Time Intelligence: Why ‘Last Month’ Is Harder Than It Sounds

TL;DR

• Time logic must be explicit and consistent.
• AI needs to know which date field is used.

The problem

• Multiple date fields and inconsistent logic lead to different results.
• AI can’t infer which time definition is intended.

Why it matters

• Time comparisons are core to business decisions.
• Inconsistent logic causes misleading trends.

Symptoms

• Last month results vary by report.
• Year‑over‑year comparisons don’t align.

Root causes

• No dedicated date table or inconsistent relationships.
• Time intelligence implemented differently across measures.

What good looks like

• Dedicated date table with standard time measures.
• Explicit time logic in canonical measures.

How to fix

• Create a single date table and use it consistently.
• Standardize time intelligence measures.
• Document time basis in each KPI.

Pitfalls

• Mixing calendar and fiscal definitions without labels.
• Using implicit date fields.

Checklist

• Single date table used across facts.
• Time measures standardized.
• Time basis documented.

Framework placement

Primary ARF layer: Context Stability. Diagnostic bridge: data-movement-reliability, semantic-reliability, execution-reliability.