Profiling and auditing data quality at the ETL stage — before it reaches your models
Published 23 September 2026
Following my recent post, “Power Query: A Missed Opportunity for Many Finance Teams,” I wanted to explore one of Power Query’s most underrated strengths: its ability to check, review, and audit data quality long before the data reaches your warehouse or reporting models.
Quality data sits at the foundation of all analysis, insights, and AI. Just like any process, the end result can only be as strong as the raw material feeding it — your data.
If you start your journey with Power Query, one of the biggest advantages you gain — often unintentionally — is powerful data profiling and quality assurance. Traditional spreadsheets make data quality checks painful, manual, and often ineffective. But with Power Query, it becomes a breeze.
With its built-in profiling tools, you can scan entire datasets in seconds and quickly identify issues such as null values, duplicate records, inconsistent data types, and error rows. Addressing these problems at the ETL stage is crucial — if left unresolved, they can have a significant negative impact downstream in your data models, reports, and AI outputs.
Instantly view the percentage of valid, error, and empty values in each column — providing a clear snapshot of data health.
See how values are spread across a column — identify distinct values, duplicates, and unexpected data patterns with ease.
Access deeper insights into data structure, including:
These features equip finance teams with the ability to understand, validate, and prepare clean, trustworthy data — before it ever reaches a dashboard or data model.
Want clean, trustworthy data feeding your reports and AI? Talk to us, or read more about how we automate manual Excel reporting.