The Noise Problem

There is a specific kind of meeting where everyone leaves feeling productive and nothing changes. The agenda was full. The slides were dense. Numbers were cited. Opinions were shared. Actions were assigned. And within 48 hours, every participant has returned to doing exactly what they were doing before, because nothing in that meeting was signal. It was noise — dressed in a suit, projected onto a wall, and treated with the seriousness of verified intelligence.

This is the noise problem. It is not that organisations lack information. It is that they have so much information that they have lost the ability to distinguish what is true from what merely sounds true.

The Volume Illusion

The assumption behind most data strategies is simple: more data produces better decisions. This is true only when the additional data is signal. When it is noise — which, in most organisations, it overwhelmingly is — more data produces worse decisions. Not marginally worse. Categorically worse.

More noise creates more complexity. More complexity demands more analysis. More analysis consumes more time. More time creates more pressure. More pressure rewards faster conclusions. And faster conclusions from noisy inputs produce confident errors — decisions that feel data-driven but are actually noise-driven.

The executive who says “I have more data than I know what to do with” is not describing an abundance of insight. They are describing a classification failure. They have data, certainly. What they do not have is a system for determining which of it deserves their attention.

Why Noise Persists

Noise persists because it is useful to someone. The consultant who produces a 60-page report has a commercial interest in every page. The software vendor whose dashboard shows 47 metrics has an engagement interest in every widget. The team member who surfaces a competitor’s press release has a relevance interest in being seen as informed. None of these interests are malicious. All of them produce noise that enters the decision stream with the same weight as verified intelligence.

Noise also persists because discarding it feels risky. The fear is always the same: “What if we throw away something important?” This is a reasonable fear in theory. In practice, the cost of keeping noise is almost always higher than the cost of discarding it. Every piece of noise that remains accessible will eventually be found during a search, cited during a meeting, or referenced during a debate — where it will be treated as though it means something.

The Classification Imperative

The solution to the noise problem is not better analysis. It is classification before analysis. Before any information enters a decision process, it should pass through a filter that asks: is this verified? Is this probable? Is this merely a candidate for investigation? Or is this noise?

This sounds simple. It is not. Most organisations have never built the classification muscle. They have built analytical muscle — sophisticated tools for examining data once it arrives. But they have not built the intake muscle that determines whether the data should arrive at all.

The result is analytical sophistication applied to unreliable inputs. The models are excellent. The dashboards are beautiful. The underlying data has never been classified for quality.

What Changes When You Classify

Organisations that adopt information classification report the same changes: meetings get shorter because noise is no longer discussed. Decisions get faster because the relevant information is smaller and higher quality. Confidence increases because the inputs have been verified rather than assumed. And paradoxically, people feel less overwhelmed despite having access to fewer data points, because the data points they retain are ones they can actually trust.

The shift is not technological. It is cultural. It requires an organisation to accept that most of what it currently treats as intelligence is noise — and to build the discipline to discard it rather than archive it.

This is uncomfortable. It is also necessary. Because every decision made on unclassified information is a gamble disguised as analysis. And the house always wins.

The Signal Status framework provides the classification system. Four levels. Clear criteria. Refresh rates that acknowledge degradation over time. It is not the only way to solve the noise problem. But it is a way that works, and it starts with the hardest admission any organisation can make: most of what we call data is noise.