MCCM Wiki
Data analysis without a data science team
You do not need a full data science team to start. You need one decision, one trusted data pipeline, and a repeating report or model that an owner can maintain.
Hiring ahead of clarity creates idle capacity. Starting with spreadsheets forever creates silent errors. The middle path is a focused pipeline tied to a business KPI.
A lean sequence that works
- Name the decision (pricing, stock, sales focus, ops load) and the owner.
- Inventory sources. Prefer systems of record over exported copies when possible.
- Clean only what the decision needs. Skip boiling the ocean.
- Deliver a dashboard or model with assumptions written down.
- Review weekly until the metric is trusted, then automate the refresh.
When to hire deeper talent
Hire or contract deeper machine learning talent when the decision needs prediction or multi-system inference that a maintained report cannot cover. Until then, clarity beats sophistication.
Related MCCM terms
See AI audit and AI pilot KPI examples.