From spreadsheet sprawl to a governed source of truth before AI

Outcomes

Competing versions of key operational metrics

30+ diverging spreadsheets→One governed model with a single definition per metric

Leadership reporting cycle

Weekly manual consolidation→Self-serve dashboard on governed data

The problem

The company wanted “AI-driven insights” in their ops organization. The discovery call made the real situation clear: thirty-plus spreadsheets with diverging definitions of on-time percentage, no shared metric definitions, and a weekly leadership meeting where the first twenty minutes were spent reconciling whose number was right. No AI system can add value on top of that

• it would just generate confident nonsense faster.

The approach

1. Metric definition audit. We cataloged every spreadsheet in circulation, traced each metric to its source system, and documented where definitions disagreed. This map alone changed several operational conversations.
2. One governed model. Consolidated the definitions into a single semantic model with certified metrics, lineage back to source systems, and a deprecation path for the shadow spreadsheets.
3. Self-serve reporting on the governed layer. Leadership dashboards now read from the certified metrics. The consolidation ritual disappeared from the leadership meeting.
4. A realistic AI roadmap. With trusted data in place, we sequenced the AI initiatives that were actually feasible: anomaly alerts on lane performance, then a Q&A layer over the governed metrics.

The outcome

The metric-definition work was the least glamorous and most valuable part of the engagement. The company now has a foundation where an AI feature can cite a number that everyone agrees is correct - the precondition for anything intelligent to be built on top.

Sequencing matters: the AI roadmap was written after the data foundation, not before it, and that is why it will actually be delivered.

Stack

• [ SQL ]
• [ Python ]
• [ Semantic modeling ]
• [ Power BI ]