directly into operational systems. In agriculture, that means field-level prescriptions for input timing and quantity, not NDVI maps. In energy, it’s methane detection with quantified emissions and verified abatement SLAs, not raw spectral signatures. In insurance, it’s automated underwriting and claims triage linked to policy systems—with explainability for auditors and regulators.
Productisation requires three layers. First, data plumbing: harmonised archives, provenance tracking and latency guarantees. Second, modelling: crop growth, asset integrity and peril models tuned with local ground truth. Third, workflow integration: APIs, connectors to ERP/SCADA/policy platforms, and user interfaces that reflect how agronomists, operators and adjusters actually work. As authors across the Newspace literature have argued, the companies that escape commodity pricing are those that package EO as an outcome (yield lift, leak reduction, loss ratio improvement), not as imagery.
Revenue then shifts from one-off data sales to subscriptions and risk-sharing contracts. Buyers pay for measurable deltas in KPIs, enabling vendors to capture a share of the value created. This aligns incentives for continuous improvement and sticky retention.
Commercial Takeaways
Businesses should measure success by customer KPIs moved—yield, unplanned downtime, loss ratios—and price against those outcomes via tiers or performance credits. Investors should look for domain depth (agronomists, pipeline engineers, actuaries) alongside ML talent, plus evidence of integration into core systems where switching costs are real. Enterprise buyers should start with narrowly scoped pilots tied to a baseline, then scale across regions and business units once uplift is proven and reproducible. Policy-makers should encourage open ground-truth datasets and outcome-based procurement to catalyse private investment without piling on prescriptive rules.
