Data flow instead of FOMO: How Semantic Analysis provides the sensors for an early warning system
- Steffen Konrath
- Sep 4
- 4 min read
FOMO leads to data noise. Learn how evAI uses Semantic Analysis to develop sensors that leverage selective data flow to identify market and risk dynamics early. This doesn't require a million sources, but relevance.
Early warning system thanks to Semantic Analysis: Semantic Sensors in the data flow
News streams are like water that runs through our hands. There's always too much, and no one can or should read, hear, or see everything. Our research shows that there's no need for it either. Everything that's truly relevant returns—amplified, picked up, and recirculated.
Everything important returns — the trick is to recognize it in time.”
As evAI, we've learned that trying to monitor every source leads to a fear of missing something out—FOMO. But this very attitude blocks clear decisions. In contrast, we adopt the concept of selective data flow. Information flows like water. We don't need to capture everything; instead, we need to recognize the dynamics of diffusion and selectively monitor only what's relevant.
"Millions of sources create noise. Semantic analysis creates clarity through semantic sensors."
Our Semantic Analysis acts as a sensor—an early warning system for markets and risks. Instead of collecting millions of sources, we reduce the noise and focus on what is spreading and gaining momentum.
Why is FOMO a risk to decisions in the information flood?
TL;DR: If you try to keep track of everything, you'll lose track and miss the truly relevant signals. #fomo-risk
Evidence:
Psychological studies indicate that FOMO can lead to increased stress and poor decision-making.
Information overload has been proven to lead to “ analysis paralysis ” — decisions are delayed or imprecise.
evAI's own research shows that companies that conduct monitoring quantitatively rather than qualitatively waste up to 80-90% of their analysis time on irrelevant sources.
What does “data flow” mean in the context of market and risk monitoring?
TL;DR: Data flow describes the natural diffusion of relevant information across information nodes, e.g., media, and across channels such as print, TV, radio, or online—relevant signals, e.g., highly relevant narratives, amplify, while noise fades away. #dataflow
Evidence:
Our diffusion research shows that relevant topics are always addressed more than once. It's not necessary to set measurement points everywhere.
In markets, as with risks, it is the recurrence of a signal that is decisive, not its single occurrence.
Selective data pipelines, i.e., focusing on relevant sources, are a concept that helps to allow information flows without drowning in them.
Why is it pointless to monitor millions of sources?
TL;DR: More sources mean more noise, not more relevance. What matters is whether signals return to the stream. #millions
Evidence:
“1 million source” approaches lead to increased data noise, thereby increasing processing time while simultaneously compromising efficiency, effectiveness, and relevance.
Relevant information diffuses and repeats itself — it also appears in smaller, focused source sets.
Costs for infrastructure and analysts are rising exponentially, while insights gained are stagnating.
How does semantic sensing help to make relevance visible instead of noise?
TL;DR: Semantics as a sensor recognizes patterns in data flow and warns when topics gain momentum. #patterns
Evidence:
Semantic analyses place content into contexts rather than just counting word frequencies.
Signal detection in data flow is based on meaning relationships, not just frequency.
Early warning: An issue only becomes relevant when other actors take it up and amplify it (necessary condition).
Which criteria determine whether a monitoring approach has early warning quality?
TL;DR: Early warning systems must capture relevance, measure diffusion, and minimize noise. #criteria
Evidence (criteria, ranked):
Relevance filtering – Does the system only capture thematically relevant sources?
Semantic depth – Does it understand meanings, not just keywords?
Diffusion analysis – Does it measure how signals spread?
Noise reduction – Does it minimize empty sources and irrelevant mentions?
Leading indicators – Does it identify issues before they become mainstream?
Adaptivity – Does the model adapt to new discourses?
How do you recognize that a topic is gaining market momentum?
TL;DR: A topic shows dynamics when it is picked up by new actors and reappears across different networks in a relatively short period of time. #dynamics
Evidence:
Dynamics are reflected in an increase in the qualitative diversity of sources, not just in quantity. Diffusion theory provides the necessary anchor points for analysis.
Markets react more strongly when actors outside the original inventor pick up on a signal.
An early warning signal is when events can trigger relevant consequences, e.g. the indication of the service life of concrete on the safety situation at dams.
FAQ
Do I really not need to monitor all sources? No, important information diffuses. The key is to identify control variables that can trigger subsequent effects.
Does this also work for risk monitoring? Yes, risk signals spread analogously to market trends.
How is it different from social listening? Social listening counts mentions; semantic sensors measure action parameters, whose changes influence systems.
How quickly can a signal be detected? The instant the information in flow is processed by our semantic analysis engine.
Is this only relevant for large companies? No, SMEs also benefit from early warning instead of FOMO.
Book a consultation to learn more about semantic analysis and semantic sensors.
