Signal Status

The Problem

Every organisation is drowning in information. Dashboards, reports, alerts, updates, opinions, market signals, customer feedback, competitor moves, industry trends. The volume is not the problem. The problem is that all of it arrives with equal weight.

A verified customer complaint and an unattributed rumour from an industry blog land in the same inbox with the same urgency. A validated quarterly metric and a speculation from a board meeting get discussed in the same strategy session with the same seriousness. The result is noise treated as signal, signal buried in noise, and decisions made on information that was never classified for reliability.

Signal Status was built to solve this. It creates a shared vocabulary for information quality — a classification system that every person in an organisation can use to distinguish what they know from what they think they know.

The Four Levels

Signal Status classifies all information into four levels. The specific criteria for each level are proprietary, but the categories are public because the vocabulary itself changes behaviour. Simply naming the levels creates awareness of the problem.

The four levels are: Verified Signal, Probable Signal, Candidate Signal, and Noise. Each level carries different implications for action. Verified Signals can be acted upon immediately. Probable Signals can inform direction but require confirmation before commitment. Candidate Signals warrant investigation but not action. Noise should be discarded — not stored, not filed, not “kept for later.” Discarded.

Signal Degradation

One of the most important principles in Signal Status is that all signals degrade over time. A Verified Signal from six months ago may be Probable today and Noise tomorrow. Information that was accurate when collected may no longer reflect current reality. The classification is not permanent — it has a refresh rate, and that refresh rate varies by domain.

Market data degrades faster than legal precedent. Customer sentiment degrades faster than demographic data. Competitive intelligence degrades faster than macroeconomic trends. Acknowledging this degradation prevents organisations from acting on stale intelligence with outdated confidence.

Why Noise Must Be Discarded

Most organisations archive noise. They keep it in databases, dashboards, and shared drives because “you never know when it might be useful.” This is a storage problem disguised as a knowledge problem. Keeping noise accessible means it will be found during future searches, where it will be treated with the same weight as actual signals because it occupies the same space.

Signal Status requires active discarding of Noise. Not archiving. Discarding. The discipline of deletion is as important as the discipline of classification.

Who Signal Status Is For

Any leader who has ever said “I have more data than I know what to do with” or “We’re data-rich and insight-poor” is describing a Signal Status problem. The framework serves executives, analysts, and operators who need to make decisions from imperfect information — which is to say, all of them.

Frequently Asked Questions

What is signal vs. noise?

Signal is information that accurately represents reality and can inform action. Noise is information that does not. The challenge is that noise often looks like signal — it arrives in the same formats, from the same sources, with the same apparent authority. Signal Status provides a classification system for distinguishing between them.

How do you assess information reliability?

Information reliability depends on source credibility, recency, corroboration, and domain relevance. Signal Status formalises these factors into a repeatable classification process rather than relying on individual judgement, which is subject to bias and inconsistency.

Why doesn’t more data improve decisions?

More data improves decisions only when the additional data is signal. When the additional data is noise — which it usually is — it actively degrades decision quality by creating false confidence, analytical complexity, and attention dilution.

What causes information overload in leadership?

Information overload is caused by the absence of classification. When all information is treated equally, the volume of incoming data exceeds the capacity to process it. Signal Status reduces the volume by classifying and discarding noise, leaving only information that warrants attention.