What if the events we perceive aren’t objective truths but rather interpretations shaped by our cognitive frameworks? This question lies at the heart of a paradigm shift in computer science, moving us from a rigid, event-centric view to a more nuanced, sign-centric understanding of systems. In this new paradigm, understanding isn’t about what objectively occurs, but how meaning emerges through interpretation and context.
Author: William David Louth
The False Promise of “Semantic” Observability
Current observability standards misappropriate terms like "semantics" while delivering mere data collection. This post critiques OpenTelemetry's "Semantic Conventions" as standardized naming taxonomies that lack genuine interpretive power. Rather than capturing meaning, these approaches create cognitive burden through maximum data capture—contradicting how intelligence functions as a selective abstraction engine.
The Gap Between Human Cognition and Observability
Our observability tools are working against the very way our brains make sense of the world. While we think in high-level situations and narratives, our dashboards bombard us with disconnected metrics and fragmented data points. This fundamental mismatch forces engineers to waste precious cognitive resources translating numbers into meaning—a burden that becomes dangerous during critical incidents when clarity matters most.
Beyond Dashboards and Databoards
The world of modern software is a symphony of interconnected services, microservices, and cloud infrastructure, constantly evolving and adapting to changing demands. Yet our tools for understanding these complex systems often remain rooted in a paradigm of static dashboards and isolated metrics. While these tools might suffice for simpler systems, they fall short when applied to the dynamic complexity of cloud-native architectures, digital twins, and large-scale service ecosystems.
Countering Counting – Breaking Free of Metricity
The following post challenges the epistemological assumption that quantitative data provides the most reliable and objective form of knowledge. It argues that our obsession with counting and measuring has led to a distorted understanding of reality, where numbers replace the richness and complexity of human experience. This critique aligns with postmodern and phenomenological perspectives, which emphasize the importance of subjective experience and the limitations of objective measurement. We’ve become prisoners of our invention.
The Lost Dimension of Meaning
For decades, we’ve built computational systems under a grand illusion: that by mastering data collection, storage, and movement, intelligence and insight will naturally emerge. The reality we face today across observability, digital transformations, data engineering, and artificial intelligence proves otherwise. Our systems are efficient but not effective. They process but don’t understand. They inform but don’t enlighten. This isn’t just a technical failure—it’s philosophical. We’ve engineered complexity but not comprehension. We’ve built means for data but not for meaning.
From Configuration Management to Situational Awareness
We trace the evolution of system management from manual configuration to dynamic situational awareness, highlighting the shift from maintaining static states to enabling systems to self-regulate and adapt in real-time. As we consider the future of system observability, it has become evident that data collection alone is insufficient. We require systems capable of comprehending the significance of the data they generate. This is where our Semiotic Twin concept comes into play. Just as human experts interpret numbers beyond mere numerical values, our monitoring systems must transcend mere metric recording to attain genuine situational awareness.
The Uncomputable Moment
In our quest to model human experience through computation, we often forget that a situation is more than the sum of its measurable parts. While we can map events, log data, and predict patterns, we cannot fully capture the ineffable quality of human awareness—the way a mother’s intuition sparks before conscious thought, or how a shared glance between strangers can rewrite social rules in an instant.
The Situation Room of the Known Unknown
In the drive to “observe all the things,” we’ve built comprehensive pipelines that churn out terabytes of data. Yet data alone isn’t understanding. Without context, without synthesis, even the most powerful observability suite is just a loudspeaker blaring noise.
Semiosphere: A Foundation for Perception and Control
In our exploration of system observability and situational intelligence, we established a universal pattern for understanding system behavior through sources, subjects, signs, and signals (including states). This recursive pattern is foundational to what we call Semiosphere, a living semiotics system, which builds upon Substrates and Signetics—a computational execution model enabling the processing, routing, and transformation of signs and signals. Together, these elements create an architecture that supports situational awareness, digital twins, and business process intelligence.