To compare it to the music industry, we are still in the 90s, when people bought albums to listen to a fraction of it, their favourite song. Max Gulde, constellr However, the influx of commercial entrants is driving innovation in business models, aligning EO investment requirements more closely with expected returns. The business model is shifting, and soon, organizations may only need to pay for the specific pixels or data they need. So, while costs can appear prohibitive at present, the long-term costs and return on investment through efficiency improvements, regulatory compliance and sustainability outcomes can vastly outweigh these costs. Understanding the urgency of EO-derived insights When immediate access to operational EO tools is needed, such as responding to regulatory pressures or unexpected environmental events, organizations may opt for pre-built EO solutions. This is especially beneficial when the organization’s use case aligns with industry-standard workflows – such as monitoring urban heat islands, assessing flood risk or managing deforestation. While it may lack deep customization, buying delivers speed, scalability and immediate integration into existing workflows, allowing organizations to quickly gain value from EO data. Identifying the level of control needed For organizations handling sensitive data, EO systems may need to be built in-house to maintain control over the data life cycle. This is especially important when EO data intersects with data- sensitive and highly regulated environments like defence, financial services and energy. In-house EO systems can aid in compliance with privacy regulations like the EU’s General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA), or industry- specific regulations that dictate how geospatial data is handled. Furthermore, implementing robust encryption and secure APIs (application programming interfaces) for data sharing helps protect EO data throughout its life cycle. This helps organizations avoid fines, legal action or reputational damage. Beyond protecting sensitive data, it is crucial for organizations to identify and leverage their existing non-EO data to drive business advantages. Understanding the types of proprietary data a company possesses can help in creating a unique competitive moat. For instance, a seed business may have proprietary farmer data that can be used to train in-house EO and AI services for better crop estimates. By integrating EO data with proprietary datasets, organizations can develop more accurate models, enhance decision-making processes and create tailored solutions that provide a competitive edge in the market.3.2 Pathways to implementation The Executive’s Playbook on Earth Observation: Strategic Insights for a Changing Planet 24

The Executive%E2%80%99s Playbook on Earth Observation