Data quality assessment

Garbage in, garbage out; bad data leads to bad decisions. Data quality issues hide everywhere: missing values, duplicate entries, inconsistent formats, and measurement errors. A single misplaced decimal point can make metrics look catastrophic or miraculous. Regular quality checks prevent embarrassing retractions and misguided strategies.

Common quality problems have telltale signs. Sudden metric spikes often indicate tracking bugs rather than user behavior changes. Perfectly round numbers suggest manual entry or estimation. Missing data patterns reveal collection blind spots: mobile events dropping during app updates, or certain browsers failing to fire analytics.

To prevent this, build quality checks into your workflow:

• Set reasonable bounds for metrics. For example, conversion rates shouldn't exceed 100% or go negative
• Compare multiple data sources to catch discrepancies
• Document data lineage so you know where numbers originate