Data Quality:
Garbage In, Garbage Out

The Silent Saboteur

Data quality problems are invisible until they cause visible damage: wrong prices in orders, incorrect sizes in shipments, mismatched product data across systems, duplicate records, and outdated information that no one corrected. The cumulative effect: decisions based on data you can't trust.

The solution is not better data cleaning — it's better data capture. When data is born structured in a unified platform, quality is built in from the start. No manual entry. No format conversions. No version conflicts. This is why platforms like FIRE achieve near-100% data accuracy — because data never needs to be transferred, translated, or manually entered.