Operational KPI root-cause analysis

The challenge

A dashboard reports that a metric fell. Finding out which upstream change caused it is a manual hunt through the tables that feed it.

The scenario

A weekly operational KPI drops eleven percent. The dashboard shows the drop; nobody can say whether it is a real change, a data-pipeline problem, or a definition that shifted.

What goes in

KPI definition

The tables and logic the metric is built from.

Source tables

The upstream data feeding the metric.

What you can ask

Illustrative prompts. Results depend on your own data and are not deterministic.

• “Which tables feed this KPI?”Copy
• “What changed upstream of this metric last week?”Copy
• “Is this a real change or a data problem?”Copy

How Clearception approaches it

How the work is structured. Each step shows what its output is grounded in.

1. 1

   Trace the lineage

   Observed

   Establish which tables and logic produce the metric.

2. 2

   Compare periods

   Observed

   Look at each contributing input across the period of the change.

3. 3

   Isolate the mover

   AI-inferred

   Identify which input accounts for the movement.

4. 4

   State confidence

   AI-inferred

   Report how strongly the data supports the attribution.

What you get

The shape of what comes back — not a promised result.

• Contribution breakdown

  Observed

  Each input's contribution to the change, with the rows behind it.

• Confidence statement

  AI-inferred

  How much of the movement is explained, and how much is not.

Why it matters

Cause, not just symptom

The investigation starts from lineage rather than guesswork.

Data problems caught

A pipeline fault stops being mistaken for a business change.