Think you need one big, perfect standard to achieve interoperability? Think again.
The truth is: interoperability doesn\u2019t require perfection\u2014it requires progress.<\/p>\n\n\n\n
Too often, organizations fall into the trap of thinking that if a standard can\u2019t do everything, it\u2019s not worth doing anything. Custom systems dominate because they\u2019re seen as faster or more tailored, and teams defer standardization for \u201clater\u201d\u2014once the future magically simplifies. But waiting for the perfect, universal standard is not only unrealistic, it\u2019s unnecessary.<\/p>\n\n\n\n
The real value of standards lies in their return on investment (ROI)\u2014and that ROI isn\u2019t all-or-nothing. Even partial standardization of specific aspects can unlock major benefits. Some elements of a system may yield high ROI from standardization, while others may not yet be worth the effort. Interoperability is not a binary state\u2014it\u2019s an optimization process.<\/p>\n\n\n\n
This is especially true in complex systems like supply chains, analytical workflows, and digital twins. These ecosystems involve many actors, technologies, and data sources. Interoperability in such systems is best approached incrementally\u2014by standardizing specific components of how systems interact over time.<\/p>\n\n\n\n
In the beginning, most system interactions rely on human-readable descriptions\u2014manuals, specs, emails, and informal agreements. These help people understand each other, but they don\u2019t help machines. Without standardized, machine-readable structures, systems can\u2019t exchange data efficiently, and automation becomes costly or impossible.<\/p>\n\n\n\n
So how do we move from that fragmented starting point to a fully interoperable ecosystem?<\/p>\n\n\n\n
We break it down. Step by step.<\/p>\n\n\n\n
At the outset, data and processes are typically expressed through natural language documentation<\/strong>, such as manuals, specifications, or informal agreements between parties. These documents provide semantic guidance<\/strong> but do not enable automated data processing or seamless system integration. The absence of machine-readable standards introduces ambiguity<\/strong>, making data exchange and automation costly, inefficient, and error-prone.<\/p>\n\n\n\n Example:<\/p>\n\n\n\n To optimize interoperability<\/strong>, standards can be incrementally introduced, focusing on specific aspects of system transactions. This approach reduces disruption while progressively improving system efficiency. Incremental standardization often starts by addressing:<\/p>\n\n\n\n At each stage, the system becomes more interoperable<\/strong>, allowing for more efficient data exchange<\/strong> and less dependency on manual processing<\/strong>.<\/p>\n\n\n\n The optimization target is to achieve actionable machine-readable standards<\/strong>, where:<\/p>\n\n\n\n In the earlier example, an interoperable standard might now express the temperature reading as:<\/p>\n\n\n\n {<\/p>\n\n\n\n “@context”: “https:\/\/example.org\/context.jsonld”,<\/p>\n\n\n\n “@type”: “Observation”,<\/p>\n\n\n\n “observedProperty”: “temperature”,<\/p>\n\n\n\n “unit”: “Celsius”,<\/p>\n\n\n\n “interval”: “PT10M”,<\/p>\n\n\n\n “location”: “urn:location:someIdentifier”<\/p>\n\n\n\n }<\/p>\n\n\n\n In the second example, \u201c@context\u201d maps each element to a unique identifier, which in turn can be used to retrieve detailed descriptions. This structure is both machine-readable and standardized, enabling seamless data integration between different systems.<\/p>\n\n\n\n Note \u2013 this example leverages the existing <\/strong>data exchange standards to link typical schemas, APIs, semantic models and vocabularies together for the first time. This provides a game-changing opportunity to optimise system interoperability through augmentation with machine readable annotations (at any point in a data supply chain) rather than wholesale re-engineering around particular standardised data structures.<\/p>\n\n\n\n As interoperability improves, further optimizations<\/strong> become possible by standardizing:<\/p>\n\n\n\n The result is a system where data, processes<\/strong>, and actions are increasingly standardized<\/strong>, resulting in a large-scale reduction in \u201ctransaction costs\u201d and integration complexity.<\/p>\n\n\n\n The optimization process is inherently incremental because it balances:<\/p>\n\n\n\n The final optimization goal is a system where standards are actionable by machines<\/strong>, minimizing the need for custom integration work<\/strong> and maximizing interoperability<\/strong> across heterogeneous systems. This is achieved through a staged migration from descriptive text<\/strong> to formalized, actionable standards<\/strong> that incrementally cover the full spectrum of system transactions<\/strong>.<\/p>\n\n\n\n Description:<\/strong> Core Tension:<\/strong> Key Insight:<\/strong> Optimization Outcome:<\/strong><\/p>\n\n\n\n Interoperability isn\u2019t achieved overnight. It\u2019s a process\u2014one that starts small, builds gradually, and delivers real value at every stage. By moving from loosely defined descriptions to machine-readable, standards-based systems, organizations reduce friction, improve scalability, and lay the foundation for more intelligent, automated collaboration. These basic ideas are the technical pillars of the geospatial ecosystem, discussed in our previous blog – The Shift That\u2019s Reshaping Geospatial\u2014and Why It Matters Now<\/a><\/p>\n\n\n\n We can also explore how to make existing and new standards more valuable by demonstrating how they can work together to optimise systems, rather than focusing too much on the implementation of specific components.<\/p>\n\n\n\n You don\u2019t need to wait for the perfect moment\u2014or the perfect standard. The sooner you begin, the sooner you see results.<\/p>\n\n\n\n Whether you\u2019re standardizing your first data model or designing systems that support global integration, your experience matters. Share your use cases, your challenges, and your goals\u2014so we can shape practical, scalable standards together.<\/p>\n\n\n\n Contact us at innovation@ogc.org<\/a> or get involved through our working groups to participate in stepwise interoperability enhancements for your system and other systems.<\/p>\n\n\n\n This blog is part of our \u201c10 Ideas in 10 Weeks\u201d series, highlighting bold ideas and real-world innovation across the OGC community.<\/strong> <\/p>\n\n\n\n\n
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<\/li>\n\n\n\nStep 3: Actionable Machine-Readable Standards<\/strong><\/h4>\n\n\n\n
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<\/li>\n\n\n\nStep 4: Recursive Optimization<\/strong><\/h4>\n\n\n\n
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<\/li>\n\n\n\nStep 5: Economies of Participation<\/strong><\/h4>\n\n\n\n
At this stage, organizations recognize that the value of interoperability<\/em> is tightly coupled to the availability and reuse<\/em> of shared components \u2014 such as vocabularies, APIs, schemas, and governance models \u2014 across a community or ecosystem.<\/p>\n\n\n\n
Organizations must balance:<\/p>\n\n\n\n\n
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Widespread participation in shared standards<\/strong> creates positive network effects: the more entities that adopt and contribute, the more reuse is possible, and the lower the cost for each new participant. Conversely, if an actor must implement everything from scratch (e.g., vocabularies, reference services, governance rules), the barrier to entry may outweigh perceived benefits<\/strong>, especially in the short term.<\/p>\n\n\n\n\n
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<\/li>\n\n\n\nWhere to Go from Here<\/h4>\n\n\n\n
Join the conversation.<\/strong><\/h4>\n\n\n\n
Explore previous insights:<\/h4>\n\n\n\n
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