The world’s fastest crewed air breathing airplane is still the SR-71 Blackbird over 60 years since it first took flight, and now 30 years retired. The plane was an amazing tool in reconnaissance in the early days of geo-intelligence, yet it was the pilot that still had to make decisions on what to do with those tools. At Mach 3.2 and over 80,000ft up, decisions had to be quick, and correct. As one pilot said years later “If you hesitated to turn on your mark, you would miss your flight line by hundreds of miles.”

While maybe not yet the equivalent of a SR-71, geospatial data and imagery are moving at a unprecedented speed and volume. As 2026 arrives and geospatial technologies continue to mature, the industry’s center of gravity is shifting. We are moving from data availability to decision-grade data. The next phase of growth will not be defined by higher resolution alone, but by the ability to translate complex, multi-source geospatial inputs into outputs that leaders can trust, defend, and act upon.  Afterall, no one ever says “I wish I had less data…”, but I have heard more than one end-user whisper “What am I going to do with all of this data?”.

Decision-grade geospatial data is characterized by traceability, quantified uncertainty, temporal relevance, and governance aligned to real decision contexts. Whether supporting infrastructure investment, climate resilience, national security, or financial risk management, stakeholders increasingly demand clarity around data provenance, confidence intervals, and fitness-for-purpose—not just pixels and vectors.  The speed and depth of the AI evolution in the context of the geospatial and earth observation industry have only added to the urgency for decision-grade data.

Several trends are accelerating this shift. First, the convergence of Earth observation, digital twins, and AI is enabling scenario-based analysis rather than static mapping. Second, enterprise and government users are embedding geospatial intelligence directly into operational workflows, raising the bar for reliability and auditability. Finally, risk-adjusted and probabilistic models are gaining traction, reflecting a broader move toward decision support rather than focusing on descriptive analytics.

To fully realize this shift means clarifying where decision-grade data delivers the greatest value. High stakes use cases such as infrastructure investment, climate adaptation planning, disaster preparedness, and financial risk underwriting are increasingly reliant on geospatial inputs that are defensible under scrutiny. In these contexts, resolution alone is insufficient; leaders must understand uncertainty, assumptions, and trade-offs embedded in the data.

Decision-grade data is distinct from analytics-grade outputs. Dashboards, AI predictions, or rapid updates without explainability can inform exploration, but they often fall short when decisions carry legal, financial, or societal consequences. As a result, organizations are rethinking procurement, governance, and internal capabilities—shifting investment from data volume toward trust, transparency, and accountability. This evolution positions geospatial and earth observation not as supporting tools, but as a core component of enterprise and public-sector decision systems.

The companies and organizations that will lead in this environment are those that treat geospatial data as critical infrastructure—investing in quality frameworks, interoperability, and governance at scale. In doing so, they will differentiate from their competitors by elevating the geospatial conversation from a supporting function to a foundational layer of modern decision-making with a seat at the table where decisions are made.