Signal Archaeology: Tracing Information Decay Across Organisational Layers

Following the signal

Every organisation processes signals — customer feedback, market intelligence, risk indicators, operational metrics. These signals enter the organisation at one point, travel through multiple layers, and eventually reach a decision-maker. The question we became interested in was simple: what happens to the signal along the way?

The answer, consistently, is that it degrades. Not through malice or incompetence, but through the ordinary mechanics of how organisations process information. Each layer that touches the signal transforms it — summarising, contextualising, filtering, prioritising. Each transformation loses something. By the time the signal reaches a decision-maker, it may bear little resemblance to the original.

We call the practice of tracing this degradation signal archaeology — the systematic excavation of how information changes as it crosses organisational boundaries.

The method

Signal archaeology follows a specific protocol:

1. Select a signal. Choose a piece of information that entered the organisation at a known point — a customer complaint, a risk event, a competitive intelligence report, a product defect.

2. Map its journey. Trace every touchpoint the signal passed through. Who received it? How did they process it? What did they pass along? To whom?

3. Measure transformation. At each touchpoint, compare the signal entering with the signal leaving. What was added? What was removed? What was changed? What was lost?

4. Identify decay points. Where did the most significant degradation occur? Was it at a specific organisational boundary? Was it in a specific transformation (e.g., summarisation for an executive audience)?

5. Classify the mechanism. Was the signal attenuated (weakened)? Amplified (distorted into something larger)? Dispersed (fragmented across channels)? Inverted (reinterpreted to mean the opposite)?

A worked example

We traced a customer friction signal through a mid-size technology company:

Origin: A customer support ticket describing a recurring authentication failure. Specific, technical, reproducible. The customer provided screenshots, error codes, and a timeline.

Layer 1 — Support team. Logged as a ticket. Categorised as “Login Issues.” The specific technical detail was preserved in the ticket notes but not in the category. Loss: specificity reduced to category.

Layer 2 — Support manager. Included in the weekly team summary as “3 login-related tickets this week.” The individual ticket’s detail — the screenshots, the error codes, the reproducibility — didn’t make it into the summary. Loss: evidence removed, quantified as frequency.

Layer 3 — VP of Customer Experience. Monthly report noted “login issues remain within normal parameters.” The three tickets were compared against a historical baseline and deemed non-exceptional. Loss: contextual significance removed by statistical normalisation.

Layer 4 — Executive leadership team. Quarterly business review showed “customer satisfaction: 4.2/5” with no mention of authentication issues. Loss: signal completely absent from decision-making context.

The original signal — a specific, reproducible, fixable authentication failure affecting customers — had been reduced to nothing by the time it reached the people with authority to allocate engineering resources to fix it.

What signal archaeology reveals

The method consistently surfaces three types of findings:

Structural decay points — boundaries where signal degradation is built into the process. Summarisation for executive audiences is the most common. The act of making information “executive-friendly” strips the specificity that makes it actionable.

Ghost loops — feedback pathways that should exist but don’t. In the example above, there was no mechanism for the support team to escalate a technical issue directly to engineering without going through the management summarisation chain. The loop didn’t exist. Nobody had noticed it was missing.

Measurement interference — cases where the measurement system itself degrades signal. The “within normal parameters” classification in Layer 3 is a measurement decision that actively suppresses escalation. The metric does its job (categorising volume) while destroying the signal (this specific issue needs fixing).

Why this matters

Signal archaeology isn’t an academic exercise. It’s a diagnostic tool that reveals the structural conditions affecting strategic execution. When we can show an executive team exactly where and how a specific signal degraded — with evidence at each step — the conversation shifts from “we need better communication” (which changes nothing) to “we need to redesign this specific boundary crossing” (which changes something).

The archaeological metaphor is deliberate. Like physical archaeology, we’re excavating layers to understand what happened and why. The artefacts we find — the transformed signals at each layer — tell the story of how the organisation actually processes information, as distinct from how it thinks it does.