insight autopsy
drift chart

Insight Autopsy

When analytical insights silently expire

Inspiration

Dashboards are excellent at answering what is happening.
They are dangerously bad at answering a more important question:

Is this insight still true?

While working with analytical insights, we noticed a recurring failure mode: once an insight is declared “true,” it is treated as permanent. Teams continue to rely on it even though the data and assumptions underneath may have shifted.

When reality changes, dashboards do not raise alarms.
They keep reporting with confidence — quietly misleading decision-makers.

This project was inspired by one uncomfortable question:

Why did this insight look true… and when did it die?

What it does

Insight Autopsy treats analytical insights as living objects, not static results.

Instead of trusting insights forever, the system:

Models each insight as a claim
Decomposes that claim into explicit assumptions
Continuously monitors those assumptions over time
Flags or retires insights automatically when assumptions break

This shifts analytics from static reporting to assumption-aware decision intelligence.

How we built it

We designed Insight Autopsy around the lifecycle of an analytical claim.

1. Insight as a Claim

An insight such as:

“Campaign 1 outperforms Campaign 2 for high-income customers.”

is treated as a claim, not a chart.

Each claim depends on hidden assumptions, including:

Stability of the high-income customer segment
Consistency of the income distribution
Validity of the segment definition over time

Traditional dashboards visualize outcomes but never formalize these assumptions.
Insight Autopsy makes them explicit and measurable.

2. Monitoring Assumptions with Drift Metrics

Each assumption is monitored using statistical drift metrics.

For income stability, we use Population Stability Index (PSI):

$$ PSI = \sum (Actual_i - Expected_i)\ln\left(\frac{Actual_i}{Expected_i}\right) $$

Interpretation:

PSI < 0.2 → Stable
0.2 ≤ PSI < 0.4 → Warning
PSI ≥ 0.4 → Significant drift

When PSI crosses thresholds, the system signals that the insight’s assumptions are degrading — even if performance metrics still appear strong.

3. Insight Retirement

When key assumptions fail, the insight is marked as expired.

Instead of silently misleading stakeholders, Insight Autopsy explains:

Which assumption broke
When it broke
Why the insight is no longer valid

This allows teams to trust insights only while their assumptions remain valid.

Challenges we ran into

Translating abstract assumptions into measurable signals
Choosing drift thresholds that are interpretable, not just statistically correct
Avoiding false alarms while still detecting meaningful shifts early
Designing the system to feel intuitive for analysts, not academic

Balancing rigor with usability was the hardest challenge.

Accomplishments that we're proud of

Turning vague business intuition into formal, monitorable assumptions
Detecting insight failure before KPI degradation becomes visible
Explaining why an insight failed, not just that it failed
Shifting the mindset from “trust the dashboard” to “trust the assumptions”

What we learned

Analytical insights are hypotheses, not facts
Data drift can invalidate conclusions long before metrics collapse
Making assumptions explicit is more powerful than adding more charts
Monitoring why an insight works is as important as monitoring how well it performs